Date of Award
Eugene Santos Jr
This research paper presents an integrated approach that combines Long Short-Term Memory (LSTM), Q-Learning, Monte Carlo methods, and Text-to-Text Transfer Transformer (T5) to analyze and evaluate the business strategies of public companies. Leveraging a large and diverse dataset sourced from multiple reliable sources, the study examines corporate strategies and their impact on market dynamics. LSTM and Q-Learning are employed to process sequential data, enabling informed decision-making in simulated market environments and providing insights into potential outcomes of different strategies. The Monte Carlo method manages uncertainty, allowing for a comprehensive analysis of risks and rewards associated with specific strategies. T5 interprets textual data from earnings calls, press releases, and industry reports, offering a deeper understanding of strategic changes and market sentiments. The integration of these techniques enhances the evaluation of business strategies, enabling decision-makers to anticipate future market scenarios and make informed strategic shifts. Overall, this integrated approach provides a comprehensive framework for evaluating and anticipating market dynamics, enhancing the assessment and adjustment of public companies' business decisions.
Jbeniani, Lobna, "Interpreting Business Strategy and Market Dynamics: A Multi-Method AI Approach" (2023). Computer Science Senior Theses. 1.