Designing Strategies for Autonomous Stock Trading Agents using a Random Forest Approach

Aloud, Monira

doi:10.14569/ijacsa.2021.0120788

Cited by 1 publication

(2 citation statements)

References 54 publications

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“…Among all the machine learning models, some researchers found that the predictive accuracy of RF is more stable and higher than that of other models. 6,14,15 In addition, deep learning models are also used in stock prices forecasting. The vast majority of studies found deep learning models, such as LSTM neural networks, 16,17 GRU neural networks 18 and deep multilayer perceptron (DMLP), 19,20 to be better than machine learning models.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in recent years, machine learning models, such as support vector regression (SVR), 4,5 random forest (RF), 6,7 eXtreme Gradient Boosting, 8,9 artificial neural networks (ANNs), 10,11 extreme learning machine (ELM) 12,13 have been widely used in time series prediction and have yielded satisfying results. Among all the machine learning models, some researchers found that the predictive accuracy of RF is more stable and higher than that of other models 6,14,15 . In addition, deep learning models are also used in stock prices forecasting.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid approach for portfolio construction: Combing two‐stage ensemble forecasting model with portfolio optimization

Chen,

Liu,

Jia

2023

Computational Intelligence

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Combining the stock prediction with portfolio optimization can improve the performance of the portfolio construction. In this article, we propose a novel portfolio construction approach by utilizing a two‐stage ensemble model to forecast stock prices and combining the forecasting results with the portfolio optimization. To be specific, there are two phases in the approach: stock prediction and portfolio optimization. The stock prediction has two stages. In the first stage, three neural networks, that is, multilayer perceptron (MLP), gated recurrent unit (GRU), and long short‐term memory (LSTM) are used to integrate the forecasting results of four individual models, that is, LSTM, GRU, deep multilayer perceptron (DMLP), and random forest (RF). In the second stage, the time‐varying weight ordinary least square model (OLS) is utilized to combine the first‐stage forecasting results to obtain the ultimate forecasting results, and then the stocks having a better potential return on investment are chosen. In the portfolio optimization, a diversified mean‐variance with forecasting model named DMVF is proposed, in which an average predictive error term is considered to obtain excess returns, and a 2‐norm cost function is introduced to diversify the portfolio. Using the historical data from the Shanghai stock exchange as the study sample, the results of the experiments indicate the DMVF model with two‐stage ensemble prediction outperforms benchmarks in terms of return and return‐risk characteristics.

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