predicting market movements: from breaking news to emerging social media dr. hsinchun chen director,...
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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
• $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
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
11
Data Collection
Messages
Yahoo! Movie
Parsing
Sales Data
Professional Evaluation
Firms Strategy
Data Processing
OpinionFinder SentiWordNet
Measures and Metrics
Online WOM measures
Number of messagesNumber of sentences
ValenceSubjectivity
Number of valence words
New-product performance metrics
Opening-week box office salesTotal box office sales
Opening strengthLongevity
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
AZ WOM: events, volume, sentiment
12
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
13
IT’S THE BUZZ!
AZ STOCK TRACKER I & II
15
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
16
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
17
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
18
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
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, 2010AI 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
21
Conversation
analysis
AZ STOCK TRACKER I: mass, social media, topic, volume, sentiment
Sentiment identification
Data collection Topic extraction
Discussion
topics
Mutual information phrase extractor
Database
Spider/
Parser
Sentiment grader
Message
sentiments
Online
newsWeb Forums
Traffic dynamics
Message
A
uth
or
S
en
tim
en
t
Topic correlation and evolution
Sentiment correlation and evolution
Active topics and sentiments
Market predictionSentiment aggregator
Topic
22
User-Generated Contents (UGC): Conversations of 30,000 Wal-Mart Constituents and 500,000 Responses
Data sources Duration # of Threads
# of Message
s
# of Users
Wall Street Journal - WalMart-related News (WSJ)
Aug 1999 - Mar 2007 N/A 4,081 657
Yahoo! Finance - WalMart Message Board (YAHOO)
Jan 1999 - Jun 2008
139,062 441,954 25,500
Walmart-blows Forum - Employee Department Board (EMP)
Dec 2003 - Oct 2008 7,440 102,240 2,930
Walmart-blows Forum - WalMart Sucks Board (WSB)
Nov 2003 - Nov 2008
1,354 19,624 1,855
Wakeupwalmart Forum- General WalMart Discussion Board (GDB)
Aug 2005 - Nov 2008
2,136 23,940 967
23
0
40
80
120
160
200
240
280
320
99 00 01 02 03 04 05 06 07 08Year
# of
new
s
0
2000
4000
6000
8000
10000
12000
14000
16000
# of
mes
sage
s
WSJ
YAHOO
EMP
WSB
GDB
Post Dynamics
24
Sentiment Trend
-0.04
-0.03
-0.02
-0.01
0
0.01
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Year
Avera
ge s
entim
ent
WSJ
YAHOO
EMP
WSB
GDB
-0.04
-0.03
-0.02
-0.01
0
0.01
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Year
3 m
onth
s' m
ovin
g
avera
ge s
entim
ent
YAHOO
WSJ
EMP
WSB
GDB
25
Market Modeling
Correlation Return Volatility Trading Volume
Return 1
Volatility 0.0348 1
Trading Volume 1
Sentiment 0.0338
Disagreement -0.0507 -0.03578
Message Volume -0.3186 0.3131
Message Length 0.0473 -0.1840
Subjectivity
Sentiment One Day Lag
Disagreement One Day Lag -0.0527 -0.0475
Message Volume One Day Lag -0.3433 0.3026
Message Length One Day Lag 0.0859 -0.1795
Subjectivity One Day Lag -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
26
Market Predictive Results (cont’d)
Overall Forum
Markett Sentimentt-1 Disagreementt-1 Message Volumet-1 Message Lengtht-1 Subjectivityt-1
Returnt 0.8723***(31.33)
0.0025(0.31)
0.0000(0.04)
-0.0007**(-2.29)
0.0002(1.42)
0.0015(1.46)
Volatilityt -0.0010(-0.25)
0.0074(0.47)
-0.0023***(-4.94)
-0.0122***(-19.09)
0.0030***(7.82)
0.0149***(7.27)
TradingVolumet
0.7627***(15.06)
-0.4275**(-2.06)
0.0140**(2.29)
0.1957***(23.18)
-0.0668***(-13.24)
-0.3014***(-11.11)
Note: *p<0.10;**p<0.05;***p<0.01
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
27
AZ STOCK TRACKER II: stakeholder analysis
28
Experimental Design: Description of Prediction Models
Variables DescriptionDependent:
RETURN tStock return on day t (log difference of share price)
Fundamental:
FFSIZEFFBTMFFMARKET t-1
FFMARKET t-2
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)
Technical:
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)
29
Experimental Design: Description of Prediction Models
Variables DescriptionForum:
MESSAGES t-1
LENGTH t-1
SENTI t-1
VARSENTI t-1
SUBJ t-1
VARSUBJ t-1
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 – 1Variance in sentiment of messages posted in the forum on day t – 1Average subjectivity of messages posted in the forum on day t – 1Variance in subjectivity of messages posted in the forum on day 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
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 – 1Variance in sentiment of messages posted by stakeholder cluster s on day t – 1Average subjectivity of messages posted by stakeholder cluster s on day t – 1Variance 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)
30
Experimental Design: Description of Prediction Models
Baseline Model – Baseline-FF Fundamental variables: Fama-French model
Baseline Model – Baseline-Tech Technical variables: Lagged stock returns, volatility, trading volume, day-of-week dummies
Baseline Model – Baseline-Comp Comprehensive: all fundamental and technical variables
Where t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4)
RETURN t = β0 + β1 FFSIZE + β2 FFBTM + β3 FFMARKET t-1 + β4 FFMARKET t-2 + εt
RETURN t = β0 + β1 FFSIZE + β2 FFBTM + β3 FFMARKET t-1 + β4 FFMARKET t-2 + β5 RETURN t-1 + β6 RETURN t-2 + β7 VOLATILITY t-1 + β8 VOLATILITY t-2 + β9 VOLUME t-1 + β10 VOLUME t-2 + (β11 DAY1t + … + β14 DAY4t) + εt
RETURN t = β0 + β1 RETURN t-1 + β2 RETURN t-2 + β3 VOLATILITY t-1 + β4 VOLATILITY t-2 + β5 VOLUME t-1 + β6 VOLUME t-2 + (β7 DAY1t + … + β10 DAY4t)+ εt
31
Experimental Design: Description of Prediction Models
Forum models Comprehensive baseline variables plus forum-level measures
Where t = days (t = 1, 2, …, n); day of the week (d = 1, …, 4); stakeholder clusters (s = 1, 2, …, c)
RETURN t = β0 + β1 FFSIZE + β2 FFBTM + β3 FFMARKET t-1 + β4 FFMARKET t-2 + β5 RETURN t-1 + β6 RETURN t-2 + β7 VOLATILITY t-1 + β8 VOLATILITY t-2 + β9 VOLUME t-1 + β10 VOLUME t-2 + (β11 DAY1t + … + β14 DAY4t) + β15 MESSAGES t-1 + β16 LENGTH t-1 + β17 SENTI t-1 + β18 VARSENTI t-1
+ β19 SUBJ t-1 + β20 VARSUBJ t-1 + εt
32
Experimental Design: Description of Prediction Models
Stakeholder models Comprehensive baseline variables plus stakeholder group-level forum
measures
RETURN t = β0 + β1 FFSIZE + β2 FFBTM + β3 FFMARKET t-1 + β4 FFMARKET t-2 + β5 RETURN t-1 + β6 RETURN t-2 + β7 VOLATILITY t-1 + β8 VOLATILITY t-2 + β9 VOLUME t-1 + β10 VOLUME t-2 + (β11 DAY1t + … + β14 DAY4t) + (β15 MESSAGES 1 t-1 + β16 LENGTH 1 t-1 + β17 SENTI 1 t-1 + β18 VARSENTI 1 t-1
+ β19 SUBJ 1 t-1 + β20 VARSUBJ 1 t-1 + … + βk MESSAGES c t-1 + βk+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) + εt
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)
33
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 Messages Discussion Threads Stakeholders Messages
per ThreadMessages perStakeholder
Yahoo Finance – WMT(finance.yahoo.com) 134,201 40,633 5,533 3.30 24.25
Wal-Mart Blows(www.walmartblows.com) 55,125 3,690 1,461 14.94 37.73
Wakeup Wal-Mart (www.wakeupwalmart.com) 10,797 1,306 915 8.27 11.80
34
Results and Discussion
Hypothesis Result
H1.1 Baseline-Comp model > Baseline-FF model Partially supported
H1.2 Baseline-Comp model > Baseline-Tech model Rejected
H2 Forum-level models > best baseline models Rejected
H3.1 Stakeholder-level models > best baseline models
Supported
H3.2 Stakeholder-level models > forum-level models Partially supported
H4.1 Social network > discussion content representation Partially supported
H4.2 Writing style > discussion content representation Rejected
H4.3 Social network > writing style representation Partially supported
H5.1 ANN > OLS Rejected
H5.2 SVR > OLS Partially supported
H5.3 SVR > ANN Partially supported
Hypothesis testing results
35
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 OLS $ OLS Accuracy ANN $ ANN Accuracy SVR $ SVR AccuracyBaseline-FF $ 9,787 55.20% $ 9,998 44.40% $ 9,408 51.20%Baseline-Tech $ 8,799 57.20% $ 9,702 57.60% $ 9,503 56.40%Baseline-Comp $ 10,763 54.40% $ 10,418 56.80% $ 10,645 56.80%
36
Results and Discussion
Wal-Mart stock return prediction model results Incorporating the Wakeup Wal-Mart web forum
Results across 250 trading days forecasted
Model OLS $ OLS Accuracy ANN $ ANN Accuracy SVR $ SVR AccuracyBest Baseline $ 10,763 57.20% $ 10,418 57.60% $ 10,645 56.80%Forum $ 10,367 57.60% $ 10,397 59.20% $ 10,303 59.20%Stakeholder-SN $ 9,873 55.20% $ 10,930 57.20% $ 10,669 59.20%Stakeholder -Content $ 10,689 60.40% $ 11,595 60.40% $ 11,976 61.20% *Stakeholder -Style $ 10,271 56.00% $ 9,653 56.80% $ 9,305 56.00%Stakeholder-SN+Content $ 10,384 61.60% $ 13,066 60.80% $ 11,866 62.80% **Stakeholder-SN+Style $ 10,744 60.00% $ 10,792 60.40% $ 11,249 57.60%Stakeholder-Content+Style $ 10,696 59.20% $ 10,590 56.40% $ 10,603 58.80%Stakeholder-SN+Content+Style $ 10,976 58.00% $ 10,778 56.40% $ 10,881 59.60%
Pair-wise t-test; improvement over best baseline model at * p < 0.10 ** p < 0.05
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
39
Goal Recognition Task Definition
EFLS Recognition Future Timing (FT) Primary content is about future events or states
Explicit Uncertainty (EU)
Explicit accounts of doubt or unreliability
Overall Assessment (ALL)
Affect decision maker’s belief about a firm’s future cash flow
EFLS Sentiment Positive (POS) Positive impact on the belief
Negative (NEG) Negative impact on the belief
40
AZ STOCK TRACKER III: EFLS
Summary of Annotation Results
Agreement Cohen’s Kappa
ALL 0.91 (0.88, 0.93)
0.81 (0.76, 0.86)
POS 0.90 (0.88, 0.93)
0.79 (0.73, 0.85)
NEG 0.89 (0.86, 0.91)
0.77 (0.71, 0.82)
41Note: (95% CI) from 1,000 Bootstrappings
• 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)Category Count Percent
ALL 1157 46%
POS 836 33%
NEG 904 36%
• Reference Standard Dataset:– 2539 sentences in total
Experiment 1: Sentence-Level Evaluation
Model Accuracy† F-Measure‡ Recall‡ Precision‡
LASSO 67.1% 66.5% 83.8% 55.1%
ENET75 69.3% 68.0% 87.7% 55.6%
ENET50 68.9% 68.7% 90.5% 55.4%
ENET25 69.4% 68.9% 91.2% 55.4%
SVM 69.5% 70.2% 83.9% 60.3%
SVM w/IG 69.1% 68.9% 84.3% 58.3%
FKC 64.7% 50.9% 69.7% 40.1%
OF_PN 54.8% 27.9% 19.1% 51.4%42
43
EFLS Impacts: Hypotheses Development
Theoretical framework (Easley and O’Hara, 2004)There are signals for stock k ()
()
: The relative amount of private-versus-public
information
Private Signals Public Signals
44
Hypotheses Development (Cont’d.)
Hypothesis 1: Firms with lower EFLS intensity are associated with higher expected return.
𝜕𝐸 [𝑣𝑘−𝑝𝑘]𝜕𝛼𝑘
=𝛿𝑥𝑘 (1−𝜇𝑘 ) 𝐼𝑘𝛾𝑘
𝐶𝑘2 (1+𝛼𝑘 𝐼𝑘𝜂𝑘𝜇𝑘
2𝛾𝑘𝜎−2 )2 >0
45
Hypotheses Development (Cont’d.)
Hypothesis 2: Firms with lower EFLS intensity are associated with the higher stock volatility.
If and then >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
𝜕𝑉𝑎𝑟 (𝑣𝑘−𝑝𝑘)𝜕𝛼𝑘
=𝛿4𝛾𝑘 𝐼𝑘 (1−𝜇𝑘 ) {2 𝛿4+𝑉 1 ,𝑘+𝑉 2 ,𝑘 }
𝜂𝑘 {𝛿2 [𝜌𝑘+𝛾𝑘 𝐼𝑘(1+𝛼𝑘(𝜇𝑘−1))]+𝛼𝑘𝜂𝑘𝛾𝑘 𝐼𝑘𝜇𝑘2(𝛾𝑘 𝐼𝑘+𝜌𝑘)}
3
𝑉 1 ,𝑘= [ (𝛾𝑘 𝐼𝑘− 𝜌𝑘 )+𝜇𝑘 (𝛾𝑘 𝐼𝑘+𝜌𝑘 ) ] [𝛼𝑘𝜂𝑘2 𝐼𝑘𝛾𝑘𝜇𝑘
2+𝛿2𝜂𝑘 ]𝑉 2 ,𝑘=(−1+2𝜇𝑘+𝜇𝑘
2 )𝛿2𝜂𝑘𝛾𝑘 𝐼𝑘𝛼𝑘
Control Variables
46
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).
47
Firm-Level Performance Evaluation (Cont’d.)
Empirical Model 1:
Empirical Model 2:
Hypothesis 1 Predicts Negative b1
Hypothesis 2 Predicts b1 ≠ 0
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 Variable Value Variable Value
-0.0026* -0.0052** -0.0039
Control Variables
0.00069*** 0.00068*** 0.00067***
-0.00081 -0.0012 -0.0015
-0.0019** -0.0019*** -0.0019***
0.0025*** 0.0025*** 0.0025***
-0.046*** -0.046*** -0.046***
0.00042 0.00042 0.00042
Intercept 0.039*** Intercept 0.039*** Intercept 0.039***
0.0031 0.0031 0.003149
***, **, * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.
50
Volatility and EFLS IntensityModel 2A () Model 2B () Model 2C (EU)
Variable Value Variable Value Variable Value
-0.074*** -0.196*** -0.254***
Control Variables
0.012*** 0.012*** 0.012***
-0.105*** -0.103*** -0.110***
0.108*** 0.108*** 0.108***
0.565*** 0.565*** 0.565***
-0.222*** -0.222*** -0.222***
-0.066*** -0.066*** -0.066***
-0.615*** -0.615*** -0.616***
0.071*** 0.071*** 0.071***
0.016*** 0.017*** 0.017***
0.095*** 0.095*** 0.095***
Intercept-1.568***
Intercept-1.566***
Intercept-1.566***
0.57 0.57 0.57
***, **, * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.
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
52
Predefined Data Sources
Data Sources for US Public Companies
SEC/Edgar NYSE.com NASDAQ.comFinance.Yahoo.com
Company Information Database
Ticker CUSIPCIK PERMNOCompany Keywords
Company Name
Dynamic Data Sources
Blogs News
Search Engines
WSJTwitter
Basic
Info
rmatio
n
Yahoo Finance Forums
Company Websites
Stock Exchange
10K Report
Data C
ollectio
nD
ata P
rocessin
g
Transformation/Integration
Topics & Sentiments
Time Series / Burst
Risk ModelSNA Data
An
alysis
Analytic Approaches
Performance Indicators
Cross Media Analysis
Single Media Analysis
PredictiveAnalysis
AZ BIZ INTEL System Design
Visualization
Static
Figures/D
ashboardsInteractive A
pplications
Simulated Trading
Hsinchun Chen, Ph.D.
Artificial Intelligence Lab, University of Arizona
[email protected] http://ai.arizona.edu