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

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Abstract

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.

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Data availability statement

The data that support the findings of this study are openly available at Yahoo Finance (http://finance.yahoo.com), Kenneth R. French Data Library (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html), and Economic Research, Federal Reserve Bank of St. Louis (http://fred.stlouisfed.org).

Notes

  1. http://finance.yahoo.com.

  2. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

  3. http://fred.stlouisfed.org.

References

  • Alexander C (2009) Market risk analysis, value at risk models, vol 4. John Wiley, Hoboken

    Google Scholar 

  • Ando T, Bai J (2017) Clustering huge number of financial time series: a panel data approach with high-dimensional predictors and factor structures. J Am Stat Assoc 112(519):1182–1198

    Article  Google Scholar 

  • Ang A, Bekaert G (2006) Stock return predictability: is it there? Rev Financ Stud 20(3):651–707

    Article  Google Scholar 

  • Ang A, Bekaert G (2007) Stock return predictability: is it there? Rev Financ Stud 20(3):651–707

    Article  Google Scholar 

  • Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques-part ii: soft computing methods. Exp Syst Appl 36(3):5932–5941

    Article  Google Scholar 

  • Aydin I, Karakose M, Akin E (2011) A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl Soft Comput 11(1):120–129

    Article  Google Scholar 

  • Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS One 12(7):e0180944

    Article  Google Scholar 

  • Benesty J, Chen J, Huang Y (2008) Microphone array signal processing, vol 1. Springer Science & Business Media, Berlin

    Google Scholar 

  • Bondt WF, Thaler R (1985) Does the stock market overreact? J Finance 40(3):793–805

    Article  Google Scholar 

  • Campbell JY (1987) Stock returns and the term structure. J Financ Econ 18(2):373–399

    Article  Google Scholar 

  • Campbell JY, Thompson SB (2007) Predicting excess stock returns out of sample: can anything beat the historical average? Rev Financ Stud 21(4):1509–1531

    Article  Google Scholar 

  • Chen AS, Leung MT, Daouk H (2003a) Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index. Computers Op Res 30(6):901–923

    Article  Google Scholar 

  • Chen H, Xiao K, Sun J, Wu S (2017) A double-layer neural network framework for high-frequency forecasting. ACM Trans Mana Inf Syst (TMIS) 7(4):1–17

    Article  Google Scholar 

  • Chen J, Benesty J, Huang Y (2003b) Robust time delay estimation exploiting redundancy among multiple microphones. IEEE Trans Speech Audio Process 11(6):549–557

    Article  Google Scholar 

  • Chen X, Liu Y, Ralescu DA (2013) Uncertain stock model with periodic dividends. Fuzzy Optim Decis Mak 12(1):111–123

    Article  Google Scholar 

  • Cutler DM, Poterba JM, Summers LH (1991) Speculative dynamics. Rev Econ Stud 58(3):529–546

    Article  Google Scholar 

  • Eraslan V (2013) Fama and french three-factor model: evidence from istanbul stock exchange. Bus Econ Res J 4(2):11

    Google Scholar 

  • Fama EF, French KR (1988) Permanent and temporary components of stock prices. J Political Econ 96(2):246–273

    Article  Google Scholar 

  • Fama EF, French KR (1989) Business conditions and expected returns on stocks and bonds. J Financ Econ 25(1):23–49

    Article  Google Scholar 

  • Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56

    Article  Google Scholar 

  • Ghorbani M, Chong EK (2020) Stock price prediction using principal components. Plos One 15(3):e0230124

    Article  Google Scholar 

  • Hermus K, Wambacq P et al (2007) (2006) A review of signal subspace speech enhancement and its application to noise robust speech recognition. EURASIP J Adv Signal Process 1:045821

    Google Scholar 

  • Hiransha M, Gopalakrishnan EA, Menon VK, Soman K (2018) Nse stock market prediction using deep-learning models. Proced Computer Sci 132:1351–1362

    Article  Google Scholar 

  • Hodrick RJ (1992) Dividend yields and expected stock returns: alternative procedures for inference and measurement. Rev Financ Stud 5(3):357–386

    Article  Google Scholar 

  • Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(6):417

    Article  Google Scholar 

  • Ince H, Trafalis TB (2007) Kernel principal component analysis and support vector machines for stock price prediction. IIE Trans 39(6):629–637

    Article  Google Scholar 

  • Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating lstm with multiple garch-type models. Exp Syst Appl 103:25–37

    Article  Google Scholar 

  • Kj Kim (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1–2):307–319

    Google Scholar 

  • Klein U, Võ TQ (2012) Direction-of-arrival estimation using a microphone array with the multichannel cross-correlation method. In: 2012 IEEE International symposium on signal processing and information technology (ISSPIT), IEEE, pp. 000251–000256

  • Kumatani K, McDonough J, Lehman JF, Raj B (2011) Channel selection based on multichannel cross-correlation coefficients for distant speech recognition. In: 2011 Joint workshop on hands-free speech communication and microphone arrays, IEEE, pp. 1–6

  • Lamont O (1998) Earnings and expected returns. J Finance 53(5):1563–1587

    Article  Google Scholar 

  • Lettau M, Ludvigson S (2001) Consumption, aggregate wealth, and expected stock returns. J Finance 56(3):815–849

    Article  Google Scholar 

  • Lof M (2012) Heterogeneity in stock prices: a star model with multivariate transition function. J Econ Dyn Control 36(12):1845–1854

    Article  Google Scholar 

  • Ohno S, Ando T (2018) Stock return predictability: a factor-augmented predictive regression system with shrinkage method. Econom Rev 37(1):29–60

    Article  Google Scholar 

  • Pafka S, Potters M, Kondor I (2004) Exponential weighting and random-matrix-theory-based filtering of financial covariance matrices for portfolio optimization. arXiv preprint cond-mat/0402573

  • Patel J, Shah S, Thakkar P, Kotecha K (2015) Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Exp Syst Appl 42(1):259–268

    Article  Google Scholar 

  • Powell N, Foo SY, Weatherspoon M (2008) Supervised and unsupervised methods for stock trend forecasting. In: 2008 40th Southeastern symposium on system theory (SSST), IEEE, pp. 203–205

  • Raza N, Shahzad SJH, Tiwari AK, Shahbaz M (2016) Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets. Resour Policy 49:290–301

    Article  Google Scholar 

  • Rozeff MS (1984) Dividend yields are equity risk premiums. J Portf Manag 11:68–75

    Article  Google Scholar 

  • Santos T, Veronesi P (2006) Labor income and predictable stock returns. Rev Financ Stud 19(1):1–44

    Article  Google Scholar 

  • Scharf LL, Demeure C (1991) Statistical signal processing: detection, estimation, and time series analysis, vol 63. Addison-Wesley Reading, MA

    Google Scholar 

  • Shah D, Isah H, Zulkernine F (2019) Stock market analysis: a review and taxonomy of prediction techniques. Int J Financ Stud 7(2):26

    Article  Google Scholar 

  • Shukla R, Trzcinka C (1990) Sequential tests of the arbitrage pricing theory: a comparison of principal components and maximum likelihood factors. J Finance 45(5):1541–1564

    Article  Google Scholar 

  • Sitorus T, Elinarty S (2017) The influence of liquidity and profitability toward the growth at stock price mediated by the dividends paid out (case in banks listed in indonesia stock exchange). J Econ Bus Account Ventura 19(3):377–392

    Google Scholar 

  • Taylor MP, Allen H (1992) The use of technical analysis in the foreign exchange market. J Int Money Finance 11(3):304–314

    Article  Google Scholar 

  • Tsai CF, Hsiao YC (2010) Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches. Decis Support Syst 50(1):258–269

    Article  Google Scholar 

  • Tufts DW, Kumaresan R, Kirsteins I (1982) Data adaptive signal estimation by singular value decomposition of a data matrix. Proc IEEE 70(6):684–685

    Article  Google Scholar 

  • Van Binsbergen J, Brandt M, Koijen R (2012) On the timing and pricing of dividends. Am Econ Rev 102(4):1596–1618

    Article  Google Scholar 

  • Welch I, Goyal A (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev Financ Stud 21(4):1455–1508

    Article  Google Scholar 

  • Xianya J, Mo H, Haifeng L (2019) Stock classification prediction based on spark. Proced Computer Sci 162:243–250

    Article  Google Scholar 

  • Yao J, Tan CL (2000) A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 34(1–4):79–98

    Article  Google Scholar 

  • Zhong X, Enke D (2017) Forecasting daily stock market return using dimensionality reduction. Exp Syst Appl 67:126–139

    Article  Google Scholar 

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Authors and Affiliations

  1. Program of Systems Engineering, Colorado State University, Fort Collins, USA

    Mahsa Ghorbani

  2. Department of ECE, Colorado State University, Fort Collins, USA

    Edwin K. P. Chong

Authors
  1. Mahsa Ghorbani
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  2. Edwin K. P. Chong
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Correspondence to Mahsa Ghorbani.

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Ghorbani, M., Chong, E.K.P. A dimension reduction method for stock-price prediction using multiple predictors. Oper Res Int J 22, 2859–2878 (2022). https://doi.org/10.1007/s12351-021-00636-3

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  • DOI: https://doi.org/10.1007/s12351-021-00636-3

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