P REDICTING THE D AILY R ETURNS USING F INANCIAL Q UANTITATIVE D ATA AND ASX A NNOUNCEMENTS Zhendong Zhao (4238 8910) Supervisor: Mark Johnson.

Slides:

Advertisements

Similar presentations

A Support Vector Method for Optimizing Average Precision

Advertisements

Contemporary Investments: Chapter 13 Chapter 13 COMPANY ANALSYSIS: LOOKING FORWARD Is there a long-term relationship between earnings and stock prices?

Regularization David Kauchak CS 451 – Fall 2013.

A CTION R ECOGNITION FROM V IDEO U SING F EATURE C OVARIANCE M ATRICES Kai Guo, Prakash Ishwar, Senior Member, IEEE, and Janusz Konrad, Fellow, IEEE.

Title: The Author-Topic Model for Authors and Documents

P REDICTION OF NBA G AME P ACE Fa Wang. I NTRODUCTION Possession based NBA game prediction dominates What is Pace? Total score = ASPM (points/possession)

EventCube Aviation Safety Data Analysis System Fangbo Tao, Xiao Yu, Jiawei Han 08/10/13.

Anindya Ghose Panos Ipeirotis Arun Sundararajan Stern School of Business New York University Opinion Mining using Econometrics A Case Study on Reputation.

Mateusz Radzimski (ATOS) The Sentify Portal. Sentiments + Finance = Sentify.

On the Importance of Text Analysis for Stock Price Prediction

O PTICAL C HARACTER R ECOGNITION USING H IDDEN M ARKOV M ODELS Jan Rupnik.

The Information Content of Initial Firm Rating Announcements Paper Presenter: Chih-Hsiang Chang Department of Finance National University of Kaohsiung.

Stock Volatility Prediction using Earnings Calls Transcripts and their Summaries Naveed Ahmad Aram Zinzalian.

Personalizing Search via Automated Analysis of Interests and Activities Jaime Teevan Susan T.Dumains Eric Horvitz MIT,CSAILMicrosoft Researcher Microsoft.

1 Transfer Learning Algorithms for Image Classification Ariadna Quattoni MIT, CSAIL Advisors: Michael Collins Trevor Darrell.

Sentiment analysis of news articles for financial signal prediction Anand Atreya Nicholas Cohen Jinjiang James Zhai.

Text Categorization Moshe Koppel Lecture 9: Top-Down Sentiment Analysis Work with Jonathan Schler, Itai Shtrimberg Some slides from Bo Pang, Michael Gamon.

1 Investor Protection and the Information Content of Annual Earnings Announcements: International Evidence Mark L. DeFond Mingyi Hung Robert Trezevant.

© 2003 The McGraw-Hill Companies, Inc. All rights reserved. Return, Risk, and the Security Market Line Lecture 11.

Price and Earnings Momentum: An Explanation Using Return Decomposition Qinghao Mao K.C. John Wei Hong Kong University of Science and Technology NTUICF.

Dr Kriti Arekar Dr Rinku Jain Keshav Trehan. Volatility is the key word in stock markets. What is volatility? It is a measure of how far the current price.

© 2003 The McGraw-Hill Companies, Inc. All rights reserved. Return, Risk, and the Security Market Line Chapter Thirteen.

Data Mining BS/MS Project Decision Trees for Stock Market Forecasting Presentation by Mike Calder.

Expected Returns Expected returns are based on the probabilities of possible outcomes In this context, “expected” means average if the process is repeated.

Panos Ipeirotis Stern School of Business New York University Opinion Mining Using Econometrics.

Predictive Modeling with Heterogeneous Sources Xiaoxiao Shi 1 Qi Liu 2 Wei Fan 3 Qiang Yang 4 Philip S. Yu 1 1 University of Illinois at Chicago 2 Tongji.

Chapter 11 Risk and Return. Expected Returns Expected returns are based on the probabilities of possible outcomes In this context, “expected” means average.

13-0 Figure 13.1 – Different Correlation Coefficients LO2 © 2013 McGraw-Hill Ryerson Limited.

Xiaoxiao Shi, Qi Liu, Wei Fan, Philip S. Yu, and Ruixin Zhu

1 Sentiment Polarity Identification in Financial News: A Cohesion-based Approach Author:Ann Devitt Khurshid Ahmad (School of Computer Science & Statistics,

Transfer Learning Task. Problem Identification Dataset : A Year: 2000 Features: 48 Training Model ‘M’ Testing 98.6% Training Model ‘M’ Testing 97% Dataset.

Forex-foreteller: A News Based Currency Predictor Fang Jin (fang8), Nathan Self (nwself), Parang Saraf (parang), Patrick Butler (pabutler), Wei Wang (tskatom)

Macroeconomic News Announcement Effects on Stocks Allison Keane.

A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting Huang, C. L. & Tsai, C. Y. Expert Systems with Applications 2008.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. A quantitative stock prediction system based on financial news Presenter : Chun-Jung Shih Authors :Robert.

Forex-foreteller: A News Based Currency Predictor Fang Jin, Nathan Self, Parang Saraf, Patrick Butler, Wei Wang, Naren Ramakrishnan Department of Computer.

A Discrete Stock Price Prediction Engine Based on Financial News.

Statistical Arbitrage Ying Chen, Leonardo Bachega Yandong Guo, Xing Liu February, 2010.

Personalization with user’s local data Personalizing Search via Automated Analysis of Interests and Activities 1 Sungjick Lee Department of Electrical.

Author Age Prediction from Text using Linear Regression Dong Nguyen Noah A. Smith Carolyn P. Rose.

Matic Perovšek, Anže Vavpeti č, Nada Lavra č Jožef Stefan Institute, Slovenia A Wordification Approach to Relational Data Mining: Early Results.

K ERNEL - BASED W EIGHTED M ULTI - VIEW C LUSTERING Grigorios Tzortzis and Aristidis Likas Department of Computer Science, University of Ioannina, Greece.

Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales Bo Pang and Lillian Lee Cornell University Carnegie.

CpSc 881: Machine Learning

Comparative Experiments on Sentiment Classification for Online Product Reviews Hang Cui, Vibhu Mittal, and Mayur Datar AAAI 2006.

M ETA - PATH BASED M ULTI - N ETWORK C OLLECTIVE L INK P REDICTION Speaker: Jim-An Tsai Advisor: Jia-ling Koh Author: Jiawei Zhang, Philip S. Yu, Zhi-Hua.

Analysis of Complex Systems John Sherwood Period 2.

G REEDY U NSUPERVISED M ULTIPLE K ERNEL L EARNING Grigorios Tzortzis and Aristidis Likas Department of Computer Science, University of Ioannina, Greece.

Inferring gene regulatory networks from multiple microarray datasets (Wang 2006) Tiffany Ko ELE571 Spring 2009.

© K. Cuthbertson and D. Nitzsche Figures for Chapter 16 Intertemporal Asset Allocation : Empirics (Quantitative Financial Economics)

F EATURE -E NHANCED P ROBABILISTIC M ODELS FOR D IFFUSION N ETWORK I NFERENCE Stefano Ermon ECML-PKDD September 26, 2012 Joint work with Liaoruo Wang and.

 Effective Multi-Label Active Learning for Text Classification Bishan yang, Juan-Tao Sun, Tengjiao Wang, Zheng Chen KDD’ 09 Supervisor: Koh Jia-Ling Presenter:

Stock market forecasting using LASSO Linear Regression model

An Application of Divergence Estimation to Projection Retrieval for Semi-supervised Classification and Clustering MADALINA FITERAU ARTUR DUBRAWSKI ICML.

G ENDER AND I NTEREST T ARGETING FOR S PONSORED P OST A DVERTISING AT T UMBLR Speaker: Jim-An Tsai Advisor: Professor Jia-ling Koh Author: Mihajlo Grbovicy,

Collaborative Deep Learning for Recommender Systems

GP FOR ADAPTIVE MARKETS Jake Pacheco 6/11/2010. The Goal  Produce a system that can create novel quantitative trading strategies for the stock market.

Energy System Control with Deep Neural Networks

StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent

Team TLT Taehee Jung Lev Golod Temi N Lal

Linear Regression (continued)

Quantitative Methods

Quantitative Methods

Quantitative Methods

Quantitative Methods

Quantitative Methods

Quantitative Methods

Learning with information of features

Linear-Time Selection

Presentation transcript:

P REDICTING THE D AILY R ETURNS USING F INANCIAL Q UANTITATIVE D ATA AND ASX A NNOUNCEMENTS Zhendong Zhao ( ) Supervisor: Mark Johnson

O UTLINE Motivations Framework & formulation Dataset & features Experiments

M OTIVATIONS – T O PREDICT THE DAILY RETURNS Previous works Use either textual or financial quantitative features Our work Use heterogeneous features (both textual and financial quantitative features) Textual Features (Announcements, Financial News, etc.) Financial Quantitative Features (past daily returns, past trading value, etc.) The daily Returns

E XAMPLES

T HE DAILY RETURNS ( LOG )

O UTLINE Motivations Framework & formulation Dataset & features Experiments

P ROPOSED F RAMEWORK A regression model using heterogeneous features Textual Features (Announcements, Financial News, etc.) Regression Algorithms The daily Returns Financial Quantitative Features (past daily returns, past trading value, etc.)

F ORMULATION

R EGRESSION S OLVER

O UTLINE Motivations Framework & formulation Dataset & features Experiments

D ATASET & F EATURES

O UTLINE Motivations Framework & formulation Dataset & features Experiments

E XPERIMENTS Objectives: to find 1. The best features; Combined vs. individual features; 2. The best textual features; Unigram vs. sentiment features; 3. The best regression solver; Equal vs. unequal penalty factors on quantitative features.

O BJECTIVE 1 -- T HE BEST FEATURES C OMBINED VS. INDIVIDUAL FEATURES Textual Features (Announcements, Financial News, etc.) Quantitative Features (Panel data) Regression Algorithms The Stock Returns

O BJECTIVE 1 -- T HE BEST FEATURES C OMBINED VS. INDIVIDUAL FEATURES

O BJECTIVE 2 -- T HE BEST TEXTUAL FEATURES U NIGRAM VS. SENTIMENT FEATURES Unigram features (all words in corpus) Huge size of vocabulary (100,000 features) but sparse for each document Sentiment features (negative, positive, uncertainty) Smaller size of features But may loss information

O BJECTIVE 2 -- T HE BEST TEXTUAL FEATURES U NIGRAM VS. SENTIMENT FEATURES

O BJECTIVE 3 -- T HE BEST REGRESSION SOLVER E QUAL VS. UNEQUAL PENALTY FACTORS Quantitative Features (dense) Textual Features (sparse) Quantitative Features (dense) Textual Features (sparse)

O BJECTIVE 3 -- T HE BEST REGRESSION SOLVER E QUAL VS. UNEQUAL PENALTY FACTORS

Q & A Thanks!