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Dr. Hsinchun Chen Director, Artificial Intelligence Lab

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Presentation on theme: "Dr. Hsinchun Chen Director, Artificial Intelligence Lab"— Presentation transcript:

1 Predicting Market Movements: From Breaking News to Emerging Social Media
Dr. Hsinchun Chen Director, Artificial Intelligence Lab University of Arizona Acknowledgements: NSF CRI; NSF EXP-LA; DOD DTRA, CTFP, NPS; (ARFL WMD, CIA, FBI)

2 PREDICITNG MARKET MOVEMENTS

3 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

4 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…

5 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

6 BUSINESS MASS MEDIA, SOCIAL MEDIA,
AZ BIZ INTEL: BUSINESS MASS MEDIA, SOCIAL MEDIA, TEXT ANALYTICS, SENTIMENT ANALYSIS, SPIKE DETECTION, FINANCE/ACCOUNTING/MARKETING MODELING, PREDICTING MARKET MOVEMENTS

7 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

8 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)

9 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)

10 AZ ONLINE WOM

11 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!

14 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

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

20 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

21 AZ STOCK TRACKER I: mass, social media, topic, volume, sentiment
Data collection Topic extraction Conversation analysis Topic Mutual information phrase extractor Online news Web Forums Traffic dynamics Discussion topics Topic correlation and evolution Spider/ Parser Sentiment correlation and evolution Sentiment aggregator Author Sentiment Sentiment identification Sentiment grader Active topics and sentiments Database Market prediction Message sentiments Message

22 User-Generated Contents (UGC): Conversations of 30,000 Wal-Mart Constituents and 500,000 Responses
Data sources Duration # of Threads # of Messages # 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 Sucks Board (WSB) Nov 2003 - Nov 2008 1,354 19,624 1,855 Wakeupwalmart Forum - General WalMart Discussion Board (GDB) Aug 2005 2,136 23,940 967

23 Post Dynamics

24 Sentiment Trend

25 Correlation coefficients with p<0.10 are shown (two-tailed test)
Market Modeling Correlation Return Volatility Trading Volume 1 0.0348 Sentiment 0.0338 Disagreement Message Volume 0.3131 Message Length 0.0473 Subjectivity Sentiment One Day Lag Disagreement One Day Lag Message Volume One Day Lag 0.3026 Message Length One Day Lag 0.0859 Subjectivity One Day Lag 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) ** (-2.29) 0.0002 (1.42) 0.0015 (1.46) Volatilityt (-0.25) 0.0074 (0.47) *** (-4.94) *** (-19.09) 0.0030*** (7.82) 0.0149*** (7.27) Trading Volumet 0.7627*** (15.06) ** (-2.06) 0.0140** (2.29) 0.1957*** (23.18) *** (-13.24) *** (-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 Description Dependent: RETURN t Stock return on day t (log difference of share price) Fundamental: FFSIZE FFBTM FFMARKET 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 Description Forum: 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 – 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 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 – 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)

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)

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)

32 Experimental Design: Description of Prediction Models
Stakeholder models Comprehensive baseline variables plus stakeholder group-level forum measures 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 per Thread Messages per Stakeholder 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 testing results 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 H3.1 Stakeholder-level models > best baseline models Supported H3.2 Stakeholder-level models > forum-level models 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

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 Accuracy Baseline-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

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 Accuracy Best Baseline $ 10,763 57.20% $ 10,418 57.60% $ 10,645 56.80% Forum $ 10,367 $ 10,397 59.20% $ 10,303 Stakeholder-SN $ 9,873 55.20% $ 10,930 $ 10,669 Stakeholder -Content $ 10,689 60.40% $ 11,595 $ 11,976 61.20% * Stakeholder -Style $ 10,271 56.00% $ 9,653 $ 9,305 Stakeholder-SN+Content $ 10,384 61.60% $ 13,066 60.80% $ 11,866 62.80% ** Stakeholder-SN+Style $ 10,744 60.00% $ 10,792 $ 11,249 Stakeholder-Content+Style $ 10,696 $ 10,590 56.40% $ 10,603 58.80% Stakeholder-SN+Content+Style $ 10,976 58.00% $ 10,778 $ 10,881 59.60% Pair-wise t-test; improvement over best baseline model at * p < 0.10 ** p < 0.05

37 AZ STOCK TRACKER III

38 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)

39 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 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

41 Summary of Annotation Results
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) Agreement Cohen’s Kappa ALL 0.91 (0.88, 0.93) 0.81 (0.76, 0.86) POS 0.90 0.79 (0.73, 0.85) NEG 0.89 (0.86, 0.91) 0.77 (0.71, 0.82) Category Count Percent ALL 1157 46% POS 836 33% NEG 904 36% Reference Standard Dataset: 2539 sentences in total kappa reference value 0.75 alpha reference value 0.7 DT=1  = 307 DT>1  184 rows Note: (95% CI) from 1,000 Bootstrappings

42 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% 91.2% SVM 69.5% 70.2% 83.9% 60.3% SVM w/IG 69.1% 84.3% 58.3% FKC 64.7% 50.9% 69.7% 40.1% OF_PN 54.8% 27.9% 19.1% 51.4%

43 EFLS Impacts: Hypotheses Development
Theoretical framework (Easley and O’Hara, 2004) There are 𝐼 𝑘 signals for stock k ( 𝑠 𝑘1 , 𝑠 𝑘2 , …, 𝑠 𝑘 𝐼 𝑘 ) 𝑠 𝑘𝑖 ~𝑁 𝑣 𝑘 , 1 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 𝑘 𝐼 𝑘 ) 𝛼 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[ 𝑣 𝑘 − 𝑝 𝑘 ] 𝜕 𝛼 𝑘 = 𝛿 𝑥 𝑘 1− 𝜇 𝑘 𝐼 𝑘 𝛾 𝑘 𝐶 𝑘 𝛼 𝑘 𝐼 𝑘 𝜂 𝑘 𝜇 𝑘 2 𝛾 𝑘 𝜎 − >0

45 Hypotheses Development (Cont’d.)
Hypothesis 2: Firms with lower EFLS intensity are associated with the higher stock volatility. If 𝐼 𝑘 𝛾 𝑘 > 𝜌 𝑘 and 𝜇 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 𝜕𝑉𝑎𝑟( 𝑣 𝑘 − 𝑝 𝑘 ) 𝜕 𝛼 𝑘 = 𝛿 4 𝛾 𝑘 𝐼 𝑘 1− 𝜇 𝑘 2 𝛿 4 + 𝑉 1,𝑘 + 𝑉 2,𝑘 𝜂 𝑘 𝛿 2 𝜌 𝑘 + 𝛾 𝑘 𝐼 𝑘 (1+ 𝛼 𝑘 ( 𝜇 𝑘 −1)) + 𝛼 𝑘 𝜂 𝑘 𝛾 𝑘 𝐼 𝑘 𝜇 𝑘 2 ( 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 𝑘 2 𝐼 𝑘 𝛾 𝑘 𝜇 𝑘 2 + 𝛿 2 𝜂 𝑘 𝑉 2,𝑘 = −1+2 𝜇 𝑘 + 𝜇 𝑘 2 𝛿 2 𝜂 𝑘 𝛾 𝑘 𝐼 𝑘 𝛼 𝑘

46 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).

47 Firm-Level Performance Evaluation (Cont’d.)
Empirical Model 1: Empirical Model 2: Hypothesis 1 Predicts Negative b1 𝑟 𝑖,𝑡+1 = 𝑎 0 + b 1 𝐴𝐿𝐿_𝐼𝑁 𝑖,𝑡 + 𝑐 1 𝑁𝑒𝑤𝑠𝐹𝑟𝑒 𝑞 𝑖,𝑡 + 𝑐 2 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡 𝑖 𝑖,𝑡 + 𝑑 1 𝐿𝑜𝑔𝑆𝑖𝑧 𝑒 𝑖,𝑡 + 𝑑 2 𝐿𝑜𝑔𝐵 𝑀 𝑖,𝑡 + 𝑑 3 𝑟 𝑖,𝑡 + 𝑑 4 𝐿𝑜𝑔 𝑉 𝑖,𝑡 + 𝑒 𝑖𝑡 Hypothesis 2 Predicts b1 ≠ 0 𝐿𝑜𝑔𝑉 𝑖,𝑡+1 = 𝑎 0 + b 1 ALL_IN i,t + 𝑐 1 𝑁𝑒𝑤𝑠𝐹𝑟𝑒 𝑞 𝑖,𝑡 + 𝑐 2 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡 𝑖 𝑖,𝑡 + 𝑑 1 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚 𝑒 𝑖,𝑡 + 𝑑 2 𝐿𝑜𝑔 𝑉 𝑖,𝑡 + 𝑑 3 𝐿𝑜𝑔𝑆𝑖𝑧 𝑒 𝑖,𝑡 + 𝑑 4 𝐿𝑜𝑔𝐵 𝑀 𝑖,𝑡 + 𝑑 5 𝑟 i,t + 𝑑 6 𝐼𝑛𝑑𝑣𝑂𝑤 𝑛 𝑖,𝑡 + 𝑑 7 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒 𝑟 𝑖,𝑡 + 𝑑 8 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆 𝐷 𝑖,𝑡 + 𝑒 𝑖,𝑡

48 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

49 Expected Return and EFLS Intensity
Variable Value * ** Control Variables *** *** *** ** *** 0.0025*** -0.046*** Intercept 0.039*** 0.0031 ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.

50 Volatility and EFLS Intensity
Model 2A (𝐴𝐿𝐿_𝐼 𝑁 𝑖,𝑡 ) Model 2B (𝐹𝑇_𝐼 𝑁 𝑖,𝑡 ) Model 2C (EU_𝐼 𝑁 𝑖,𝑡 ) Variable Value 𝐴𝐿𝐿_𝐼 𝑁 𝑖,𝑡 -0.074*** 𝐹𝑇_𝐼 𝑁 𝑖,𝑡 -0.196*** 𝐸𝑈_𝐼 𝑁 𝑖,𝑡 -0.254*** Control Variables 𝑁𝑒𝑤𝑠𝐹𝑟𝑒 𝑞 𝑖,𝑡 0.012*** 𝑁𝑒𝑤𝑠𝑆𝑒𝑛𝑡 𝑖 𝑖,𝑡 -0.105*** -0.103*** -0.110*** 𝐿𝑜𝑔𝑉𝑜𝑙𝑢𝑚 𝑒 𝑖,𝑡 0.108*** 𝐿𝑜𝑔 𝑉 𝑖,𝑡 0.565*** 𝐿𝑜𝑔𝑆𝑖𝑧 𝑒 𝑖,𝑡 -0.222*** 𝐿𝑜𝑔𝐵 𝑀 𝑖,𝑡 -0.066*** 𝑟 𝑖,𝑡 -0.615*** -0.616*** 𝐼𝑛𝑑𝑣𝑂𝑤 𝑛 𝑖,𝑡 0.071*** 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝐶𝑜𝑣𝑒 𝑟 𝑖,𝑡 0.016*** 0.017*** 𝐿𝑜𝑔𝐴𝑛𝑎𝑙𝑦𝑆 𝐷 𝑖,𝑡 0.095*** Intercept -1.568*** -1.566*** 𝑅 2 0.57 ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.1 levels, respectively.

51 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 ???

52 AZ BIZ INTEL System Design
Predefined Data Sources Data Sources for US Public Companies SEC/Edgar NYSE.com NASDAQ.com Finance.Yahoo.com Company Information Database Ticker CUSIP CIK PERMNO Company Keywords Company Name Dynamic Data Sources Blogs News Search Engines WSJ Twitter Basic Information Yahoo Finance Forums Company Websites Stock Exchange 10K Report Data Collection Data Processing Transformation/Integration Topics & Sentiments Time Series / Burst Risk Model SNA Data Analysis Analytic Approaches Performance Indicators Cross Media Analysis Single Media Analysis Predictive AZ BIZ INTEL System Design Static Figures/Dashboards Interactive Applications Visualization Simulated Trading

53 Hsinchun Chen, Ph.D. Artificial Intelligence Lab, University of Arizona


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