The strategy trades correlations amongst both stocks and options. “Correlations between options are different from those between stocks,” says Chowdhury.
In addition, the model sells overpriced and buys underpriced options. This is dubbed “statistical arbitrage”; this is clearly very different from the traditional description of “statistical arbitrage” and would be perceived as “volatility arbitrage” by most allocators.
“Rebalancing no more often than monthly reduces transaction costs and results in very stable portfolios,” says Chowdhury. Options must be rebalanced monthly, and stocks can also be reshuffled at the same time but may be rebalanced somewhat less often. Al execution is electronic and algorithmic, though there is no direct market access.
“We could not do all of this without our own AI. We use supervised deep learning neural networks and matrix algebra where appropriate. I still use 80% of the maths and matrix algebra from my Masters in AI but now we also have high performance engineering (HPE) expertise providing an edge. HPE is more widely used in games, but I wrote the world’s fastest Black Scholes pricer using HPE techniques to exploit the benefits of 2020s computers,” reveals Chowdhury.
Performance drivers
The portfolio lagged the S&P 500 in 2023 and 2024 when the MAG 7 were driving returns but has also shown better downside protection in down periods. The aim is to make a steadier 12-20% return, which was attained in both of the past two years. “The options are expected to make an average of 1% a month, though they will not all profit. There might be 15 positive and 15 negative option positions. If all our options were positive that would imply that we had no protection. In a small up market, option costs can outweigh stock gains. It is in a sharp down market when options really come into play,” explains Chowdhury.
