Motivation Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. This is with the intention that ensembles will achieve better prediction accuracy than individual classifiers. In machine learning research, most research papers focus on evaluating the performance of single […]
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Entries by Golden Compass
Delving deeper into machine learning techniques, we compare the performance of popular supervised learning algorithms on trend strategy on Hang Seng Index Futures.
Building upon our last post which used Support Vector Machines (SVM) as a supervised learning method for trend strategy on Nikkei 225 mini futures, we explore a comparison of other supervised learning methods including Neural Networks, Random Forest, Naïve Bayes, K-nearest neighbors as well as SVM on a similar classification problem on long/short strategy. As part of a broad effort to explore feasibility of machine learning based strategies across Asian markets, we based this paper on HKEX Hang Seng Index futures, another immensely popular Asian equity index futures contract.
Based on our study and among various methods, we found SVM to have the most robust edge in trading Hang Seng index futures, although Neural Network yielded promising results.
Support Vector Machines (SVMs) are among the most popular Supervised Learning techniques for classification and regression. In recent years, there has been much research into using them as a forecasting algorithm for trading, due to their ability to classify and predict market regimes effectively. This post aims to explore the use of a SVM based trading system on JPX Nikkei 225 Mini Futures, a popular Asian index futures contract.
Based on our study, we found that using SVM does present an edge for trading Nikkei 225 mini futures at a daily rebalanced level.
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