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

Trend prediction in financial markets is a very complex task, due to the fact that prices are inherently noisy, non-stationary, and deterministically chaotic. In recent years, studies have leveraged various machine learning algorithms to tackle this task. Leung, Chen, and Daouk (2001) used a

Motivation Support Vector Machines (SVM) are among the most popular Supervised Learning techniques for classification and regression, due to their ease in usage to find non-linear patterns. They work by separating data by finding an optimal threshold – known as a decision boundary or hyperplane,

Pairs trading is one of the simplest forms of statistical arbitrage which involves exploiting relative mispricings between two similar assets. It operates based on the assumption of the law of one price; that anomalies among securities valuation will occur in the short run but in the long run, will

In his 2012 book, Nicholas Nassim Taleb defined the term antifragile for representing things that benefit from disorder. When this concept is applied to trading, an obvious example can be seen in the relative outperformance of systematic trend following strategies during market crises. In the CTA

Order book imbalance strategies have been a big alpha source in automated market making. Tick by tick observations provide important information about general market sentiment and direction, and high frequency trading firms (HFTs) have been very efficient at trading on this information at very low

This article is on the analysis of intra-arrival times of future contract trades, analysing market behaviour, and identification of other particpants’ trading strategy. Intra-arrival time has close relationship with the quantity of each trade. One reason behind this is that many participants use