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In the first part of a two-part article, David Aronson, President of Hood River Research, introduces the concept of performance boosting strategies and explains the selection process for their predictor inputs.

Using Trading Dynamics to Boost Strategy Performance

David Aronson

The objective of performance boosting is to increase the alpha of the buy and sell signals issued by an existing trading model. This process is premised on the notion that if a model is completely objective1, it may be possible to predict the outcomes of its buy and sell signals to an economically meaningful degree. Alpha is boosted by taking larger than normal positions on trade signals that are predicted to have above-average outcomes and taking smaller than normal positions (or no position) on recommendations predicted to be below average.

The predictions may either be in the form of a forecast of the recommendation’s return or in the form a probability that the recommendation will result in a profit. The predictions are based on a set of predictive variables or indicators that quantify the market’s trading dynamics at the time a new position is signalled by the original model. We refer to these variables as ‘trading-dynamics indicators’. The indicator values serve as input to the second-stage model. The model’s output is a forecast of the original recommendation’s outcome.

Predicting strategy returns

To explain how the performance-boosting model forecasts the outcome of a buy or sell signal, it is useful to consider the situation confronted by an investor who does not use a performance-boosting model. Suppose this investor is ranking stocks on a monthly basis and buying the stocks in the lowest PE decile. Assume that a backtest of the strategy has shown that stocks in the lowest PE decile earn an excess return of 0.50 per cent versus the universe over the one-month period following purchase.

Now consider another investor who follows the same strategy, but who is also using a performance-boosting model to predict the returns of recommendations generated by the low-PE strategy. His enhancement model contains only two predictor variables which were discovered to contain information that helps predict the excess returns of low-PE stocks. The first is the RSI (relative strength index) and VOL_CHG (see Figure 1) which quantifies the recent rate of change in the stock’s trading volume.

Assume that as of the date the stock is recommended the RSI has a value of 15 and VOL_CHG has a value -10. Note that in Figure 1 these two values represent a set of coordinates that denote a specific location in a two-dimensional space, where one dimension (axis) represents RSI while the other represents VOL_CHG. Also note that this location is associated with a specific location on the grid surface in Figure 1 and has an altitude of +1.8 per cent with respect to a third dimension, which represents the predicted return for the recommendation.

The grid surface as a whole represents the relationship between just two predictor variables – RSI and VOL_CHG – and the variable we wish to predict, i.e. the return on the stock recommendation. However, in practice the performance-boosting model may contain numerous predictor variables (dimensions) and so the model surface would be a multi-dimensional generalisation of a surface known as a hyper-surface, which cannot be illustrated. Thus, one can think of the model as a hyper-surface in an abstract mathematical space of a number of dimensions ‘n’, where n minus 1 of the dimensions represent predictor variables while the remaining dimension represents the return on the strategy’s recommendations.

In Figure 1, the state of knowledge of the investor operating without an enhancement model is represented by the level flat orange surface at 0.5 per cent. By contrast, the state of knowledge of the investor using an enhancement model is represented by the grid surface. This investor’s expectation of a strategy recommendation’s excess return is conditional upon the values of the predictor variables (RSI and VOL_CHG) that characterise the stock being recommended.

Figure 1: Boosting model return prediction

Booster model development

The development of a boosting model involves discovering two things: ...

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