Schneider L, Stübinger J (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 8
Article Number: 1627
Journal Issue: 9
DOI: 10.3390/math8091627
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.
APA:
Schneider, L., & Stübinger, J. (2020). Dispersion trading based on the explanatory power of S&P 500 stock returns. Mathematics, 8(9). https://doi.org/10.3390/math8091627
MLA:
Schneider, Luisa, and Johannes Stübinger. "Dispersion trading based on the explanatory power of S&P 500 stock returns." Mathematics 8.9 (2020).
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