Case Study - Building a Quantitative Strategy from Scratch › Limitations and Practical Considerations

This case study follows the full research process but intentionally simplifies several dimensions that would matter in production. The most significant limitation is our use of synthetic data. Real financial data presents challenges that synthetic data cannot replicate: earnings announcements that cause discrete jumps in the spread, short-selling constraints that make one leg more expensive to maintain, and periods of market stress where correlations spike and cointegration relationships temporarily break down. In practice, pair selection itself is a challenging problem: with $n$ stocks, there are $\binom{n}{2}$ possible pairs to test, creating a severe multiple comparison problem that inflates the false discovery rate. Bonferroni or Benjamini-Hochberg corrections should be applied when scanning for cointegrated pairs, but even these cannot fully protect against overfitting.

Another key limitation is parameter stability. We estimated a single hedge ratio from the training period and used it throughout the test period. In reality, hedge ratios drift over time as companies evolve, and a static $\beta$ will gradually become stale. More sophisticated implementations use Kalman filters or rolling regressions to dynamically update the hedge ratio, at the cost of introducing additional parameters that must themselves be tuned. The strategy also assumes continuous market access and the ability to short sell without constraints, neither of which is guaranteed. Short-selling costs (borrow fees) can be substantial for hard-to-borrow stocks and should be modeled explicitly. Finally, our transaction cost model of 10 basis points round-trip is a rough approximation; real costs depend on order size, time of day, market volatility, and the specific execution algorithm used, all topics we covered in the transaction costs and execution chapters.

Despite these limitations, the framework we built, with its strict train-test separation, explicit risk controls, and monitoring infrastructure, represents the disciplined approach that separates quantitative research from gambling. The specific numbers matter less than the process.