Logistic Regression Model

Logistic regression model has been widely used for researching issues in many industries, especially in predicting probability of bankruptcy and default risks of corporations and clients (in banking field). Among those researches, the work Ohlson (1980) could be considered to be outstanding. In his paper, Ohlson used logistic regression model to quantitatively measure and predict the probability of bankruptcy. His trend of work was later studied and upgraded by Zavgren (1985) and Zmijewski (1984). Although the multivariate discriminant analysis (MDA) was the dominating method in studying such fields, regression model arose with many advantages compared to the MDA. The logistic regression model is more appropriate for circumstances where the

In their work “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression” in 2013, Avijan Dutta, Gautam Bandopadhyay and Suchismita Sengupta obtained the testing result with 74.6 percent of accuracy in predictability. This success helps to widen a new innovation in predictability of share movement in the market, and as well, the practical implication of using logistic regression model for such work. Additionally, Carol Hargreaves and Yi Hao (2013) contributed their study and supported for this idea. Their finding and conclusion was that simulation results show that their selected stock portfolios outperform the Australian All-Ordinaries Index. Moreover, Jerry K. Bilbrey, Jr., Neil F. Riley, Caitlin L. Sams (2013) stated as the regression based model shows great promise for developing strategies using individual risk based

First of all, dependent variables are expected to be binary. There is a drawback for this point, since reducing an independent variable to dichotomous level would obviously lose lots of information. Secondly, as the logistic regression model conducts the response variable in form of probability of the event occurring, it is essential to code the variable accordingly. Thirdly, the model should be fitted correctly with meaningful independent variables, while also all meaningful variables should be included. A good approach to ensure such condition is using a stepwise method to estimate logistic regression. For next assumption, logistic regression requires each observation to be independent, or in other words, the model should have little or no multicollinearity. However, there is the option to include interaction effects of categorical variables in the analysis and the model: if multicollinearity is present centering the variables might resolve the issue, i.e. deducting the mean of each variable. Fifthly, logistic regression assumes there is a linearity of independent variables and log odds. According to the Journal of Statistics Solution Internet-based Organization, whilst it does not require the dependent and independent variables to be related linearly, the independent variables are required to be