Towards an improved Adaboost algorithmic method for computational financial analysis

Chang, Victor; Li, Taiyu; Zeng, Zhiyang

doi:10.1016/j.jpdc.2019.07.014

Cited by 30 publications

(13 citation statements)

References 25 publications

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“…The application of AdaBoost algorithm in the literature is very diverse. Recent usages of AdaBoost include time series classification based on Arima and AdaBoost [47], an AdaBoost algorithmic method for computational financial analysis [48], and an AdaBoost classifier using stochastic diffusion search model for data optimization in Internet of Things [49].…”

Section: Adaboost Classifiermentioning

confidence: 99%

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

2020

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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

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Section: Adaboost Classifiermentioning

confidence: 99%

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

2020

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“…Chang et al [7] proposed an alternative approach was based on the development of the Adaboost algorithm [14]. The purpose was to enable computational financial modeling to be conducted and completed with both accuracy and performance achieved.…”

Section: Comparison With An Alternative Approachmentioning

confidence: 99%

Application of deep reinforcement learning in stock trading strategies and stock forecasting

Chang

2019

Computing

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The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of Deep Reinforcement Learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of Deep Reinforcement Learning in financial markets and the credibility and advantages of strategic decision-making.

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“…In financial computing, it is important to compare the actual and predicted prices. For example, our previous work (Chang, Li, & Zeng, 2019) has demonstrated an accurate comparison between the actual and predicted prices and risks management. Our program can simulate long‐term trading for both aggressive and defensive strategies.…”

Section: Numerical Resultsmentioning

confidence: 99%

Pairs trading on different portfolios based on machine learning

Chang

Man

et al. 2020

Expert Systems

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This article presents an advanced visualization and analytics approach for financial research. Statistical arbitrage, particularly pairs trading strategy, has gained ground in the financial market and machine learning techniques are applied to the finance field. The cointegration approach and long short‐term memory (LSTM) were utilized to achieve stock pairs identification and price prediction purposes, respectively, in this project. This article focused on the US stock market, investigating the performance of pairs trading on different types of portfolios (aggressive and defensive portfolio) and compare the accuracy of price prediction based on LSTM. It can be briefly concluded that LSTM offers higher prediction precision on aggressive stocks and implementing pairs trading on the defensive portfolio would gain higher profitability during a specific period between 2016 and 2017. However, predicting tools like LSTM only offer limited advice on stock movement and should be cautiously utilized. We conclude that analytics and visualization can be effective for financial analysis, forecasting and investment strategy.

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Towards an improved Adaboost algorithmic method for computational financial analysis

Cited by 30 publications

References 25 publications

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Application of deep reinforcement learning in stock trading strategies and stock forecasting

Pairs trading on different portfolios based on machine learning

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