Using Machine Learning to Predict Short-Term Movements of the Bitcoin Market

Jaquart, Patrick; Dann, David; Weinhardt, Christof

doi:10.1007/978-3-030-64466-6_2

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…So far, it can be shown that machine learning models can predict the market to a certain extent. Although within this study a first trading strategy cannot compensate for arising transaction costs, the proportion of explainable variance nevertheless encourages further research in this context (Jaquart et al 2020b).…”

Section: Modelling Asset Developmentmentioning

confidence: 83%

Information and Market Engineering at KIT: Quo Vadis?

et al. 2021

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Information systems (IS) are nowadays at the core of many personal and institutional activities and influence daily life more than ever before. To understand, evaluate and envision the forms of how we interact with IS, interdisciplinary and multifaceted research efforts are required. At the Information and Market Engineering chair at the Karlsruhe Institute of Technology, this task is taken head-on via research that stretches from user experiences to system design. In this review, the present research foci at the department are outlined, together with a brief description of its origins and the global developments that underly the necessity of conducting these particular IS studies.

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Section: Modelling Asset Developmentmentioning

confidence: 83%

Information and Market Engineering at KIT: Quo Vadis?

et al. 2021

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“…This study is expected to make a substantial contribution to our comprehension of the dynamic for stakeholders and investors navigating the constantly changing world of digital assets. Patrick Jaquart, David Dann, and Carl Martin [3]. The survey provides a methodical analysis of previous research in this field and a structured synopsis of the methods and strategies used by investigators.…”

Section: Literature Reviewmentioning

confidence: 99%

A survey on cryptocurrency price prediction

S. Venkatesh,

B Rashmitha,

S Manjunadha

et al. 2024

World J. Adv. Res. Rev.

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A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

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