A dimension reduction method for stock-price prediction using multiple predictors

Stock-price prediction has been the focus of extensive studies. Historical price values have been proven to have power to predict future prices. At the same time, different economic variables have also been used in the literature to predict stock-price values with high accuracy. In this work, we develop a general method for stock-price prediction using multiple predictors. First, we use multichannel cross-correlation coefficient as a measure for selecting the most correlated set of variables for each stock. We then construct the temporally local covariance matrix of the data and use this as the basis for a dimension-reduction method for prediction. This method involves resolving the predictive data (predictors) onto a principal subspace and from there producing a prediction that is consistent with the resolved data. Our method is easily implemented and can accommodate an arbitrary number of predictors. We investigate the optimal number of predictors based on two performance metrics: mean squared error of the prediction and the directional change statistic. We illustrate our results based on historical daily price data for 50 companies.

[1]  Matthijs Lof,et al.  Heterogeneity in Stock Prices: A STAR Model with Multivariate Transition Function , 2012 .

[2]  K. P. Soman,et al.  NSE Stock Market Prediction Using Deep-Learning Models , 2018 .

[3]  Yuhan Liu,et al.  Uncertain stock model with periodic dividends , 2013, Fuzzy Optim. Decis. Mak..

[4]  S. B. Thompson,et al.  Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? , 2008 .

[5]  A. Tiwari,et al.  Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets , 2016 .

[6]  Michael W. Brandt,et al.  On the Timing and Pricing of Dividends , 2010 .

[7]  Mark P. Taylor,et al.  The use of technical analysis in the foreign exchange market , 1992 .

[8]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[9]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[10]  E. Fama,et al.  Common risk factors in the returns on stocks and bonds , 1993 .

[11]  An-Sing Chen,et al.  Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index , 2001, Comput. Oper. Res..

[12]  Tigor Sitorus,et al.  The Influence of Liquidity and Profitability toward the growth of Stock price mediated by the Dividen Paid out (Case in banks listed in Indonesia Stock Exchange) , 2017 .

[13]  R. Hodrick.  Dividend Yields and Expected Stock Returns: Alternative Procedures for Interference and Measurement , 1991 .

[14]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[15]  Li Haifeng,et al.  Stock Classification Prediction Based on Spark , 2019 .

[16]  Hugo Van hamme,et al.  A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition , 2007, EURASIP J. Adv. Signal Process..

[17]  N. Powell,et al.  Supervised and Unsupervised Methods for Stock Trend Forecasting , 2008 .

[18]  Tomohiro Ando,et al.  Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures , 2015 .

[19]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[20]  T. Ando,et al.  Stock return predictability: A factor-augmented predictive regression system with shrinkage method , 2018 .

[21]  Mehmet Karaköse,et al.  A multi-objective artificial immune algorithm for parameter optimization in support vector machine , 2011, Appl. Soft Comput..

[22]  Charles Trzcinka,et al.  Sequential Tests of the Arbitrage Pricing Theory: A Comparison of Principal Components and Maximum Likelihood Factors , 1990 .

[23]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[24]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[25]  Michael S. Rozeff.  Dividend yields are equity risk premiums , 1984 .

[26]  Owen A. Lamont.  Earnings and Expected Returns , 1996 .

[27]  Jill Fain Lehman,et al.  Channel selection based on multichannel cross-correlation coefficients for distant speech recognition , 2011, 2011 Joint Workshop on Hands-free Speech Communication and Microphone Arrays.

[28]  Theodore B. Trafalis,et al.  Kernel principal component analysis and support vector machines for stock price prediction , 2007 .

[29]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[30]  Sydney C. Ludvigson,et al.  Consumption, Aggregate Wealth and Expected Stock Returns , 1999 .

[31]  J. Poterba,et al.  Speculative Dynamics , 1990 .

[32]  Pietro Veronesi,et al.  Labor Income and Predictable Stock Returns , 2006 .

[33]  Mahsa Ghorbani,et al.  Stock price prediction using principal components , 2020, PloS one.

[34]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[35]  Ha Young Kim,et al.  Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models , 2018, Expert Syst. Appl..

[36]  Boaz Rafaely,et al.  Microphone Array Signal Processing , 2008 .

[37]  Jacob Benesty,et al.  Robust time delay estimation exploiting redundancy among multiple microphones , 2003, IEEE Trans. Speech Audio Process..

[38]  R. Kumaresan,et al.  Data adaptive signal estimation by singular value decomposition of a data matrix , 1982, Proceedings of the IEEE.

[39]  Andrew Ang,et al.  Stock Return Predictability: Is it There? , 2001 .

[40]  Xiao Zhong,et al.  Forecasting daily stock market return using dimensionality reduction , 2017, Expert Syst. Appl..

[41]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

[42]  Haruna Isah,et al.  Stock Market Analysis: A Review and Taxonomy of Prediction Techniques , 2019, International Journal of Financial Studies.

[43]  V. Eraslan,et al.  Fama and French Three-Factor Model: Evidence from Istanbul Stock Exchange , 2013 .

[44]  Tomohiro Ando,et al.  Clustering Huge Number of Financial Time Series: A Panel Data Approach With High-Dimensional Predictors and Factor Structures , 2017 .

[45]  Udo Klein,et al.  Direction-of-arrival estimation using a microphone array with the multichannel cross-correlation method , 2012, 2012 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[46]  I. Welch,et al.  A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II , 2004, SSRN Electronic Journal.

[47]  J. Campbell.  Stock Returns and the Term Structure , 1985 .

[48]  E. Fama,et al.  BUSINESS CONDITIONS AND EXPECTED RETURNS ON STOCKS AND BONDS , 1989 .

[49]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[50]  Song Wu,et al.  A Double-Layer Neural Network Framework for High-Frequency Forecasting , 2017, ACM Trans. Manag. Inf. Syst..