Evaluating machine learning classification for financial trading: An empirical approach

Abstract

Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

“…Those models can learn from complex data and adaptively extract optimal relationships between the input and output variables. The early success in stock market movement prediction and asset value estimation has further promoted its popularity [19]. ML itself is undoubtedly a powerful tool for data mining.…”

A derivatives trading recommendation system: The mid‐curve calendar spread case

“…Inference (Baker et al, 2017), Forecasting Combination (Elliott et al, 2013), Generalized Exponential Weighted Moving Average (Nakano et al, 2017), Support Vector Machines (SVM) (Karathanasopoulos et al, 2016), Shallow and Deep Neural Networks (NN) architectures (Gerlein et al, 2016;Chong et al, 2017;Zhou et al, 2016;Deng et al, 2017), Random Forest and Gradient Boosting Trees (Krauss et al, 2017), and so forth. The list of proposed methodologies continue to grow (Reveiz-Herault, 2016;Resta, 2016;Galeshchuk & Mukherjee, 2017), in which equities or indices appears as the dominant asset class to apply these algorithms.…”

“…When we scan the literature for cash instruments (equities, bonds, foreign exchange, etc.) focused only in using past returns as the main source for prediction, we can find works that tap into Bayesian forecasting (Zhou et al, ), Nonparametric Predictive Inference (Baker et al, ), Forecasting Combination (Elliott et al, ), Generalized Exponential Weighted Moving Average (Nakano et al, ), Support Vector Machines (SVM) (Karathanasopoulos et al, ), Shallow and Deep Neural Networks (NN) architectures (Gerlein et al, ; Chong et al, ; Zhou et al, ; Deng et al, ), Random Forest and Gradient Boosting Trees (Krauss et al, ), and so forth. The list of proposed methodologies continue to grow (Reveiz‐Herault, ; Resta, ; Galeshchuk & Mukherjee, ), in which equities or indices appears as the dominant asset class to apply these algorithms.…”

Mid-Curve Recommendation System: a Stacking Approach Through Neural Networks

“…In this sense, researchers have employed different modelling approaches and information sets to predict price changes across a range of assets; for cash instruments (equities, bonds, foreign exchange, etc.) we can find a vast amount of research using statistical and machine learning methods [4], [6], [11], [15], [19], [29], [30].…”