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PERFORMANCE BOOSTING: ENHANCING THE
PROFITABILITY OF QUANTITATIVE INVESTING
STRATEGIES
Editor’s note: this was originally posted at HoodRiverResearch.com and is reprinted here to introduce readers to the
philosophy of David Aronson who will be a presenter at the MTA’s Annual Symposium.
There is a need for quantitative strategies to differentiate themselves from competitors and to maximize returns. There
are two approaches to doing this:
1. Improving execution algorithms to minimize the price impact of strategy buy and sell orders, or
2. Increasing the returns that can be earned from the strategy’s buy and sell signals.
Hood River Research (HRR) favors taking the second approach. The return of your strategy is increased by predicting which
signals are likely to be the most and least profitable, thereby allowing you to take larger positions on signals with the
highest expectations and smaller positions on those with the lowest expected returns. The predictions are based on
indicators that quantify the trading dynamics of a security at the time your strategy issues a signal to initiate a position.
Hood River’s process relies on sophisticated data preprocessing and an ensemble of non-parametric modeling techniques
designed to uncover patterns invisible to less powerful modeling methods.
The deliverable is a performance boosting model, which is a second-stage prediction model, designed to work in
conjunction with your existing strategy. Past projects have shown that signals predicted to generate the highest returns
produce gains that are 1.5 to 3 times larger than average signal returns while signal predicted to perform poorly can have
negative expected returns.
The Benefit of Performance Boosting: More Knowledge
To clarify the knowledge conferred by the second-stage model, consider the knowledge possessed by the investor not
using one. Suppose this investor employs a low-PE strategy. Each month all stocks in the investor’s universe are broken
into deciles based on their PE ratio. A typical strategy would be to buy the stocks in the lowest PE decile (decile 1). Assume
that a historical back-test of the strategy has shown that stocks in the lowest PE decile produce an excess return versus
the universe of 0.50% over the one-month period following purchase. From this investor’s state of knowledge, all that can
be said each time the low-PE strategy signals the purchase of a security is that it has an expected one-month excess return
APRIL 2016
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