Abstract
In recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the non-stationary nature and the likelihood of drastic structural or concept changes in the markets. Traditional systems are unable or slow to adapt to these changes. Ensemble-based systems are widely known for their good results predicting both cyclic and non-stationary data such as stock prices. In this work, we propose RCARF (Recurring Concepts Adaptive Random Forests), an ensemble tree-based online classifier that handles recurring concepts explicitly. The algorithm extends the capabilities of a version of Random Forest for evolving data streams, adding on top a mechanism to store and handle a shared collection of inactive trees, called concept history, which holds memories of the way market operators reacted in similar circumstances. This works in conjunction with a decision strategy that reacts to drift by replacing active trees with the best available alternative: either a previously stored tree from the concept history or a newly trained background tree. Both mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The experimental validation of the algorithm is based on the prediction of price movement directions one second ahead in the SPDR (Standard & Poor’s Depositary Receipts) S&P 500 Exchange-Traded Fund. RCARF is benchmarked against other popular methods from the incremental online machine learning literature and is able to achieve competitive results.
Highlights
Financial market forecasting is a field characterized by data intensity, noise, non-stationary, unstructured nature, a high degree of uncertainty, and hidden relationships [1], being the financial markets complex, evolutionary, and non-linear dynamical systems [2]
Recurring Concepts Adaptive Random Forest (RCARF) relies on the assumption that, in the case of abrupt drift, the background tree learned from scratch from the beginning of the warning window may be at a disadvantage compared to an old tree adapted to obtain good results but subsequently discarded
This would be consistent with the notion that RCARF should benefit from the use of its concept history to adjust faster to drifts than Adaptive Random Forests (ARF), which would eventually accumulate enough evidence to converge to a similar model
Summary
Financial market forecasting is a field characterized by data intensity, noise, non-stationary, unstructured nature, a high degree of uncertainty, and hidden relationships [1], being the financial markets complex, evolutionary, and non-linear dynamical systems [2]. There is a relatively new line of work based on machine learning, whose success has surprised experts given the theory and evidence from the financial economics literature [4,5,6] Many of these algorithms are able to capture nonlinear relationships in the input data with no prior knowledge [7]. When a structural change is detected, it replaces drifting classifiers with either a new concept model or with a concept extracted from the history, using dynamic time-windows to make the decision As this concept representation is already trained, our algorithm is able to react faster than its predecessor, which is unable to profit from previous models.
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