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

Mrinalini Smita

doi:https://doi.org/10.14445/22315373/IJMTT-V67I7P515

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 67 | Issue 7 | Year 2021 | Article Id. IJMTT-V67I7P515 | DOI : https://doi.org/10.14445/22315373/IJMTT-V67I7P515

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

Mrinalini Smita

Citation :

Mrinalini Smita, "Logistic Regression Model For Predicting Performance of S&P BSE30 Company Using IBM SPSS," International Journal of Mathematics Trends and Technology (IJMTT), vol. 67, no. 7, pp. 118-134, 2021. Crossref, https://doi.org/10.14445/22315373/IJMTT-V67I7P515

Abstract

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.

Keywords

Financial ratios, stock performance, stock price prediction, logistic curves ,logistic regression model.

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