Stock Price Prediction Based on Social Network A survey Presented by: CHEN En.

Slides:



Advertisements
Similar presentations
MODULE - IV: Security pricing: Factors influencing valuation, Constant growth modal, Equity valuation, Dividend capitalization, Earnings capitalization,
Advertisements

© PHI Learning, All rights reserved.1 Financial Accounting: A Managerial Perspective Third Edition Prepared by R. Narayanaswamy Indian Institute.
Twitter: The mental state of humankind Daniel Allen COMP 2903 X1 Fall 2010.
Tweet this PresentationTweet this Presentation Share on Facebook Share on LinkedIn Share on Facebook Share on LinkedIn 1.
Part One Introduction.
1 Market Efficiency in the Emerging Securitized Real Estate Markets Felix Schindler Centre for European Economic Research (ZEW) Milan, 26 th of June 2010.
The Effect Of Past Price Patterns and Price Memory In Asset Markets: A Behavioral Hypothesis Test With Brazilian Young Investors Bernardo Fonseca Nunes.
Company LOGO Stock Price Forecasting with Support Vector Machines based on Web Financial Information Sentiment Analysis Run Cao School of Information Renmin.
Machine Learning Applications in Algorithmic Trading Ryan Brosnahan Ross Rothenstine.
McGraw-Hill/Irwin Copyright © 2008 The McGraw-Hill Companies, Inc., All Rights Reserved. Behavioral Finance and Technical Analysis CHAPTER 9.
Copyright ©2004 Pearson Education, Inc. All rights reserved. Chapter 14 Stock Analysis and Valuation.
Mr. Perminous KAHOME, University of Nairobi, Nairobi, Kenya. Dr. Elisha T.O. OPIYO, SCI, University of Nairobi, Nairobi, Kenya. Prof. William OKELLO-ODONGO,
Twitter Mood Predicts the Stock Market Authors: Johan Bollen, Huina Mao, Xiao-Jun Zeng Presented By: Krishna Aswani Computing ID: ka5am.
Neural Networks And Its Applications By Dr. Surya Chitra.
Forecasting with Twitter data Presented by : Thusitha Chandrapala MARTA ARIAS, ARGIMIRO ARRATIA, and RAMON XURIGUERA.
Stock Valuation – Technical Analysis Essentials of Corporate Finance Chapters 7 and 10 Materials Created by Glenn Snyder – San Francisco State University.
NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI 授課教師:楊婉秀 報告人:李宗霖.
Yale School of Management The Dow Theory William Peter Hamilton’s Track Record Re-Considered Stephen J. Brown (NYU Stern School) William N. Goetzmann (Yale.
Chapter 17 TECHNICAL ANALYSIS The Visual Clue.
Requests for permission to make copies of any part of the work should be mailed to: Thomson/South-Western 5191 Natorp Blvd. Mason, OH Chapter 17.
Stock Value Ratio Classification Yan SuiZheng Chai.
The Security Market Line (SML) aka The Capital Asset Pricing Model (CAPM) The Capital Asset Price Model is E(R A ) = R f + [E(R M ) - R f ] x A Expected.
Achieving Better Reliability With Software Reliability Engineering Russel D’Souza Russel D’Souza.
1 Three Approaches to Security Selection Technical Analysis Fundamental Analysis –Economic Analysis –Industry Analysis –Company Analysis Efficient Markets.
Efficient Market Hypothesis EMH Presented by Inderpal Singh.
Chapter 4 Security Market Indicator Series As benchmarks to evaluate the performance of professional money managers 2. To create and monitor an.
Security Market Indexes Security Market Indexes: A statistical measure of change in a securities market. An index is an imaginary portfolio of securities.
8 8 C h a p t e r Stock Price Behavior And Market Efficiency second edition Fundamentals of Investments Valuation & Management Charles J. Corrado Bradford.
BASIC INVESTMENT INFORMATION. Investment information key concepts stock quotations investment information resources.
Applying Neural Networks to Day-to-Day Stock Prediction by Thomas Eskebaek.
Chapter 16 Jones, Investments: Analysis and Management
CHAPTER EIGHTEEN Technical Analysis CHAPTER EIGHTEEN Technical Analysis Cleary / Jones Investments: Analysis and Management.
Dr. Tucker Balch Associate Professor School of Interactive Computing CS 7646: Machine Learning for Trading Company Value Find out how modern electronic.
20th ERES Conference 3th - 6th July 2013 Vienna Change of the Tools Used for Real Estate Risk Analysis Rafał Wolski, PhD Department of Industry Economics.
Practical Personal Investing 2 Online access is essential. While this course is a continuation of those offered in the winter and fall of 2014, it is open.
Stock Price Prediction Using Reinforcement Learning
1 Prediction method for stock market Student : Dah-Sheng Lee Professor: Hahn-Ming Lee Date:30 January 2004.
 Title : Discussion of The Book-to-Price Effect in Stock Returns: Accounting for Leverage  Topic : Securities Valuation  Theory used by the article.
1 1 Ch11&12 – MBA 566 Efficient Market Hypothesis vs. Behavioral Finance Market Efficiency Random walk versus market efficiency Versions of market efficiency.
Prediction of Influencers from Word Use Chan Shing Hei.
Incorporating News into Algorithmic Market Trading Presented by Philip Gagner, Vice President RavenPack International, S.L. With the kind assistance of.
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
Contemporary Investments: Chapter 11 Chapter 11 ECONOMIC AND INDUSTRY ANALYSIS Why are economic and industry analyses important? How are investment decisions.
Market Efficiency.
Soft Computing methods for High frequency tradin.
PORTFOLIO MANAGEMENT.
Financial Data mining and Tools CSCI 4333 Presentation Group 6 Date10th November 2003.
INTRODUCTION RSI- RELATIVE STRENGTH INDEX MOVING AVERAGE BOLLINGER BANDSSUMMARY.
Stock market forecasting using LASSO Linear Regression model
1 A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting Reporter : Zhao-Wei Luo Che-Jung Chang,Der-Chiang.
Stock Market. General Terms Earnings per share: Amt of profit each share is entitled Going Public: Company plans to sell stock Share: investor’s ownership.
PREDICTING STOCK MARKET MOVEMENT USING SENTIMENTS For EECSE 6898-From Data to Solutions class Presented by-Tulika Bhatt(tb2658)
Economics 101 Class #4 Hand Out:. Investing Advice ? What will you do / Who will you use ? -Full Service Stockbroker: +Understands markets generally,
More than words: Social network’s text mining for consumer brand sentiments Expert Systems with Applications 40 (2013) 4241–4251 Mohamed M. Mostafa Reporter.
Behavioral Finance and Technical Analysis
INTRODUCTION TO INVESTING
Investment Management
Market Intelligence Analysis
Security Analysis & portfolio management
F Chapter 17 FUNDAMENTAL ANALYSIS vs TECHNICAL ANALYSIS 7/30/2018
Review Fundamental analysis is about determining the value of an asset. The value of an asset is a function of its future dividends or cash flows. Dividends,
The Dow Theory William Peter Hamilton’s Track Record Re-Considered
3 Cases on Business Intelligence MIS
Alexandros Dimitriadis
Behavioral Finance and Technical Analysis
國際金融專題 期中報告 Cointegration And The Causality Between Stock Prices And Exchange Rates Of The Korean Economy 授課教授:楊奕農 教授 國貿碩一 梁璇德.
Corporate Financial Theory
Happiness and Stocks Ali Javed, Tim Stevens
Beating the market -- forecasting the S&P 500 Index
Presentation transcript:

Stock Price Prediction Based on Social Network A survey Presented by: CHEN En

Outline Introduction Related work Methodology Conclusion

Introduction Related work Methodology Conclusion 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

Related work Methodology Conclusion 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 informationeasily 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

Introduction Related work Methodology Conclusion 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

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 ith 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 Introduction Related work Methodology 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.