Advances of Machine Learning Forecasting within the FinTech Revolution

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 46960

Special Issue Editors


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

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 submissions that pass pre-check are 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 1800 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 446 KiB  
Article
On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles
by Kate Murray, Andrea Rossi, Diego Carraro and Andrea Visentin
Forecasting 2023, 5(1), 196-209; https://doi.org/10.3390/forecast5010010 - 29 Jan 2023
Cited by 23 | Viewed by 16193
Abstract
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning [...] Read more.
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of 0.0222 and MAE of 0.0173, respectively, 2.7% and 1.7% better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments. Full article
Show Figures

Figure 1

20 pages, 2743 KiB  
Article
Modelling Financial Markets during Times of Extreme Volatility: Evidence from the GameStop Short Squeeze
by Boris Andreev, Georgios Sermpinis and Charalampos Stasinakis
Forecasting 2022, 4(3), 654-673; https://doi.org/10.3390/forecast4030035 - 19 Jul 2022
Cited by 5 | Viewed by 3577
Abstract
Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in [...] Read more.
Ever since the start of the coronavirus pandemic, lockdowns to curb the spread of the virus have resulted in an increased interest of retail investors in the stock market, due to more free time, capital, and commission-free trading brokerages. This interest culminated in the January 2021 short squeeze wave, caused in no small part due to the coordinated trading moves of the r/WallStreetBets subreddit, which has rapidly grown in user base since the event. In this paper, we attempt to discover if coordinated trading by retail investors can make them a market moving force and attempt to identify proactive signals of such movements in the post activity of the forum, to be used as a part of a trading strategy. Data about the most mentioned stocks is collected, aggregated, combined with price data for the respective stock and analysed. Additionally, we utilise predictive modelling to be able to better classify trading signals. It is discovered that despite the considerable capital that retail investors can direct by coordinating their trading moves, additional factors, such as very high short interest, need to be present to achieve the volatility seen in the short squeeze wave. Furthermore, we find that autoregressive models are better suited to identifying signals correctly, with best results achieved by a Random Forest classifier. However, it became apparent that even the best performing model in our experimentation cannot make accurate predictions in extreme volatility, evidenced by the negative returns shown by conducted back-tests. Full article
Show Figures

Figure 1

11 pages, 497 KiB  
Article
A Monte Carlo Approach to Bitcoin Price Prediction with Fractional Ornstein–Uhlenbeck Lévy Process
by Jules Clément Mba, Sutene Mwambetania Mwambi and Edson Pindza
Forecasting 2022, 4(2), 409-419; https://doi.org/10.3390/forecast4020023 - 30 Mar 2022
Cited by 3 | Viewed by 5804
Abstract
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting [...] Read more.
Since its inception in 2009, Bitcoin has increasingly gained main stream attention from the general population to institutional investors. Several models, from GARCH type to jump-diffusion type, have been developed to dynamically capture the price movement of this highly volatile asset. While fitting the Gaussian and the Generalized Hyperbolic and the Normal Inverse Gaussian (NIG) distributions to log-returns of Bitcoin, NIG distribution appears to provide the best fit. The time-varying Hurst parameter for Bitcoin price reveals periods of randomness and mean-reverting type of behaviour, motivating the study in this paper through fractional Ornstein–Uhlenbeck driven by a Normal Inverse Gaussian Lévy process. Features such as long-range memory are jump diffusion processes that are well captured with this model. The results present a 95% prediction for the price of Bitcoin for some specific dates. This study contributes to the literature of Bitcoin price forecasts that are useful for Bitcoin options traders. Full article
Show Figures

Figure 1

24 pages, 3146 KiB  
Article
A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance
by Yixuan Li, Charalampos Stasinakis and Wee Meng Yeo
Forecasting 2022, 4(1), 184-207; https://doi.org/10.3390/forecast4010011 - 29 Jan 2022
Cited by 18 | Viewed by 8880
Abstract
Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk [...] Read more.
Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit risk of Digital SCF (DSCF). In this paper, 1357 observations from 85 Chinese-listed SMEs over the period 2016–2019 are selected as the empirical sample, and the important features of credit risk assessment in DSCF are automatically selected through the feature selection of the XGBoost model in the first stage, then followed by credit risk assessment through the MLP in the second stage. Based on the empirical results, we find that the XGBoost-MLP model has good performance in credit risk assessment, where XGBoost feature selection is important for the credit risk assessment model. From the perspective of DSCF, the results show that the inclusion of digital features improves the accuracy of credit risk assessment in SCF. Full article
Show Figures

Figure 1

44 pages, 1531 KiB  
Article
Is It Possible to Forecast the Price of Bitcoin?
by Julien Chevallier, Dominique Guégan and Stéphane Goutte
Forecasting 2021, 3(2), 377-420; https://doi.org/10.3390/forecast3020024 - 28 May 2021
Cited by 11 | Viewed by 10413
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
Show Figures

Figure 1

Back to TopTop