Stock Portfolio Prediction by Multi-Target Decision Support

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.

Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models

“…The final predictions are then generated by the meta-regressor, which is trained using the new meta training set Wu et al [25]. The stacking regression methodology has gained popularity in various domains, including molecular quantum characteristics [44], daily reference evapotranspiration estimation [25], genome prediction [47], and stock portfolio prediction [48]. In this particular study, XGB, SVR, and ENMLR models were utilized as the base regressors, while RF was employed as the meta-regressor.…”

Towards meta-learning for multi-target regression problems

“…Multi-target Regression (MTR) is an alternative approach that, besides using the original input features, exploits the statistical correlation among the outputs. The MTR methods have been applied to solve many problems [1]- [5], leading to improvement in the predictive performance over ST methods. However, each method has specific characteristics and has been effective for different problems.…”