Novel Modelling Strategies for High-frequency Stock Trading Data
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Abstract (arXiv)
Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as inputs for prediction models (e.g., data thinning and feature engineering) can primarily affect the performance of the prediction methods. However, researchers rarely discuss this topic. This motivated us to propose three novel modelling strategies for processing raw data. We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks. In these experiments, our strategies often lead to statistically significant improvement in predictions. The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 43 references that are in the map. First posted on arXiv 30 Nov 2022.
Builds on
7 of 43 references.
References · 7
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
- 2018Machine Learning for Forecasting Mid Price Movement using Limit Order Book Datacited by 14
- 2019Feature Engineering for Mid-Price Prediction with Deep Learningcited by 6
- 2019Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicatorscited by 4
- 2017Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methodscited by 2
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