Abstract
Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators, the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure for Tracking Asynchronous Regressor Stacking, Deep Regressor Stacking, Multi-output Tree Chaining, Multi-target Augment Stacking and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models.
Highlights
The prediction of stock market is a very challenging task because it is affected by several macro-economic factors, for instance exchange rates, political events, recession or expansion periods, and investor’s expectations [Atsalakis and Valavanis 2009, Hu et al 2017].Cite as: Santana, E.J., Silva, J
Our goal was to evaluate whether the multi-target regression (MTR) solutions would achieve satisfactory predictions results, enabling the creation of a decision support system to help in predicting the tendencies in the action market
Different approaches have been used in stock market to predict economic metrics, including the usage of computational intelligence and machine learning (ML) algorithms
Summary
The prediction of stock market is a very challenging task because it is affected by several macro-economic factors, for instance exchange rates, political events, recession or expansion periods, and investor’s expectations [Atsalakis and Valavanis 2009, Hu et al 2017].Cite as: Santana, E.J., Silva, J. Stock Portfolio Prediction by Multi-Target Decision Support. iSys: Revista Brasileira de Sistemas de Informacao (Brazilian Journal of Information Systems), 12(1), 05-27
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