Algorithmic Investment Strategies and Other Quantitative Finance Dilemmas with Focus on Information Theory

The unexpected and recurrent turmoil in the financial markets clearly increases the awareness of the need for better and more effective models and tools that serve as the basis for the development of efficient algorithmic investment strategies. Access to data of higher frequencies, ultrafast graphic cards, more powerful computers and continuously developing financial instruments within new types of asset classes and all advancements in AI and ML enable us to estimate and test theoretical approaches and models, which investment systems were unable to use in the past. Furthermore, we are aware of the fact that investment tools and models currently used in financial markets, with new types of assets, such as volatility or cryptocurrencies, are only a small part of what we will see in the near future.

For this reason, we invite all researchers focused on designing, implementing, testing, estimating, and optimizing algorithmic investment strategies and on other quantitative finance dilemmas to participate in this Special Issue of Entropy, which will collect new ideas and

describe promising methods arising from the field of algorithmic investment strategies. This Special Issue aims to provide a forum for the presentation of new and improved techniques and models. It will accept unpublished original papers and comprehensive reviews including, but not restricted to,

the following research areas:

• Algorithmic investment strategies, overoptimization and overfitting problems, and sensitivity analysis;
• Entropy measures for assessing volatile markets;
• Data preparation, preprocessing, and transformation: data cleansing, data normalization, data quantization, missing value treatment, feature creation, feature selection, and feature extraction;
• Volatility modelling and option pricing, forecasting volatility, and volatility trading;
• Portfolio allocation;
• Entropy as a measure of implied volatility in the options market;
• Modeling, learning, and analysis: supervised learning, unsupervised learning, reinforcement learning, neural architecture search and design, statistical learning, error optimization, optimization, algorithms and modeling, and hybrid search algorithms;
• Risk management, VaR models, and CoVaR models.

Prof. Dr. Dariusz Gątarek
Prof. Dr. Robert Slepaczuk
Prof. Dr. Daniel Traian Pele
Prof. Dr. Ronald Hochreiter
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. Entropy 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 2600 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.

• algorithmic investment strategies
• volatility modeling
• quantitative finance
• statistical learning
• information theory
• entropy
• overoptimization dilemma
• asset classes
• machine learning
• portfolio allocation
• risk management
• financial data analysis