Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization

Abstract

Future earnings indicate whether the trend of earnings is increasing or decreasing in the future of a business. It is beneficial to investors and users in the analysis and planning of investments. Consequently, this study aimed to identify future earnings patterns from financial statements on the Stock Exchange of Thailand. We proposed a novel approach based on FP-Growth and ECLAT algorithms with optimized discretization to identify associated future earnings patterns. The patterns are easy to use and interpret for the co-occurrence of associated future earnings patterns that differ from other studies that have only predicted earnings or analyzed the earnings factor from accounting descriptors. We found four strongly associated increases in earnings patterns and nine strongly associated decreases. Moreover, we also established ten accounting descriptors related to earnings: 1) %∆ in long-term debt, 2) %∆ in debt-to-equity ratio, 3) %∆ in depreciation/plant assets, 4) %∆ in operating income/total assets, 5) %∆ in working capital/total assets, 6) debt-to-equity ratio, 7) issuance of long-term debt as a percentage of total long-term debt, 8) long-term debt to equity, 9) repayment of long-term debt as a percentage of total long-term debt, and 10) return on closing equity. Doi: 10.28991/ESJ-2022-06-06-07 Full Text: PDF

Keywords

• future earnings patterns
• financial statement
• association rule mining
• optimized discretization.