The value premium and uncertainty: An approach by support vector regression algorithm

Abstract Risk premium plays an important role in stock investing. Experiments have shown that value stocks typically have a higher average return than growth stocks; however, this effect persists indefinitely, even disappearing in some stages. Some studies suggested high volatility in the series of returns, broken structures, market volatility, or the impact of financial crises. This study aimed to build the uncertainty index and control it in the regression analysis model to solve the limitations above. The empirical analysis in Ho Chi Minh Stock Exchange (HOSE) showed that a value premium exists, and value stocks have a higher average return than growth stocks due to the higher overall risk. Furthermore, this study combined the Support Vector Regression (SVR) algorithm with the risk premium theoretical framework for the forecasting model; consequently, it is the most efficient model.

[1]  T. Huynh,et al.  Forecasting stock price movement direction by machine learning algorithm , 2022, International Journal of Electrical and Computer Engineering (IJECE).

[2]  Mahmoud Qadan,et al.  The value premium and investors' appetite for risk , 2022, International Review of Economics & Finance.

[3]  T. Huynh,et al.  The impact of Company Characteristics and Macro Factors on Stock Prices: Machine Learning approach , 2022, 2022 International Conference on Decision Aid Sciences and Applications (DASA).

[4]  T. Huynh,et al.  Factors Affecting Forecast Accuracy of Individual Stocks: SVR Algorithm Under CAPM Framework , 2022, 2022 International Conference for Advancement in Technology (ICONAT).

[5]  Bui Thanh Khoa,et al.  Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading , 2021, Comput. Intell. Neurosci..

[6]  Jianguo Zheng,et al.  The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm , 2021, Algorithms.

[7]  Constantin Zopounidis,et al.  Preface: Regression methods based on OR techniques and computational aspects in management, economics and finance , 2021, Annals of Operations Research.

[8]  E. F. Simões Vieira,et al.  Value and growth stock returns: international evidence (JES) , 2021, International Journal of Accounting & Information Management.

[9]  Oscar Varela.  Duration‐adjusted betas , 2021, Review of Financial Economics.

[10]  Alibey Kudar,et al.  A new sustainable dividend policy and valuation model: Decreasing growth rate model , 2021 .

[11]  Weizhong Wang,et al.  Portfolio optimization with return prediction using deep learning and machine learning , 2021, Expert Syst. Appl..

[12]  Simon C. Smith,et al.  Have Risk Premia Vanished? , 2020, Journal of Financial Economics.

[13]  Lan Li,et al.  Predicting Stock Price Using Two-Stage Machine Learning Techniques , 2020, Computational Economics.

[14]  Zhuo Qiao,et al.  The value premium puzzle, behavior versus risk: New evidence from China , 2020, The Quarterly Review of Economics and Finance.

[15]  Josep M. Guerrero,et al.  Hybrid machine intelligent SVR variants for wind forecasting and ramp events , 2019, Renewable and Sustainable Energy Reviews.

[16]  Praveen Kumar,et al.  Leverage and the Cross‐Section of Equity Returns , 2019, The Journal of Finance.

[17]  Fan Chen,et al.  The Aumann-Serrano risk factor and asset pricing: evidence from the Chinese A-share market , 2019, Quantitative Finance.

[18]  J. Jaffe,et al.  Can Mispricing Explain the Value Premium? , 2019, Financial Management.

[19]  Herbert Kimura,et al.  Stock price prediction using support vector regression on daily and up to the minute prices , 2018, The Journal of Finance and Data Science.

[20]  Chris Kirby.  The Value Premium and Expected Business Conditions , 2018, Finance Research Letters.

[21]  Maja Koprčina.  Fama-French three-factor model , 2018 .

[22]  R. Stambaugh,et al.  Size and Value in China , 2018, Journal of Financial Economics.

[23]  G. N. Pillai,et al.  Prediction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm , 2017 .

[24]  Yongtao Hao,et al.  A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction , 2017, Expert Syst. Appl..

[25]  Barbara Rossi,et al.  Uncertainty and deviations from uncovered interest rate parity , 2017, Journal of International Money and Finance.

[26]  Zhi Xiao,et al.  A multiple support vector machine approach to stock index forecasting with mixed frequency sampling , 2017, Knowl. Based Syst..

[27]  Joachim Freyberger,et al.  Dissecting Characteristics Nonparametrically , 2017, The Review of Financial Studies.

[28]  Yongquan Zhou,et al.  A sparse method for least squares twin support vector regression , 2016, Neurocomputing.

[29]  Olivier Scaillet,et al.  Time-Varying Risk Premium in Large Cross-Sectional Equity Data Sets , 2016 .

[30]  Timotheos Angelidis,et al.  Stock Market Dispersion, the Business Cycle and Expected Factor Returns , 2015 .

[31]  E. Fama,et al.  A Five-Factor Asset Pricing Model , 2014 .

[32]  Jonathan M. Williams,et al.  Rationalizing the value premium in emerging markets , 2014 .

[33]  R. Koijen,et al.  The Cross-Section and Time-Series of Stock and Bond Returns , 2012 .

[34]  Lu Zhang,et al.  The Value Premium , 2002 .

[35]  Sydney C. Ludvigson,et al.  Expected Returns and Expected Dividend Growth , 2002 .

[36]  R. Stambaugh,et al.  Predictive Regressions , 1999 .

[37]  Feng Zhang,et al.  Risk and Return of Value Stocks , 1998 .

[38]  R. Green,et al.  Optimal Investment, Growth Options, and Security Returns , 1998 .

[39]  Tim Loughran.  Book-to-Market across Firm Size, Exchange, and Seasonality: Is There an Effect? , 1997, Journal of Financial and Quantitative Analysis.

[40]  E. Fama,et al.  Value Versus Growth: The International Evidence , 1997 .

[41]  R. Jagannathan,et al.  The Conditional CAPM and the Cross-Section of Expected Returns , 1996 .

[42]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[43]  Josef Lakonishok,et al.  Contrarian Investment, Extrapolation, and Risk , 1993 .

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

[45]  E. Fama,et al.  The Cross‐Section of Expected Stock Returns , 1992 .

[46]  Josef Lakonishok,et al.  Fundamentals and Stock Returns in Japan , 1991 .

[47]  E. Fama,et al.  Efficient Capital Markets : II , 2007 .

[48]  M. Gordon,et al.  Dividends, Earnings, and Stock Prices , 1959 .

[49]  N. Ouerhani,et al.  Data-Driven Thermal Deviation Prediction in Turning Machine-Tool - A Comparative Analysis of Machine Learning Algorithms , 2021, ISM.

[50]  Libo Yin,et al.  Adjusted dividend-price ratios and stock return predictability: Evidence from China , 2021 .

[51]  Tatevik Sekhposyan,et al.  Macroeconomic uncertainty indices based on nowcast and forecast error distributions , 2015 .

[52]  R. H. Smith,et al.  Asset Pricing Models and Financial Market Anomalies , 2006 .

[53]  Dana Kiku.  Is the Value Premium a Puzzle , 2006 .

[54]  Richard G. Sloan,et al.  Earnings Surprises, Growth Expectations, and Stock Returns or Don't Let an Earnings Torpedo Sink Your Portfolio , 1999 .

[55]  W. Sharpe,et al.  International Value and Growth Stock Returns , 1993 .