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Improved Accuracy Stock Price Change Prediction Model Using Trading Volume

  • Zhen Wei
  • Chao Wu
  • Yike Guo
  • Zhongwei Yao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

This research aims to model the relationship between the change in stock price and the volume. Linear regression has been applied to the model at daily and at minute time scales; then Random Forest and Lasso regression have been applied to the model. The results show that the larger the data, the better fit the model is, and Random forest has better prediction accuracy than the linear model.

Keywords

Equity trading Finance Machine learning Volume 

References

  1. 1.
    Griffin, J.M., Lemmon, M.L.: J. Financ. 57(5), 2317–2336 (2002). Wiley Online LibraryGoogle Scholar
  2. 2.
    Bailey, W., Chung, Y.P.: Exchange rate fluctuations, political risk, and stock returns: some evidence from an emerging market. J. Financ. Quant. Anal. 30(4), 541–561 (1995)CrossRefGoogle Scholar
  3. 3.
    Basu, S.: Investment performance of common stocks in relation to their price-earnings ratios: a test of the efficient market hypothesis. J. Financ. 32(3), 663–682 (1997)CrossRefGoogle Scholar
  4. 4.
    Banz, R.W.: The relationship between return and market value of common stocks. J. Financ. Econ. 9(1), 3–18 (1981)CrossRefGoogle Scholar
  5. 5.
    Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Financ. 48(1), 65–91 (1993)CrossRefGoogle Scholar
  6. 6.
    Levine, R., Zervos, S.: Stock markets, banks, and economic growth. Am. Econ. Rev. 88, 537–558 (1998)Google Scholar
  7. 7.
    Fama, E.F., French, K.R.: Dividend yields and expected stock returns. J. Financ. Econ. 22(1), 3–25 (1988)CrossRefGoogle Scholar
  8. 8.
    Charest, G.: Dividend information, stock returns and market efficiency-II. J. Financ. Econ. 6(2–3), 297–330 (1978)CrossRefGoogle Scholar
  9. 9.
    Amihud, Y.: Illiquidity and stock returns: cross-section and time-series effects. J. Financ. Mark. 5(1), 31–56 (2002)CrossRefGoogle Scholar
  10. 10.
    Wang, H., Li, G., Tsai, C.-L.: Regression coefficient and autoregressive order shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B Stat. Methodol. 69(1), 63–78 (2007)MathSciNetGoogle Scholar
  11. 11.
    Nadaraya, E.A: On Estimating Regression, Theory of Probability & Its Applications. SIAM (2006)Google Scholar
  12. 12.
    Pástor, L., Stambaugh, R.F.: Liquidity risk and expected stock returns. J. Polit. Econ. 111(3), 642–685 (2003)CrossRefGoogle Scholar
  13. 13.
    Pan, J., Poteshman, A.M.: The information in option volume for future stock prices. Rev. Financ. Stud. 19(3), 871–908 (2006)CrossRefGoogle Scholar
  14. 14.
    Augustin, P., Brenner, M., Subrahmanyam, M.G.: Informed Options Trading prior to Takeover Announcements: Insider Trading? (2016)Google Scholar
  15. 15.
    French, K.R., Roll, R.: Stock return variances: the arrival of information and the reaction of traders. J. Financ. Econ. 17(1), 5–26 (1986)CrossRefGoogle Scholar
  16. 16.
    Campbell, J.Y., Grossman, S.J., Wang, J.: Trading volume and serial correlation in stock returns. Q. J. Econ. 108(4), 905–939 (1993)CrossRefGoogle Scholar
  17. 17.
    Keim, D.B.: Size-related anomalies and stock return seasonality: further empirical evidence. J. Financ. Econ. 12(1), 13–32 (1983)CrossRefGoogle Scholar
  18. 18.
    Kandel, S., Stambaugh, R.F.: Modelling expected stock returns for short and long horizons. Working Paper 42–88, Rodney L. White Center for Financial Research, Wharton School, University of Pennsylvania (1988)Google Scholar
  19. 19.
    Osborne, M.F.M.: Brownian motion in the stock market. Oper. Res. 7(2), 145–173 (1959)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Karpoff, J.M.: The relation between price changes and trading volume: a survey. J. Financ. Quant. Anal. 22(1), 109–126 (1987)CrossRefGoogle Scholar
  21. 21.
    Hiemstra, C., Jones, J.D.: Testing for linear and nonlinear Granger causality in the stock price-volume relation. J. Financ. 49(5), 1639–1664 (1994)Google Scholar
  22. 22.
    Epps, T.W.: Security price changes and transaction volumes: theory and evidence. Am. Econ. Rev. 65(4), 586–597 (1975)Google Scholar
  23. 23.
    Epps, T.W.: Security price changes and transaction volumes: some additional evidence. J. Financ. Quant. Anal. 12(1), 141–146 (1977)CrossRefGoogle Scholar
  24. 24.
    Harris, L., Gurel, E.: Price and volume effects associated with changes in the S&P 500 list: new evidence for the existence of price pressures. J. Financ. 41(4), 815–829 (1986)CrossRefGoogle Scholar
  25. 25.
    Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Applied Linear Statistical Models, vol. 4. Irwin, Chicago (1996)Google Scholar
  26. 26.
    Seber, G.A.F., Lee, A.J.: Linear Regression Analysis, vol. 936. Wiley, Hoboken (2012)zbMATHGoogle Scholar
  27. 27.
    Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis. Wiley, Hoboken (2015)zbMATHGoogle Scholar
  28. 28.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)Google Scholar
  29. 29.
    Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)CrossRefGoogle Scholar
  30. 30.
    Segal, M.R.: Machine Learning Benchmarks and Random Forest Regression. Center for Bioinformatics & Molecular Biostatistics (2004)Google Scholar
  31. 31.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B Methodol. 267–288 (1996)Google Scholar
  32. 32.
    Hans, C.: Bayesian lasso regression. Biometrika 96(4), 835–845 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1(2), 270–280 (1989)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Science InstituteImperial College LondonLondonUK

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