Predicting Market Movements: From Breaking News to Emerging Social Media Dr. Hsinchun Chen Director, Artificial Intelligence Lab University of Arizona [email protected] http://ai.arizona.edu

Acknowledgements: NSF CRI; NSF EXP-LA; DOD DTRA, CTFP, NPS; (ARFL WMD, CIA, FBI)

PREDICITNG MARKET MOVEMENTS

Predicting Markets

  

Markets: international markets, emerging markets, import/export markets, financial market, stock market, commodity market, retail market Economics (macro), international relations (trade, geopolitics), finance (international/banking/stock), accounting (market return), marketing (sales/retailing) US (NSF SBE, social behavioral economics; governments, think tanks), Europe/Asia



Business school research in not science (cannot be funded by NSF in US)!

 

Economics, finance, accounting, political science, social science, marketing, computer science (small, no funding in US!), MIS (business intelligence) Geopolitical/econ/finance/accounting models/theories, market metrics/parameters, analytical techniques, results interpretations, predicating markets



EMH (efficiency market hypothesis), RWT (random walk theory), CAPM (capital asset pricing model), quant/algorithm trading

Research Opportunities



Sophisticated econ/finance/accounting/marketing models/theories, established analytical techniques and metrics (numeric), abundant structured databases (financial metrics, economic indicators, stock quotes)

 

New, diverse unstructured (text) web-enabled business data sources, e.g., 10K/10Q SEC reports, mass media news, local news, Internet news, financial blogs, investor forums, tweets… Topic extraction, named entity recognition, sentiment/affect analysis, multilingual language models, social network analysis, statistical machine learning, temporal data/text mining, time series analysis…

Nerds on Wall Street

“Future technological stars…(1) Advanced electronic market tools; (2) Understanding both quantitative and qualitative information…” “The Text Frontier, Collective Intelligence, Social Media, and Market Monitors” “Stocks are stories, bonds are mathematics.” David Leinweber, 2009

AZ BIZ INTEL:

BUSINESS MASS MEDIA, SOCIAL MEDIA, TEXT ANALYTICS, SENTIMENT ANALYSIS, SPIKE DETECTION, FINANCE/ACCOUNTING/MARKETING MODELING, PREDICTING MARKET MOVEMENTS

Business Intelligence & Analytics

• • • •

$3B BI revenue in 2009 (Gartner, 2006) The Data Deluge (The Economists, March 2010); internet traffic 667 Exabytes by 2013, Cisco; Total amount of information in 2010, 1.2 Zettabyte (KB-MB-GB-TB-PB-EB-ZB YB) $9.4B BI software M&A spending in 2010 and $14.1B by 2014 (Forrester) IBM spent $14B in BI in five years; $9B BI revenue in 2010 (USA Today, November 2010); 24 acquisitions, 10,000 BI software developers, 8,000 BI consultants, 200 BI mathematicians



Acquired i2/COPLINK in 2011

Business Intelligence & Analytics

•

BI: “skills, technologies, applications, and practices used to help an enterprise better understand its business and market.”

• •

Technologies: data warehousing; Extraction, Transformation, and Load(ETL); Business Performance Management (BPM); visual dashboards; and advanced knowledge discovery using data and text mining BI 2.0: web intelligence, web analytics, web 2.0, social media analytics, opinion mining; cloud computing and web services; real-time monitoring and mining; enterprise performances (marketing/accounting/finance/healthcare)

AZ BIZ INTEL

• • •

Mass media, social media contents Text & social media analytics techniques Finance/accounting/marketing models (Tetlock/Columbia, Antweiler/UBC, Das/Santa Clara)



NYU (Dhar), Arizona (Dhaliwal, Kelly, Jiang, Lusch, Yong), National Taiwan U (Li, Hong, Lu)

• • • •

Bag of words, named entities, proper nouns, topics (1, 2-, 3- grams) Sentiment/valence, lexicons, machine learning, stakeholder analysis, EFLS analysis Time series models, spike detection, decaying function, trading windows, targeted sentiment Econometrics/regression models (R-sqr, p-value), 10-fold validation (F, accuracy), simulated trading (cost, frequency, exit)

AZ ONLINE WOM

AZ WOM: events, volume, sentiment

Data Collection

Yahoo! Movie Parsing Messages Sales Data Professional Evaluation Firms Strategy

Data Processing

OpinionFinder SentiWordNet

Measures and Metrics Online WOM measures

Number of messages Number of sentences Valence Subjectivity Number of valence words

New-product performance metrics

Opening-week box office sales Total box office sales Opening strength Longevity Professional evaluation

Statistical Analysis Online WOM evolution

Correlation between different WOM measures Correlation of WOM measure across new product lifecycle

Correlation between online WOM and product performance

Correlation between online WOM measures and new-product performance across the whole new-product lifecycle 11

Results



Evolution of online WOM through new-product lifecycle

   WOM communication starts early in preproduction, becomes highly active before movie release, then diminishes gradually Valence has a clear decreasing trend over time, indicating that WOM becomes more negative after movie release Subjectivity, number of sentences and number of valence words stay stable over time 12

IT’S THE BUZZ!

13

AZ STOCK TRACKER I & II

Literature Review: Stock Performance Prediction



Theoretical perspectives on stock behavior

   Efficient market hypothesis (Fama 1964)  Price of a stock reflects all available information  Market reacts instantaneously; impossible to outperform Random walk theory (Malkiel 1973)   Price of a stock varies randomly over time Future prediction, outperforming the market is impossible Pessimistic assessments of the predictability of stock behavior refuted through empirical studies  Lo and MacKinlay 1988; Jaffe et al 1989; Pesaran and Timmermann 1995

15

Literature Review: Stock Performance Prediction



Predominant approaches to stock prediction

   Fundamentalists utilize fundamental and financial measures of economy, industry, and firm  Economy and sector indicators, financial ratios of the firm   Fama-French three factors model (Fama and French 1993)  Market return, market capitalization, book to market ratio Currency exchange rates, interest rates, dividends Technicians utilize historical time-series information of the stock and market behavior  Historical price, volatility, trading volume Various machine learning models applied  Regression, ANN, ARIMA, support vector machines

16

Literature Review: Stock Performance Prediction

 

In addition to financial and stock variables, researchers have incorporated firm-related news article measures

   Developed trend-based language models for news articles  Lavrenko et al. 2000 Categorized press releases (good, bad, neutral)  Mittermayer 2004 Examined various textual representations of news articles  Schumaker and Chen, 2009a; 2009b

But few have incorporated firm-related web forums

 Thomas and Sycara (2000) utilize text classifications of discussions on Raging Bull to inform stock trading strategies

17

Literature Review: Firm-Related Web Forums and Stock



Studies relating web forums and stock behavior

  Examined firm-related web forums on major web portals Early studies focused on activity, without content analysis   Supported market efficiency; only concurrent relationships identified  Wysocki 1998; Tumarkin and Whitelaw 2001 Subsequently challenged; forum activity predicted stock behavior  Antweiler and Frank 2002; 2004; Das and Chen 2007   Analysis advanced to measure opinions in discussions  ‘Bullishness’ classifiers to distinguish investment positions   Antweiler and Frank 2004; Das and Chen 2007 Classified buy, hold, or sell positions with 60 – 70% accuracy  Identified predictive relationships between forum discussion sentiment and subsequent stock returns, volatility, trading volume Shortcomings  Retrospective analyses, shareholder perspective of major forums

18

AZ FinText: numbers + text

• Techniques: bag of words, named entities, proper nouns, past stock prices + • SVR Testbed: S&P 500 5 weeks, Oct-Nov 2005, 2,809 news, 10M stock quotes, • GICS industry classification Evaluation: Return, vs. Quant funds; 20-minute prediction

AZ FinText in the news

Thursday, June 10, 2010

AI That Picks Stocks Better Than the Pros

A computer science professor uses textual analysis of articles to beat the market.

WSJ

Technology News and Insights June 21, 2010, 1:45 PM ET

Using Artificial Intelligence to Digest News, Trade Stocks

AZ STOCK TRACKER I: mass, social media, topic, volume, sentiment

Data collection

Online news Web Forums

Spider/ Parser

Database

Topic extraction

Mutual information phrase extractor

Discussion topics

Sentiment identification

Sentiment grader Sentiment aggregator

Message sentiments

Conversation analysis



Topic Traffic dynamics Topic correlation and evolution Sentiment correlation and evolution Active topics and sentiments Market prediction



Message

21

User-Generated Contents (UGC): Conversations of 30,000 Wal-Mart Constituents and 500,000 Responses

Data sources

Wall Street Journal - WalMart-related News (WSJ)

Yahoo! Finance - WalMart Message Board (YAHOO)

Walmart-blows Forum - Employee Department Board (EMP)

Duration # of Threads # of Messages # of Users

Aug 1999 - Mar 2007 Jan 1999 - Jun 2008 Dec 2003 - Oct 2008 Walmart-blows Forum - WalMart Sucks Board (WSB) Nov 2003 - Nov 2008 Wakeupwalmart Forum - General WalMart Discussion Board (GDB) Aug 2005 - Nov 2008 N/A 139,062 7,440 1,354 2,136 4,081 441,954 102,240 19,624 23,940 657 25,500 2,930 1,855 967

22

Post Dynamics

320 280 240 200 160 120 80 40 0 99 00 01 02 03 04 Year 05 06 07 08 16000 14000 12000 10000 8000 6000 4000 2000 0 WSJ YAHOO EMP WSB GDB

23

Sentiment Trend

0.01

0 -0.01

-0.02

-0.03

-0.04

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year 0.01

0 -0.01

-0.02

-0.03

-0.04

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year WSJ YAHOO EMP WSB GDB YAHOO WSJ EMP WSB GDB

24

Market Modeling

Correlation Return Volatility Trading Volume Return Volatility Trading Volume Sentiment Disagreement

1 0.0348

0.0338

1 1

Message Volume Message Length Subjectivity

-0.0507

-0.3186

0.0473

-0.03578

0.3131

-0.1840

Sentiment One Day Lag Disagreement One Day Lag Message Volume One Day Lag Message Length One Day Lag Subjectivity One Day Lag

-0.0527

-0.3433

0.0859

-0.0475

0.3026

-0.1795

-0.0425

Correlation coefficients with p<0.10 are shown (two-tailed test) 

Correlation

  Sentiment expressed in the forum contemporaneously correlates significantly with stock return Disagreement, volume, and length expressed in the forum also hold significant correlations with volatility and trading volume

25

Market Predictive Results (cont’d)

Overall Forum Return t Volatility t Trading Volume t Market t Sentiment

0.8723*** (31.33) -0.0010

(-0.25) 0.7627*** (15.06) 0.0025

(0.31) 0.0074

(0.47) -0.4275** (-2.06)

t-1 Disagreement t-1 Message Volume t-1

0.0000

(0.04) -0.0023*** (-4.94) 0.0140** (2.29) -0.0007** (-2.29) -0.0122*** (-19.09) 0.1957*** (23.18) Note: *p<0.10;**p<0.05;***p<0.01

Message Length t-1 Subjectivity t-1

0.0002

(1.42) 0.0030*** (7.82) -0.0668*** (-13.24) 0.0015

(1.46) 0.0149*** (7.27) -0.3014*** (-11.11) • • 

Predictive regression (t-1)

The significant measures of forum discussions identified in contemporaneous regressions maintain their significance in the predictive regression models Additionally, sentiment expressed in the web forum holds a significant relationship with the trading volume on the following day • Positive sentiment reduces trading volume; negative sentiment induces trading activity

26

AZ STOCK TRACKER II: stakeholder analysis

27

Experimental Design: Description of Prediction Models

Variables

Dependent:

Description

RETURN t

Fundamental:

Stock return on day t (log difference of share price) FFSIZE FFBTM FFMARKET t-1 FFMARKET t-2

Technical:

Fama-French firm size (prior year; market capitalization = share price * shares outstanding) Fama-French book-to-market ratio (prior year; book value / market value of shares) Fama-French market return on day t – 1 (log difference of S&P 500 index price) Fama-French market return on day t – 2 (log difference of S&P 500 index price) RETURN t-1 RETURN t-2 VOLATILITY t-1 VOLATILITY t-2 VOLUME t-1 VOLUME t-2 DAY d t Stock return on day t – 1 (log difference of share price) Stock return on day t – 2 (log difference of share price) Stock price volatility on day t – 1 (volatility modeled using a GARCH(1,1)) Stock price volatility on day t – 2 (volatility modeled using a GARCH(1,1)) Stock trading volume on day t – 1 (in log) Stock trading volume on day t – 2 (in log) Dummy variables for trading day of the week on day t t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4)

28

Experimental Design: Description of Prediction Models

Variables

Forum:

MESSAGES t-1 LENGTH t-1 SENTI t-1 VARSENTI t-1 SUBJ t-1 VARSUBJ t-1

Stakeholder:

MESSAGES s t-1 LENGTH s t-1 SENTI s t-1 VARSENTI s t-1 SUBJ s t-1 VARSUBJ s t-1

Description

Number of messages posted in the forum on day t – 1 (in log (1 + messages)) Average length of messages posted in the forum on day t – 1 (in number of sentences) Average sentiment of messages posted in the forum on day t – 1 Variance in sentiment of messages posted in the forum on day t – 1 Average subjectivity of messages posted in the forum on day t – 1 Variance in subjectivity of messages posted in the forum on day t – 1 Number of messages posted by stakeholder cluster s on day t – 1 (in log (1 + messages)) Average length of messages posted by stakeholder cluster s on day t – 1 (in number of sentences) Average sentiment of messages posted by stakeholder cluster s on day t – 1 Variance in sentiment of messages posted by stakeholder cluster s on day t – 1 Average subjectivity of messages posted by stakeholder cluster s on day t – 1 Variance in subjectivity of messages posted by stakeholder cluster s on day t – 1 t = days (t = 1, 2, …, n); stakeholder clusters (s = 1, 2, …, c)

29

Experimental Design: Description of Prediction Models



Baseline Model – Baseline-FF

 Fundamental variables: Fama-French model RETURN t = β 0 + β 1 FFSIZE + β 2 FFBTM + β 3 FFMARKET t-1 + β 4 FFMARKET t-2 + ε t 

Baseline Model – Baseline-Tech

 Technical variables: Lagged stock returns, volatility, trading volume, day-of-week dummies RETURN t = β 0 + β 1 RETURN t-1 + β 2 RETURN t-2 + β 3 VOLATILITY t-1 + β 4 VOLATILITY t-2  + β 5 VOLUME t-1 + β 6 VOLUME t-2 + (β 7 DAY 1t + … + β 10 DAY 4t )+ ε t

Baseline Model – Baseline-Comp

 Comprehensive: all fundamental and technical variables RETURN t = β 0 + β 1 FFSIZE + β 2 FFBTM + β 3 FFMARKET t-1 + β 4 FFMARKET t-2 + β 5 RETURN t-1 + β 6 RETURN t-2 + β 7 VOLATILITY t-1 + β 8 VOLATILITY t-2 + β 9 VOLUME t-1 + β 10 VOLUME t-2 + (β 11 DAY 1t + … + β 14 DAY 4t ) + ε t Where t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4)

30

Experimental Design: Description of Prediction Models



Forum models

 Comprehensive baseline variables plus forum-level measures RETURN t = β 0 + β 1 FFSIZE + β 2 FFBTM + β 3 FFMARKET t-1 + β 4 FFMARKET t-2 + β 5 RETURN t-1 + β 6 RETURN t-2 + β 7 VOLATILITY t-1 + β 8 VOLATILITY t-2 + β 9 VOLUME t-1 + β 10 VOLUME t-2 + (β 11 DAY 1t + … + β 14 DAY 4t ) + β 15 MESSAGES t-1 + β 16 LENGTH t-1 + β 17 SENTI t-1 + β 18 VARSENTI t-1 + β 19 SUBJ t-1 + β 20 VARSUBJ t-1 + ε t Where t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4); stakeholder clusters (s = 1, 2, …, c)

31

Experimental Design: Description of Prediction Models



Stakeholder models

 Comprehensive baseline variables plus stakeholder group level forum measures RETURN t = β 0 + β 1 FFSIZE + β 2 FFBTM + β 3 FFMARKET t-1 + β 4 FFMARKET t-2 + β 5 RETURN t-1 + β 6 RETURN t-2 + β 7 VOLATILITY t-1 + β 8 VOLATILITY t-2 + β 9 VOLUME t-1 + β 10 VOLUME t-2 + (β 11 DAY 1t + … + β 14 DAY 4t ) + (β 15 MESSAGES 1 t-1 + β 16 LENGTH 1 t-1 + β 17 SENTI 1 t-1 + β 18 VARSENTI 1 t-1 + β 19 SUBJ 1 t-1 + β 20 VARSUBJ 1 t-1 + … + β k MESSAGES c t-1 + β k+1 LENGTH c t-1 + β k+2 SENTI c t-1 + β k+3 VARSENTI c t-1 + β k+4 SUBJ c t-1 + β k+5 VARSUBJ c t-1 ) + ε t Where t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4); stakeholder clusters (s = 1, 2, …, c); index k = (((c - 1) * 6) + 15)

32

Experimental Design: Social Media Data



A 17 month period was utilized for analysis and experimentation

    November 1, 2005 to March 31, 2007 First five months were utilized to calibrate the initial stock return prediction models  November1, 2005 – March 31, 2006  Calibrated models applied for prediction during each trading day in the next month Each subsequent month, new models were calibrated using five previous months of time-series variables, for stock return prediction during the next month of trading In total, stock return prediction was performed daily for one year (250 trading days)  April 1, 2006 – March 31, 2007

Forum

Yahoo Finance – WMT (finance.yahoo.com) Wal-Mart Blows (www.walmartblows.com) Wakeup Wal-Mart (www.wakeupwalmart.com)

Messages

134,201 55,125 10,797

Discussion Threads Stakeholders

40,633 5,533

Messages per Thread

3.30

3,690 1,306 1,461 915 14.94

8.27

Messages per Stakeholder

24.25

37.73

11.80

33

Results and Discussion



Hypothesis testing results Hypothesis

H1.1 Baseline-Comp model > Baseline-FF model H1.2 Baseline-Comp model > Baseline-Tech model H2 Forum-level models > best baseline models

H3.1 Stakeholder-level models > best baseline models H3.2 Stakeholder-level models > forum-level models Result

Partially supported Rejected Rejected

Supported

H4.1 Social network > discussion content representation H4.2 Writing style > discussion content representation H4.3 Social network > writing style representation H5.1 ANN > OLS H5.2 SVR > OLS H5.3 SVR > ANN

Partially supported

Partially supported Rejected Partially supported Rejected Partially supported Partially supported

34

Results and Discussion



Wal-Mart stock return prediction model results

  Baseline models using fundamental and technical variables  Results across 250 trading days forecasted Baselines for simulated trading (initial investment of $10,000):   Holding Wal-Mart stock for the year results in $10,096 Holding S&P 500 for the year results in $11,012

Model

Baseline-FF Baseline-Tech

Baseline-Comp OLS $

$ 9,787 $ 8,799 $ 10,763

OLS Accuracy

55.20% 57.20% 54.40%

ANN $

$ 9,998 $ 9,702 $ 10,418

ANN Accuracy

44.40% 57.60% 56.80%

SVR $

$ 9,408 $ 9,503

$ 10,645 SVR Accuracy

51.20% 56.40%

56.80% 35

Results and Discussion



Wal-Mart stock return prediction model results

 Incorporating the Wakeup Wal-Mart web forum  Results across 250 trading days forecasted

Model

Best Baseline Forum Stakeholder-SN Stakeholder -Content Stakeholder -Style Stakeholder-SN+Content Stakeholder-SN+Style Stakeholder-Content+Style Stakeholder-SN+Content+Style

OLS $

$ 10,763 $ 10,367 $ 9,873 $ 10,689 $ 10,271 $ 10,384 $ 10,744 $ 10,696 $ 10,976

OLS Accuracy

57.20% 57.60% 55.20% 60.40% 56.00% 61.60% 60.00% 59.20% 58.00%

ANN $

$ 10,418 $ 10,397 $ 10,930 $ 11,595 $ 9,653 $ 13,066 $ 10,792 $ 10,590 $ 10,778

ANN Accuracy

57.60% 59.20% 57.20% 60.40% 56.80% 60.80% 60.40% 56.40% 56.40% Pair-wise t-test; improvement over best baseline model at * p < 0.10 ** p < 0.05

SVR $

$ 10,645 $ 10,303 $ 10,669 $ 11,976 $ 9,305 $ 11,866 $ 11,249 $ 10,603 $ 10,881

SVR Accuracy

56.80% 59.20% 59.20% 61.20% * 56.00% 62.80% ** 57.60% 58.80% 59.60%

36

AZ STOCK TRACKER III

Introduction



Forward-looking statements (FLS) refer to

 Projections, forecasts, or other predictive statements  Made by firm management  Section 21E of the Securities Exchange Act (1934) 

Extended forward-looking statements (EFLS)

 Statements that may have implications for a firms future development  Similar to FLS, but broader  Including information from information intermediaries (e.g., newspapers, newswires) and individuals (e.g., blogs)

38

Recognizing EFLS



EFLS: Extends FLS to include statements about firm’s future performance from other sources such as financial press, analysts’ reports, and individuals Goal

EFLS Recognition EFLS Sentiment

Recognition Task

Future Timing (FT) Explicit Uncertainty (EU) Overall Assessment (ALL) Positive (POS) Negative (NEG)

Definition

Primary content is about future events or states Explicit accounts of doubt or unreliability Affect decision maker’s belief about a firm’s future cash flow Positive impact on the belief Negative impact on the belief

39

AZ STOCK TRACKER III: EFLS 40

Summary of Annotation Results

ALL POS NEG

Category

ALL POS NEG

Agreement

0.91 (0.88, 0.93) 0.90 (0.88, 0.93) 0.89 (0.86, 0.91)

Count

1157 836 904

Cohen’s Kappa

0.81 (0.76, 0.86) • 0.79 (0.73, 0.85) • 0.77 (0.71, 0.82)

Percent

46% 33% 36% • High kappa values (>0.7) on risks supports the coding scheme being empirically valid Agreement upper bound • 89% to 91% (for ALL, POS, and NEG) Reference Standard Dataset: – 2539 sentences in total Note: (95% CI) from 1,000 Bootstrappings 41

Experiment 1: Sentence-Level Evaluation

Model LASSO ENET75 ENET50 ENET25 SVM SVM w/IG FKC OF_PN Accuracy †

67.1% 69.3% 68.9%

69.4% 69.5%

69.1% 64.7% 54.8%

F-Measure ‡

66.5% 68.0% 68.7%

68.9% 70.2%

68.9% 50.9% 27.9%

Recall ‡

83.8% 87.7% 90.5%

91.2%

83.9% 84.3% 69.7% 19.1%

Precision ‡

55.1% 55.6% 55.4% 55.4%

60.3%

58.3% 40.1% 51.4%

42

EFLS Impacts: Hypotheses Development



Theoretical framework (Easley and O’Hara, 2004)



There are 𝐼

𝑘

signals for stock k ( 𝑠

𝑘1

, 𝑠

𝑘2

, … , 𝑠

𝑘𝐼 𝑘

)

 1

𝑠

𝑘𝑖

~𝑁 𝑣

𝑘

,

𝛾 𝑘 

( 𝑠

𝑘1

, 𝑠

𝑘2

, 𝑠

𝑘3

, 𝑠

𝑘(𝛼 𝑘 𝐼 𝑘 )

, 𝑠

𝑘(𝛼 𝑘 𝐼 𝑘 +1)

, … , 𝑠

𝑘(𝐼 𝑘 −1)

, 𝑠

𝑘𝐼 𝑘

)

Private Signals Public Signals 

𝛼

𝑘

: The relative amount of private-versus-public information

43

Hypotheses Development (Cont’d.)



Hypothesis 1: Firms with lower EFLS intensity are associated with higher expected return.

𝜕𝐸[𝑣 𝑘 − 𝑝 𝑘 ] = 𝜕𝛼 𝑘 𝐶 𝑘 2 𝛿𝑥 𝑘 1 − 𝜇 𝑘 𝐼 𝑘 𝛾 𝑘 1 + 𝛼 𝑘 𝐼 𝑘 𝜂 𝑘 𝜇 2 𝑘 𝛾 𝑘 𝜎 −2 2 > 0

44

Hypotheses Development (Cont’d.)



Hypothesis 2: Firms with lower EFLS intensity are associated with the higher stock volatility.

𝜕𝑉𝑎𝑟(𝑣 𝑘 − 𝑝 𝑘 ) 𝜕𝛼 𝑘 = 𝜂 𝑘 𝛿 2 𝜌 𝑘 + 𝛾 𝑘 𝐼 𝛿 𝑘 4 𝛾 𝑘 𝐼 𝑘 (1 + 𝛼 1 − 𝜇 𝑘 𝑘 (𝜇 𝑘 2𝛿 4 + 𝑉 1,𝑘 + 𝑉 2,𝑘 − 1)) + 𝛼 𝑘 𝜂 𝑘 𝛾 𝑘 𝐼 𝑘 𝜇 2 𝑘 (𝛾 𝑘 𝐼 𝑘 + 𝜌 𝑘 ) 3 𝑉 1,𝑘 = 𝛾 𝑘 𝐼 𝑘 − 𝜌 𝑘 + 𝜇 𝑘 𝛾 𝑘 𝐼 𝑘 + 𝜌 𝑘 𝛼 𝑘 𝜂 2 𝑘 𝐼 𝑘 𝛾 𝑘 𝜇 2 𝑘 + 𝛿 2 𝜂 𝑘   If 𝐼 𝑘 𝛾 𝑘 𝑉 2,𝑘 = −1 + 2𝜇 𝑘 > 𝜌 𝑘 and 𝜇 𝑘 + 𝜇 2 𝑘 𝛿 2 𝜂 𝑘 𝛾 𝑘 𝐼 𝑘 𝛼 𝑘 > 2 − 1 then 𝜕𝑉𝑎𝑟 𝑣−𝑝 𝑘 𝜕𝛼 𝑘 >0 Intuition: if there are enough signals and the fraction of informed investors is larger than 41%, then firms with lower amounts of EFLS  Higher Volatility

45

Control Variables

Variable Definition

Number of news articles mentioning firm i in month t.

Logarithm of market value, computed using the closing market price of month t-1.

Logarithm of book-to-market ratio, computed following Fama and French ( 1993 ).

Log(Dollar trading volume of firm i in month t) Log(variance); variance of firm i in month t is computed using daily stock returns.

Proportion of individual ownership of stock i, using the latest available data, computed by aggregating 13f filings ( Fang and Peress 2009 ).

Log(1+number of analysts covering firm i in month t).

Log(1+standard deviation of analyst’s earnings predictions).

46

Firm-Level Performance Evaluation (Cont’d.)



Empirical Model 1:

Hypothesis 1 Predicts Negative b1 𝑟 𝑖,𝑡+1 = 𝑎 0 + b 1 𝐴𝐿𝐿_𝐼𝑁 𝑖,𝑡 𝑑 1 𝐿𝑜𝑔𝑆𝑖𝑧𝑒 𝑖,𝑡 + 𝑑 2 + 𝑐 𝐿𝑜𝑔𝐵𝑀 1 𝑁𝑒𝑤𝑠𝐹𝑟𝑒𝑞 𝑖,𝑡 + 𝑑 3 𝑟 𝑖,𝑡 𝑖,𝑡 + 𝑐 + 𝑑 4 2 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡𝑖 𝐿𝑜𝑔𝑉 𝑖,𝑡 + 𝑒 𝑖𝑡 𝑖,𝑡 + 

Empirical Model 2:

Hypothesis 2 Predicts b1 ≠ 0 𝐿𝑜𝑔𝑉 𝑖,𝑡+1 = 𝑎 0 + b 1 ALL_IN i,t + 𝑐 1 𝑁𝑒𝑤𝑠𝐹𝑟𝑒𝑞 𝑖,𝑡 𝑑 1 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚𝑒 𝑖,𝑡 + 𝑑 2 𝐿𝑜𝑔𝑉 𝑖,𝑡 + 𝑑 + 𝑐 3 2 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡𝑖 𝐿𝑜𝑔𝑆𝑖𝑧𝑒 𝑖,𝑡 + 𝑖,𝑡 𝑑 4 𝐿𝑜𝑔𝐵𝑀 𝑖,𝑡 + 𝑑 𝑑 7 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒𝑟 𝑖,𝑡 5 𝑟 i,t + 𝑑 8 6 𝐼𝑛𝑑𝑣𝑂𝑤𝑛 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆𝐷 𝑖,𝑡 𝑖,𝑡 + + 𝑒 𝑖,𝑡 +

47

Experiment Two: Firm-Level Evaluation



Research Testbed: January 1986 to May 2008, 1,134,321 Wall Street Journal news articles

 Merged with CRSP, Compustat, and IBES  Stock prices lower than $5 at the end of a month were removed (Cohen and Frazzini 2008; Fang and Peress 2009) 

1,274,711 firm-months, spanning 269 months 48

Expected Return and EFLS Intensity

Variable

Value -0.0026

*

Variable

Value -0.0052

**

Control Variables Variable

Value -0.0039

0.00069

*** -0.00081

-0.0019

** 0.0025

*** -0.046

*** 0.00042

Intercept

0.039

***

Intercept

0.00068

-0.0012

-0.0019

0.0025

-0.046

*** 0.00042

0.039

*** *** *** ***

Intercept

0.00067

-0.0015

-0.0019

0.0025

-0.046

*** , ** , * 0.0031

0.0031

0.0031

indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.

*** 0.00042

0.039

*** *** *** ***

49

Volatility and EFLS Intensity

Model 2A (

𝐴𝐿𝐿_𝐼𝑁 𝑖,𝑡

) Variable

𝐴𝐿𝐿_𝐼𝑁 𝑖,𝑡 Value -0.074

*** 𝑁𝑒𝑤𝑠𝐹𝑟𝑒𝑞 𝑖,𝑡 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡𝑖 𝑖,𝑡 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚𝑒 𝑖,𝑡 𝐿𝑜𝑔𝑉 𝑖,𝑡 𝐿𝑜𝑔𝑆𝑖𝑧𝑒 𝑖,𝑡 𝐿𝑜𝑔𝐵𝑀 𝑖,𝑡 𝑟 𝑖,𝑡 𝐼𝑛𝑑𝑣𝑂𝑤𝑛 𝑖,𝑡 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒𝑟 𝑖,𝑡 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆𝐷 𝑖,𝑡

Intercept

𝑅 2 0.012

*** -0.105

*** 0.108

*** 0.565

*** -0.222

*** -0.066

*** -0.615

*** 0.071

*** 0.016

*** 0.095

*** -1.568

*** 0.57

Model 2B (

𝐹𝑇_𝐼𝑁 𝑖,𝑡

) Variable

Value 𝐹𝑇_𝐼𝑁 𝑖,𝑡 -0.196

***

Control Variables

𝑁𝑒𝑤𝑠𝐹𝑟𝑒𝑞 𝑖,𝑡 0.012

*** 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡𝑖 𝑖,𝑡 -0.103

*** 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚𝑒 𝑖,𝑡 0.108

*** 𝐿𝑜𝑔𝑉 𝑖,𝑡 0.565

*** 𝐿𝑜𝑔𝑆𝑖𝑧𝑒 𝑖,𝑡 -0.222

*** 𝐿𝑜𝑔𝐵𝑀 𝑖,𝑡 -0.066

*** 𝑟 𝑖,𝑡 -0.615

*** 𝐼𝑛𝑑𝑣𝑂𝑤𝑛 𝑖,𝑡 0.071

*** 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒𝑟 𝑖,𝑡 0.017

*** 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆𝐷 𝑖,𝑡 0.095

***

Intercept

-1.566

*** 𝑅 2 0.57

Model 2C (EU

_𝐼𝑁 𝑖,𝑡

) Variable

Value 𝐸𝑈_𝐼𝑁 𝑖,𝑡 -0.254

*** 𝑁𝑒𝑤𝑠𝐹𝑟𝑒𝑞 𝑖,𝑡 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡𝑖 𝑖,𝑡 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚𝑒 𝑖,𝑡 𝐿𝑜𝑔𝑉 𝑖,𝑡 𝐿𝑜𝑔𝑆𝑖𝑧𝑒 𝑖,𝑡 𝐿𝑜𝑔𝐵𝑀 𝑖,𝑡 𝑟 𝑖,𝑡 𝐼𝑛𝑑𝑣𝑂𝑤𝑛 𝑖,𝑡 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒𝑟 𝑖,𝑡 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆𝐷 𝑖,𝑡

Intercept

𝑅 2 0.012

*** -0.110

*** 0.108

*** 0.565

*** -0.222

*** -0.066

*** -0.616

*** 0.071

*** 0.017

*** 0.095

*** -1.566

*** 0.57

*** , ** , * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.

50

Take-Away and WIP (20%)

    

Mass and social media texts provide additional signals for market prediction (in addition to numbers) Message volume important; aggregate sentiment may not (EMH) Business sentiment processing difficult; may require additional content pre-processing (stakeholder; EFLS) Predicting return hard; predicting volatility easier (VIX Chicago Board) Large-scale stock news tracking and text analytics can be automated



Trading windows; decay function; targeted sentiment; extensive trading periods (up/down); industry and news category (oil/banking); firm & index size (Russell/NYSE); emerging markets (China)



All the firms (10K), all the news (1M each), all the time ???



Trading strategy ???

51

Data Sources for US Public Companies SEC/Edgar NYSE.com

Finance.Yahoo.com

NASDAQ.com

Company Information Database Ticker CIK CUSIP PERMNO Predefined Data Sources Yahoo Finance Forums Twitter Company Websites Stock Exchange WSJ 10K Report Company Name Dynamic Data Sources Search Engines Blogs Company Keywords News Transformation/Integration Performance Indicators Topics & Sentiments Time Series / Burst Risk Model SNA Data

52

Analytic Approaches Single Media Analysis Cross Media Analysis Predictive Analysis Simulated Trading

AZ BIZ INTEL System Design Visualization

Hsinchun Chen, Ph.D.

Artificial Intelligence Lab, University of Arizona [email protected] http://ai.arizona.edu