State-of-the-Art in Applying Machine Learning to Electronic Trading

Rabhi, Fethi A.; Mehandjiev, Nikolay; Baghdadi, Ali

doi:10.1007/978-3-030-64466-6_1

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…Short-term dependencies are concerned with intraday fluctuations and fast market movements, whereas long-term dependencies are concerned with trends and patterns that last weeks, months, or even years. It has been revealed that neglecting the temporal dependencies may result in poor prediction performance [13,14]. 3.…”

Section: Characteristics Of Financial Time Series Datamentioning

confidence: 99%

“…Real-life raw data could have too many features that suffer the curse of dimensionality, highlighting the complexity and difficulty brought about by high-dimensional data. It has been reported that feeding a large number of raw features into ML gives rise to poor performance [13]. Thus, feature selection and feature extraction are often conducted before feeding data into ML models.…”

Section: Feature Selection and Extractionmentioning

confidence: 99%

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Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Chen,

Hussain,

Cauteruccio

et al. 2024

CMES

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Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an overview of techniques, components and frameworks for financial time series prediction, with an emphasis on state-of-the-art deep learning models in the literature from 2015 to 2023, including standalone models like convolutional neural networks (CNN) that are capable of extracting spatial dependencies within data, and long short-term memory (LSTM) that is designed for handling temporal dependencies; and hybrid models integrating CNN, LSTM, attention mechanism (AM) and other techniques. For illustration and comparison purposes, models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input, output, feature extraction, prediction, and related processes. Among the state-of-the-art models, hybrid models like CNN-LSTM and CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model. Some remaining challenges have been discussed, including non-friendliness for finance domain experts, delayed prediction, domain knowledge negligence, lack of standards, and inability of real-time and highfrequency predictions. The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review, compare and summarize technologies and recent advances in this area, to facilitate smooth and informed implementation, and to highlight future research directions.

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Section: Characteristics Of Financial Time Series Datamentioning

confidence: 99%

Section: Feature Selection and Extractionmentioning

confidence: 99%

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Chen,

Hussain,

Cauteruccio

et al. 2024

CMES

View full text Add to dashboard Cite

show abstract

“…The study found that the hybrid integration of knowledge approach performed better than other approaches. Rabhi et al (2020) surveyed several machine‐learning algorithms in electronic financial market trading. The study found a mismatch between existing academic literature, which tends to concentrate on asset price prediction and certain areas in electronic trading, for example, smart order routing, that need more attention.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating interpretable machine learning predictions for cryptocurrencies

Majzoub

Rabhi

Hussain

2023

Intell Sys Acc Fin Mgmt

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SummaryThis study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N‐BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.

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“…There are numerous software platforms used for this purpose, such as RapidMiner [9], R project [10] or Pentaho/WEKA [11]. However, when machine learning classifiers are used for solving large scale classification problems, like data mining, or in real time data processing applications, such as network anomaly detection [12] or real-time trading [13,14], the instance classification duration becomes a critical metric for the classifier performance evaluation. One way to improve the instance classification duration is to implement ML classifiers directly in hardware.…”

Section: Introductionmentioning

confidence: 99%

Universal Reconfigurable Hardware Accelerator for Sparse Machine Learning Predictive Models

2022

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This study presents a universal reconfigurable hardware accelerator for efficient processing of sparse decision trees, artificial neural networks and support vector machines. The main idea is to develop a hardware accelerator that will be able to directly process sparse machine learning models, resulting in shorter inference times and lower power consumption compared to existing solutions. To the author’s best knowledge, this is the first hardware accelerator of this type. Additionally, this is the first accelerator that is capable of processing sparse machine learning models of different types. Besides the hardware accelerator itself, algorithms for induction of sparse decision trees, pruning of support vector machines and artificial neural networks are presented. Such sparse machine learning classifiers are attractive since they require significantly less memory resources for storing model parameters. This results in reduced data movement between the accelerator and the DRAM memory, as well as a reduced number of operations required to process input instances, leading to faster and more energy-efficient processing. This could be of a significant interest in edge-based applications, with severely constrained memory, computation resources and power consumption. The performance of algorithms and the developed hardware accelerator are demonstrated using standard benchmark datasets from the UCI Machine Learning Repository database. The results of the experimental study reveal that the proposed algorithms and presented hardware accelerator are superior when compared to some of the existing solutions. Throughput is increased up to 2 times for decision trees, 2.3 times for support vector machines and 38 times for artificial neural networks. When the processing latency is considered, maximum performance improvement is even higher: up to a 4.4 times reduction for decision trees, a 84.1 times reduction for support vector machines and a 22.2 times reduction for artificial neural networks. Finally, since it is capable of supporting sparse classifiers, the usage of the proposed hardware accelerator leads to a significant reduction in energy spent on DRAM data transfers and a reduction of 50.16% for decision trees, 93.65% for support vector machines and as much as 93.75% for artificial neural networks, respectively.

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State-of-the-Art in Applying Machine Learning to Electronic Trading

Cited by 10 publications

References 35 publications

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Evaluating interpretable machine learning predictions for cryptocurrencies

Universal Reconfigurable Hardware Accelerator for Sparse Machine Learning Predictive Models

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