Logistic Regression Model For Predicting Performance of S&P BSE30 Company Using IBM SPSS

International Journal of Mathematics Trends and Technology (IJMTT)
© 2021 by IJMTT Journal
Volume-67 Issue-7
Year of Publication : 2021
Authors : Mrinalini Smita


MLA Style: Mrinalini Smita  "Logistic Regression Model For Predicting Performance of S&P BSE30 Company Using IBM SPSS" International Journal of Mathematics Trends and Technology 67.7 (2021):118-134. 

APA Style: Mrinalini Smita(2021). Logistic Regression Model For Predicting Performance of S&P BSE30 Company Using IBM SPSS International Journal of Mathematics Trends and Technology, 118-134.

Stock price prediction, the method of determining future values of a company’s stocks and other financial values, is an important topic in finance and economics which has captured the interest of researchers over the years to develop predictive models. A stock performance can, do some extent, be analysed based on financial indicators presented in the company’s annual report. Financial ratios help to form the basis of investor stock price expectations and hence, influence investment decision-making. Stock market prediction with the help of binary logistic regression using relation between financial ratios and stock performance has been recognized that financial ratios can enhance an investor’s stock price forecasting ability. The purpose of this study is to apply statistical methods to survey and analyze financial data in order to develop a simplified model for interpretation. The main purpose of the study to apply logistic regression model (binary) model for classifying S&P BSE 30 stocks into two categories GOOD or POOR performance stock. The logistic regression model, by applying variable to logistic curves, can be used to predict the likelihood of good performing stocks.


[1] Miao, K., Chen, F. & Zhao, Z.G., Stock price forecast based on bacterial colony RBF neural network., Journal of Qingdao University (Natural Science Edition), ISSN: 1006-1037 2(11) (2007).
[2] Ngunyi, A., Mwita, P.N., Odhiambo,R.O., On the estimation of properties of logistic regression Parameters, IOSR Journals of Mathematics.,e-ISSN:2278-5728,10(4) (2014) 57-68
[3] Ali, S.S. , Mubeen, M., Lal, I., Hussain , A., Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX), Asian Journal of Empirical Research, 8(7) (2018) 247-258 ISSN (P): 2306-983X, ISSN (E): 2224-4425 Journal of Business Intelligence and Data Mining, ISSN:1743-8195,9(2) 145-160.
[4] Fama, E. F., & French, K. R., The cross‐section of expected stock returns”. The Journal of Finance, 47(2) (1992) ISSN: 0022-1082, 427-465.
[5] Aminian, F., Suarez, E. D., Aminian, M., & Walz, D. T., Forecasting economic data with neural networks”. Computational Economics, 28(1) (2006) ISSN: 0927-7099, 71-88.
[6] Harvey, C. R., Predictable risk and returns in emerging markets”. The Review of Financial Studies, ISSN: 0893-9454, 8 (1995) 773-816.
[7] Jung, C., & Boyd, R., Forecasting UK stock prices., Applied Financial Economics, ISSN: 09603107, 14664305, 6 (1996) 279-286.
[8] Al-Loughani, N., & Chappell, D., Modelling the day-of-the-week effect in the Kuwait Stock Exchange: a nonlinear GARCH representation., Applied Financial Economics, ISSN: 9603107, 14664305,11(4) (2001) 353-359.
[9] Chen, A. S., Leung, M. T., & Daouk, H., Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index., Computers and Operations Research, ISSN: 0305-0548,30(6) (2003) 901-923
[10] Lee, S., Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS., Environmental Management, ISSN: 1095-8630,34(2) (2004) 223-232.
[11] Pardo, J. A., Pardo, L., & Pardo., Minimum Ө-divergence estimator in logistic regression models., Statistical Papers, ISSN: 09325026, 16139798, 47 (2005) 91-108.
[12] Yumlu, S., Gürgen, F. S., & Okay, N., A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction., Pattern Recognition Letters, 26(13) (2005) ISSN: 0167-8655,2093-2103.
[13] Lee, S., Ryu, J., & Kim, L., Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models”. Case study of Youngin, Korea, Landslides, CFKO200430710677778, 4 (2007) 327-338.
[14] Kumar, P. R., & Ravi, V., Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review., European Journal of Operational Research, ISSN: 0377-2217, 180(1) (2007) 1-28
[15] Öğüt, H., Doganay, M. M., & Aktaş, R., Detecting stock-price manipulation in an emerging market: The case of Turkey., Expert Systems with Applications 36(9) (2009) ISSN: 0957-4174, 11944-11949.
[16] Min, J. H., & Jeong, C., A binary classification method for bankruptcy prediction., Expert Systems with Applications, ISSN: 0957-4174,36(3) (2009) 5256-5263.
[17] Mostafa, M. M. (2010). “Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait”. Expert Systems with Application, ISSN: 0957-4174, 37(9), 6302-6309.
[18] Chen, Mu-Yen. (2011) “Predicting corporate financial distress based on integration of decision tree classification and logistic regression”, Expert Systems with Applications, ISSN: 0957-4174, 38(9), 11261-11272.
[19] Guresen, Erkam, et al. (2011). “Using artificial neural network models in stock market index prediction”, Expert Systems with Applications, ISSN: 0957-4174, 38 (8), 10389-10397.
[20] Dutta, A., et al. (2008). “Classification and Prediction of Stock Performance using Logistic Regression: An Empirical Examination from Indian Stock Market”, Redefining Business Horizons: McMillan Advanced Research Series, ISSN: 0007-6813, 46-62.

Keywords : financial ratios, stock performance, stock price prediction, logistic curves ,logistic regression model.