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Mathematics
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Computational Intelligence in Management Science and Finance
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Computational Intelligence in Management Science and Finance

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 5195

Special Issue Editor

Dr. Giacomo Di Tollo

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods (D.E.M.M.), Università degli Studi del Sannio, Benevento, Italy
Interests: applied artificial intelligence; computational intelligence; quantitative finance; financial risk management; computational methods for economic and finance

Special Issue Information

Dear Colleagues,

Computational intelligence methods are currently among the most studied tools in finance, economics, and management science: their introduction by academics and practitioners is changing the way of organising and modelling business processes and research projects, jointly with an increased efficiency of forecasting and classification exercises. Moreover, in the global, interconnected, and increasingly digitalized economies of today, featuring massive amounts of data paired with imperfect and incomplete information, these tools may  provide useful  information at different scale, having a crucial role for academics, industry professionals, the social community and policy makers.

This Special Issue, titled "Computational Intelligence in Management Science and Finance", will closely analyse some of the most recent aspects related to the introduction of computational intelligence methods in management, economics, and finance. Examples of topics include (but are not limited to): decision tree methods for asset pricing, credit risk analysis, decision making, and financial trading; fuzzy logic for financial modelling, optimization in economics, finance and insurance; heuristics and metaheuristics for complex portfolio selection, optimization in economics, finance and insurance; supervised machine learning methods for asset pricing, financial forecasts, market sentiment analysis, ranking and rating, and volatility estimation; reinforcement learning methods for derivative pricing and hedging, financial trading, and portfolio management; and support vector machines for classification, financial forecasts, and financial trading.

Dr. Giacomo Di Tollo
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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.

Published Papers (3 papers)

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Research

30 pages, 1750 KiB  
Article
An Analytic Network Process to Support Financial Decision-Making in the Context of Behavioural Finance
by Roberta Martino and Viviana Ventre
Mathematics 2023, 11(18), 3994; https://doi.org/10.3390/math11183994 - 20 Sep 2023
Cited by 2 | Viewed by 963
Abstract
Following the financial crisis of the last decade and the increasing complexity of financial products, the European Union has introduced investor protection tools that require professionals to carry out a client profiling process. The aim is to offer products that are in line [...] Read more.
Following the financial crisis of the last decade and the increasing complexity of financial products, the European Union has introduced investor protection tools that require professionals to carry out a client profiling process. The aim is to offer products that are in line with the characteristics of the individual. The classes of variables for comprehensive profiling are obtained by matching the elements proposed by the Markets in Financial Instruments Directive and studies of classical finance. However, behavioural finance studies, which emphasise the importance of behavioural attitudes, are not clearly considered in this structured profiling. The present paper discusses the implementation of an analytic network process to support financial decision-making in a behavioural context, combining regulatory guidance and qualitative and quantitative evidence from the literature. The Kersey Temperament Model is used as the behavioural model to construct the network cluster that incorporates personality into the valuation. Uncertainty management is incorporated through recent studies in the context of intertemporal choice theory. The functionality of the network is verified through a case study, where two alternatives with different characteristics are considered to meet the same investment objective. The present approach proves how the generated structure can provide strong support for financial decision-making. Full article
(This article belongs to the Special Issue Computational Intelligence in Management Science and Finance)
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18 pages, 1311 KiB  
Article
The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs
by Giacomo di Tollo, Joseph Andria and Gianni Filograsso
Mathematics 2023, 11(16), 3441; https://doi.org/10.3390/math11163441 - 8 Aug 2023
Viewed by 2334
Abstract
Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of [...] Read more.
Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies. Full article
(This article belongs to the Special Issue Computational Intelligence in Management Science and Finance)
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26 pages, 1132 KiB  
Article
User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks
by Pegah Eslamieh, Mehdi Shajari and Ahmad Nickabadi
Mathematics 2023, 11(13), 2950; https://doi.org/10.3390/math11132950 - 1 Jul 2023
Cited by 1 | Viewed by 1413
Abstract
Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks [...] Read more.
Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction. Full article
(This article belongs to the Special Issue Computational Intelligence in Management Science and Finance)
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