Transcript Tőzsdei ismeretek I. - Technical University of Košice

Sándor Bozsik (Ph.D)
Miskolc University
Hungary
TECHNICAL ANALYSIS
EFFICIENT MARKET
In efficient market the NPV of all investment decisions is
0.
Assumptions:
 Information efficiency
 Transaction efficiency
 Allocation efficiency
Consequence:
 Price movement is a random walk.
TYPES OF EFFICIENT MARKET (FAMA)
 Weak
 Semi
form
strong form
 Strong
form
TECHNICAL ANALYSIS – DENIES THE WEAK
FORM
The price movement has a trend
 The history repeats itself
 The price perfectly reflects the effort of market
forces.
 The market has got memory.
 The prices are sticky.

STAGES OF TREND
Accumulation
 Expansion
 Dispersion
 Exhaustion

Discovering with support and resistance lines
METHODS OF TECHNICAL ANALYSIS

Graphic tools




Statistical tools




Bar chart
Japanese candlestick
O-X diagram
Moving average, EMA, MACD
Momentum, oscillator
Market strength, Money Flow Index
Combined tools



Fibonacci-lines
Bollinger-band
Elliott-wave
BAR CHART (MOL)
10000000
6000
9000000
5500
8000000
7000000
5000
6000000
5000000
4500
4000000
4000
3000000
2000000
3500
1000000
0
3000
JAPANESE - CANDLESTICK
Japanese chandlestick for MOL
12000000
18000
17000
10000000
16000
8000000
15000
6000000
14000
13000
4000000
12000
2000000
11000
0
10000
2005.01.01-2005.04.18
TRENDS

Trend strengthening forms
triangles
 Channels
 Mast and flag


Trend changing forms
Double peak
 Saucer
 Key reversal or inland reversal
 Head and shoulders
 Spike

MOVING AVERAGE


Trading rule: if the shorter moving average crosses the
longer one below – buying signal, on the contrary –
selling signal
Grouping:




By term: 3, 7, 14 days
Simply, weighted or exponential
Direct or Indirect average
The longer is the average, the better follows the trend,
the shorter is the average, the quicker gives a signal.
EXPONENTIAL MOVING AVERAGE (EMA)

Equation
2 
2

EMA [ i ]   1 
* X [i ]
 * EMA [ i  1] 
N 1
N 1

EMA [1]  X [1]
Stage analysis (Stan Weinstein)
Stage 1 – the asset moves in a relative narrow band
Stage 2 – developing stage – the asset price increases
above the 200 and the 50 days EMA
Stage 3 – Peak, the asset price is permanently above the
200 day EMA (profit realisation)
Stage 4 – Price drop
TWO DERIVATIVE FROM EMA
McClellan oscillator and summary index
 Daily breadth – difference between the
number of up-closing and down-closing
shares – they are cumulated and an EMA with
10% and 5% adjusting parameter is created.
The difference between them is the oscillator.
 MACD – Difference between two EMA (12
days and 25 days) Then the 9 days EMA is
taken. If it crosses the difference – trading
signal.

MOMENTUMS AND OSCILLATORS

Oscillator
Daily highest
- previous
Daily highest

closing
- daily lowest
Momentum
Closing
- opening
period

Relative strength index (RSI)
Relative
Relative
strength
strength

Upper closing
in 14 days
Lower closing
in 14 days
index  1 
1
1  Relative
strength
MONEY FLOW INDEX


Measures the money in and out of the market
Equations:
Daily average price 
Maximum
 Minimum
 Closing
3
Money flow  daily average price * daily turn
Money flow ratio 
over
Positive
money flow in 14 days
Negative
money flow in 14 days
Money flow index  1 -
1
1  Money flow ratio
FIBONACCI NUMBERS
What does it show? – Resistance and support
level
 fn=fn-1+fn-1
 The next figure is 1,618 higher than previous
one (gold cut)
 From 100% we get the followings:
 100%; 61,8%; 38,2%; 23,6%; 14,6%; 9%
 100% is the gap between maximum and
minimum price in a given period
FIBONACCI LINE
30 000
Richter
25 000
25 390
20 000
20 271
15 000
17 104
14 545
11 986
10 000
8 819
5 000
3 700
0
12/5/1997 5/22/1998 11/6/1998 4/23/1999 10/8/1999 3/24/2000
BOLLINGER - BAND
Usage: To determine the eruptions
Based on:
•Relative support and resistance
•Moving average + standard deviation
The larger is the volatility the larger is the width
of band.
Normal difference

1
n

n
i 1
Band - moving



 xi  x 


2
average  2 * normal difference
APPLYING THE BOLLINGER-BAND
21 000
19 000
17 000
15 000
13 000
11 000
9 000
7 000
5 000
11/9/1998
80
60
40
20
0
1,2
1,0
0,8
0,6
0,4
0,2
0,0
-0,2
Richter (záró)
20 napos mozgóátlag
felső
alsó
2/1/1999
4/26/1999
7/19/1999 10/11/1999
1/3/2000
3/27/2000
ASSUMPTION OF BOLLINGER-BAND





Narrowing band projects meaningful change in price
If the price reaches the upper or lower limit, then the
trend may go on.
If the price leaves one of the limit, but doesn’t reach
the another one, then the current trend continues.
If the price breaks the moving average, then reaches
the opposite limit.
The break out of the band is a sign of eruption.
FUNDAMENTAL ANALYSIS
PRINCIPLES OF FUNDAMENTAL ANALYSIS
The market is efficient in weak form, but
inefficient in semi-strong form.
 Not everybody can evaluate properly the public
information.
 Analyse the fundamentals to determine the
company’s intrinsic value.
 Invest in medium or long term.

HOW THE FUNDAMENTAL ANALYSIS
WORKS
Find a benchmark (similar company or industry
average)
 Calculate a market ratio
 Collect the financial statements, market
projections, data on macroeconomic
circumstances
 Analyse and compare the results
 Try to explain the differences in market ratio

HOW TO CHOOSE PROPER BENCHMARK
 Operated
in the same industry
 Located in similar region
 Similar size
 Similar financial risk profile
FAMOUS MARKET RATIOS

P/E – Price per earning

Market to book value

P/EBITDA
P/E RATIO
P/E – share price/net income per share
 Value of shares:

PX  EPS
X
*
V X  PX * DB

P
E
X
*
* 1  d 
Where:
•Px – firm’s share price
•EPSX – firm’s earnings per share
•P/E* - benchmark’s P/E indicator
•d – adjusting factor
•DBX – number of share issued
•VX – value of equity
Usage: manufacturing companies
MORE SOPHISTICATED METHODS
DCF analysis
 Real option models
