Special Issue "Advances of Machine Learning Forecasting within the FinTech Revolution"

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Charalampos Stasinakis
E-Mail Website1 Website2
Guest Editor
Accounting and Finance, Adam Smith Business School, University of Glasgow, Glasgow G12 8QQ, UK
Interests: quantitative finance; financial forecasting; artificial intelligence; machine learning; technical analysis; portfolio optimization; financial technology; big data analytics
Prof. Dr. Georgios Sermpinis
E-Mail Website1 Website2
Guest Editor
Accounting and Finance, Adam Smith Business School, University of Glasgow, Glasgow G12 8QQ, UK
Interests: machine learning; financial trading; forecasting; econometrics; financial risk management; operations research; financial technology; big data analytics

Special Issue Information

Dear Colleagues,

Machine learning methods are key aspects of interdisciplinary operational research. Their interaction with financial decision-making and their suitability for solving complex quantitative problems demonstrates their contemporary importance in the field of finance. Financial forecasting, trading, risk modelling and asset pricing, to name a few, are research domains in which these techniques offer efficient solutions. However, the financial world is gradually shifting towards a digital domain of high-volume information and high-speed data transformation and processing. This, combined with technological innovation, has led to the Financial Technology (FinTech) revolution. Recent advances in data mining and deep learning make machine learning algorithms ideal tools for analysing trends and extracting forecasts from big data, a task with which traditional econometric techniques cannot cope. Considering that FinTech is tied with big data analytics, digital payments, alternative financing and automated wealth management, the value of machine learning is becoming even more prominent in that field. This is the main motivation for this Special Issue in Forecasting. In this Special Issue, we encourage authors to submit high-quality papers that focus on but are not limited to the following topics:

  • Methodological advances in deep learning networks and machine learning;
  • Machine learning applications of financial forecasting and trading;
  • Cryptocurrencies’ forecasting and trading;
  • FinTech risk and wealth management;
  • Data mining and natural language processing financial applications.

Dr. Charalampos Stasinakis
Prof. Dr. Georgios Sermpinis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • forecasting
  • FinTech
  • data mining
  • big data analytics

Published Papers (1 paper)

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Research

Article
Is It Possible to Forecast the Price of Bitcoin?
Forecasting 2021, 3(2), 377-420; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020024 - 28 May 2021
Viewed by 1017
Abstract
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge [...] Read more.
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented. Full article
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