Abstract
This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.
Highlights
Relative value trading strategies, often referred to as statistical arbitrage, were developed by Morgan Stanley’s quantitative group in the mid-1980s and describe a market neutral trading approach [1]
Since data snooping is an important issue in many research studies, we examine the sensitivity of our PCA straddle delta hedged (PSD) results with respect to variations of these hyperparameters
We developed a dispersion trading strategy based on a statistical stock selection process and applied our approach to the S&P 500 index and its constituents from January 2000 to December 2017
Summary
Often referred to as statistical arbitrage, were developed by Morgan Stanley’s quantitative group in the mid-1980s and describe a market neutral trading approach [1]. Those strategies attempt to generate profits from the mean reversion of two closely related securities that diverge temporarily. Dispersion approaches are one of the most common trading algorithms and attempt to profit from implied volatility spreads of related assets and changes in correlations. Since index options usually incorporate higher implied volatility and correlation than an index replicating basket of single stock options, returns are generated by selling index options and buying the basket. As shown by [8,9], volatility based strategies generate meaningful and reliable returns
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