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Mathematics
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Business Analytics and Decision-Making: Models, Algorithms and Applications
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Business Analytics and Decision-Making: Models, Algorithms and Applications

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1467

Special Issue Editor

Prof. Dr. Liping Liu

E-Mail Website
Guest Editor
Department of Management, The University of Akron, Akron, OH 44325-4801, USA
Interests: uncertainty reasoning in artificial intelligence, belief functions, decision theories, information systems, research methodologies

Special Issue Information

Dear Colleagues, 

Business analytics is currently one of the fastest growing research fields. Its growth can be attributed to two primary factors: (a) the need to process a staggering amount of data in various organizations; and (b) the availability of a wide range of mathematical and statistical, as well as decision theories, operations research, machine learning, information systems, and artificial intelligence models and algorithms. This Special Issue seeks to publish a collection of current research on the use of mathematical models and/or machine learning algorithms for solving business problems ranging from productivity to sustainability. It welcomes articles that advance the classic analytics models and algorithms in response to emerging business needs and their applications to real-world business problems such as fraud reduction, production planning, actuarial estimates of financial damages, customer segmentation, etc. It also welcomes proposals of new theoretical models and algorithms such as latent Dirichlet allocation and non-parametric Bayesian learning that overcome the limitations of the classical ones.  Due to the recent growth in the use of generative AI tools such as ChatGPT, this Special Issue also seeks articles that explore and experiment how businesses can integrate generative AI capabilities into the existing business processes.

Prof. Dr. Liping Liu
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.

Keywords

  • business analytics
  • predictive analytics
  • machine learning
  • Bayesian learning
  • deep learning
  • fuzzy logic
  • Dempster–Shafer theory
  • generative AI

Published Papers (2 papers)

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Research

26 pages, 4749 KiB  
Article
Incorporating Multi-Source Market Sentiment and Price Data for Stock Price Prediction
by Kui Fu and Yanbin Zhang
Mathematics 2024, 12(10), 1572; https://doi.org/10.3390/math12101572 - 17 May 2024
Viewed by 457
Abstract
The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor [...] Read more.
The problem of stock price prediction has been a hot research issue. Stock price is influenced by various factors at the same time, and market sentiment is one of the most critical factors. Financial texts such as news and investor comments reflect investor sentiment in the stock market and influence market movements. Previous research models have struggled to accurately mine multiple sources of market sentiment information originating from the Internet and traditional sentiment analysis models are challenging to quantify and combine indicator data from market data and multi-source sentiment data. Therefore, we propose a BERT-LLA stock price prediction model incorporating multi-source market sentiment and technical analysis. In the sentiment analysis module, we propose a semantic similarity and sector heat-based model to screen for related sectors and use fine-tuned BERT models to calculate the text sentiment index, transforming the text data into sentiment index time series data. In the technical indicator calculation module, technical indicator time series are calculated using market data. Finally, in the prediction module, we combine the sentiment index time series and technical indicator time series and employ a two-layer LSTM network prediction model with an integrated attention mechanism to predict stock close price. Our experiment results show that the BERT-LLA model can accurately capture market sentiment and has a strong practicality and forecasting ability in analyzing market sentiment and stock price prediction. Full article
(This article belongs to the Special Issue Business Analytics and Decision-Making: Models, Algorithms and Applications)
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18 pages, 1298 KiB  
Article
A Comparative Analysis of Multi-Criteria Decision Methods for Personnel Selection: A Practical Approach
by Pablo A. Pinto-DelaCadena, Vicente Liern and Andrea Vinueza-Cabezas
Mathematics 2024, 12(2), 324; https://doi.org/10.3390/math12020324 - 19 Jan 2024
Viewed by 784
Abstract
This research focused on decision-making supported by multi-criteria decision methods, specifically TOPSIS, OWA, and their respective variants within personnel selection. The study presented models aimed at facilitating the selection of the best candidate for a job through competency-based assessments and comparing the application [...] Read more.
This research focused on decision-making supported by multi-criteria decision methods, specifically TOPSIS, OWA, and their respective variants within personnel selection. The study presented models aimed at facilitating the selection of the best candidate for a job through competency-based assessments and comparing the application of four methods across various scenarios. We employed methods such as TOPSIS, OWA, and two variations (Canós–Liern method and an OWA model based on mathematically replicating expert opinion). Each model provided distinct rankings and demonstrated adaptability to specific situations within a company. Furthermore, it was emphasized that each method could and should be tailored according to the company’s reality to derive maximum benefit from its implementation. A crucial aspect of securing the best candidates involves understanding the context and identifying the appropriate methodology. Full article
(This article belongs to the Special Issue Business Analytics and Decision-Making: Models, Algorithms and Applications)
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