Value Relevance of Key Accounting Information in Predicting Market Performance: A Machine Learning Approach

This study aims to examine the value relevance of key accounting information in predicting market performance and to explore which accounting information has the most value relevance in predicting market performance. The dataset is comprised of a total of 1401 observations from 117 companies over a period of 2010 to 2021. To conduct the study, total 12 indicators were taken from financial statements to represent the major accounting information (AI) and year-end market price and Tobin’s Q were taken as a proxy for market performance. Under the principal component analysis (PCA), it was found that only 3 AIs namely earnings per share (EPS), book value per share (BVPS) and cash flow per share (CFPS) could contribute to the whole explained variance ratio of market performance. The study employs several machine learning approaches: random forest regression (RFR) was chosen as the base line model and the result was compared with decision tree regression (DTR), long short term memory (LSTM), neural network model and multivariate regression model. After splitting the total observation into a 70:30 training-testing dataset and controlling for noise reduction, it was found that over any other models, LSTM and RFR models can predict the market performance with higher R-square value of 62% and 65% respectively along with the lowest MSE of all other models. It was found that EPS has the highest value relevance (factor importance) in predicting market profitability whereas BVPS and CFPS were found to have less than 10% factor importance in predicting market profitability.