Assessing the Implementation and Performance of Automated Trading Software with Non-Biased Human Decisions in the Derivatives Market: Evidence from Thailand

Abstract

In recent years, most financial firms have utilized Artificial Intelligence (AI) technology in Algorithmic Trading (AT) software instead of human traders due to their susceptibility to numerous behavioral anomalies. Therefore, novel automated trading software's profitability and robustness have become an important research area. These software systems leverage historical market data containing a closing price, volume, and technical indicators to produce bi-directional trading signals for long and short positions. However, the AT-based strategies cannot be formulated as a formal mathematical model due to their non-linear nature. Thus, applying an AI-based fuzzy logic approach can make a qualitative trading system feasible for development. Many state-of-the-art AI-based strategies are ineffective regarding risk reduction, robustness, and profit growth. Therefore, we proposed a novel trading algorithm called the Robustness Trading Model in this research study. The recommended model integrates random generation methodology to generate AT strategies for derivative markets. For a case study, we particularly leverage a vital trading market, SET50 Index, and evaluate the approach's effectiveness by comparing results with the Buy & Hold and Mean Reversal strategies. The results show that our approach outperforms these two strategies by minimizing risk and improving profits. More specifically, we have demonstrated that the proposed AI-based approach can predict the future performance of SET50 index futures with up to 57.28% accuracy. Furthermore, this approach can reduce risk (maximum Drawdown) and enhance the total profit per maximum Drawdown (return/risk) when compared with the Buy & Hold and the Mean Reversal strategies. Moreover, we found that the profit-sharing business model has the best commercial software for innovative evaluation systems using financial feasibility compared to the commission-based and subscription business models. Based on these outcomes, we recommend that Thailand's market regulators and policymakers can utilize our model to maximize profits and eliminate any human biases. Finally, we also suggest that the proposed model can be substantially improved by including an extensive set of parameters during the training phase.