| Summary: | 碩士 === 國立政治大學 === 金融學系 === 106 === Over the past decades, predicting trends in financial products prices has been an area of interest, but due to the interaction effects of different factors from all sides, the nature of market is always complex and dynamic. In general, error rate is seen as a proxy of risk of trading strategy, and it needs to be minimized to improve strategy effectiveness. To simplify the problem, the forecasting problem in our research is treated as a classification problem, and Machine Learning is used to solve it. Because of some attractive characteristics, our research used one of Ensemble Learning, which is Random Forest, to construct trading strategies.
Our research selected technical and chip indicators as the features to train model, and the ways to analyze predictions contained OOB error rate, which derived from Random Forest, and the performance indicators. Because TAIEX Futures historical returns are non-normal distribution, our research introduced an intuitive performance indicator- Calmar Ratio as the evaluation criteria, and the other performance indicators have been added to improve the robustness.
Our research have tested the performance of strategies and the robustness from different angle, and the result shows that our strategies truly beat the benchmark in whole period, not just training period. Besides, there is a lot of evidence that testing period in our research was in recovery to the peak, and this will lower the discrimination between strategies and benchmark performance. However, from the point of view of OOB error rate, our strategies are truly sufficiently robust.
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