Predicting Stock Returns Using Regression Modeling
Introduction
There are many empirical studies in the stock return that shows economically and statistically relevant in forecasting the gains on its return. The prediction of the analysts is entirely based on their knowledge and past experiences in the stock exchange. Most of the analysts enjoyed the freedom to collaborate and explore the other opportunities in close prediction of the stock return especially when they are hired by the company. In a deep sense, a business analyst or an economist is not effective in the field of work if he or she did manage to forecast the future of the company.
Background of the Study and Problem Statement
The composition of the regression modeling is said to be an advisable predictive tool which is also widely used in the professional business. Regression, according to the mathematical context is composed of variables that can help an individual or an analyst to predict the stock return of a corporation (2008). Based on that particular idea, the other elements of the stock market such are dividend are needed to be assessed in the attempt to develop a sound regression model. However, the question on how can analysts measure its usefulness in the stock exchange and global trading remains the center of the study.
Research Objectives
The study has various objectives but only two are emphasized which is first, the attempt of creating a sound predictive regression model. And second, to deliver the created model as an effective predictor in stock return. The standard model is basically inspired by the changes happened in the business environment such as sudden economical trends which is a great burden not only on the analysts but as well as on the business leaders.
Research Questions
The study provided certain questions in its journey toward the determination of predictive regression model.
1. What is the importance of regression model when it is applied in the business setting?
2. What are the proposed strategies to accommodate the need in predicting the stock return?
3. What would be the factors that need to be considered in the formulation of an predictive regression model?
Literature Review
It is often argued that if stock markets are efficient then it should not be possible to predict stock returns. Using a simple regression model this means that none of the variables in the following stock return regression should be statistically significant (2004). The predictive regression model basically examines the evidences and afterward predicts in the excess of stock returns or for predicting the return in equities (1996; 2008). There are two types of regression which is the linear regression and multiple regressions that both utilized as an effective predictive tool in stock exchange. The stock market volatility is another considered factor or predictor of the future returns. Although there are other models that are available to use in predicting the stock return, the regression model is still recognized as an essential tool that measure the generated predicted results than of the other predicting models. It is now well known that if the predictor variables are highly persistent then, the predictive regression is considered biased and their limiting distribution is non-standard when the innovations of the predictor variable are correlated with returns. This is because of the dividend-price and earnings-price ratios while the innovations and the long-short interest rate ( 2008).
Methodology
The applied method utilized in the study is the comparative case study method that serves as an engine to keep the research to reach the highest positive answer to the main topic. The comparative case study method is an effective tool to review, examine, compare, and promote other better ideas in predicting stock return using the regression model. Significantly, the information gathered are suitable to the questions adhering in the main topic which is another advantage of the entire study.
Analysis
A robust regression method can be used in the approach that yields quite different conclusions than the currently accepted wisdom on the stock return. These results illustrate the important role that regression model plays in careful financial analysis and modeling, by providing modern methods. The regression models are subject in the analysts’ portfolio in determining the effective position of the company’s share in the stock market.
Conclusion
The regression model in business settings aims to change the calculation of the velocity of the return in the stock market as well as the equities. Furthermore, the business analysts are anticipated to look for a solution that can be easily derive to the predictions in the stock returns without enduring the crucial phases of computations.
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