Deep Learning Predicts Stock Market Using Financial News Events
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This document describes research on using deep learning models to predict stock market movements based on news events. It presents a method to extract event representations from news articles, generalize the events, embed the events, and feed the embedded events into deep learning models. Experimental results show that using embedded events as inputs to convolutional neural networks achieved more accurate stock market predictions than baseline methods, and modeling long, mid, and short-term event effects further improved performance. The research demonstrates that deep learning can effectively capture hidden relationships between news events and stock prices.
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Deep Learning Predicts Stock Market Using Financial News Events
- 1. Deep Learning for Stock Prediction Yue Zhang
- 2. My research areas Machine Learning Natural Language Processing Applications Text synthesis Machine translation Information extractionMarket prediction Sentiment analysis Syntactic analysis
- 3. This talk • Reading news from the Internet and predicting the stock market
- 4. Outline • Event-‐driven predict • Two extensions
- 5. Introduction • Is it possible? – Random walk theory – Efficient market hypothesis – Human/algorithm trading • Examples – Shares of Apple Inc. fell as trading began in New York on Tuesday morning, the day after former CEO Steve Jobs passed away – Google’s stock falls after grim earnings come out early
- 6. Why events? • Previous work – Bag-‐of-‐words – Named Entities – Noun Phrases • Examples – Oracle Corp would sue Google Inc., claiming Google’s Android operating system… – Microsoft agrees to buy Nokia’s mobile phone business for $ 7.2 billion.
- 7. Method • Event Representation – E=(O1, P, O2, T) – Actor – Event – Object – Time
- 8. Method • Event Extraction – Syntactic parsing – Open information extraction
- 9. Method • Event Generalization – First, we construct a morphological analysis tool based on the WordNet stemmer to extract lemma forms of inflected words – Second, we generalize each verb to its class name in VerbNet • For example – Instant view: Private sector adds 114,000 jobs in July. – (Private sector, adds, 114,000 jobs) – (private sector, multiply_class, 114,000 job)
- 10. Method • Model – Input: events – Output: two-‐way movement • Training: historical data • Testing: coming data
- 11. Method • Prediction Model – Linear model • Most previous work uses linear models to predict the stock market. To make direct comparisons, this paper constructs a linear prediction model by using SVM with linear kernel – Nonlinear model • Intuitively, the relationship between events and the stock market may be more complex than linear, due to hidden and indirect relationships. We exploit a deep neural network model, the hidden layers of which is useful for learning such hidden relationships
- 12. … News documents φ1 Class +1 The polarity of the stock price movement is positive Class -‐1 The polarity of the stock price movement is negative Input Layer Output Layer Hidden Layers … … φ2 φ3 φM
- 13. Method • Feature Representation – Bag-‐of-‐words • TF*IDF – Events • O1, P, O2, O1 + P, P + O2, O1 + P + O2 • For Example – (Microsoft, buy, Nokia's mobile phone business) – (#arg1=Microsoft, #action=buy, #arg2= Nokia's mobile phone business, #arg1_action=Microsoft buy, #action_arg2=buy Nokia's mobile phone business, #arg1_action_arg2= Microsoft buy Nokia's mobile phone business)
- 14. Experiments • Data Description – We use publicly available financial news from Reuters and Bloomberg over the period from October 2006 to November 2013. This time span witnesses a severe economic downturn in 2007-‐2010, followed by a modest recovery in 2011-‐2013. There are 106,521 documents in total from Reuters News and 447,145 from Bloomberg News. – We mainly focus on predicting the Standard &Poor's 500 stocks (S&P 500) index, obtaining indices and stock price from Yahoo Finance.
- 15. Experiments • Evaluation Metrics – Accuracy and MCC • Overall Results
- 16. Experiments • Experiments with Different Number of Hidden Layers of the Deep Neural Network Model
- 17. Experiments • Experiments with Different Quantities of Data
- 18. Experiments • Individual Stock Prediction
- 19. Experiments • Individual Stock Prediction
- 20. Experiments • Individual Stock Prediction
- 21. Conclusion • Events are useful. – Events are more useful representations compared to bags-‐of-‐words for the task of stock market prediction. • Hidden relations useful. – A deep neural network model can be more accurate on predicting the stock market compared to the linear model. • Robust results obtained. – Our approach can achieve stable experiment results on S&P 500 index prediction and individual stock prediction over a large amount of data (eight years of stock prices and more than 550,000 pieces of news). • Quality of information is more important than quantity. – The most relevant information (i.e. news title vs news content, individual company news vs all news) is better than more, but less relevant information.
- 22. Two extensions • Better event encodings • Long term history
- 23. Two extensions • Event sparsity – Using structured event to predict stock market movement suffers from increased data sparsity (Actor = Microsoft, Action = sues, Object = Barnes & Noble)
- 24. Two extensions • Modeling long-‐term effect of events – The effect becomes weaker – Little has been done
- 25. Event Embedding • Previous work – Learning entity embedding (Socher et al. 2013)
- 26. Neural Tensor Network 𝑓(𝑒$ % 𝑊 $:( 𝑒) + 𝑉 ,- ,. + 𝑏)𝑓(𝑊𝑥 + 𝑏) Neural Network Neural Tensor Network
- 27. Neural Tensor Network for Event Embedding O1 T1 P T2 O2 R1 R2 U T3
- 28. O1 T1 P R1 𝑅$ = 𝑓(𝑂$ % 𝑇$ [$:(] 𝑃 + 𝑉 :- ; + 𝑏) Neural Tensor Network for Event Embedding
- 29. Training • Minimize the margin loss • 500 iterations • Standard back-‐propagation Random replace with an object Regulation weight,set to 0.0001 Parameters
- 30. Deep Prediction Model • We model long-‐, mid-‐, short-‐term events – Long-‐term events (Last month) – Mid-‐term events (Last week) – Short-‐term events (Last day)
- 31. Deep Prediction Model • Architecture
- 32. Deep Prediction Model • Convolution and Max-‐pooling – Convolution layer to obtain local feature – Max-‐pooling to determine the global representativefeature
- 33. Experiments • Baselines Input Method Luss and d’Aspremont [2012] Bag of words NN Ding et al. [2014] (E-NN) Structured event NN WB-NN Word embedding NN WB-CNN Word embedding CNN E-CNN Structured event CNN EB-NN Event embedding NN EB-CNN Event embedding CNN
- 34. Experiments • Finds – Events are better features than words – Reducing sparsity if helpful in the task – CNN-‐based is more powerful
- 35. Experiments • 15 companies from S&P 500 – Consists of High-‐,mid-‐ and low-‐ranking companies – Evaluation metric: Accuracy and MCC
- 36. Conclusion • Event embeddings-‐based document representations are better than discrete events-‐based methods • Deep CNN can help capture longer-‐term influence of news event
- 37. Current • More technical enhancements • More markets – China’s A market – Chinese syntactic and semantic analysis – Chinese Open IE