Problem Statement
Building a long short trading strategy that could work consistently in US stock markets and that can also be used as a hedge during volatile markets.
JMI Implementation
- The JMI team understood the total long-short exposure requirement from the client and mapped it to the liquidity of various index and stock futures and options. Based on ADTV (Average Daily Trading Value) data, the team proposed to develop an index Long-Short strategy.
- Collected 20 years of tick-by-tick index trading data from the exchanges and sampled the data into 1 hour, 2 hours, daily, and weekly time series.
- Identified key price, volume, and volatility-based quantitative factors using statistical methods, developed a hypothesis, and back-tested it on in-sampled data.
- Using JMI’s automated AI/ML platform, the JMI team ran millions of scenarios based on the values of the factors.
- JMI developed an optimized strategy as per the client’s needs, which showed consistent results across timeframes.
- Re-tested the strategy on out-sampled data and risk and return parameters matched with in-sampled data and validated that.
Results
- The output strategy had a Sharpe ratio of 1.95, a 4% peak-to-trough drawdown, and an 11.5% annualized return on 20 years of data.
- Long-short strategy approved by the fund for actual capital deployment after some tweaks as per the fund’s risk-reward matrix.
- The short arm of the long-short strategy is used by the fund separately as well during times of major events and heightened volatility.