Recursive Portfolio Selection with Decision Trees

Andriyashin, Anton; HHrdle, Wolfgang K.; Timofeev, Roman

doi:10.2139/ssrn.2894287

Cited by 6 publications

(5 citation statements)

References 9 publications

(5 reference statements)

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“…The recent boom in machine learning techniques offers an alternative paradigm for illustrating the relationships between the stock price forward process and its relevant company features, thereby providing a higher degree of model diversification compared to traditional approaches. It is proved that by using powerful model classes, such as artificial neural networks (ANN) (Khashei and Bijari, 2010; Alberg and Lipton, 2017; Belciug and Sandita, 2017), decision trees (DT) (Sorensen et al., 2000; Andriyashin et al., 2008; Zhu et al., 2011, 2012), deep neural networks (DNN) (Chong et al., 2017), gradient-boosted trees (GBDT), random forests (RF) (Krauss et al., 2017), etc., the classification and prediction efficiency of stocks are significantly enhanced.…”

Section: Introductionmentioning

confidence: 99%

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Stock selection with random forest: An exploitation of excess return in the Chinese stock market

Tan¹,

Yan²,

Zhu

2019

Heliyon

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In recent years, a variety of research fields, including finance, have begun to place great emphasis on machine learning techniques because they exhibit broad abilities to simulate more complicated problems. In contrast to the traditional linear regression scheme that is usually used to describe the relationship between the stock forward return and company characteristics, the field of finance has experienced the rapid development of tree-based algorithms and neural network paradigms when illustrating complex stock dynamics. These nonlinear methods have proved to be effective in predicting stock prices and selecting stocks that can outperform the general market. This article implements and evaluates the robustness of the random forest (RF) model in the context of the stock selection strategy. The model is trained for stocks in the Chinese stock market, and two types of feature spaces, fundamental/technical feature space and pure momentum feature space, are adopted to forecast the price trend in the long run and the short run, respectively. It is evidenced that both feature paradigms have led to remarkable excess returns during the past five out-of-sample period years, with the Sharpe ratios calculated to be 2.75 and 5 for the portfolio net value of the multi-factor space strategy and momentum space strategy, respectively. Although the excess return has weakened in recent years with respect to the multi-factor strategy, our findings point to a less efficient market that is far from equilibrium.

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Section: Introductionmentioning

confidence: 99%

“…Andriyashin et al. (2008) applied decision trees to a ternary classification of stocks in DAX constituents by training the model on both the fundamental and technical variables.…”

Section: Introductionmentioning

confidence: 99%

Stock selection with random forest: An exploitation of excess return in the Chinese stock market

Tan¹,

Yan²,

Zhu

2019

Heliyon

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“…The multitask model proposed improved the predictability, returns, and profitability for S&P500, SSE Composite Index, and KOSPI200 as compared to the baseline Random Forest model. Andriyashin et al (2008) applied the decision trees technique to a ternary classification of stocks, who are the constituents of DAX. They have trained the model on both the technical as well as fundamental variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This type of model is useful in providing more diverse approaches than traditional models (Neely et al 2014;Dai et al 2020). The literature has shown that by applying powerful modeling approaches, such as ANN (artificial neural networks) (Khashei and Bijari 2010;Alberg and Lipton 2017;Belciug and Sandita 2017), decision trees (Sorensen et al 2000;Andriyashin et al 2008;Zhu et al 2011Zhu et al , 2012, deep neural networks (Chong et al 2017;Kraus and Feuerriegel 2017), random forests (Krauss et al 2017), the classification and prediction efficiency of stocks have increased significantly.…”

Section: Introductionmentioning

confidence: 99%

Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

Mohapatra¹,

Mukherjee²,

Roy

et al. 2022

JRFM

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This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.

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“…It is notable that most studies on the short-term reversal employ the linear quantile partition scheme, i.e., sorting stocks based on the size of a past return and building a reversal portfolio by buying losers (bottom quantile) and selling winners (top quantile). Although machine learning approaches have become popular in asset pricing over the past few years [1,15,20,23], the study of the short-term reversal based on the machine learning is relatively primitive. Preliminary investigations of AI assisted momentum and reversal trading can be seen in Li and Tam [17], where the market state defined by the returns in the past observation period is learned to predict the possible stock selection policies.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for the Short-term Reversal Strategy

Tan¹,

Li²,

Han³

2021

IJDSA

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The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.

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Recursive Portfolio Selection with Decision Trees

Cited by 6 publications

References 9 publications

Stock selection with random forest: An exploitation of excess return in the Chinese stock market

Stock selection with random forest: An exploitation of excess return in the Chinese stock market

Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

A Machine Learning Approach for the Short-term Reversal Strategy

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