Regression Techniques for the Prediction of Stock Returns Performance

Abstract

This paper examines the theory and practice of regression techniques for prediction of stock price trend by using a transformed data set in ordinal data format. The original pre-transformed data source contains data of heterogeneous data types used for handling of currency values and financial ratios provides a process to measure ranking of stock price trends. The outcomes of both processes are examined and appraised. The primary design is based on regression analysis from WEKA machine learning software. The stock price movement in Bursa Malaysia is used as our research setting. The data sources are corporate annual reports which included balance sheet, income statement and cash flow statement. The variables included in the date set were formed based on stock market trading fundamental analysis approach. Classified in WEKA were used that the outcomes of regression techniques can be improved for the prediction of stock price trend by using a dataset in standardized ordinal data format.