Decision Support from Financial Disclosures with Deep Reinforcement Learning Considering Different Countries and Exchange Rates

Abstract

The era of low-interest rates is coming. In addition to their basic salary, people hope to increase their income by doing part-time work, understanding how to use assets already on hand, and investing in assets to earn extra rewards. Goldman Sachs reports that over the past 140 years, the 10-year stock market return has averaged 9.2%. The investment firm also noted that the S&P 500 outperformed its 10-year historical average with an annual average return of 13.6% between 2010 and 2020. Nowadays, with increased computing power and advancements in artificial intelligence algorithms, the effective use of computing power for investment has become an important topic. In the investment process, venture capitalists form portfolios, a practice that improves investment efficiency and reduces risks in a relatively safe situation. Current investments are not limited to one country, allowing for investments in other countries. Given this situation, we must pay attention to Uncovered Equity Parity (UEP) conditions. Thus, we aim to find optimal dynamic trading strategies with Deep Reinforcement Learning, considering the aforementioned properties.

Keywords

• decision support
• deep reinforcement learning
• Investment portfolio