Market Anomaly (January Effect) in the Indonesian Capital Market

Abstract

The company's strategy to improve its financial performance is to sell stocks that are expected to incur losses. This study uses secondary data on closing stock prices on the Indonesia Stock Exchange (IDX30) for the study period of 2020 to 2024. The January effect occurs when there is a difference in return values in January compared to other months. The variables used in this study include stock returns, abnormal returns, and trading volume activity. A company's strategy to improve its financial performance involves divesting stocks projected to incur losses at low prices at the end of the year and repurchasing large amounts, boosting the prices of stocks deemed to have good future prospects. The market anomaly discussed in this study is the January Effect. Investors and financial managers will divest stocks projected to incur losses. Another reason is to reduce taxes on stock ownership. When January is optimistic and data analysis is strong, they will repurchase large amounts, boosting the prices of stocks deemed to have good future prospects.This study was conducted to determine the best ARIMA model for the January Effect anomaly, reflecting the differences in stock returns in January compared to other months for companies listed on the Indonesia Stock Exchange (IDX). The dynamic market conditions in each research period illustrate the need for a study on testing market anomalies in the Indonesian Capital Market by providing important information regarding the potential and a real picture of the prospects and potential returns at the beginning of the year. The study results show several model estimations for abnormal return including ARMA (2,0), ARMA (0,3) and ARMA (2,3). The best ARIMA model estimation is ARMA (2.3) represented by the equation Y_t=β_0+β_1 Y_(t-1)+β_2 Y_(t-2)+e_t+α_1 e_(t-1)+α_2 e_(t-2)+α_3 e_(t-3). The January effect is indicated by the discovery of abnormal returns, fitting within the semi strong form of the efficient market hypothesis. Meanwhile, the best model for trading volume activity is ARMA (1,1) with the equation for the model being Y_t=β_0+β_1 Y_(t1)+α_0 e_t+α_1 e_(t-1). Market anomalies are indicated by the discovery of abnormal returns, which is in line with the efficient market hypothesis.