The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

Aloud, Monira

doi:10.1002/isaf.1466

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Although these traditional tools are useful, new techniques in the field of ML have been developed for time‐series forecasting. The work of Aloud (2020) analyzed the role of attribute selection on the development of a simple DL‐ANN multiagent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluated the performance of the DL‐ANN multiagent framework over different time spans of high‐frequency intraday asset time‐series data and determined how a set of the framework attributes produced effective forecasting for profitable trading.…”

Section: Discussionmentioning

confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

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Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

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Section: Discussionmentioning

confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

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“…On the other hand data driven AI approaches must evaluate the training needs and data used for training. For example the high‐frequency trading (HFT) approach uses financial raw tick big data (including information from the order book) but the predictive interval of such methods is very short and neural networks are rarely used because of the high computational power needed (often supercomputer access is required) (Aloud, 2020; Carapuço et al, 2018; Degiannakis & Filis, 2018; Shakeel & Srivastava, 2021; Shintate & Pichl, 2019; Zhang, Chan, et al, 2019). The problem of the lack of processing power to train raw tick big data extends even to publicly available GPU or TPU processors.…”

Section: Introductionmentioning

confidence: 99%

Reduction of financial tick big data for intraday trading

Milke,

Luca,

Wilson

2024

Expert Systems

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Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated with intraday trading using the EURO/USD currency pair. This time series reduction method focuses on short periods preceding significant movements in financial features and allows the most popular neural network architectures to be applied using less powerful but more readily available computer resources. It is shown that the proposed data preprocessing method for machine learning and other AI‐techniques successfully reduced the size of the selected dataset covering a three‐year period (2018–2021) by 275 times.

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“…The use of non‐conventional forecasting techniques based on machine and deep learning has been gaining popularity in recent years due to their ability to capture complex patterns in data that traditional statistical methods may miss (Zhang et al, 2017; Zhang & Yan, 2018). Firstly, this study contributes toward introducing the non‐conventional models of time series based on machine and deep learning models by using two different frequency intervals of intraday, which have been adopted by previous studies, that is, Aloud (2020) and Li et al (2020) to forecast stock markets prices.…”

Section: Introductionmentioning

confidence: 99%

Forecasting multi‐frequency intraday exchange rates using deep learning models

Arslan,

Hunjra,

Ahmed

et al. 2024

Journal of Forecasting

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This paper examines the behavior of currencies' intraday exchange rates with mainly focuses on predicting these behaviors through deep learning models. The time series data are used in this study and comprise intraday exchange rate data for seven volatile currencies, recorded at two different frequency intervals: 1 h and 30 min. The data cover the time frame from January 1, 2018, to December 31, 2020. Firstly, wavelet maximal overlap discreet wavelet transform (MODWT) type “haar” is applied in order to identify the noise representing the volatile trend. Then learning models are applied to historical data that includes support vector regression (SVR), recurrent neural network (RNN), and long short‐term memory (LSTM). The main findings of the study provide strong evidence that the deep learning technique (i.e., LSTM) outperforms the other compatible models. This has been confirmed by the statistical measures for accuracy purposes. This study intends to address complex trends exhibited by volatile behavior of intraday exchange rates through non‐conventional learning techniques for the time of need‐based for the currency traders and other stakeholders.

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The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

Cited by 6 publications

References 41 publications

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Reduction of financial tick big data for intraday trading

Forecasting multi‐frequency intraday exchange rates using deep learning models

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