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Many recent papers conclude on short-term profitable patterns based on trading strategies relying on the belief that best (worst) performing securities over the past short-term period tend to continue to perform well (poorly) over the subsequent period of up to 12 months. Along with exploiting past price patterns, ever since the early 1980s the researchers' attention is drawn on the usefulness of company financial statements in predicting stock market returns. We argue on the necessity to develop a framework that would enable investors to support their everyday decisions by taking into account information retrieved from financial statements in an automated manner. We propose an ML-based AI system that inputs historic price information as well as information on a number of balance sheet items, profit and loss items, and cash flow items for the company of interest and predicts the probability of its stock price going down in the next period. The prediction relies on a dynamic automated selection of significant accounting and historic features. Tracking the dynamics in the structure of significant features could shed additional light on company specific issues that might be taken into account by investors when making their decisions. [ABSTRACT FROM AUTHOR]

In a technology-rich business environment, accounting educators are challenged to develop the required skill set in accounting graduates and to prepare them for a successful career in a data-driven environment. As the deployment of AI and ML advances may substantially improve almost every area of accounting, future professionals have to be well equipped with the needed knowledge and competences to benefit from their capabilities. Universities are searching for innovative approaches and good practices for implementing AI and ML techniques into accounting curriculum. This paper introduces an AI-based case study for accounting students, supported by clear learning objectives, methods and detailed implementation guidance. By playing their assigned role as company management's consultants under the case hypothetical scenario, students have to complete several individual and group tasks that demonstrate how the quality of investment decisions might be improved by enhancing the usefulness of companies' financial statements. Students are also encouraged to develop their analytical thinking and interpretation skills as well as their communication and deliberation proficiencies complementing the targeted competences in AI and ML. [ABSTRACT FROM AUTHOR]