Trading strategy of structured mutual fund based on deep learning network

Chen, Jiao; Chengji, Luo; Pan, Lurun; Jia, Yun

doi:10.1016/j.eswa.2021.115390

Cited by 20 publications

(8 citation statements)

References 19 publications

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“…addition, previous studies have shown that DNN trading models perform well in financial markets [3], and the articles referenced in [4]- [8] and [9]- [10] significantly aided our research. Tuning hyperparameters and data selection are essential for training and developing an ML strategy [11].…”

Section: A Problem Findingssupporting

confidence: 60%

Trade Ease With Machine Learning and AWS

Kamurthi

Liu

2023

IEEE Access

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Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research.

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Section: A Problem Findingssupporting

confidence: 60%

Trade Ease With Machine Learning and AWS

Kamurthi

Liu

2023

IEEE Access

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“…This phenomenon has not left the mutual fund industry unaffected (e.g., Dehghanpour and Esfahanipour 2018;Zhang and Hamori 2020;Park et al 2020;Haq et al 2021). According to Chen et al (2021), as well as the remarkable advantages of machine learning, there are also limitations, such as the lack of inclusion of the prior knowledge and experience that play an important role in the learning process.…”

Section: Literature Reviewmentioning

confidence: 99%

You Learn When It Hurts: Evidence in the Mutual Fund Industry

Gimeno

Sarto

Vicente

2022

JRFM

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This paper aims to contribute to the lack of research on the learning process of mutual fund markets. The empirical design is focused on the ability of the Spanish equity mutual fund industry to learn from its important errors. The choice of this industry is justified by both its relevance in the European mutual fund markets and some specific characteristics, such as the concentration and the banking control of the industry, which may affect the learning process. Our main objectives are to identify important trading errors in mutual fund management by applying three independent filters based on the relative importance of each decision, and then testing the evolution of these errors both at the industry level and at the fund family level. We apply the dynamic model of generalized method of moments (GMM), and we find an overall significant decrease in the percentage of important trading errors over time, thereby providing evidence of the global learning process of the industry. In addition, we find that a large number of fund families drive this evidence. Finally, we obtain that the family size and its dependence on financial groups do not seem to play significant roles in explaining the learning process. Therefore, we conclude that fund managers have incentives to learn from their important trading errors, in order to avoid them in future decisions, due to their serious negative consequences on fund performance, regardless of the characteristics of the families to which they belong.

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“…Except for Christmas, Saturday and Sunday, there are a few days when there is no corresponding gold price. To enhance the accuracy of the result and find the optimal trading strategy as far as possible, we cannot ignore the day corresponding to the vacancy value, so we need to complete the data [4].…”

Section: Data Cleaning and Preprocessingmentioning

confidence: 99%

Research on asset trading strategy based on forecasting model and decision-making trading model

2022

AJCIS

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Quantitative investment is becoming more and more popular among traders. Market traders who seek to maximize returns by buying and selling volatile assets focus more on quantitative trading. They try to find the optimal trading strategy by quantitative investment. We developed a decision trading model to try to find the optimal trading strategy to obtain the optimal returns. Our model consists of four parts, data processing, Arima prediction model, risk prediction model and bull-bear market predicting model.

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Trading strategy of structured mutual fund based on deep learning network

Cited by 20 publications

References 19 publications

Trade Ease With Machine Learning and AWS

Trade Ease With Machine Learning and AWS

You Learn When It Hurts: Evidence in the Mutual Fund Industry

Research on asset trading strategy based on forecasting model and decision-making trading model

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