Data fusion with factored quantization for stock trend prediction using neural networks

Chaudhari, Kinjal; Thakkar, Ankit

doi:10.1016/j.ipm.2023.103293

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…1) Stock data refers to numerical data that characterizes stocks in time series. For instance, [4] proposed a novel data quantization and fusion way of stock data for stock time series predictions. 2) News data is unstructured text that contains complex time-sensitive information.…”

Section: Introductionmentioning

confidence: 99%

FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model

Li,

Shen,

Zeng

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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The task of stock earnings forecasting has received considerable attention due to the demand investors in real-world scenarios. However, compared with financial institutions, it is not easy for ordinary investors to mine factors and analyze news. On the other hand, although large language models in the financial field can serve users in the form of dialogue robots, it still requires users to have financial knowledge to ask reasonable questions. To serve the user experience, we aim to build an automatic system, FinReport, for ordinary investors to collect information, analyze it, and generate reports after summarizing.Specifically, our FinReport is based on financial news announcements and a multi-factor model to ensure the professionalism of the report. The FinReport consists of three modules: news factorization module, return forecasting module, risk assessment module. The news factorization module involves understanding news information and combining it with stock factors, the return forecasting module aim to analysis the impact of news on market sentiment, and the risk assessment module is adopted to control investment risk. Extensive experiments on real-world datasets have well verified the effectiveness and explainability of our proposed FinReport. Our codes and datasets are available at https://github.com/frinkleko/FinReport.

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Section: Introductionmentioning

confidence: 99%

FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model

Li,

Shen,

Zeng

et al. 2024

Companion Proceedings of the ACM Web Conference 2024

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show abstract

“…One of the challenges in achieving stock trading based on DRL is to correctly analyze the state of the stock market. Most of the existing research on stock trading based on DRL analyzes the stock market through the time series features of stock data [1], such as recurrent neural network (RNN), long short-term memory (LSTM) [2]- [4] and gated recurrent unit (GRU). However, stock data are not only time-dependent, but also has certain spatial information, which can affect the final analysis results [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Convolutional Neural Networks for Stock Trading Based on DDQN Algorithm

Cui

Huang²,

et al. 2023

IEEE Access

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In deep learning based stock trading strategy models, most of the research just use simple convolutional neural networks (CNN) to process stock data. But task-specific neural network structures have been proposed extensively, and their effectiveness has been demonstrated in computer vision (CV) and natural language processing (NLP) tasks. In this paper, we proposed a multi-scale convolutional neural feature extraction network (MS-CNN) for stock data, which can better extract stock trend features and thus make better decisions. The network structure inspired by the human stock trading model: in human behavior, we do not only look at a single set of stock data, but rather combine all the stock data, such as opening, closing, and trading volume, to make a comprehensive judgment. And humans will consider the current stock trend on different time scales, such as 3-Day Line and 5-Day Line. This is consistent with the two-dimensional convolution kernels commonly used in CV tasks, so we used convolution kernels of 3×3 and 5×5 in the network with two-dimensional convolution size and constructed a novel network structure for stock data. With double deep Q networks (DDQN) algorithm, we get the best performance for our network. The experimental results show that we can obtain high yield on the datasets of Dow Jones (DJI), AAPLE (AAPL), and General Electric (GE).

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CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion

Sun

Liu

2023

Interdiscip Sci Comput Life Sci

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Data fusion with factored quantization for stock trend prediction using neural networks

Cited by 12 publications

References 37 publications

FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model

FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model

A Novel Convolutional Neural Networks for Stock Trading Based on DDQN Algorithm

CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion

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