Lagged Co-movement Prediction of Sectoral Indices in Stock Market using Frequent Itemset Mining

Stock price prediction has become a critical area of interest for investors and market analysts, though forecasting stock market trends remains a challenging endeavor due to the inherent volatility and unpredictability of the market. The process of stock price prediction typically involves estimating future prices based on historical data, market trends, and various socioeconomic factors. However, factors like market fluctuations, incomplete or erroneous data, and investor behavior add complexity to these predictions. Several methods are employed for stock price forecasting, including fundamental analysis, technical analysis, and machine learning approaches such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. This study focuses on using sectoral indices as benchmarking tools to evaluate sector performance. Specifically, it explores the co-movements of thirteen NSE sectoral indices, with one index chosen as the target. The analysis centers on using closing prices to measure sector performance and calculates the correlations between the target index and others. The six most highly correlated indices are identified, and association rule mining is used to uncover the relationships between these indices and the target index. The study aims to: (i) examine the interdependencies between the target sector and other sectors, and (ii) generate predictive rules for a sector’s performance based on the behavior of correlated sectors, providing valuable insights for making informed investment decisions.