Special Issue "New Approaches in Economics and Finance"
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 219
Interests: time-series analysis with financial applications; machine learning and deep neural networks with financial applications; fintech; sentiment analysis; blockchain technology; cryptocurrencies; forward–backward stochastic differential equations (FBSDEs) with financial applications
Interests: stochastic partial differential equations (SPDEs) in both finite and infinite dimensions; asymptotic expansion of finite/infinite integrals; interacting particle systems; random walk in random media; stochastic mean field games with applications in finance; time series analysis with applications in finance; machine learning and mathematical foundations of neural networks with applications in real markets
Special Issues, Collections and Topics in MDPI journals
Special Issue in Risks: New Challenges in Mathematical Finance: From S(P)DEs to Machine Learning
Special Issue in Energies: A Holistic Overview of the Energy Sector: From Engineering Approaches to Innovative ML Solutions
The last few years have seen a disruptive increase in interest concerning how to manage and forecast complex economical systems. Ranging from the Energy sector to structured financial products, passing from forecast socio-economical behaviors to predict technology outputs and their energy impacts, there has been a huge focus on how to develop new and effective approaches to manage such challenges, the task being further overcomplicated by the enormous amount of data pervading each of the aforementioned scenarios, often interconnecting their own peculiarities.
Consequently, the main aim of this Special Issue is to gather new ideas, position papers as well as collect open problems with the possible view of unifying solutions, helping the international scientific community to propose state-of-the-art models in view of a concrete enhancement in solving the challenges posed by the tumultuous growth of both economics systems and financial settings, more and more intrinsically linked between them and to the energy production/consumption challenges.
Possible contributions could be related to Mathematics, Computer Science, Physics of Complex Systems, Economics, Finance, Machine Learning, Artificial Intelligence solutions, Energy, FinTech, Dynamical Systems, Game Theory, Operative Research, and Optimization Theory, though not limited to these.
Dr. Marco Patacca
Dr. Luca Di Persio
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. Risks is an international peer-reviewed open access monthly 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 1400 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.
- artificial intelligence (ML/NNs and applications)
- game theory
- operative research
- optimization theory
- stochastic analysis and applications