Predicting Stock Markets Using Binary Logistic Regression Based on Bry-Boschan Algorithm

Abstract

In the stock market, there are bullish and bearish terms that are reflected in the movement of the stock price index. One of the stock price indexes listed on the Indonesia Stock Exchange (IDX) is the IDX Composite. Stock market conditions fluctuate along with changes in stock prices that move randomly, while investors expect market conditions to be active (bullish market). Several factors influence the movement of the IDX Composite, one of which is macroeconomic factors. The purpose of this research is to find out the condition of stock market as well as predict its condition using macroeconomics indicators. The method used to determine stock market conditions (bullish or bearish) is the Bry-Boschan algorithm, while the method used to predict the stock market using macroeconomic indicators is the binary logistic regression method. The Bry-Boschan algorithm is widely used to detect peaks and troughs in business cycle analysis. Binary logistic regression is used to model data with responses that have two categories or are in the form of binary numbers. Results show that the IDX Composite experienced 42 times (month) bearish periods and 191 times (month) experienced bullish periods. The obtained model has an accuracy value of 81.55%.