Sentiment Analysis in Finance Market Forcast. Dong Wang 2017/7/16

Transcription

1 Sentiment Analysis in Finance Market Forcast Dong Wang 2017/7/16

2 Basic assumption Movements in financial markets and movements in (financial) news are intrinsically interlinked (Alanyali et al., 2013).

3 Some hypothesis Market impacts media Less predictive power of the media Media conveys some information about fundamentals Quick absorption, long impact Media impacts noise and liquidity traders Impacts in short term, and then reverse Media leads to risk-aversion Negative words lead to more on large-scale stocks (Dow)

4 Modules Text retrieve (sources, spiders, constraint ) Text feature collection (scale or vector, discrete or continuous, word or discourse )(text, meta data, statistics, projection) Discriminative analysis (attention, tone, mood, sentiment..) Prediction model (regression, SVM, Bayesian, NN, genetic, ) Strategy (hand-crafted or auto trading; conditional or compositional )

5 Start from a simple example The Role of Media in the Stock Market, Paul Tetlock, 2016, News and Finance conference

6 The Role of Media in the Stock Market, Paul Tetlock, 2016, News and Finance conference

7 Start from a simple model Construct a text factor by looking at the negative words in news (e.g., WSJ column). Design a linear regression, to test the predictive capability of the text factor. Design a strategy. Paul C. Tetlock, Giving Content to Investor Sentiment: The Role of Media in the Stock Market, Journal of Finance 62, , 2007.

8 Media factor Collect the raw vector based on category word counts. Using PCA to find the direction that accounts for the most variance. The variance explained by the PCs is computed as the sum of the eigen values corresponding to the selected eigen vectors. Media factor = <word-count-vector, PC1> Pessimism factor involving mainly 4 categories: negative, weak, fail, fall. σ = K i=1 λ i

9 Predict reuturns using media factor

10 Economic significance A one standard deviation increase in pessimism in WSJ column predicts a decrease in Dow Jones returns euqal to 8.1 basis points over the next day The average daily return on the Dow Jones over the sample period is 6.3 basis points, which would be completely off set by a one standard deviation increase in pessimism The five lags of the pessimism factor explain just 1.52% of the residual variation in Dow Jones returnsowever; however the other economic control variables used in this study such as five lags of Dow returns,five lags of volume, and five time period dummies explain only 0.16%, 0.30%, and 0.17% respectively Can be used to construct a trading strategy that earns 7.3% annualized return, but trading cost is significant.

11 Tetlock s findings The reaction of the market is negative and then recover to negative words The negative & recover pattern does not support the information hypothesis The strength of the pattern depends on period A trading strategy is possible, but less practically valuable. All the finds are based on the text source (WSJ Abreast of the Market)

12 Some hypothesis Market impacts media Less predictive power of the media Media conveys some information about fundamentals Quick absorption, long impact Media impacts noise and liquidity traders Impacts in short term, and then reverse Media leads to risk-aversion Negative words lead to more on large-scale stocks (Dow)

13 Extend to predict firms (TETLOCK 08) We examine whether a simple quantitative measure of language can be used to predict individual firms accounting earnings and stock returns The same approach but use different text sources more specific to individual firms and about more fundamentals Negative word fraction The Role of Media in the Stock Market, Paul Tetlock, 2016, News and Finance conference

14 Findings The fraction of negative words in firm-specific news stories forecasts low firm earnings; Firms stock prices briefly underreact to the information embedded in negative words; The earnings and return predictability from negative words is largest for the stories that focus on fundamentals.

15 The Role of Media in the Stock Market, Paul Tetlock, 2016, News and Finance conference

16 Some hypothesis Market impacts media Less predictive power of the media Media conveys some information about fundamentals Quick absorption, long impact Media impacts noise and liquidity traders Impacts in short term, and then reverse Media leads to risk-aversion Negative words lead to more on large-scale stocks (Dow)

17 Over to more recent research Better (numerical) measures for news? Use deep learning Better integration with other data? Use deep learning Better strategy? Use deep learning

18 How text (news) can be represented Term frequency TF/IDF PCA other factor analysis LDA or other topic models Embedding

19 Embedding for words Put words in a low-dimensional space where the distance is measureable Make the words semantic computable Can be aggregated to infer doc semantics

20 How to embed From word to text Neural LM model Context log-linear model Mean average Both word and text Sequence to sequence model Marginal cost End to end training

21 An example for bank stress test Event data: 101 banks from 2007Q3 2012Q2, 243 distress events Government interventions State aid Direct failures Distressed mergers Text data: news articles from Reuters online archive from the years 2007 to 2014 (Q3), 6.6M articles. Link text and event Search articles with the bank name mentioned Articles that are at least 3 months earlier are used

22 Training doc vector Paragraph vector

23 Distress prediction A deep model to predict the event Can be used to select descriptions in an article

24 Performance

25 Stress index

26 Stress index for Fortis

27 Extracted text

28 Conclusion Large variance on the effectiveness was reported Deep and accurate data mining, beyond emotion and keyword seems important Isolate information seems not effective More sophisticated models seem required for both prediction and trading strategy No free lunch, no universal method. Domain and market specific approaches are important

29 Reference Paul C. Tetlock, Giving Content to Investor Sentiment: The Role of Media in the Stock Market, Journal of Finance 62, , PAUL C. TETLOCK, MAYTAL SAAR-TSECHANSKY, and SOFUS MACSKASSY, More Than Words: Quantifying Language, THE JOURNAL OF FINANCE, VOL. LXIII, NO. 3, JUNE 2008 The Role of Media in the Stock Market ( %20March%202016%20-%20The%20Role%20of%20Media%20in%20the%20Stock%20Market.pdf) Alanyali, M., Moat, H.S. and Preis, T. (2013) Quantifying relationship between financial news and stock market. Scientific Reports 3: 3578 Samuel Ronnqvist, Peter Sarlin, Detect & Describe: Deep learning of bank stress in the news, Michela Nardo, et al. Walking Down Wall Street with a Tablet: A Survey of Stock Market Predictions Using the Web, Journal of Economic Surveys, Vol. 30, Issue 2, pp , 2016 Qiang Li et al. A Tensor-Based Information Framework for Predicting the Stock Market, ACM TIOS, 2016.