Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX)
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In this article, the authors used logistic regression model to predict stock performance and found that the model was 89.77 percent accurate for prediction good as well as bad performance of stock.Abstract:
The key purpose behind the study is to use logistic regression model to predict stock performance. For this purpose different financial and accounting ratios were used as independent variables and stock performance (either “good” or “poor”) as dependent variable. The result shows that financial and accounting ratios significantly predict the stock performance. Our study consists on the sample period of annual data from 2011-2015 and comprises of 109 listed non-financial firms of Pakistan’s Stock Exchange (PSX). Our sample was shortlisted on the basis of available data of Market Capitalization. Our research examines sales growth, debt to equity ratio, book to price ratio, earning per share, return on equity and current ratio for the prediction of stock performance. The findings indicate that our prediction was 89.77 percent accurate for prediction good as well as bad performance of stock. Although we did not consider macroeconomic variable to forecast stock return performance but our six firm specific accounting and financial ratios were good enough to predict stock performance. This study shows that Logistic regression model can be used by investors, individual as well as institutions or fund managers to enhance their ability to predict “good or poor” stock.read more
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References
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Journal ArticleDOI
Applied Logistic Regression.
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Book
Applied Regression Analysis and Other Multivariable Methods
TL;DR: In this article, the authors compare two straight line regression models and conclude that the Straight Line Regression Equation does not measure the strength of the Straight-line Relationship, but instead is a measure of the relationship between two straight lines.
Book
An introduction to categorical data analysis
TL;DR: In this paper, the authors present a tour of categorical data analysis for Contingency Tables and Logit and Loglinear models for contingency tables, as well as generalized linear models for Matched Pairs.
Journal ArticleDOI
Financial ratios and the probabilistic prediction of bankruptcy
TL;DR: In this paper, the authors present some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy, and the methodology is one of maximum likelihood estimation of the so-called conditional logit model, in which the data set used in this study is from the seventies (1970-76).
Journal ArticleDOI
The Cross-section of Expected Stock Returns
TL;DR: In this paper, the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions were studied, and the authors found that the forecasts vary substantially across stocks and have strong predictive power for actual returns.