In this paper, we propose a hybrid machine learning system based on Genetic Algorithm (GA) and Support Vector Machines (SVM) for stock market prediction. A variety of indicators from the technical analysis field of study are used as input features. We also make use of the correlation between stock prices of different companies to forecast the price of a stock, making use of technical indicators of highly correlated stocks, not only the stock to be predicted. The genetic algorithm is used to select the set of most informative input features from among all the technical indicators. The results show that the hybrid GA-SVM system outperforms the stand alone SVM system.

/pdf/a-hybrid-machine-learning-system-for-stock-market-y7xki0gnk3.pdf

A Hybrid Machine Learning System for Stock Market Forecasting

Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.

Automated trading with performance weighted random forests and seasonality

Cryptocurrencies are becoming increasingly relevant in the financial world and can be considered as an emerging market. The low barrier of entry and high data availability of the cryptocurrency market makes it an excellent subject of study, from which it is possible to derive insights into the behavior of markets through the application of sentiment analysis and machine learning techniques for the challenging task of stock market prediction. While there have been some previous studies, most of them have focused exclusively on the behavior of Bitcoin. In this paper, we propose the usage of common machine learning tools and available social media data for predicting the price movement of the Bitcoin, Ethereum, Ripple and Litecoin cryptocurrency market movements. We compare the utilization of neural networks (NN), support vector machines (SVM) and random forest (RF) while using elements from Twitter and market data as input features. The results show that it is possible to predict cryptocurrency markets using machine learning and sentiment analysis, where Twitter data by itself could be used to predict certain cryptocurrencies and that NN outperform the other models.

https://www.mdpi.com/1099-4300/21/6/589/pdf

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning.

Low-complexity machine learning models are used trade in the FOREX market.A six year trading simulation in USDJPY, EURGPB and EURUSD are assessed.Periodic retraining, number of attributes and retraining set size are varied and studied.Middle range accuracies are obtained with high financial returns in the long term. 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.

Evaluating machine learning classification for financial trading

An extensive benchmark in financial time series forecasting is performed.Best machine learning(ML) methods out-perform best econometric methods.The ML methodology employed significantly affects forecasting accuracy.Market maturity, forecast horizon & model-assessment method affect forecast accuracy.Evidence against the informational value of technical indicators. Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and opportunities for profitable trading. The objective of the paper is to resolve this contradiction. To this end, we undertake an extensive forecasting simulation, based on data from thirty-four financial indices over six years. These simulations confirm that the best machine learning methods produce more accurate forecasts than the best econometric methods. We also examine the methodological factors that impact the predictive accuracy of machine learning forecasting experiments. The results suggest that the predictability of a financial market and the feasibility of profitable model-based trading are significantly influenced by the maturity of the market, the forecasting method employed, the horizon for which it generates predictions and the methodology used to assess the model and simulate model-based trading. We also find evidence against the informational value of indicators from the field of technical analysis. Overall, we confirm that advanced forecasting methods can be used to predict price changes in some financial markets and we discuss whether these results question the prevailing view in the financial economics literature that financial markets are efficient.

Bridging the divide in financial market forecasting

Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researches.

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Wun-Hua Chen

Papers

Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets