Statistical Evaluation of Deep Learning Models for Stock Return Forecasting

Yilmaz, Fırat Melih; Yildiztepe, Engin

doi:10.1007/s10614-022-10338-3

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…With the rapid development of computer technology, research on algorithms in the fields of machine learning, deep learning, and reinforcement learning has been promoted, and the application of machine-learning algorithms to quantitative financial transactions has become a hot research topic . Quantification means to rely on a powerful algorithm model to replace the traditional empirical judgment (Yilmaz & Yildiztepe, 2024). Through computer technology, we can discover all kinds of high-probability events or laws from huge historical data and test that data to obtain profits (Biju et al, 2024).…”

Section: Literature Reviewmentioning

confidence: 99%

Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things

Tang,

Wang,

Wang

2024

International Journal of Information Technology and Web Engineering

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By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which organically combines models, strategies and data, visually displays the information to users in the form of web pages to facilitate users' use and seeks the trading rules of the financial market to provide investors with a more stable trading strategy. The research results have important practical value and research significance in the field of financial investment.

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Section: Literature Reviewmentioning

confidence: 99%