Reinforcement Learning in Financial Markets

Meng, Terry Lingze; Khushi, Matloob

doi:10.3390/data4030110

Cited by 105 publications

(87 citation statements)

References 23 publications

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“…The complete workflow of deployment is shown in Figure 6. [26] Proposed Method [11] Proposed Method [24] Proposed Method [23] Proposed Method [16] Proposed Method [22] Proposed Method [54][55]. The objective of this paper is to train various supervised learning algorithms to predict the client's behavior in paying off the credit card balance.…”

Section: Deploymentmentioning

confidence: 99%

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

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Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. Oneway Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.

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Section: Deploymentmentioning

confidence: 99%

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

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“…We know that good prediction accuracy is needed not just for the day ahead, but the behaviour of the stock days ahead is also of interest. A number of automated forecasting models are part of stock markets PLOS ONE [94] and areas of machine learning such as reinforcement learning has made such automation possible [95]. Providing uncertainty quantification in automated stock forecasting models would give information to investors for better decision-making.…”

Section: Plos Onementioning

confidence: 99%

Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic

Chandra

2021

PLoS ONE

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Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor confidence. Bayesian neural networks feature Bayesian inference for providing inference (training) of model parameters that provides a rigorous methodology for uncertainty quantification in predictions. Markov Chain Monte Carlo (MCMC) sampling methods have been prominent in implementing inference of Bayesian neural networks; however certain limitations existed due to a large number of parameters and the need for better computational resources. Recently, there has been much progress in the area of Bayesian neural networks given the use of Langevin gradients with parallel tempering MCMC that can be implemented in a parallel computing environment. The COVID-19 pandemic had a drastic impact in the world economy and stock markets given different levels of lockdowns due to rise and fall of daily infections. It is important to investigate the performance of related forecasting models during the COVID-19 pandemic given the volatility in stock markets. In this paper, we use novel Bayesian neural networks for multi-step-ahead stock price forecasting before and during COVID-19. We also investigate if the pre-COVID-19 datasets are useful of modelling stock price forecasting during COVID-19. Our results indicate due to high volatility in the stock-price during COVID-19, it is more challenging to provide forecasting. However, we found that Bayesian neural networks could provide reasonable predictions with uncertainty quantification despite high market volatility during the first peak of the COVID-19 pandemic.

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“…While there is a lot of new research being done on reinforcement learning in the context of financial markets, especially on research questions like profitability, as stated by (Meng and Khushi 2019), the overall research in the field is in a rather early stage and needs more scientific attention. A lot of studies focus on general algorithms and how they can be used, and only a few focus on the inputs and how a different observation period can maybe have an influence on the actions taken.…”

Section: Literature Reviewmentioning

confidence: 99%

Observation Time Effects in Reinforcement Learning on Contracts for Difference

Wehrmann

Zengeler

Handmann

2021

JRFM

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In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) and act on the basis of different observation time spans. Each agent tries to maximize trading profit by buying or selling one of a number of contracts in a simulated market environment for Contracts for Difference (CfD), considering correlations between individual assets by architecture. To decide which action to take on a specific contract, an agent develops a policy which relies on an observation of the whole market for a certain period of time. We investigate whether or not there exists an optimal observation sequence length, and conclude that such a value depends on market dynamics.

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Reinforcement Learning in Financial Markets

Cited by 105 publications

References 23 publications

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic

Observation Time Effects in Reinforcement Learning on Contracts for Difference

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