– Artificial intelligence (AI) is being increasingly used in investing and portfolio management.
– AI models can outperform traditional benchmarks like the S&P 500.
– The analyzed AI trading model had success in predicting down days and performing well in high-volatility environments.
– AI models can avoid trading before big market-moving events.
– These findings suggest the vast potential of AI in transforming investment management.
Artificial intelligence (AI) has become a popular tool in the world of investing and portfolio management. There are various AI-based strategies being utilized, each with its own context, utility, and results. However, for many investment professionals, AI remains somewhat of a black box.
To shed some light on the subject, a specific AI equity trading model was analyzed to explore its benefits and risk-related costs. The model in question is Traders’ A.I., run by Ashok Margam and his team. The analysis focused on the model’s decisions and overall performance from 2019 to 2022.
Traders’ A.I. has the capability to take both long and short positions in the market and can flip positions throughout the day. However, it completely exits the market by the end of each day and does not hold any positions overnight.
The results of the analysis showed that Traders’ A.I. outperformed its benchmark, the S&P 500, over the three-year period. Although the model remained neutral in terms of long vs. short positions, its beta was statistically zero.
The model leveraged moments of higher skewness to achieve its results. While the S&P 500 displayed negative skewness (a strong left tail), Traders’ A.I. exhibited positive skewness (a strong right tail), indicating that it had fewer days with very high returns.
The analysis also revealed that Traders’ A.I. performed better when going short, with average returns of 0.13% on short days compared to a loss of 0.52% for the market. This pattern was consistent in bear markets as well, where the model generated excess performance relative to bull markets.
Furthermore, the AI model showed better performance on high-volatility days, outperforming the S&P 500 by 0.19% on average. However, it underperformed on low-volatility days.
Overall, the analysis of Traders’ A.I. highlights the potential of AI in investment management. By predicting down days, performing well in high-volatility environments, and avoiding trading before market-moving events, AI models can potentially transform the field.
It’s important to note that the results of this specific AI model may not serve as a proxy for all AI applications in investing. Nevertheless, these findings provide valuable insights into the potential benefits of AI in investment management.
