Transcript Capitant Belgium

Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
EFFECT OF INDEX INCLUSION AND EXCLUSION ON THE
STOCK'S RETURN PREDICTABILITY
Mathieu Ververken
Abstract
This research aims to investigate the impact of stock additions and deletions on the predictability of
stock returns on the long term. In the literature, the effect of index revision on the stock price and
trading volume is well documented. However, the impact of index revisions on the stock return
predictability has not been thoroughly investigated. Nonetheless, a number of hypotheses that
have been put forward to explain the impact of index revision on the stock price (and trading
volume) are related to the predictability of stock returns. To the best of my knowledge, this is the
first study that provides a comprehensive analysis of the impact of index revisions on the in-sample
and out-of-sample predictability of stock returns in the long term. The subject of this scientific
paper is inspired by the prior work of Liu (2009), who examined the impact of index membership
on the predictability of stock returns for the Nikkei 225 Index in the short term. The main premise
of the empirical research is that a stock that is added to the index results in an increase of the
predictability, while a stock that is deleted from the index results in a decrease of the predictability.
The empirical research is subdivided into three different parts and is based on index revisions of
the Dow Jones Index. Based on the results of the empirical research, I will be able to form a
conclusion about the market efficiency, form a conclusion about the implication for investors
(trading strategies), and provide suggestions for further research.
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Faculty of Economics & Management
Master Thesis
PART I: LITERATURE REVIEW
1. Stock Return Predictability
The predictability of stock returns is one of the most debated issues in the field of financial
economics. Until the 1980s, there was a general consensus among financial economists that stock
prices follow a “random walk” —in other words, price changes are random and therefore
unpredictable (Pesaran, 2010). This is consistent with the existence of an “efficient” market for
stocks, which is defined by Fama (1970). An “efficient” market is a market in which stock prices
immediately and fully reflect all the available information. In an efficient market, price changes
must be a response only to new information. Therefore, it is impossible to earn abnormal returns1
by forecasting future stock returns, or in other words, it is impossible to outperform the market
consistently (Malkiel B.G., 2003). If stocks would be predictable, then investors would reap
unlimited profits by purchasing those stocks that, according to the model, were about to increase in
price and by selling those stocks about to fall in price. Suppose the stock price is about to increase
by 10 %; all investors would like to buy the stock, but no one would like to sell. Therefore, the
stock price would immediately reflect the forecast news and would immediately increase by 10 %
(Bodie et al., 2009 and Granger and Timmerman, 2004).
Subsequently, the efficient market has been categorized into three forms of efficiency, depending
on the definition of the available information. The weak form efficiency states that the current
stock prices reflect all the information contained in the past history of the stock prices. This implies
that the past history of stock prices cannot be used to generate abnormal returns. The semi-strong
form of efficiency asserts that the stock prices incorporate all publicly available information. Apart
from the past prices (weak form), public information also includes macroeconomic information
(inflation, money supply, interest rates), information of the firm’s profit, dividends, etc. The strong
form of efficiency determines that the stock prices reflect public as well as private information.
Private information is information that is only available to company insiders (Bodie et al., 2009;
Fama, 1970; Malkiel B.G., 2003).
However by the beginning of the 1980s, the dominance of an efficient market had become far less
universal. Many financial economists and statisticians began to believe that stock returns are at
least partially predictable (e.g., Fama and French, 1988 and Campbell and Shiller, 1988). From this
moment, researchers started to investigate two different methods of predicting stock returns:
technical analysis and fundamental analysis. Technical analysis studies past stock price behavior to
determine future price movements and thereby guide trading decisions. Therefore, technical
analysis is inconsistent with the weak form efficient market. Fundamental analysis, on the other
hand, uses valuation ratios and economic variables to forecast future stock returns (Neely et al.,
2010; Shiller, 1984 and Summers, 1986).
In the fundamental analysis, a distinction is made between time-series predictability and crosssectional predictability of stock returns. Cross-sectional predictability consists of an analysis of the
cross-section variation in stock returns. The two basic asset pricing models in the literature are the
Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). These models give a
prediction of the relationship between the risk of a stock and its expected return. The CAPM relates
the expected return on the risk-free rate of return (time value of money) and the stock’s risk
1
Abnormal return is the return in excess of the expected return given the risk of the security and the
market return.
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Faculty of Economics & Management
Master Thesis
(which consists of the beta and the overall market risk). The APT relates the expected return to
firm specific and macroeconomic factors (Bodie et al., 2009).
In case of a time-series analysis, the stock returns are regressed on the lagged values of a set of
predictor variables (which form a predictive regression model). In other words, based on a set of
information variables, an investor will try to predict future stock returns by exploiting the
relationship between stock returns and this lagged set of information variables (Rapach and Wohar,
2006). This paper will focus on time-series predictability and hence will exclude the analysis of
cross-sectional predictability.
Numerous predictor variables have been suggested to be good predictors in the forecasting
literature. In general, the predictor variables can be divided into two categories: valuation variables
and macroeconomic variables. This section examines, for each category, the most important
variables that have considerable predictive power in the empirical research. Figure 1 presents an
overview of the different predictor variables. Furthermore, this analysis will be of importance for
my own research, where a multivariate predictive regression model is constructed based on a set
of predictor variables.
Figure 1: Schematic overview of the different predictor variables
Dividend yield (+)
Valuation
variables
Price-earnings ratio (-)
Book-to-market ratio (+)
Predictor variables
Interest rate (-)
Inflation (-)
Macroeconomic
variables
Industrial production (+)
Consumption-wealth ratio (-)
Output gap (+)
Investment/capital ratio (-)
First, a number of valuation variables have been put forward to explain the pattern of stock
returns. Valuation variables present information about the fundamental value of the company. The
most common valuation variables in the forecasting literature are the dividend yield, the priceearnings ratio and the book-to-market ratio (Lewellen, 2004).
The dividend yield is one of the valuation variables that is used as a forecasting variable in most
studies of return predictability. Fama and French (1988) and Campbell and Shiller (1988) find that
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Faculty of Economics & Management
Master Thesis
the dividend yield has significant predictive power for future stock returns. The dividend yield
expresses the dividend per share as a percentage of the share price. In other words, it tells the
investor the dollar amount of dividends paid by the company per dollar invested in a single stock.
The link between the dividend yield and future returns can be explained based on the GordonShapiro model. The Gordon-Shapiro model is written as follows:
price at time t-1,
is the expected dividends per stock,
, where
is the stock
is the required return and
is the
expected growth rate of the dividends. By rearranging the Gordon-Shapiro model, the positive
relationship of the dividend yield and the future stock return can be explained as follows:
. If I assume a constant growth rate of the dividends, then a high dividend yield indicates a high
expected return (Fama and French, 1988 and Campbell and Shiller, 1988). Besides the study of
Fama and French (1988) and Campbell and Shiller (1988), many other researchers (e.g., Lewellen,
2004; Wohar and Rapach, 2005 and Cochrane, 1997) observe a significant positive relationship
between the dividend yield and the future stock returns.
The price-earnings ratio, the price divided by the earnings rate per share, is also extensively used
as a predictor variable in the forecasting literature. The ratio shows how much investors are willing
to pay per one dollar of earnings. It is an important key ratio for investors because it is a signal for
an under- or overestimation of the stock price (Bodie et al., 2009). A high price-earnings ratio
could indicate that the stock is overvalued (i.e., the stock price is too high relative to the
fundamental value of the company, which is measured by the earnings). Intuitively, a high priceearnings ratio should therefore result in a decrease of the stock price in the future. In the
literature, there is evidence that the price-earnings ratio is negative related to future stock returns
(Campbell and Schiller, 1988, 2001 and Campbell and Yogo, 2003). A high price-earnings ratio
indicates low future expected returns, while a low price-earnings ratio indicates high future
expected returns. The relationship can also be explained based on the Gordon-Shapiro Model. If
the expected return increases, then the current price of the stock will decrease. As a consequence,
the current price-earnings ratio will decrease if we assume constant earnings (Campbell and
Schiller, 1988, 2001).
Another valuation variable that is often used in studies to predict future stock returns is the bookto-market ratio. The book-to-market ratio is the ratio of a firm’s book value to the market value of
the stock. The book-to-market ratio is positive related to future expected return. Concurrently with
the two previous valuation variables, the positive relation can be explained based on the GordonShapiro Model. If the required return (k) increases and we assume constant expected dividends,
then the stock price decreases. Subsequently, the book-to-market ratio will increase if the book
value doesn’t change. Numerous studies, including Fama and French (1992), Pontiff and Schall
(1998) and Kothari and Shanken (1997), find that the book-to-market ratio is a significant
predictor of stock returns.
Apart from the valuation variables, researchers began to investigate how well macroeconomic
variables predict stock returns. The choice of using macro variables to predict stock returns is quite
natural, since a link exists between the stock price and the macroeconomic environment (Gupta et
al., 2011). In the literature, there is evidence that expected returns vary counter-cyclical with the
real economy, so the expected return increases during a period of economic downturn and
decreases during a period of economic expansion (Lettau and Ludvigson, 2001). Campbell and
Cochrane (1999) explain this based on a change in the risk aversion of the investor. In a period of
economic downturn, the consumption is low relative to the habit, and the risk-aversion of the
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Faculty of Economics & Management
Master Thesis
investor is high. As a consequence, the investor will require a high risk premium. When the
economy is expanding, the consumption is high and the risk-aversion of the investor is low. In this
case, investors will demand a lower risk premium (Lettau and Ludvigson, 2001).
One of the macroeconomic variables that is often used by researchers to predict future stock
returns is the interest rate. Based on the Gordon-Shapiro model, it can be shown that the interest
rates and stock returns are negatively related. An increase in the interest rate leads to an increase
in the required return (k), because the required return consists of the risk-free interest rate and
the risk premium. As a consequence, the stock price will decrease. On the other hand, an increase
of the interest rate will also have in impact on the nominator of the Gordon-Shapiro model. An
increase of the interest rate leads to an increase of the financing cost for the company, which on
their end has a negative impact on the profitability of the company. Therefore, the expected
dividends (Dt) will decrease. Different sorts of interest rates are examined in the forecasting
literature. For example, Campbell (1987, 1991), Ferson (1989), Hodrick (1992) and Ang and
Bekaert (2006) examined the predictive power of the short-term interest rate and found a
significant negative impact on the stock returns.
Another macroeconomic variable that has been proposed to explain the pattern of stock returns is
inflation. There is a general consensus in the literature that the relationship between the inflation
and stock returns is negative (Fama, 1981). The ability of inflation to predict stock returns has
been investigated by numerous researchers—for example Rapach et al. (2005) who demonstrate
the predictability power of inflation in a number of countries.
Third, the industrial production index is also extensively used in the forecasting literature as a
proxy for the business cycle. An increase in the industrial production leads intuitively to an increase
in the sales and earnings of the company, which on their end will have a positive impact on the
stock price. Hence, there is a positive relationship between industrial production and stock returns
(Rapach et al., 2005 and Young, 2006). The empirical research of Fama (1990) and Chen et al.
(1986) present evidence that industrial production has predictive power for stock returns.
In addition to these three macroeconomic variables (interest rate, inflation and industrial
production), other macroeconomic variables have been proposed to explain the pattern of stock
returns (Rapach et al., 2005). Lettau and Ludvigson (2001) propose the consumption-wealth ratio2
as a predictor variable for future stock returns and found a negative significant relationship
between the consumption-wealth ratio and future stock returns. In addition, Cooper and Priestley
(2009) find that the output gap is a strong (positive) predictor of stock returns. Additionally,
Cochrane (1991) shows that the aggregate investment/capital ratio3 is negatively related to future
stock returns. These macroeconomic variables will not be discussed in more detail because only a
limited number of financial researchers demonstrate a significant predictability power. Moreover,
some of these macroeconomic variables (e.g., consumption-wealth ratio) are hard to calculate and
are therefore more difficult to use in my own research.
Within the literature of stock return predictability, there is a consensus that the predictability of
stock returns increases with the length of the return horizon, and therefore the strongest evidence
of stock return predictability comes from the longer horizon stock returns (Boudoukh et al., 2005).
2
Consumption-wealth ratio is the aggregate consumption divided by a measure of aggregate wealth.
The investment to capital ratio is the ratio of aggregate investment to aggregate capital for the whole
economy (Goyal and Welch, 2008).
3
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Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
To illustrate this, the study of Fama and French (1988) concludes that the dividend yield explains
less than 5 % of the monthly or quarterly return variance, while it explains more than 25 % of the
variances of two-to-four-year returns.
2. Index Revision
First, I will discuss the stock price (and trading volume) reaction to changes in the index, which has
been the subject of empirical financial research over the last three decades. My research focuses
on the impact of index revisions on the predictability of stock returns. However, in order to develop
a premise for my own empirical research, a number of elements from research of price impact (and
volume impact) from index revisions are relevant. For this reason, I will first discuss the impact of
index revisions on the stock price (and trading volume). However, the main focus will be on the
implication of index revision on the predictability of stock returns, which I will discuss in section
2.2.
2.1. Impact of Index Revisions on Stock Prices
The impact of index revisions on the stock price (and trading volume) is well documented in the
literature (e.g., Harris and Gurel, 1986 and Jain, 1987). However, researchers disagree about the
underlying mechanism of these phenomena. As a result, they differ on whether the pattern of the
stock price (or volume change) is permanent or temporary in case of a stock’s inclusion or
exclusion.
Therefore, several hypotheses have been put forward in the literature to explain the impact of
index revisions on the stock price (and trading volume). On the one hand, there are hypotheses
that foresee short-term effects (e.g., price-pressure hypothesis). On the other hand, some
hypotheses predict long-term effects (e.g., downward sloping demand curve hypothesis; liquidity
hypothesis; information hypothesis and attention hypothesis).
According to the price-pressure hypothesis, an addition to (deletion from) the index does not
reveal new firm-specific information, but results in a change of the demand because index fund
managers will buy (sell) the added (deleted) stock to rebalance their portfolios according to the
index changes (Harris and Gurel, 1986). Therefore, if a company is added to the index, the price
and the volume will temporarily go up, since the demand for the stock will increase significantly. In
other words, the effect of index changes on the stock price and volume is not permanent and tends
to disappear in the long term (Lynch and Mendenhall, 1997).
Harris and Gurel (1986) examined the impact of stock additions to the S&P 500 Index in the period
of 1976-1983. Their results tended to confirm the price pressure hypothesis.4 In addition to the
study of Harris and Gurel, Lynch and Mendenhall (1997), Madhavan (2003), Vespro (2006) and
Mase (2007) also provide corroborating evidence for the price pressure hypothesis.
The downward sloping demand curve hypothesis suggests that when a firm is added to the
index, index fund managers buy the added stock to rebalance their portfolios (Shleifer, 1989).
However, in comparison with the price pressure hypothesis, this hypothesis holds that a stock does
not have perfect substitutes and therefore has a downward sloping demand curve rather than a
perfect elastic (horizontal) demand curve. In the case of an index addition, index funds buy the
4
Harris and Gurel (1986) found an immediate price increase of more than 3 % after inclusion, which was
fully reversed after two weeks.
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Faculty of Economics & Management
Master Thesis
stock, thereby removing a considerable part of the firm’s shares. Therefore, the stock’s availability
reduces, which causes a permanent price increase. When a firm is removed from the index,
analogous logic predicts a permanent price decrease (Lynch and Mendenhall, 2001).
Contrary to studies that demonstrate a temporary price effect, Shleifer (1986) is one of the first to
document a persistent price effect.5 Later studies such as Beneish and Whaley (1996), Lynch and
Mendenhall (1997) and Wurgler and Zhuravskaya (2002), among others, also supported the
downward sloping demand curve hypothesis.
The liquidity hypothesis suggests that the liquidity of a stock is affected by its addition to or
deletion from an index (Amihud and Mendelson, 1986). When a stock is included into an index, it
will get increased coverage by analysts, investors and institutions. This higher attractiveness
results in an increase of the public information and in an increase of the number of trades of the
stock. Hence, the increased trading volume decreases the transaction costs (bid-ask spread).6 As a
result, the required rate of return decreases and eventually leads to a permanent price increase
(Docking and Dowen, 2006).7 In the same way, a stock that is deleted from the index results in a
fall of public information, decrease of the stock’s liquidity and an increase of the bid-ask spread.
Therefore, the required rate of return increases and leads to a price decrease (Shleifer, 1986;
Goetzmann and Garry, 1986).
The study of Hedge and McDermott (2003) support the liquidity hypothesis. They examine index
changes of the S&P 500 Index from 1993-1998 and found an improvement in the liquidity for stock
additions. Furthermore, Edmister et al. (1996), Erwin and Miller (1998) and Chakrabarti et al.
(2005) provide evidence for index revisions consistent with the liquidity hypothesis.
The information hypothesis is based on the concept that when a stock is added to the index, it
conveys new positive information to the investors about the company’s future performance
(increase perception of the quality of the management) (Jain, 1987).8 Therefore, investors perceive
the inclusion of a firm in the index as a reduction in the riskiness of firm’s stocks (Docking and
Dowen, 2006). So, according to the information hypothesis, a stock inclusion should result in a
permanent positive effect on the stock price (Cai, 2007). Analogous logic predicts a permanent
price decrease when a firm is removed from the index.
Jain (1987) finds strong empirical evidence that stock additions to the S&P 500 Index are
consistent with information content that is caused by index inclusion. Later on, studies such as
Dhillon and Johnson (1991), Denis et al. (2003), Hegde and McDermott (2003) and Chen et al.
(2004), show that the permanent price effects of index additions are consistent with the
information hypothesis.
5
Specifically, the 3 % price increase for stocks added to the S&P 500 Index in the period 1981-1983
does not disappear within 10 days after the inclusion of the stock.
6
The bid-ask spread is the difference between the bid price (the highest price investors are willing to pay
for a stock) and the ask price (the lowest price at which investors are willing to sell a stock) (Bodie et al.,
2009)
In general, the bid-ask spread increases when the liquidity decreases.
7
The relationship between the required rate of return and the price of a stock is documented in the
formula of the Gordon-Shapiro Model.
8
To illustrate, the S&P 500 Index consists of leading companies of the United Stated of leading
industries. An inclusion of a stock into the S&P 500 Index may act as a certification that the company in
question is a leading company. Because the index membership committee monitors the companies
closely and prefers stable firms in the S&P 500 Index, they select firms that, according to their beliefs,
will be able to meet the index criteria for a long period of time.
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Faculty of Economics & Management
Master Thesis
According to the attention hypothesis, a stock’s inclusion in the index should result in an
increase of the investor awareness of that particular stock, and therefore more investors would be
willing to invest in the company. Moreover, more information is produced by analysts because a
greater number of analysts follow the firm, and the degree of analyst coverage increases.
Subsequently, there is more attention given by the media. Therefore, the firm’s “shadow costs”9
and subsequently the required rate of return reduce, thereby leading to a permanent increase of
the stock price (Merton, 1987; Docking and Dowen, 2006). In contrast, when investors are familiar
with an index stock, they will not become immediately unaware of it when the firm is deleted from
the index. Thus, the investor’s awareness of the company will not diminish immediately and the
“shadow cost” will therefore not increase quickly for a deleted stock (Yun and Kim, 2010).
The study of Chen et al. (2004) provides corroborating evidence for the attention hypothesis. They
report an asymmetric price behavior of stock additions and deletions in the S&P 500 Index for the
period of 1962-2000. This hypothesis is also supported by Poloncheck and Krehbiel (1994), which
investigated changes in the Dow Jones Index, and Elliott et al. (2006), which investigated changes
in the S&P 500 Index.
The impact of index revisions on the stock price (and trading volume) is summarized in Table 1. In
brief, only the price-pressure hypothesis foresees a temporary price effect (and volume effect) for
stock’s additions and deletions. Moreover, the downward sloping demand curve-, liquidity- and
information hypothesis predicts a permanent effect on the stock price for index revisions. Also the
liquidity hypothesis predicts a permanent effect on the volume and price for stock’s additions and
deletions. Finally, the attention hypothesis foresees only a permanent price effect for stock’s
additions.
Table 1: Overview of the five hypotheses
Price pressure hypothesis
DSDC hypothesis
Liquidity hypothesis
Information hypothesis
Attention hypothesis
9
Addition
Deletion
Addition
Deletion
Addition
Deletion
Addition
Deletion
Addition
+
-
No effect
Permanent
Temporary
Volume effect
No effect
Permanent
Temporary
Price effect
+
+
+
+
+
+
+
-
As a result of the investors’ limited attention investors only know a subset of all stocks (for example,
the investor may only be aware of stocks that are member of the S&P 500 Index). Because investors are
not able to invest in stocks of which they are unaware, firms have a “shadow cost” for being unknown.
This “shadow cost” decreases if the firm becomes more recognized (Chen et al., 2004).
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Master Thesis
Deletion
x
+: positive effect, -: negative effect, x: no effect
DSDC hypothesis: downward sloping demand curve hypothesis
2.2. Impact of Index Changes on Stock Return Predictability
A number of elements that are discussed in the previous section (“Impact of index revisions on
stock prices”) have an impact on the predictability of stock returns. However, existing studies have
not directly examined the impact of index revisions on the predictability of stock returns on the
long term. 10 Only prior work of Lui (2009) demonstrates that index revisions do have an impact on
the stock’s return predictability in the short term.
11
Therefore, this study attempts to extend the
literature by examining the long-term impact of index revisions on the predictability of stock
returns.
I consider the mechanism of two hypotheses (attention hypothesis and liquidity hypothesis) of
extreme importance to explain the impact of index revisions on the stock’s return predictability.
The underlying reasoning is summarized in figure 2 and is discussed in the next paragraphs.
Figure 2: Overview of the impact of index revisions on the stock’s return predictability
EFFECT ON
PREDICTABILITY
CONSEQUENCES
INCLUSION IN
INDEX
more analyst
coverage
Attention
hypothesis
↑
more media
coverage
Attention
hypothesis
↑
higher volumes
Liquidity
hypothesis
↑
less analyst
coverage
Attention
hypothesis

less media
following
Attention
hypothesis

lower volumes
Liquidity
hypothesis

STOCK
EXCLUSION IN
INDEX
10
I consider short term as less than one year and long term as more than one year.
Lui (2009) investigates the impact of membership changes in the Nikkei 225 Index on the stock’s
return predictability (on short term).
11
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Faculty of Economics & Management
Master Thesis
First, in line with the attention hypothesis, we assume that if a company is added to the index, a
shift will occur in the information environment of the stock. On the one hand, more information will
be produced by financial analysts because more analysts will have more interest in that particular
stock and hence will begin to analyze and estimate its earnings.12 On the other hand, an index
inclusion results in an increase of attention by the media. The media (including financial
newspapers) play an important role in disseminating information to individual investors. As a
consequence, a stock’s inclusion in the index should result in an increase of information provided
by analysts and media. It is likely that this improvement in the stock’s information environment in
the case of a stock addition leads to a higher predictability. In contrast, for a stock deletion, I
expect a decrease in analyst and media coverage. This results in a decrease of information
provided by analysts and media and therefore I foresee that the predictability of stock returns will
decrease when a stock is deleted from the index.
Along with a shift in the stock’s information environment, the stock’s addition results in an increase
of awareness among investors. This is according to the attention hypothesis, which states that
investors only know a subset of all stocks as a result of the investors’ limited attention. When a
stock is added to the index, more investors become aware of this particular stock. Because
investors are not able to invest in stocks of which they are unaware, a firm’s addition to the index
leads to an increase of potential investors. Therefore, it is likely that the volume increases when
the stock is a member of the index (liquidity hypothesis). The notion that volume increases when
the stock is added to the index is consistent with the work of Frieder and Subrahmanyam (2005),
who present evidence that individual investors prefer stocks with high recognition. An increase in
the stock’s liquidity has the consequence that the stock price reflects more of the information in an
unbiased manner. This improvement of the pricing efficiency should result in a higher predictability
of stock returns. On the contrary, when a firm is removed from the index, analogous logic predicts
a decrease in the stock’s return predictability.
To conclude, the premise of this paper is that when a stock is added to the index, the stock return
predictability increases. In contrast, when a stock is deleted from the index, the stock return
predictability should decrease.
The central research question can be stated as:
“What is the impact of index inclusion and exclusion on the stock’s return predictability?”
To examine this central research question, I will make use of three different research objectives.
The first research objective examines whether a difference in predictability exists between index
component stocks and a matched control group of stocks that are not member of the index. I
expect that stocks that are members of the index have a higher predictability than stocks that are
not members of the index.
Hypothesis 1: Stocks that are members of the index have a higher predictability than stocks that
are not members of the index.
The second research objective is to examine the impact of index changes on the predictability of
stock returns. Therefore, a distinction is made between stocks that are added to the index and
12
Stock analysts provide the investors firm-specific information, which reduces the information
asymmetry between market participants (Marhfor et al., 2010).
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stocks that are deleted from the index. In the case of a stock addition, I examine whether the
predictability is significantly higher for the period after the stock’s addition in comparison with the
previous period. In the same way, I investigate whether the predictability of stock returns is faced
with a significant drop when stocks are deleted from the index. This second research objective
forms the most extensive part of the thesis.
Hypothesis 2: A stock addition in the index results in an increase of predictability, while a stock
deletion from the index results in a decrease of predictability.
For the third research objective, I examine whether we can allocate a possible difference in the
predictability of stock returns from index revisions to a change in analyst following and volume.
Hypothesis 3 (a): A difference in stock return predictability from index revisions is allocated to a
change in analyst following.
Hypothesis 3 (b): A difference in stock return predictability from index revisions is allocated to a
change in volume.
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PART II: DATA AND METHODOLOGY
1. Data
1.1. Selection of the Index and Stocks
I examine the impact of index membership on the predictability of stock returns for the Dow Jones
Index. The choice is motivated by the fact that the Dow Jones Index is considered one of the most
important economic stock market indexes in the world. Thus, the Dow Jones Index is the bestknown and most widely followed stock market index by investors. In addition, the choice for the
Dow Jones Index also limits certain concerns relating to the data. Some data used in the own
research is only available for the US market (namely, the data obtained from the I/B/E/S
database). Furthermore, Dow Jones Index revisions are limited, which makes it more feasible to
analyze the index revisions individually.13
The Dow Jones (Industrial Average) Index was created by Charles Dow in May of 1896. The Dow
Jones Index includes some of the best known, largest, and most liquid companies in the United
States. It consists of 30 companies and covers all industries, with the exception of transportation
and utilities. The Dow Jones index is maintained by editors of The Wall Street Journal. While stock
selection is not governed by quantitative rules, a stock typically is added only if the company has
an excellent reputation, demonstrates sustained growth, is of interest to a large number of
investors, and is representative in its industry sector. Components of the Dow Jones index are
added and deleted on an as-needed basis. Such changes in the index are rare, and when one
component is replaced, the whole index is reviewed.14
In the first research part, I investigate index component stocks and a matched control group of
stocks that are not members of the index for the period of 2000-2009. I only take into account
stocks that are members of the Dow Jones Index for the full period of investigation. As a
consequence, the two groups of stocks consist each of 22 stocks. Table 2 presents the Dow Jones
component stocks with, for each one, a matched control stock.15
Table 2: The Dow Jones Index Sample and the Control Sample
Dow Jones Index Group
Control Group
Industry
3M
Eaton
Diversified Industrials
American Express
SLM
Consumer finance
Boeing
Precision Castparts
Aerospace
Caterpillar
Deere
Commercial vehicles
Coca-Cola
Pepsico
Soft drinks
13
If I would opt to examine index revisions of the S&P 500 index, then it would not be feasible to
examine each index revision individually (unless I examine a very short period of index revisions). To
illustrate, from September 1976 until December 2001, 566 stocks were added to the S&P 500 index. As
there is one deletion for every addition, 566 stocks were deleted from the S&P 500 index during that
period of time.
14
Apple and Google are not member of the Dow Jones Index. This is remarkable because Apple and
Google are the representative in its industry. Therefore, we should critical ask the question if the Dow
Jones Index is still representative for the global business landscape.
15
The method by which the matched control group is constructed is discussed in section 2.1.
12
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Du Pont
Dow Chemical
Commodity Chemicals
Exxon Mobil
ConocoPhillips
Integrated Oil and Gass
General Electric
Danaher
Diversified Industrials
Hewlett-Packard
Dell
Computer hardware
Home Depot
Lowe’s Companies
Home improvement retailer
Intel
Qualcomm
Semiconductors
International Business Machines
Cognizant
Computer services
JP Morgan Chase
Wells Fargo
Banking
Johnson & Johnson
Abbott Laboratories
Pharmaceuticals
McDonald’s
Starbucks
Restaurants and Bars
Merck & Co
Elli Lilly
Pharmaceuticals
Microsoft
Oracle
Software
Procter & Gamble
Clorox
Nondurable household products
AT&T
CenturyLink
Fixed Line Telecommunications
United Technologies
Textron
Aerospace
Wal-Mart Stores
Target
Broadline retailers
Walt Disney
Comcast 'A'
Broadcasting and Entertainment
For the second and third research parts, I examine index revisions of the Dow Jones Index from
1983 until 2001. Appendix A gives a detailed list of changes in the Dow Jones Index since 1929,
which is obtained from the Dow Jones website (http://www.djindexes.com). During the period of
1983–2001, 14 stocks were added to the Dow Jones Index, while at the same time, 14 stocks were
deleted. From the deletion sample, it is only possible to examine three of them because limited
data is available for the other deleted stocks due to mergers, bankruptcies, and delistings. The final
sample of stock additions and deletions is summarized in table 3 and 4.
Table 3: The Sample of Stock Additions
Additions
Industry
Market
Capitalization*
Date of addition
Altria
Tobacco
$ 51 145,86
October 30, 1985
Restaurants and Bars
$ 24 475,16
October 30, 1985
Coca Cola
Soft drinks
$ 64 419,09
March 12, 1987
Boeing
Aerospace
$ 22 597,73
March 12, 1987
Commercial vehicles
$ 15 043,68
May 6, 1991
Broadcasting and Antertainment
$ 27 973,03
May 6, 1991
JP Morgan
Banking
$ 42 897,89
May 6, 1991
Travelers
Insurance
$ 8 200,59
March 17, 1997
McDonalds
Caterpillar
Walt Disney
13
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Master Thesis
HP
Computer hardware
$ 38 162,83
March 17, 1997
JNJ
Pharmaceuticals
$ 69 608,88
March 17, 1997
Wal Mart
Broadline retailers
$ 88 852,30
March 17, 1997
Microsoft
Software
$ 169 978,76
November 1, 1999
Intel
Semiconductors
$ 66 960,73
November 1, 1999
AT&T
Fixed Line Telecommunications
$ 76 970,82
November 1, 1999
* Average of the market capitalization in million dollars for the period 01:1973 - 12:2011
Table 4: The Sample of Stock Deletions
Deletions
Industry
Market
Capitalization*
Date of deletion
Navistar
Automotive
$ 1 487,56
May 6, 1991
Chevron
Integrated Oil and Gass
$ 52 682,22
November 1, 1999
Goodyear
Manufacturing
$ 3 457,17
November 1, 1999
* Average of the market capitalization in million dollars for the period 01:1973 - 12:2011
1.2. Selection of the Explanatory Variables
The choice of the predictor variables that are taken into account in the predictive regression model
is based on the revision of the forecasting literature. Only those predictor variables that in the
literature are well documented to be good predictors for stock returns are taken into consideration.
Moreover, I took into account only those predictor variables that are available on a monthly basis.
Furthermore, the data of the predictive variables should be available (for example, for the book-tomarket ratio too limited data is available, while the consumption-wealth ratio is very difficult to
calculate). Finally, the predictive regression model consists of five predictor variables: the dividend
yield, the price-earnings ratio, the interest rate (three-month Treasury bill rate), the inflation, and
the industrial production growth.
All data of the predictor variables are collected from Thomson Reuters Datastream, which is a
comprehensive economic and financial time series database. The data of the dividend yield, the
price-earnings ratio, and the interest rate are not transformed. The inflation is measured as the
difference in the levels of the Consumer Price Index (CPI):
. The industrial
production growth is calculated based on the difference in the levels of the industrial production
index.
For the monthly returns of the different stocks, I use the real log returns. This is calculated based
on the total return index (RI), which takes into account the dividends and is available on Thomson
Reuters Datastream. The returns are expressed in logs, according to most of the researchers in this
area (for example, Welch and Goyal, 2003 and Rapach and Wohar, 2006). Furthermore, I use real
returns, which is the nominal return deflated with the CPI. Within the literature of stock return
predictability, there is no general consensus as to whether to use nominal, real, or excess returns
in the predictive regression model. For example, Rapach and Wohar (2006) and Rapach et al.
14
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
(2005) use real returns, while Rangvid (2006) uses the nominal and excess return as the
independent variable. The calculation for the real log return is as follows:
(
)
(
)
1.3. Summary Statistics
The summary statistics of the data are reported in Table 5. This consists of the average, the
median, the minimum, the maximum, and the standard deviation of the predictive variables and
the real log returns on monthly basis. The summary statistics are presented for all stocks together.
Moreover, I present a comprehensive analysis of the summary statistics for each individual stock in
appendix B. In addition, the relationship between the different predictive variables, based on a
correlation analysis, is investigated in appendix B.
Table 5: Summary Statistics of the Stock Return and the Predictive Variables
(All Individual Stocks)
Mean
Median
Minimum
Maximum
SD
log RR
0,0024
0,0036
-0,2691
0,2440
0,0400
DY
0,0245
0,0202
0
0,1755
0,0218
PE
21,8334
16,3000
1,6000
840,600
29,3158
IR
0,0542
0,0519
0,0001
0,1552
0,0324
INF
0,0036
0,0033
-0,0189
0,0176
0,0039
IP
0,0017
0,0024
-0,0414
0,0217
0,0075
Log RR: real log return, DY: dividend yield, PE: price-earnings ratio, IR: interest rate (threemonth Treasury bill rate), INF: inflation, IP: industrial production growth, SD: standard
deviation
Furthermore, I examine the stationarity of the stock return and the predictive variables based on
the Dickey-Fuller test (Dickey and Fuller, 1979). The null hypothesis states that the variable has a
unit root, while the alternative hypothesis states that the variable has no unit root (is stationary).
In general, a regression model with non-stationary variables gives spurious results. Therefore, the
variables should be stationary. Table 6 (A) en (B) reports the results of the Dickey-Fuller test. The
analysis of the stock return, the dividend yield, and the price-earnings ratio are presented as a
percentage of the stationarity of all of the 17 stocks.
Table 6 (A): Stationarity of the Log RR, DY and PE
% stationary
Log RR
DY
PE
100 %
(17 out of 17)
41 %
(7 out of 17)
70 %
(12 out of 17)
Log RR: real log return, DY: dividend yield, PE: price-earnings ratio
15
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Faculty of Economics & Management
Master Thesis
Table 6 (B): Stationarity of the IR, INF and IP
t-value
(test value)
IR
INF
IP
-1,3768
(-2,89)
-10,6840
(-2,89)
-14,9395
(-2,89)
IR: interest rate (three-month Treasury bill rate), INF: inflation, IP:
industrial production growth
The analysis confirms that the stock returns are stationary for all the stocks. Moreover, I can reject
the null hypothesis of a unit root for most of the predictive variables. However, for more than half
of the stocks, the dividend yield is not significant. Also, the interest rate contains a unit root.
Therefore, it is necessary to take this into account in the analysis of the regression model.16
2. Methodology
The methodology is subdivided into three different parts, according to the three formulated
hypotheses. The methodology is discussed step by step and is summarized in table 7.
Table 7: Summary of the methodology
Hypothesis
Part I
Hypothesis 1:
Stocks which are member of the
index have a higher
predictability than stocks which
are not member of the index.
Part II
Hypothesis 2:
A stock addition in the index
results in an increase of
predictability, while a stock
deletion from the index results
in a decrease of predictability.
Methodology
Period
 OS analysis
 Comparison of MSE
[2000:01-2009:12]
 IS analysis
 Dummy variable
 Chow test
[1973:01-2011:12]
 OS analysis
 Comparison of MSE
 MDM test
[1983:01-2001:12]
o
o
o
o
IS estimation period: 7 yr
OS forecast period: 3 yr
IS estimation period: 7 yr
OS forecast period: 3 yr
(incl. analysis of quarterly and yearly
stock returns)
Part III
Hypothesis 3 (a) and (b):
A difference in stock return
predictability from index
revisions is allocated to a
change in analyst following/
volume.
 OS forecast errors regressed on
volume and analyst following (with
interaction terms for dummy
variable)
[1983:01-2001:12]
o
o
IS estimation period: 4 yr
OS forecast period: 2,5 yr
OS: out-of-sample, IS: in-sample, MSE: Mean Square Error, MDM: modified Diebold Mariano, yr: year
16
The problem of the interest rate that is not stationary can be solved by taking the differences. I have
done the analysis with the first differences of the interest rate for a number of important regressions, but
the main conclusions can be formed as the regressions where interest rate is not expressed as first
differences.
16
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
2.1. First Part: Comparison of the Predictability Between Dow Jones Component Stocks
and a Matched Control Group of Stocks
The first part is an exploratory research part of the paper. I examine whether a stock that is a
member of the Dow Jones Index has a higher predictability than a stock that is not member of the
index. Therefore, I make use of two groups: a group of stocks that are members of the Dow Jones
Index and a matched control group of stocks that are not members of the index.
To construct the control sample, I match each stock with a control stock that (1) is active in the
same industry as the index stock and (2) has close to similar firm size. In most financial studies,
the control stocks are selected by matching industry and size (see, e.g., Kim and Yang, 2004 and
Yi, 2001). Matching industry can isolate any industry-specific factors, because it is likely that two
firms in the same industry are subject to the same industry conditions. Similarly, matching firm
size can isolate any factors that can affect companies of certain size (Hwan Yi, 2001). Alternatively,
control firms could be matched on the basis of some predictor variables, such as the book-tomarket ratio and/or the price-earnings ratio, but I consider this approach to be beyond the scope
of the current paper. Therefore, it would be of interest for further research to integrate some
predictor variables as matching variables.
The matching procedure is as follows. First, I search all stocks within the same industry that are
listed on the US Stock Exchange. I obtain industry affiliation for the stocks from Datastream. Based
on “equity screening” it is possible to search for stocks that match your chosen criteria.
Subsequently, I require the control stock not to be member of the Dow Jones Index for the full
period of investigation. Second, the matching firm is this firm for which the average firm size is as
close as possible to the size of the index stock. As proxy for firm size, I use the average market
capitalization17 of the investigation period. Thus, I select this control stock with the lowest absolute
difference in average market capitalization (proxy for firm size) in comparison with the index stock
(Hrazdil and Scott, 2009 and Kaut et al., 2006).
The Dow Jones component stocks with the selected control stocks are presented in Table 2.18 A
more detailed table with the corresponding market capitalization of each stock is presented in
Appendix B.
In the exploratory research part, I focus on the out-of-sample predictability of stock returns.
Because in the out-of-sample forecast exercise we use sample information to forecast stock returns
outside the sample, this research mimics the situation of a forecaster in real time (Rapach et al.,
2007). Therefore, the ten-year data set is divided into two parts:
the in-sample data set and the
out-of-sample data set. The in-sample estimation period consists of seven years (01:2000 –
12:2006), while the out-of-sample period consists of three years (01:2007 – 12:2009).19
17
The market capitalization (or market value) is the share price multiplied by the number of shares in
issue.
18
Danaher and Textron were chosen twice as a control firm. Therefore, I take into account both index
companies and choose the best size matched control stock for United Technologies/ General Electric and
the second best size matched control stock for Boeing/3M.
19
An in-sample and out-of-sample period of, respectively, seven and three years is chosen to make it
consistent with the second part of the research. Moreover, an investigation period of ten years makes it
possible to examine a high number of index stocks, while enough observations are obtained to have
reliable results. The longer the dataset would be the, the less index stocks could be investigated,
17
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
The out-of-sample predictions can be obtained from a sequence of recursive or rolling regressions
(Inoue and Kilian, 2004). I will use the method of “rolling estimation windows” to forecast the
monthly return instead of “recursive estimation windows”. In the former case, the number of
observations for the estimation period is constant for every out-of-sample prediction. The term
“rolling” means that the coefficients of the predictive regression model are monthly re-estimated
because the estimation period moves up every month of the out-of-sample forecast period. In case
of a “recursive estimation window” method, the coefficients are re-estimated with one more
observation added to the estimation window each month (Rapach and Wohar, 2006).20
The methodology of a rolling scheme can be best explained based on an illustration. Figure 3
presents the in-sample estimation period and the out-of-sample forecast period.
Figure 3: In-sample and out-of-sample period for analysis
To forecast the return for January 2007, the coefficients of the predictive regression model are
estimated via OLS using the data of the estimation period from January 2000 until December 2006.
Based on the estimated coefficients, a forecast is constructed for the return using
̂
̂
̂
̂
̂
̂
̂
In order to forecast the return for the next month (February 2007), we use the same procedure,
but the estimation period is now moved up one month (from February 2000 until January 2007).
The next step is to compare the out-of-sample forecasts of the index stock with the control stock.
In the literature, various forecast accuracy measures are used. One of the most widely used
accuracy measures is the Mean Square Error (MSE). The MSE measures the average of the square
of the forecast errors. The forecast error is the amount by which the forecast return differs from
the real return. So, when the forecast error is positive, the forecast return is too low, and when the
forecast error is negative, the forecast return is too high. Therefore, the forecast errors are
squared before they are added up in order to avoid offsetting effects of errors with different signs.
∑
̂
The period with the smallest value of MSE gives the best out-of-sample prediction performance. In
other words, in accordance with hypothesis 1, I expect the MSE of the index firm to be less
compared to the MSE of the control firm.
because I only take into account those stocks that are member of the Dow Jones Index for the full
investigation period.
20
I chose for using rolling scheme to have a consistent approach as the second part of the research.
18
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Faculty of Economics & Management
Master Thesis
2.2. Second Part: Comparison of the Predictability of Revisions of the Dow Jones Index
Predictability tests can be conducted based on the in-sample fit of the predictive regression model,
or they can be based on the out-of-sample fit (Inoue and Kilian, 2004). For this second research
part, we compare both the in-sample and out-of-sample performances of the predictive regression
model for the period before and after the index revision.
(A) In-Sample Analysis
The in-sample forecast relies on the entire sample and predicts returns within the sample range.
Therefore, it provides a mean of evaluating the predictive regression model, but it does not
evaluate the forecasting ability of returns outside the sample. First, I examine whether the stock
return prior to the index revision is different from the one after the index revision. Therefore, I
included a dummy variable in the predictive regression model. The dummy variable is equal to one
if the stock is member of the Dow Jones Index, and zero otherwise. The regression model can be
presented as:
For the analysis of the regression model, I expect a significant positive coefficient of the dummy
variable. This is consistent with the notion that the stock return is higher when the firm is member
of the Dow Jones index.
Second, I examine whether the predictive regression model is faced with a structural break
because of the index revision. To detect a structural change in the explanatory power of the
predictor variables for returns, I use the Chow test (Chow, 1960).21 The sample data is split into
two sub-periods: the pre-index revision period and the post-index revision period. The Chow test
provides a test of whether the set of regression parameters (i.e., the intercepts and slopes) is
equal across the two periods. The predictive regression is estimated over the whole sample and
then for the two sub-periods separately. Afterward, the sum of squared residuals is obtained for
the full sample (RSS), the pre-index revision sample (RSS1), and the post-index revision sample
(RSS2). Intuitively, a large difference between RSS and the sum of RSS 1 and RSS2 indicates a
structural break (Boudoukh et al., 2003; Brooks, 2008 and Gujarati, 1995). Specifically, the test
statistic is
[
]
The null hypothesis of the Chow test is that the pre-index revision period and the post-index
revision period are equal, so there is no structural change between the two periods. A rejection of
the null hypothesis indicates that there is a structural change between the two periods. To provide
corroborating evidence for hypothesis 2, I expect that the F-statistics with the corresponding pvalue will be significant, so that the null hypothesis of no structural break at the index revision can
21
In the literature, different tests to detect structural breaks exist. I use the Chow test because this test
is used to detect known breakpoints (in my paper this is the time of index revision). Apart from this test,
several others exists for testing unknown breakpoints, like e.g. the CUSUM test, the CUSUMQ test, the
Quandt test, the Bai and Perron test (Bai and Perron, 1998).
19
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Faculty of Economics & Management
Master Thesis
be rejected. Moreover, I expect an upward (downward) structural change after the index addition
(deletion). Therefore, I expect for stock additions (deletions) the adjusted R² of the pre-index
revision period to be lower (higher) than the adjusted R² of the post-index revision.
(B) Out-of-Sample Analysis
The second part focuses on the out-of-sample forecasting, where it is attempted to mimic the
behavior of a practitioner in real time. The period of investigation consists of ten years before and
ten years after the index revision for each stock individually. If I would opt for a longer period,
then a limited number of stock additions and deletions could be investigated. If on the other hand,
the investigation period would be much shorter, too few observations would be obtained.
To decide where to split the ten-year data set in an in-sample and out-of-sample dataset, we are
faced with a trade-off. If the out-of-sample forecast period is too limited, we have very few out-ofsample observations to use in calculating the out-of-sample test statics. This makes our
conclusions regarding the out-of-sample predictability less reliable. If we, on the other hand, begin
our out-of-sample forecasts very early in the sample, we don’t have many in-sample observations
available with which to estimate the predictive regression model (Rapach and Wohar, 2006). I
decided to reserve the first seven years of the sample to estimate the predictive regression model
used to form out-of-sample forecasts of three years (or 36 observations).
I prefer to use the method of “rolling estimation windows” to forecast the monthly return instead of
“recursive estimation windows”. A recursive scheme might lead to better fit and better forecasts,
because we have more information in the sample. Hence, better forecasts might be caused by
index membership or by different sample size. Therefore, I use a rolling scheme to generate outof-sample predictions.
To illustrate, McDonald’s was added to the Dow Jones Index on October 30, 1985. The in-sample
estimation period and out-of-sample forecast period are shown in figure 4.
Figure 4: In-Sample and Out-of-Sample Period for Analysis
To forecast the stock return for November 1982, the coefficients of the predictive regression model
are estimated using the data of the estimation period from November 1975 to October 1982. Based
on the estimated coefficients, a forecast is constructed for the stock return of November 1982. In
order to forecast the return for the next month (December 1982), we use the same procedure, but
the estimation period is now moved up one month (to the period spanning December 1975 to
November 1982).
The next step is to compare the out-of-sample forecasts of the regression model from both periods.
I compare the out-of-sample forecasts based on the Mean Square Error (MSE). As discussed in the
first part of the research, the period with the smallest value of MSE gives the best out-of-sample
prediction performance. Therefore, in line with hypothesis 2, I expect that for an index addition the
20
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Faculty of Economics & Management
Master Thesis
MSE in the post-index revision period (i.e., the period of index membership) is less than the MSE in
the pre-index revision period. In contrast, for an index deletion, I expect the MSE in the post-index
revision period to be higher than the MSE in the pre-index revision period.
Although even if the MSE for the period of index membership is less than the MSE of the
alternative period, this does not imply that the out-of-sample forecasts are superior during the
index membership, since the difference between the MSE might not be statistically significant. The
standard way to compare the forecasting performance of the pre- and post-index revision periods
is to apply the forecast accuracy test proposed by Diebold and Mariano (1995). In this paper, I
apply the modified Diebold-Mariano test (MDM test) proposed by Harvey et al. (1997) and Diebold
and Mariano (1995). Because the samples are relatively small, it is better to use the MDM test
because this test corrects for a small sample bias. It is been shown by simulations that the MDM
test has a better performance for small samples (Harvey et al., 1997).
The MDM test takes into account the loss differentials that the predictive regression model
generates for the out-of-sample forecasting in both periods. The loss differentials are the
differences between the squared forecast errors of the two periods:
(
)
(
)
The MDM test statistic is:
[
Where
̅
]
√
̅
is the forecast horizon and where the mean of the difference between the MSEs is:
̅
∑
Where the variance of the mean of the difference between the MSEs is:
̅
Where
is the th autocovariance of
[
∑
]
, which can be estimated as:
∑
̅
̅
The MDM test statistic is compared with a critical value from the t-distribution with n-1 degrees of
freedom rather than the standard normal distribution. The null hypothesis of the MDM test is that
the forecasts based on the predictive regression model are in both periods idem. Meanwhile, the
alternative hypothesis suggests that for one of the periods (pre- or post-index revision period) the
predictive regression model performs better. Consequently, a negative MDM coefficient implies that
21
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Faculty of Economics & Management
Master Thesis
the post-index revision period have better predictive accuracy in comparison with the pre-index
revision period. In contrast, a positive MDM coefficient means the reverse. I expect, according to
hypothesis 2, for stock additions a significant negative MDM coefficient, while for stock deletions I
expect a significant positive MDM coefficient.
Up to this point, the out-of-sample analysis is based on monthly real log returns. Moreover, I
examine the out-of-sample predictability of quarterly and yearly returns. To calculate the quarterly
and yearly returns, I sum up the individual monthly log real returns. In other words, the quarterly
return is the sum of the last three monthly returns, and the yearly return is the sum of the last
twelve monthly returns. Apart from that, the same methodology is used as that for the out-ofsample predictability of monthly returns.
2.3. Third Part: The Impact of Volume and Analyst Following on the Forecast Accuracy
For the third hypothesis, I investigate whether the volume and analyst following have an impact on
the forecast accuracy of the predictive regression model. According to my premise, the volume as
well as the analyst following should increase (decrease) when the stock is added to (deleted from)
the index. Consequently, this increase (decrease) of the volume and analyst following should result
in an increase (decrease) of the out-of-sample predictability.
First, the out-of-sample returns are forecasted for the pre-index and post-index revision periods for
the individual stocks. The methodology is consistent with the out-of-sample analysis of the second
part of the research (the “rolling estimation window” method).
22
The in-sample estimation period
and out-of-sample period are not consistent with the first and second parts of the research,
because the available data of the analyst following is limited.23 If I would choose for an in-sample
and out-of-sample period consistent with the first and second parts of the research, then only a
very limited number of firms could be investigated. Therefore, I opted for an in-sample and out-ofsample period of respectively 4 and 2.5 years. In this way, I obtain as many observations as
possible, but I am still able to examine a large number of stocks.
Afterward, the squared forecast errors are calculated based on the forecasted returns. Then, the
forecast errors are regressed on the analyst following and volume (with interaction terms for the
dummy variable). Data of the volume is obtained from Datastream, while data of analyst following
is collected from the I/B/E/S Database. I measure analyst coverage for each firm by the number of
quarterly earnings estimates. The regression model that is used for the analysis is as follows:
Based on the first dummy variable, I examine whether there exists a significant difference in the
squared forecast errors of the out-of-sample analysis when the company is member of the Dow
Jones Index or not. Moreover, I investigate the impact of volume and analyst following on the
forecast errors. Last, I examine the difference in impact of the volume and the analyst following on
the forecast errors before and after the index revision by the interaction of the volume and analyst
following with the dummy variable.
22
The motivation of the “rolling estimation window” method discussed in the second part of the research
is also valid for the third part of the research.
23
Data of analyst following is available from 1983-2006.
22
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Faculty of Economics & Management
Master Thesis
PART III: RESULTS
In the first part, I examine if a difference in predictability exists between stocks that are members
of the Dow Jones Index and an industry- and size-matched control sample of stocks that are not
member of the index for the period 01:2000 – 12:2009. Table 8 reports the results of the MSEs
and the ratio of the MSE of the index stock to the control stock.
Table 8: Out-of-Sample Predictability Performance of the Dow Jones Index Group and
the Control Group
Dow Jones Index
Group
MSE1
Control Group
MSE2
MSE1 / MSE2
3M
0,0010
Eaton
0,0021
0,4728
American Express
0,0068
SLM
0,0118
0,5782
Boeing
0,0023
Precision Castparts
0,0024
0,9671
Caterpillar
0,0028
Deere
0,0027
1,0422
Coca-Cola
0,0005
Pepsico
0,0007
0,7187
Du Pont
0,0021
Dow Chemical
0,0065
0,3216
Exxon Mobil
0,0005
ConocoPhillips
0,0018
0,2682
General Electric
0,0043
Danaher
0,0008
5,6584
Hewlett-Packard
0,0015
Dell
0,0027
0,5477
Home Depot
0,0015
Lowe’s Companies
0,0021
0,7211
Intel
0,0018
Texas Inst
0,0023
0,8127
IBM
0,0016
Cognizant
0,0026
0,6027
JP Morgan Chase
0,0035
Wells Fargo & Co
0,0068
0,5147
Johnson & Johnson
0,0005
Abbott Laboratories
0,0009
0,6037
McDonald’s
0,0007
Starbucks
0,0032
0,2192
Merck & Co
0,0018
Elli Lilly
0,0013
1,3861
Microsoft
0,0014
Oracle
0,0017
0,8135
Procter & Gamble
0,0007
CLX
0,0007
0,9963
SBC Communications
0,0041
Centurylink
0,0523
0,0779
United Technologies
0,0013
Textron
0,0080
0,1627
Wal-Mart Stores
0,0006
Target
0,0025
0,2428
Walt Disney
0,0012
Comcost 'A'
0,0018
0,6383
MSE1: Mean Square Error of index stock, MSE2: Mean Square Error of control stock
According to hypothesis 1, I expect the MSE of the index stock to be lower than the MSE of the
control stock because the smaller the value of the MSE, the better the out-of-sample prediction
performance. Therefore, I expect the ratio “MSE1 / MSE2” to be lower than 1. The results confirm
the hypothesis that stocks that are members of the Dow Jones Index have a higher predictability
performance compare to the control group of stocks that are not members of the index. Only for
three out of the 22 stocks, it is the case that the ratio “MSE1 / MSE2” is higher than 1, which means
that the MSE of the control stock is lower than the MSE of the index stock.
23
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
In the second part of the research, I examine the impact of Dow Jones composition changes on
the predictability of stock returns. Tables 9 and 10 present the results from the analysis of the
predictive regression model with the index dummy variable. It gives an overview of the estimated
coefficients for the predictor variables and the dummy variables from the whole sample period
(01:1973 – 12:2011). The corresponding p-values are reported in parentheses.
Table 9: In-Sample Analysis of Stock’s Additions
Altria
McDonalds
Coca Cola
Boeing
Caterpillar
Walt Disney
JP Morgan
Travelers
HP
JNJ
Wal Mart
Microsoft
Intel
AT&T
DY
PE
IR
INF
IP
Dummy
0,0371
-0,0016
0,0827
-0,5639
0,1293
0,0068
(0,8089)
(0,0043**)
(0,2156)
(0,2044)
(0,5097)
(0,1271)
0,2679
-0,0003
0,0357
-0,8707
0,1792
0,0011
(0,1897)
(0,0801*)
(0,5954)
(0,0497**)
(0,3648)
(0,7950)
0,0919
-0,0003
0,0652
-0,5942
0,1695
0,0087
(0,5923)
(0,0721*)
(0,2851)
(0,1306)
(0,3472)
(0,0411**)
0,5164
0,0000
-0,0868
-0,1528
0,4124
-0,0026
(0,0081**)
(0,5369)
(0,2706)
(0,7796)
(0,1060)
(0,6351)
0,4810
-0,0001
-0,2055
0,3158
0,9522
0,0004
(0,0091**)
(0,3066)
(0,0178**)
(0,5622)
(0,0003**)
(0,9397)
0,9267
-0,0001
-0,0516
-1,2325
0,0497
-0,0042
(0,0188**)
(0,2707)
(0,5725)
(0,0249**)
(0,8416)
(0,4490)
0,4276
0,0000
0,0226
-0,8189
-0,2510
0,0154
(0,0083**)
(0,2084)
(0,7943)
(0,1300)
(0,3133)
(0,0632*)
0,8524
-0,0001
-0,2183
0,3381
0,3922
0,3922
(0,0001*)
(0,4704)
(0,0136**)
(0,5067)
(0,0839*)
(0,6669)
1,2912
-0,0004
0,0488
-0,7123
0,0653
-0,0096
(0,0622*)
(0,0661*)
(0,5743)
(0,2278)
(0,8074)
(0,1236)
0,3054
-0,0001
0,0871
-0,3291
0,1449
0,0055
(0,1500)
(0,3772)
(0,1000)
(0,3724)
(0,3851)
(0,1162)
-0,6092
-0,0008
0,1129
-1,1178
-0,4154
0,0018
(0,2849)
(0,0125**)
(0,1923)
(0,0411**)
(0,0914*)
(0,7320)
-0,3529
-0,0006
-0,0129
-0,7330
0,5905
-0,0181
(0,5112)
(0,0113**)
(0,9375)
(0,3585)
(0,1424)
(0,0154**)
0,1461
-0,0001
-0,2039
-0,0507
-0,1421
-0,0206
(0,7039)
(0,3914)
(0,0795*)
(0,94603)
(0,68325)
(0,01952**)
0,2298
0,0001
0,0359
-0,2613
0,3844
-0,0040
(0,0389**)
(0,6606)
(0,6870)
(0,6068)
(0,1271)
(0,3634)
Adj R²
4,46 %
2,11 %
1,79 %
0,95 %
4,22 %
1,65 %
1,46 %
3,67 %
1,11 %
1,47 %
2,48 %
4,54 %
0,68 %
1,96 %
DY: dividend yield, PE: price-earnings ratio, IR: interest rate (3 Month Treasury Bill rate), INF: inflation, IP: industrial
production growth, Dummy: index dummy (1 = member of index; 0 = not member of index), *: significance level of 10
%, **: significance level of 5 %
Table 10: In-Sample Analysis of Stock’s Deletions
24
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Navistar
Chevron
Goodyear
DY
PE
IR
INF
IP
Dummy
0,2758
0,0001
0,0207
-1,7233
0,7050
0,0098
(0,1011)
(0,4071)
(0,8939)
(0,0486**)
(0,0714*)
(0,3517)
0,3559
-0,0001
-0,0916
-0,7056
0,5669
0,0023
(0,0036**)
(0,3139)
(0,1338)
(0,0715*)
(0,0022**)
(0,5931)
0,2208
0,0002
-0,0379
-0,5620
0,5179
-0,0062
(0,1092)
(0,2016)
(0,7551)
(0,3954)
(0,0834*)
(0,3751)
Adj R²
1,10 %
3,86 %
0,99 %
DY: dividend yield, PE: price-earnings ratio, IR: interest rate (3 Month Treasury Bill rate), INF: inflation, IP:
industrial production growth, Dummy: index dummy (1 = member of index; 0 = not member of index), *:
significance level of 10 %, **: significance level of 5 %
I expect that the stock returns are significantly higher (lower) for the period after the index
addition (deletion) in comparison with the period before. The dummy variable is not consistently
significant for all stocks added to the Dow Jones Index. Nonetheless, for some stock additions
(Coca Cola, JP Morgan, Microsoft, and Intel), the stock return changes significantly after the index
revision. However, for Microsoft and Intel, the stock return significantly decreases in the period of
index membership. In the case of stock deletions, no firm is confronted with a significant change of
the return after the index revision. To conclude, stock additions (deletions) do not result in an
increase (decrease) of the stock return in the long term. A short-term effect of index revisions on
the stock return is not captured by this regression.
Based on the Chow test, I investigate whether the predictive regression model is faced with a
structural break between the pre- and post-index revision period. The next two tables (tables 11
and 12) report the results of the Chow test where the predictive regression’s F-statistics are given
with the corresponding p-values.
25
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Table 11: The Chow Test of the Stock’s
Additions
Table 12: The Chow Test of the Stock’s
Deletions
F-statistic
p-value
Navistar
1,3354
0,2492
0,6766
Chevron
2,3658
0,0292**
3,9264
0,0008**
Goodyear
3,5734
0,0019**
Boeing
2,6909
0,0141**
Caterpillar
2,2853
0,0351**
Walt Disney
4,5989
0,0002**
JP Morgan
3,2586
0,0038**
Travelers
1,9207
0,0762*
HP
2,2730
0,0359**
JNJ
2,5117
0,0211**
Wal Mart
2,6615
0,0151**
Microsoft
2,7196
0,0202**
Intel
2,2788
0,0355
AT&T
4,1942
0,0004**
F-statistic
p-value
Altria
2,2697
0,0361**
McDonalds
0,6668
Coca Cola
*: significance level of 10 %, **: significance
level of 5 %
*: significance level of 10 %, **: significance
level of 5 %
The results of the Chow test show that for most of the stocks, the predictive regression model
experiences a structural break between the pre- and post-index revision period. For 13 out of the
17 stocks, I can reject the null hypothesis of no structural break at a 5 % significance level.
Therefore, I can conclude that the index revision does have a significant impact on the in-sample
predictability of the regression model. Nonetheless, I cannot conclude that index membership leads
to a significantly higher explanatory power of the forecasting model because the adjusted R² is not
consistently higher (lower) for the period after the index addition (deletion). For many of the stock
additions, the adjusted R² decreases after the index addition. Moreover, the adjusted R² of two out
of the three stock deletions do not decrease after the index deletion. The results of the analysis are
reported in appendix D. To summarize, I cannot support hypothesis 2 because the explanatory
power of the predictive model of many stock additions (deletions) do not increase (decrease).
Apart from the in-sample analysis, part two consists of an out-of-sample analysis. Tables 13 and
14 report the results of the MSE for the pre-index revision period (MSE1) and the post-index
revision period (MSE2). The third column presents the ratio of the MSE of pre-index revision period
to post-index revision period. A number greater than one indicates that the predictive regression
model has lower forecasting errors for the post-index revision period than for the pre-index
revision period. The modified Diebold-Mariano test statistic appears in the column labeled “MDM
test”. The MDM test statistic is compared with a critical value from the t-distribution with n-1
degrees of freedom, where n is the number of out-of-sample forecast observations. The 95 percent
critical value for this statistic based on a t35 distribution is 2,03.
26
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Consequently, in order to reject the null hypothesis, the MDM statistic should be higher than 2,03
or lower than -2,03. The first case means that MSE2 is significantly higher than MSE1, and
therefore, the predictive regression model performs significantly better for the pre-index revision
period in comparison with the post-index revision. The second case implies that MSE1 is
significantly higher than MSE2, which means that in the post-index revision period the predictive
regression model produces superior forecast in comparison with the pre-index revision period.
Table 13: Out-of-Sample Analysis of Stock’s Additions
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Altria
0,0007
0,0013
0,4887
1,1170
H0: MSE1 = MSE2
McDonalds
0,0005
0,0005
0,9241
0,4650
H0: MSE1 = MSE2
Coca Cola
0,0009
0,0004
2,4853
-2,1250
H1: MSE1 > MSE2
Boeing
0,0010
0,0010
1,0895
-0,2500
H0: MSE1 = MSE2
Caterpillar
0,0014
0,0024
0,5694
3,0510
H1: MSE1 < MSE2
Walt Disney
0,0012
0,0019
0,6425
1,1710
H0: MSE1 = MSE2
JP Morgan
0,0033
0,0038
0,8703
0,1980
H0: MSE1 = MSE2
Travelers
0,0007
0,0009
0,7847
0,6810
H0: MSE1 = MSE2
HP
0,0012
0,0008
1,5022
-1,0860
H0: MSE1 = MSE2
JNJ
0,0006
0,0002
2,5883
-3,7740
H1: MSE1 > MSE2
Wal Mart
0,0011
0,0005
2,1767
-2,6500
H1: MSE1 > MSE2
Microsoft
0,0026
0,0014
1,8867
-1,5490
H0: MSE1 = MSE2
Intel
0,0030
0,0018
1,6254
-26,6020
H1: MSE1 > MSE2
AT&T
0,0016
0,0009
1,7827
-2,5950
H1: MSE1 > MSE2
MSE1: Mean Square Error of pre-index revision period, MSE2: Mean Square Error of post-index revision period, MDM:
modified Diebold Mariano
Table 14: Out-of-Sample Analysis of Stock’s Deletions
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Navistar
0,0018
0,0043
0,4161
2,2100
H1: MSE1 < MSE2
Chevron
0,0007
0,0008
0,8561
0,6710
H0: MSE1 = MSE2
Goodyear
0,0018
0,0095
0,1870
2,1420
H1: MSE1 < MSE2
MSE1: Mean Square Error of pre-index revision period, MSE2: Mean Square Error of post-index revision period, MDM:
modified Diebold Mariano
27
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
For most of the stock additions, it is the case that the MSE in the post-index revision period is less
than the MSE in the pre-index revision period, although there are a number of stock additions that
do not fit with this premise. The difference between the MSEs is statistically significant for roughly
half of the stock additions. For one stock addition (Coca Cola), I can conclude that the MSE in the
post-index revision period is significantly higher than the MSE in the pre-index revision period. For
the other five stock additions, it is the case that the MSE in the post-index revision period is
significant lower than the MSE in the pre-index revision period, which is consistent with hypothesis
2.
For all index deletions, the MSE in the post-index revision period is higher than the MSE in the preindex revision period. Based on the MDM test, I can conclude that two stocks experience a
significant decrease of the out-of-sample predictability after the deletion, which is in line with
hypothesis 2.
To summarize, the analysis of the out-of-sample predictability confirms to an extent hypothesis 2
that stocks’ additions (deletions) results in an increase (decrease) of the out-of-sample prediction
performance of the predictive regression model. This is because for most stock additions (and all
stock deletions) it is the case that MSE in the post-index revision period is less (higher) compared
the MSE in the pre-index revision period. However, for only a limited number of stocks, the MSEs
are significantly different.24
In addition to the predictability of monthly returns, I also examine the out-of-sample predictability
of quarterly and yearly returns. The results of the analysis are shown in appendix E.
For the
predictability of yearly stock returns, I come to more or less the same conclusion as with the
predictability of monthly returns. For most of the stock additions, the MSE in the post-index
revision period is less than the MSE in the pre-index revision period. For all of the stock deletions,
it is the case that the MSE in the post-index revision period is higher than the MSE in the pre-index
revision period. Nevertheless, the differences between the MSEs are for more than half of the
stocks not statistically significant. The evidence for the yearly returns is much weaker. Although
the MSE in the post index revision period is less (greater) than the MSE in the pre-index revision
period for most of the stock additions (deletions), for almost none of the stocks it is the case that
the out-of-sample forecasts are significant superior during the index membership.
For the third part of the research, I investigate whether the volume and analyst following have a
positive impact on the out-of-sample forecasts and whether this impact is higher or lower in the
period of index membership.25 The next table presents the parameter estimates for the regression
discussed in the methodology part. The corresponding p-values are reported in parentheses.
Table 15: Impact of Volume and Analyst Following for Stock’s Additions
Boeing
Dummy
VOL
ANALYST
DmVOL
DmANALYST
Adj R²
0,0019
1,216E-08
0,0004
-1,158E-08
-0,0004
3,15 %
24
The same analysis is done based on the Root Mean Square Error (RMSE), which is also a frequently
used method to compare the out-of-sample forecasts. The RMSE is the square root of the average of the
square of the forecast errors and is given by following formula:
√ ∑
̂
. Logically, the
same conclusions can be formed as the results of the MSE.
25
Due to missing data of the analyst following, a couple of firms had to be deleted, and a sample of ten
firms, of which eight are stock additions and two are stock deletions, is left for observation.
28
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Caterpillar
Walt Disney
JP Morgan
JNJ
Microsoft
Intel
AT&T
(0,3837)
(0,5846)
(0,0916*)
(0,6839)
(0,0931*)
0,0019
0,0000
0,0002
0,0000
-0,0001
(0,3643)
(0,0839*)
(0,0770*)
(0,3061)
(0,3787)
-0,0004
1,696E-08
0,0001
-1,563E-09
4,278E-05
(0,7891)
(0,0346**)
(0,3380)
(0,9114)
(0,6410)
1,879E-05
4,828E-09
-1,32E-05
-4,827E-09
1,317E-05
(0,8796)
(0,0148**)
(0,4167)
(0,0212**)
(0,4632)
0,0014
9,799E-09
0,0001
-7,29E-09
-0,0001
(0,4544)
(0,4733)
(0,4721)
(0,6374)
(0,2800)
0,0002
2,117E-09
1,454E-06
-1,403E-09
7,553E-06
(0,9466)
(0,1789)
(0,9783)
(0,5658)
(0,9121)
0,0016
2,747E-09
1,21E-05
-9,887E-10
-0,0001
(0,7274)
(0,1988)
(0,8175)
(0,7621)
(0,3282)
-0,0011
-1,081E-08
0,0002
1,046E-08
-0,0002
(0,7360)
(0,6705)
(0,1121)
(0,7102)
(0,1388)
24,14 %
17,27 %
9,42 %
- 0,05 %
6,66 %
0,31 %
10,44 %
Dummy: index dummy (1 = member of index; 0 = not member of index), VOL: Volume, ANALYST: Analyst following,
DmVOL: index dummy x volume, DmANALYST: index dummy x analyst following, Adj R²: Adjusted R², * =
significance level of 10 %, ** = significance level of 5 %
Table 16: Impact of Volume and Analyst Following for Stock’s Additions
Chevron
Goodyear
Dummy
VOL
ANALYST
DmVOL
DmANALYST
0,0031
4,264E-08
-2,447E-05
-4,726E-08
-9,308E-06
(0,0684*)
(0,042**)
(0,4808)
(0,0286**)
(0,8714)
0,0016
1,195E-08
0,0003
0,0000
-0,0003
(0,7311)
(0,9389)
(0,2020)
(0,9441)
(0,5288)
Adj R²
5,46 %
- 10,37 %
Dummy: index dummy (1 = member of index; 0 = not member of index), VOL: Volume, ANALYST: Analyst following,
DmVOL: index dummy x volume, DmANALYST: index dummy x analyst following, Adj R²: Adjusted R², * =
significance level of 10 %, ** = significance level of 5 %
I expected the dummy variable to be significant and negative, which means that the forecast errors
are lower for the period of index membership in comparison with the period of non-membership.
Based on the results of the regression, I conclude that the forecast errors of the pre- and postindex revision period are not significantly different, with the exception of Chevron.
Furthermore, the coefficients of the volume and analyst following should be significant and
negative. This is because, according to my premise, an increase in volume and analyst following
should improve the predictability of stock returns. However, for only a limited number of stocks,
the volume and/or analyst following has significant impact on the forecast errors. In addition, the
positive sign of the coefficients of the volume and analyst following is not consistent with my
premise, because the results suggest a negative impact of the volume and the analyst following on
the out-of-sample forecasts.
Last, I expect a significant negative coefficient for the interaction of the volume and analyst
following with the dummy variable, which means that the impact of volume/analyst following on
the out-of-sample predictability is significant higher for the period of index membership in
29
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
comparison with the period of non- membership. The coefficient of the interaction term is for most
of the stocks negative, though the impact of the volume and analyst following on the forecast
errors is for almost none of the stocks significant different when comparing the period of
membership in the index with the period of non-membership.
To summarize, I cannot support hypotheses 3 (a) and (b). I expect the impact of volume and
analyst following on the out-of-sample predictability to be significant higher for the period of index
membership, and therefore this should result in better out-of-sample forecasts. However, the
results of the regression do not confirm this premise.
A reason for the weak evidence of the volume might be the fact that stocks which are added to the
Dow Jones Index are already large companies that are well known to the investors, and therefore
do not lead to an increase of the investors’ awareness. A stock that is added to the Dow Jones
Index is a company that already has an excellent reputation, is of interest to a large number of
investors, and is representative of its industry sector.
One possible reason for the lack of significance of the analyst following could be a lack of variance
of the data of analyst following. If there is a very low variance of the analyst following before and
after the index revision, then it is reasonable that this will lead to non-significant coefficients of the
analyst following. Unfortunately, by calculating the coefficient of time-series variation (i.e.,
standard deviation/mean), this argument is not valuable because the coefficient of variation of the
individual stocks range from a minimum of 0,44 to a maximum of 1,24.
Furthermore, I examine the impact of index revision on the information environment of the stock
based on an analysis of the analyst following. However, as stated in the literature review, there
exists also other channels that distribute information to the investors, e.g., the media. The media
(such as newspapers) play an important role in disseminating information to individual investors.
Therefore, this model could be further refined by examining the impact of media coverage on the
forecast errors (which is often proxied by the number of financial newspaper articles about the
stock).
30
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
PART IV: CONCLUSION
First, I investigated whether stocks that are members of the Dow Jones Index have better out-ofsample forecasts then a matched control group of stocks that are not members of the index
(hypothesis 1). Based on the results, I am able to confirm hypothesis 1, because the MSE is for
nearly all of the index stocks higher than the MSE of the control stock. Second, I examined
whether a stock’s addition to (or deletion from) the Dow Jones Index results in an increase (or
decrease) in the in-sample and out-of-sample predictability of stock returns (hypothesis 2). For the
in-sample analysis, I cannot confirm hypothesis 2. Although a structural break is detected for many
of the stocks, the adjusted R² for stock additions (stock deletions) is not preponderantly higher in
the period after the index revision. In other words, I cannot conclude that an index addition
(deletion) results in an upward (downward) structural change of the in-sample predictability. For
the out-of-sample analysis, I conclude that the MSE decreases for a high number of stock additions
after the index revision (i.e., a stock addition results in an increase of the out-of-sample
predictability). For all stock deletions, it is the case that the MSE is lower after the index revision
(i.e., a stock deletion results in a decrease of the out-of-sample analysis). However, based on the
MDM test, an index addition results for only a limited number of stocks to significant superior outof-sample forecasts. For the predictability of yearly returns, the same conclusion can be
formulated, while for the predictability of quarterly stock returns, almost none of the stock
additions (deletions) results in a significant increase (decrease) of the out-of-sample forecasts.
Lastly, I investigated the impact of volume and analyst following on the out-of-sample
predictability. Furthermore, I investigated whether volume and analyst following have a
significantly higher impact on the out-of-sample predictability for the period of index membership
in comparison with the period of non-membership (hypothesis 3). Based on my research, I cannot
support hypothesis 3 because volume and analyst following do not have significant impact on the
out-of-sample predictability for most stocks. Moreover, the results suggest a negative impact of the
volume and analyst following on the out-of-sample predictability. Additionally, the impact of
volume and analyst following on the out-of-sample predictability is not significantly higher for the
period of index membership.
To conclude, my research part shows that a stock that is added to the index has a very limited
significant impact on the predictability of stock returns. Therefore, this empirical study provides
evidence of the market efficiency. This is because, according to the market efficiency, it should not
be the case that the predictability increases when the stock is added to the index. If this would be
the case, then trading strategies could be generated based on my predictor variables. So, if a stock
is added to the index, this would lead to higher predictability. Then the investor could use the
model to forecast the returns and buy and sell stocks based on the predicted returns.
Finally, I would like to provide a number of suggestions for further research. First, further research
could focus on the implications of index revisions on predictability in the short-term, because my
empirical research is based on the long-term effect on predictability. A second possible avenue for
further research would be to construct a predictive regression model with more comprehensive and
nonstandard predictor variables. My empirical study is based on five standard predictor variables,
but it does not show a high level of predictability. Third, it would be worthwhile to construct a
control group based on a detailed number of benchmark criteria. This would improve the reliability
of the first part of the research, as the index stocks would be better matched. Another possible
avenue for further research would be to explore whether media following has a positive effect on
31
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
the predictability of stock returns. In my research, I only take into account analyst following to
detect a change in the information environment of the stock due to an index revision. A final
suggestion for further research would be to investigate the impact of stock additions and deletions
on the predictability of stock returns for other stock indices. In contrast to the Dow Jones Index,
the predictability in a relatively small stock market could be investigated. One may argue that the
level of market inefficiency in a small stock market is higher, and this therefore results in a greater
predictability.
32
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
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Master Thesis
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Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
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38
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
APPENDIX A: Additions and deletions of the Dow Jones Index since 1929
Date
January 8, 1929
September 14, 1929
January 29, 1930
Additions
Deletions
National Cash Register
Victor Talking Machine
Curtiss-Wright
Wright Aeronautical
Johns-Manville
Borden, Eastman Kodak Company,
Goodyear, Liggett & Myers, Standard Oil
of California, United Air Transport
and Hudson Motor
American Tobacco B, Drug Incorporated,
Procter & Gamble Company, Loew’s,
Nash Motors, International Shoe,
International Business Machines and
Coca-Cola Company
Corn Products Refining and United
Aircraft
National Distillers
North American
Borden and Coca-Cola Company
August 30, 1982
Du Pont and National Steel
United Aircraft and American Telephone
& Telegraph
International Paper Company
Anaconda Copper, Swift & Company,
Aluminum Company of America and
Owens-Illinois Glass
Minnesota Mining & Manufacturing
International Business Machines and
Merck
American Express Company
October 30, 1985
Philip Morris Companies and McDonald’s
Corporation
March 12, 1987
Coca-Cola and Boeing Company
May 6, 1991
Caterpillar Incorporated, Walt Disney
Company and J.P. Morgan & Company
July 18, 1930
May 26, 1932
August 15, 1933
August 13, 1934
November 20, 1935
March 14, 1939
July 3, 1956
June 1, 1959
August 9, 1976
June 29, 1979
March 17, 1997
November 1, 1999
January 27, 2003
April 8, 2004
February 19, 2008
September 22, 2008
June 8, 2009
Travelers Group, Hewlett-Packard
Company, Johnson & Johnson and WalMart Stores Incorporated
Microsoft Corporation, Intel Corporation,
SBC Communications and Home Depot
Incorporated
Philip Morris Companies
American International Group
Incorporated, Pfizer Incorporated and
Verizon Communications Incorporated
Bank of America Corporation and
Chevron Corporation
Kraft Foods Inc
The Travelers Companies and Cisco
Systems
American Sugar, American Tobacco B, Atlantic
Refining, General Railway Signal,
Goodrich, Nash Motors and Curtiss-Wright
Liggett & Myers, Mack Trucks, United Air
Transport, Paramount Publix, Radio
Corporation, Texas Gulf Sulphur, National Cash
Register and Hudson Motor
Drug Incorporated and International Shoe
United Aircraft
Kelvinator and International Business Machines
Loew’s
American
Smelting, Corn Products Refining, National
Steel and National Distillers
Anaconda Copper
Chrysler and Esmark
Manville Corporation (Johns-Manville)
General Foods and American Brands
Incorporated
(American Tobacco B)
Owens-Illinois Glass and Inco
Navistar International
Corp., USX Corporation and Primerica
Corporation
Westinghouse Electric, Texaco Incorporated,
Bethlehem Steel and Woolworth
Chevron Corporation, Goodyear Tire & Rubber
Company, Union Carbide Corporation and
Sears, Roebuck
Altria Group, Incorporated
Corporation, Eastman Kodak Company and
International Paper Company
Altria Group, Incorporated and Honeywell
International, Incorporated
American International Group
Citigroup and General Motors Corporation
39
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
APPENDIX B: Summary statistics of the individual stocks
LOG RR
Altria
McDonalds
Boeing
Caterpillar
0,0026
Coca
Cola
0,0024
JP
Morgan
0,0017
Travelers
HP
JNJ
0,0020
Walt
Disney
0,0015
Microsoft
Intel
AT&T
Navistar
Chevron
Goodyear
0,0022
Wal
Mart
0,0052
Mean
Median
Min
Max
SD
0,0043
0,0036
0,0019
0,0017
0,0072
0,0046
0,0030
-0,0032
0,0028
-0,0003
0,0068
0,0054
0,0034
0,0057
0,0032
0,0033
0,0018
0,0039
0,0015
0,0028
0,0050
0,0085
0,0063
0,0044
-0,0033
0,0036
-0,0020
-0,1357
-0,1599
0,1283
0,1523
-0,1559
-0,1872
-0,1824
-0,2059
-0,1899
0,0981
0,1801
0,1465
0,1497
0,1225
-0,1562
-0,1674
-0,0837
-0,1836
-0,1828
-0,2691
-0,0914
-0,2537
-0,0908
-0,2333
0,1846
0,1312
0,0810
0,1993
0,1790
0,2102
0,1114
0,1925
0,1315
0,2440
0,0322
0,0318
0,0288
0,0399
0,0386
0,0399
0,0416
0,0363
0,0431
0,0270
0,0396
0,0449
0,0546
0,0288
0,0566
0,0294
0,0501
DY
Altria
McDonalds
Boeing
Caterpillar
Mean
0,0426
0,0121
Coca
Cola
0,0280
JP
Morgan
0,0529
Travelers
HP
JNJ
Intel
AT&T
Navistar
Chevron
Goodyear
0,0338
0,0083
0,0204
Wal
Mart
0,0084
Microsoft
0,0249
Walt
Disney
0,0091
0,0246
Median
0,0420
0,0104
0,0258
0,0226
0,0055
0,0056
0,0472
0,0131
0,0458
0,0345
0,0238
0,0076
0,0478
0,0323
0,0072
0,0205
0,0061
0,0000
0,0000
0,0479
0,0000
0,0417
0,0274
Min
0,0097
0,0000
0,0070
0,0081
0,0052
0,0011
0,0043
0,0000
0,0017
0,0041
0,0029
0,0000
0,0000
0,0168
0,0000
0,0245
0,0000
Max
0,0951
0,0385
0,0775
0,0903
0,0800
0,0253
0,1755
0,0732
0,0274
0,0410
0,0281
0,0322
0,0440
0,1011
0,0952
0,0950
0,1180
SD
0,0156
0,0087
0,0158
0,0117
0,0116
0,0051
0,0276
0,0113
0,0044
0,0076
0,0056
0,0087
0,0103
0,0175
0,0262
0,0151
0,0284
PE
Altria
McDonalds
Boeing
Caterpillar
JNJ
Intel
AT&T
Navistar
Chevron
Goodyear
20,104
19,891
15,195
21,611
22,818
Wal
Mart
24,835
Microsoft
19,934
JP
Morgan
30,394
HP
13,040
Walt
Disney
30,460
Travelers
Mean
Coca
Cola
24,976
31,309
29,805
16,746
16,823
15,995
15,963
Median
12,700
17,850
21,500
13,650
13,000
22,800
11,850
8,950
19,050
18,500
23,700
29,850
20,450
15,600
8,750
10,500
10,650
Min
5,800
8,400
8,200
3,800
4,300
8,000
2,600
4,100
5,300
10,500
7,900
8,700
7,000
7,600
1,600
3,100
4,100
Max
31,300
69,400
94,300
253,90
307,00
369,50
840,60
166,50
61,90
141,40
59,100
71,60
184,40
53,900
412,50
156,40
116,40
SD
3,819
9,575
13,639
27,211
30,764
34,389
84,034
21,773
9,227
13,297
9,701
13,598
25,261
7,284
38,912
16,669
18,418
40
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
The correlation matrix of the predictor variables for each individual stock is given below. The results show that the predictor variables are not strong
correlated with each other in many cases. Therefore, there is no problem of multicollinearity in the predictive regression model. Only for a limited number
of stocks, there is a relatively high negative correlation between the dividend yield and the price-earnings ratio. Moreover for some stocks, the interest
rate and the dividend yield is high correlated.
Altria
DY
DY
PE
IR
INF
IP
1
-0,55
-0,419
-0,3567
-0,0432
PE
IR
1
-0,1767
0,0435
0,0438
1
0,4511
-0,0134
INF
IP
1
-0,0122 1
DY
DY
PE
IR
INF
IP
1
-0,3514
-0,3532
-0,2274
-0,1088
Boeing
DY
DY
PE
IR
INF
IP
1
-0,3229
0,4158
0,1883
-0,1924
PE
1
-0,2521
-0,202
0,0891
IR
1
0,4461
-0,0104
INF
IP
1
-0,0088 1
DY
DY
PE
IR
INF
IP
1
-0,1418
0,3625
0,2358
-0,271
McDonalds
PE
IR
1
-0,2067
0,0302
0,0665
1
0,4511
-0,0134
Caterpillar
PE
IR
1
-0,0685
-0,0654
0,0803
1
0,4788
-0,049
INF
IP
1
-0,0122 1
INF
DY
DY
PE
IR
INF
IP
IP
1
-0,0299 1
1
-0,7216
0,6011
0,3222
-0,1077
DY
DY
PE
IR
INF
IP
1
-0,0779
0,3008
-0,0101
-0,115
Coca Cola
PE
IR
1
-0,243
-0,1669
0,0395
1
0,4511
-0,0134
Walt Disney
PE
IR
1
-0,131
-0,1378
0,001
1
0,4511
-0,0134
INF
IP
1
-0,0122 1
INF
IP
1
-0,0122 1
41
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
DY
DY
PE
IR
INF
IP
1
-0,2244
0,7153
0,4324
-0,1119
JP Morgan
PE
IR
1
-0,1779
-0,0825
0,0657
1
0,4518
-0,0153
INF
IP
1
-0,0086 1
DY
DY
PE
IR
INF
IP
1
-0,0824
0,6355
0,241
-0,021
1
-0,0381
-0,0443
0,0029
DY
Wal Mart
PE
IR
JNJ
DY
DY
PE
IR
INF
IP
1
-0,6222
-0,0443
-0,0933
-0,0358
PE
IR
1
-0,0583
0,0438
0,0255
1
0,4511
-0,0134
INF
IP
1
-0,0122 1
DY
PE
IR
INF
IP
1
-0,6501
-0,4528
-0,0732
-0,0902
Intel
DY
DY
PE
IR
INF
IP
42
1
-0,2057
-0,6172
-0,2673
-0,1067
PE
1
0,062
0,0866
0,0858
IR
1
0,4632
-0,0141
INF
IP
1
-0,0201 1
DY
DY
PE
IR
INF
IP
Travelers
PE
IR
1
-0,5161
0,1739
0,0628
-0,0145
1
-0,0501
-0,2129
0,133
AT&T
PE
1
0,1363
-0,0416
0,0952
1
0,453
-0,008
1
0,4511
-0,0134
IR
1
0,1981
0,0957
HP
INF
IP
1
-0,0208 1
INF
PE
PE
IR
INF
IP
IP
1
0,0472 1
PE
1
-0,2943
-0,5284
-0,3129
0,0053
IP
1
-0,0122 1
INF
DY
DY
PE
IR
INF
IP
1
0,3155
0,0111
0,2079
DY
DY
PE
IR
INF
IP
1
-0,0894
0,5204
0,5388
-0,0103
IR
1
0,045
0,1288
0,1377
1
0,4499
-0,0204
Microsoft
IR
1
0,1985
0,0892
Navistar
PE
IR
1
-0,0011
-0,0137
0,0238
1
0,4695
0,0291
INF
IP
1
-0,0209 1
INF
IP
1
0,0295
INF
1
IP
1
-0,0283 1
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
DY
DY
PE
IR
INF
IP
1
-0,3038
0,557
0,2683
-0,0773
Chevron
PE
IR
1
-0,1744
-0,0936
0,0748
1
0,4488
-0,0126
INF
IP
1
-0,0147 1
DY
DY
PE
IR
INF
IP
1
-0,3256
0,7518
0,5498
-0,119
Goodyear
PE
IR
1
-0,3295
-0,2048
0,0568
1
0,4847
-0,0127
INF
IP
1
0,0004 1
43
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
APPENDIX C: Sample composition of firms to the Dow Jones Index and the control sample
Dow Jones Index Group
Company
3M
American Express
Boeing
Caterpillar
Coca-Cola
Du Pont
Exxon Mobil
General Electric
Hewlett-Packard
Home Depot
Intel
IBM
JP Morgan Chase
Johnson & Johnson
McDonald’s
Merck & Co
Microsoft
Procter & Gamble
AT&T
United Technologies
44
Control Group
Industry
Market
Capitalization*
Diversified Industrials
Consumer finance
Aerospace
Commercial vehicles
Soft drinks
Commodity Chemicals
Integrated Oil and Gass
Diversified Industrials
Computer hardware
Home improvement retailer
Semiconductors
Computer services
Banking
Pharmaceuticals
Restaurants and Bars
Pharmaceuticals
Software
Nondurable household products
Fixed Line Telecommunications
Aerospace
$ 51 975,26
$ 57 174,08
$ 45 869,13
$ 28 767,81
$ 116 823,30
$ 41 366,36
$ 337 139,34
$ 339 625,69
$ 81 772,49
$ 78 755,83
$ 164 649,84
$ 152 914,17
$ 110 246,96
$ 167 309,60
$ 44 768,73
$ 107 571,38
$ 285 981,03
$ 145 775,32
$ 135 908,77
$ 47 487,12
Company
Eaton
SLM
Textron
Deere
Pepsico
Dow Chemical
ConocoPhillips
Danaher
Dell
Lowe's Companies
Qualcomm
Cognizant
Wells Fargo
Abbott Laboratories
Starbucks
Elli Lilly
Oracle
Clorox
CenturyLink
Textron
Industry
Market
Capitalization*
Diversified Industrials
Consumer finance
Aerospace
Commercial vehicles
Soft drinks
Commodity Chemicals
Integrated Oil and Gass
Diversified Industrials
Computer hardware
Home improvement retailer
Semiconductors
Computer services
Banking
Pharmaceuticals
Restaurants and Bars
Pharmaceuticals
Software
Nondurable household products
Fixed Line Telecommunications
Aerospace
$ 15 574,97
$ 30 418,17
$ 8 460,19
$ 16 905,98
$ 87 321,96
$ 32 617,57
$ 63 859,99
$ 15 574,97
$ 67 097,40
$ 37 082,02
$ 56 868,73
$ 5 039,86
$ 95 865,19
$ 73 071,42
$ 14 326,41
$ 73 071,42
$ 93 318,19
$ 9 120,90
$ 5 912,00
$ 8 460,19
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Wal-Mart Stores
Broadline retailers
$ 218 698,80
Target
Walt Disney
Broadcasting and Entertainment
$ 54 548,26
Comcast 'A'
* Average of the market capitalization in million dollars for period 01:2000-12:2009
Broadline retailers
Broadcasting and Entertainment
$ 37 949,60
$ 29 866,22
APPENDIX D: In-sample analysis of the predictive regression model for the pre- and post-index revision period
Additions
BEFORE
Altria
McDonalds
Coca Cola
Boeing
Caterpillar
Walt Disney
JP Morgan
Travelers
HP
AFTER
DY
PE
IR
INF
IP
R²
DY
PE
IR
INF
IP
R²
0,1336
0,7427
0,2737
0,7286
1,0549
0,0102**
0,5712
0,0562*
0,4712
0,0884*
1,2486
0,0417**
1,2366
0,0001**
0,9496
0,0002**
0,7068
-0,0010
0,2355
-0,0004
0,1354
0,0005
0,3174
-0,0021
0,1310
-0,0002
0,1658
-0,0003
0,0346**
0,0019
0,1542
0,0000
0,7058
-0,0004
-0,0497
0,7012
-0,0312
0,8457
-0,3616
0,0176**
-0,4719
0,0004**
-0,2802
0,0343**
-0,2064
0,2481
-0,0171
0,8900
-0,2907
0,0171**
-0,0509
-0,6845
0,3859
-1,1634
0,1546
-1,2103
0,0458**
-0,3883
0,6698
-0,5525
0,5048
-2,7369
0,0014**
-2,5127
0,0027**
0,0504
0,9432
-0,8370
0,1207
0,6659
-0,1280
0,6503
-0,1183
0,6317
0,3140
0,3773
0,2739
0,4298
-0,6267
0,0519
-0,1578
0,6347
0,2087
0,4628
-0,6003
6,52 %
0,0429
0,9256
0,2159
0,7962
-0,8987
0,085**
0,2015
0,6649
0,3258
0,4171
2,1334
0,1621
-1,0742
0,0023**
1,1344
0,0829*
0,2499
-0,0016
0,1914
0,0003
0,5372
-0,0007
0,1581
0,0022
0,1194
0,0001
0,4001
0,0003
0,0562*
-0,0019
0,1601
-0,0002
0,3282
-0,0002
0,2998
0,0705*
0,1483
0,4406
0,5135
0,0028**
0,6293
0,0003**
0,1563
0,3981
0,4683
0,07*
0,1923
0,2860
0,4021
0,0335**
0,3606
0,2401
0,8039
0,4958
0,6174
1,3644
0,0871*
0,2900
0,8026
1,7094
0,1232
3,1857
0,0049**
2,6290
0,0186**
0,9674
0,3386
0,7655
-0,0268
0,9455
0,5992
0,1336
0,5430
0,1283
0,2449
0,6296
1,5050
0,0039**
1,6143
0,0014**
-0,1503
0,7653
0,3380
0,4789
1,6243
7,62 %
5,19 %
10,20 %
8,22 %
5,25 %
10,03 %
14,65 %
5,66 %
4,88 %
2,74 %
4,22 %
3,02 %
9,88 %
5,53 %
1,38 %
11,02 %
4,51 %
Chow
test
2,2697
0,0361**
0,6668
0,6766
3,9264
0,0008**
2,6909
0,0141**
2,2853
0,0351**
4,5989
0,0002**
3,2586
0,0038**
1,9207
0,0762*
2,2730
45
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
JNJ
Wal Mart
Microsoft
Intel
AT&T
0,5298
0,6351
0,0343**
2,4533
0,1793
-0,9856
0,1979
2,2058
0,4504
0,1414
0,4009
0,2632
-0,0001
0,5975
-0,0005
0,2938
-0,0005
0,0645*
0,0000
0,7775
0,0001
0,7743
0,6860
0,0038
0,9596
0,1418
0,1423
0,2017
0,4388
-0,1144
0,4344
0,0129
0,9398
0,3059
-1,1090
0,0366**
-2,8454
0,0003**
-4,6122
0,0081**
-1,4478
0,1653
-1,2423
0,2300
0,0732*
-0,1332
0,5256
-0,7347
0,0174**
0,5821
0,3926
-0,5333
0,2090
0,3918
0,3232
5,63 %
7,89 %
5,94 %
2,66 %
2,35 %
0,8618
-1,1294
0,099*
-3,3403
0,1013
-2,3435
0,4293
0,7554
0,0121**
0,6689
-0,0007
0,2597
-0,0003
0,7333
-0,0006
0,2937
-0,0004
0,1315
-0,0028
0,0054**
0,1274
0,0412
0,7860
-0,0131
0,9480
-0,2322
0,4920
-0,1834
0,5622
1,0749
0,0003**
0,5173
1,7367
0,0203**
3,1523
0,004**
4,7172
0,0157**
2,8844
0,0564*
1,0885
0,3554
0,004**
0,6442
0,0656*
0,9087
0,0752*
0,0001
0,9999
1,0741
0,1499
-0,0837
0,8681
4,18 %
2,87 %
5,00 %
4,63 %
15,53 %
0,0359**
2,5117
0,0211**
2,6615
0,0151**
2,7196
0,0202**
2,2788
0,0355
4,1942
0,0004**
Deletions
BEFORE
Navistar
Chevron
Goodyear
46
AFTER
Chow test
DY
PE
IV
IR
IP
R²
DY
PE
IR
INF
IP
R²
0,4405
0,0192**
0,4220
0,0033**
0,2719
0,1006
0,0001
0,3725
0,0001
0,4866
0,0001
0,4326
0,0583
0,8422
-0,1009
0,1678
-0,0791
0,5302
-4,2590
0,0107**
-0,7702
0,1425
-1,6245
0,0595*
0,1785
0,7622
0,4698
0,0354**
-0,1438
0,6707
11,93 %
1,9362
0,0048**
-0,1025
0,7744
-0,0001
0,8830
-0,0005
0,0147**
0,0001
0,7872
0,0019
0,9957
0,3149
0,0482**
-0,0361
0,9315
3,8415
0,0525*
0,5053
0,5211
2,4626
0,0695*
0,8720
0,2740
0,2548
0,5165
2,7143
0,0002**
2,07 %
4,74 %
2,39 %
22,44 %
16,57 %
1,3354
0,2492
2,3658
0,0292**
3,5734
0,0019**
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
APPENDIX E: Out-of-sample comparison of the predictive regression model for quarterly
and yearly returns
Additions (Quarterly returns)
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Altria
0,0019
0,0041
0,4652
2,175
H1: MSE1 < MSE2
McDonalds
0,0014
0,0013
1,0699
-0,242
H0: MSE1 = MSE2
Coca Cola
0,0024
0,0009
2,7478
-0,946
H0: MSE1 = MSE2
Boeing
0,0048
0,0034
1,4118
-0,391
H0: MSE1 = MSE2
Caterpillar
0,0038
0,0054
0,6959
0,714
H0: MSE1 = MSE2
Walt Disney
0,0039
0,0069
0,5648
1,897
H0: MSE1 = MSE2
JP Morgan
0,0127
0,0096
1,3141
-0,379
H0: MSE1 = MSE2
Travelers
0,0011
0,0015
0,7380
0,851
H0: MSE1 = MSE2
HP
0,0037
0,0040
0,9147
0,621
H0: MSE1 = MSE2
JNJ
0,0014
0,0010
1,4242
-0,624
H0: MSE1 = MSE2
Wal Mart
0,0024
0,0008
3,0603
-2,164
H1: MSE1 > MSE2
Microsoft
0,0037
0,0067
0,5611
1,472
H0: MSE1 = MSE2
Intel
0,0089
0,0079
1,1276
-1,592
H0: MSE1 = MSE2
AT&T
0,0023
0,0022
1,0772
-0,757
H0: MSE1 = MSE2
Deletions (Quarterly returns)
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Navistar
0,0099
0,0103
0,9630
0,039
H0: MSE1 = MSE2
Chevron
0,0012
0,0019
0,6394
0,949
H0: MSE1 = MSE2
Goodyear
0,0052
0,0388
0,1343
3,075
H1: MSE1 < MSE2
47
Hogeschool - Universiteit Brussel
Faculty of Economics & Management
Master Thesis
Additions (Yearly returns)
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Altria
0,0016
0,0054
0,2974
5,861
H1: MSE1 < MSE2
McDonalds
0,0049
0,0035
1,3895
-1,001
H0: MSE1 = MSE2
Coca Cola
0,0033
0,0028
1,1584
-0,343
H0: MSE1 = MSE2
Boeing
0,0244
0,0084
2,9086
-3,249
H1: MSE1 > MSE2
Caterpillar
0,0046
0,0094
0,4873
3,297
H1: MSE1 < MSE2
Walt Disney
0,0222
0,0061
3,6566
-2,150
H1: MSE1 > MSE2
JP Morgan
0,0192
0,0203
0,9493
0,109
H0: MSE1 = MSE2
Travelers
0,0022
0,0058
0,3761
1,090
H0: MSE1 = MSE2
HP
0,0155
0,0152
1,0199
-0,059
H0: MSE1 = MSE2
JNJ
0,0066
0,0057
1,1465
-0,486
H0: MSE1 = MSE2
Wal Mart
0,0031
0,0037
0,8166
0,135
H0: MSE1 = MSE2
Microsoft
0,0062
0,0087
0,7113
0,778
H0: MSE1 = MSE2
Intel
0,0274
0,0093
2,9489
-2,344
H1: MSE1 > MSE2
AT&T
0,0072
0,0052
1,3927
-0,644
H0: MSE1 = MSE2
Deletions (Yearly returns)
MDM Test
MSE1
MSE2
MSE1 / MSE2
Test
statistic
Interpretation
Navistar
0,0129
0,0358
0,3614
1,617
H0: MSE1 = MSE2
Chevron
0,0033
0,0068
0,4808
4,436
H1: MSE1 < MSE2
Goodyear
0,0049
0,0466
0,1051
3,145
H1: MSE1 < MSE2
48