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
Interests: quantitative finance; financial forecasting; artificial intelligence; machine learning; technical analysis; portfolio optimization; financial technology; big data analytics
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
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Keywords
- machine learning
- forecasting
- FinTech
- data mining
- big data analytics
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