Stock Price Prediction Based on Social Network

— A survey

Presented by: CHEN En

Outline

IntroductionRelated workMethodologyConclusion

IntroductionRelated workMethodologyConclusion

Outline

Stock price prediction Act of trying to determine the future value of company

stock or other financial instrument trade on financial exchange

Successful prediction could yield significant profit!

Introduction

The efficient-market hypothesis Stock price movement are governed by the random walk

hypothesis Inherently unpredictable

However, the others disagree and possess myriad prediction methods to gain future price information Fundamental analysis - Performance ratio (i.e. P/E ratio) Technical analysis - Charting analysis (i.e. Head and shoulder) Alternative methods - Internet-based data source for prediction

Introduction

IntroductionRelated workMethodologyConclusion

Outline

Traditional investment decision approaches: Capital asset pricing model (CAMP) Arbitrage pricing theory (APT)

Unrealistic and time complexity of the required calculation make them not applicable in real world problem

Current soft computing techniques: Neural network (NN) (A. N. Refenes, M. Azema-Barac, and A. D. Zapranis1993)

Genetic algorithm (GA) (R. Riolo, T. Soule, B. Eorzel2008)

Support Vector Machines (SVM) (G. H. John, P. Miller, and R. Kerber1996)

Because of widely use of the social network, major prediction are based on these public information.

Related work

Why social network? Ubiquitous and important for content sharing

Facebook, Blog, Twitter feeds, etc.

Public information—easily obtained

Behavioral economics demonstrate that emotions can profoundly affect individual behavior and decision-making

Recent research suggests very early acting prediction indicators can be extracted from online social media Online chat activity predicts book sales (Gruhl, D, Guha, R, Kumar, R, Novak,

J2005)

Blog sentiment predicts movie sales (Mishne, G & Glance, N.2006)

Consumer spending indicate disease infection rates (Choi, H & Varian, H.2009)

Related work

IntroductionRelated workMethodologyConclusion

Outline

Analysis of the relation between twitter messages and stock market index Selection of happiness and unhappiness words

Method 1: Twitter message and the stock price

Method 1: Twitter message and the stock price

Analysis of the relation between twitter messages and stock market index Selection of happiness and unhappiness words Evaluating both happiness and unhappiness words in the

same tweet

Where f=frequency of i’th word, Avg_happiness(wordi)=happiness value of word and Avg(T)=average happiness of given tweet

Method 2: Twitter mood predicts the stock price

Analyzing the text content of daily Twitter feeds to find the correlation between stock price and twitter mood Phase 1: Using two mood tracking tools: OpinionFinder &

Google-Profile of Mood states (GPOMs) to extract feature of mood OpinionFinder: Positive vs. nagetive mood GPOMs: Calm, Alert, Sure, Vital, Kind, and Happy

Phase 2: Granger causality analysis to test correlation between Dow Jones Industrial average (DJIA) values and GPOMs and OF values

Phase 3: Deploying a Self-Organizing Fuzzy Neural Network model (non-linear model) to test the hypothesis

Method 2: Twitter mood predicts the stock price

Method 3: Technical analysis with sentiment

Combining technical analysis with sentiment analysis for stock prediction Extract feature (using SentiWordNet):

Time series data (price and volume) source Social network source (on Engadget)

Technical indicators Using a multiple kernel learning framework to learn and

prediction the stock price

Method 3: Technical analysis with sentiment

Technical analysis

Emotion analysis

Outline

IntroductionRelated workMethodologyConclusion

Conclusion

Method 1: It is naïve but useful to predict the stock price index by just using happiness and unhappiness

Method 2: The result showed that changes in the public mood state could indeed be tracked from the content of large-scale Twitter feed using simple text processing techniques.

Method 3: It is considerable to use multiple kernel learning that covers several features.