Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks

• URL: http://arxiv.org/abs/2412.16160v2
• Date: Fri, 27 Dec 2024 00:43:39 GMT
• Title: Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks
• Authors: Adamantios Ntakaris, Gbenga Ibikunle,
• Abstract summary: This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering.
• Score: 1.7802147489386628
• License:
• Abstract: This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering and the reliance on potentially uninformative features. More specifically, our approach involves a dual competitive mechanism for feature importance, combining the mean-decrease impurity (MDI) method and a gradient descent (GD) based feature importance mechanism. This approach, tested on HFT Level 1 order book data for 20 S&P 500 stocks, enhances the forecasting ability of the RBFNN regressor. Our findings suggest that an autonomous approach to feature selection and clustering is crucial, as each stock requires a different input feature space. Overall, by automating the feature selection and clustering processes, we remove the need for manual topological grid search and provide a more efficient way to predict LOB's mid-price.

Related papers

• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks [13.983863226803336]
  We argue that "Feature Averaging" is one of the principal factors contributing to non-robustness of deep neural networks. We provide a detailed theoretical analysis of the training dynamics of gradient descent in a two-layer ReLU network for a binary classification task. We prove that, with the provision of more granular supervised information, a two-layer multi-class neural network is capable of learning individual features.
  arXiv  Detail & Related papers  (2024-10-14T09:28:32Z)
• Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration [66.43954501171292]
  We introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. DFedCata consists of two main components: the Moreau envelope function, which addresses parameter inconsistencies, and Nesterov's extrapolation step, which accelerates the aggregation phase. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed and generalization performance on CIFAR10/100 with various non-iid data distributions.
  arXiv  Detail & Related papers  (2024-10-09T06:17:16Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks. By employing a single pre-trained self-attention network with weights shared across all members, we train member-specific low-rank matrices for the attention projections. Our method exhibits superior calibration compared to explicit ensembles and achieves similar or better accuracy across various prediction tasks and datasets.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• High-Level Parallelism and Nested Features for Dynamic Inference Cost and Top-Down Attention [4.051316555028782]
  This paper introduces a novel network topology that seamlessly integrates dynamic inference cost with a top-down attention mechanism. Drawing inspiration from human perception, we combine sequential processing of generic low-level features with parallelism and nesting of high-level features. In terms of dynamic inference cost our methodology can achieve an exclusion of up to $73.48,%$ of parameters and $84.41,%$ fewer giga-multiply-accumulate (GMAC) operations.
  arXiv  Detail & Related papers  (2023-08-09T08:49:29Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• A Dependable Hybrid Machine Learning Model for Network Intrusion Detection [1.222622290392729]
  We propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022.
  arXiv  Detail & Related papers  (2022-12-08T20:19:27Z)
• Communication-Computation Efficient Device-Edge Co-Inference via AutoML [4.06604174802643]
  Device-edge co-inference partitions a deep neural network between a resource-constrained mobile device and an edge server. On-device model sparsity level and intermediate feature compression ratio have direct impacts on workload and communication overhead. We propose a novel automated machine learning (AutoML) framework based on deep reinforcement learning (DRL)
  arXiv  Detail & Related papers  (2021-08-30T06:36:30Z)
• Adaptive Neuro Fuzzy Networks based on Quantum Subtractive Clustering [5.957580737396458]
  In this paper, an adaptive Neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. The experimental results revealed that proposed Anfis based on quantum subtractive clustering yielded good approximation and generalization capabilities.
  arXiv  Detail & Related papers  (2021-01-26T20:59:48Z)
• Provably Training Neural Network Classifiers under Fairness Constraints [70.64045590577318]
  We show that overparametrized neural networks could meet the constraints. Key ingredient of building a fair neural network classifier is establishing no-regret analysis for neural networks.
  arXiv  Detail & Related papers  (2020-12-30T18:46:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.