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Advances in Financial Decisions Modeling and Analytics
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Advances in Financial Decisions Modeling and Analytics

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9857

Special Issue Editor

Prof. Dr. C. N. V. Krishnan

E-Mail Website
Guest Editor
Department of Banking and Finance, Weatherhead School of Management, Case Western Reserve University, Cleveland, OH 44106, USA
Interests: corporate finance

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the broad topic of “Financial Decisions Modeling and Analytics”, and includes, but is not limited to, the study of a firm’s decisions and interactions with stakeholders and markets, market reactions, and value creation. This includes the modeling of corporate decisions on capital structure, investments, payouts, mergers and acquisitions, etc., as well as financial intermediary decisions, such as those of venture capital firms, banks, investment banks, hedge funds, activists, etc. Theoretical, empirical, and experimental research on new models/approaches, new results, new results on old models/approaches, etc., are all admissible. Contributions focusing on multivariate or multidimensional approaches, big data analytics, and novel measures as well as methods, and accounting for asymmetric information, agency costs, and behavioral aspects are encouraged.

Prof. Dr. C. N. V. Krishnan
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. Journal of Risk and Financial Management 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.

Keywords

  • corporate decisions modelling
  • financial intermediary decisions modelling
  • empirical analysis
  • bid data analytics
  • risk and return
  • short-run and long-run value creation
  • asymmetric information
  • agency cost
  • investor reactions
  • behavioral aspects

Published Papers (4 papers)

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Research

26 pages, 1216 KiB  
Article
Market Liquidity Estimation in a High-Frequency Setup
by Kujtim Avdiu
J. Risk Financial Manag. 2023, 16(9), 415; https://doi.org/10.3390/jrfm16090415 - 19 Sep 2023
Cited by 1 | Viewed by 888
Abstract
This article deals with the identification of a superior forecasting method for market liquidity using a calibrated Heston model for the bid/ask price path simulation instead of a standard Brownian motion, as well as a compound Poisson process and inverse transform sampling for [...] Read more.
This article deals with the identification of a superior forecasting method for market liquidity using a calibrated Heston model for the bid/ask price path simulation instead of a standard Brownian motion, as well as a compound Poisson process and inverse transform sampling for the generation of the bid/ask volume distribution. We show that the simulated trading volumes converge to one single value, which can be used as a liquidity estimator, and find that the calibrated Heston model as well as the inverse transform sampling are superior to both the use of standard Brownian motion and compound Poisson process. Full article
(This article belongs to the Special Issue Advances in Financial Decisions Modeling and Analytics)
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19 pages, 1397 KiB  
Article
An Assessment of the Benefits of Optimizing Working Capital and Profitability: Perspectives from DJIA30 and NASDAQ100
by Tarek Eldomiaty, Nourhan Eid, Farida Taman and Mohamed Rashwan
J. Risk Financial Manag. 2023, 16(5), 274; https://doi.org/10.3390/jrfm16050274 - 16 May 2023
Cited by 3 | Viewed by 2711
Abstract
The objective of this paper goes beyond the boundaries of an exploratory analysis to operationalize the association between corporate working capital and return on assets. This paper optimizes the impact of the Cash Conversion Cycle (CCC) on Return on Assets (ROA). The paper [...] Read more.
The objective of this paper goes beyond the boundaries of an exploratory analysis to operationalize the association between corporate working capital and return on assets. This paper optimizes the impact of the Cash Conversion Cycle (CCC) on Return on Assets (ROA). The paper develops a mathematical formulation that connects the components of CCC to ROA. The sample includes the non-financial firms listed in DJIA30 and NASDAQ100. The data covers the quarterly periods from June 1992 to March 2018. The paper uses standard statistical tests including linearity (RESET), the Hausman test for fixed and random effects, and the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity. The estimation is carried out using the GLS estimator. This study finds: (a) the optimal, rather than observed, components of CCC are robust and coherent, (b) if firms were to optimize the components of CCC, the ROA improves significantly, (c) the positive estimates of size show that the components of CCC help firms grow, (d) the effects of either observed or optimal CCC on ROA are reached in the short term (four quarters), (e) the results show that observed as well as optimal CCC are able to detect the structural break in the 2008 financial crisis, and (f) the results of a logit analysis show that the optimization algorithm results in significant increases in ROA that are associated with increases in degree of financial leverage and decreases in short-term debt ratio. This paper contributes to the related literature in two ways. First, the paper develops a mathematical structure that associates corporate CCC and ROA in a way that offers a guide to corporate financial managers regarding structural management of corporate CCC. Second, the paper examines the impacts of optimized CCC on ROA. Full article
(This article belongs to the Special Issue Advances in Financial Decisions Modeling and Analytics)
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18 pages, 2883 KiB  
Article
Mindsponge-Based Reasoning of Households’ Financial Resilience during the COVID-19 Crisis
by Minh-Hoang Nguyen, Quy Van Khuc, Viet-Phuong La, Tam-Tri Le, Quang-Loc Nguyen, Ruining Jin, Phuong-Tri Nguyen and Quan-Hoang Vuong
J. Risk Financial Manag. 2022, 15(11), 542; https://doi.org/10.3390/jrfm15110542 - 21 Nov 2022
Cited by 7 | Viewed by 4256
Abstract
The COVID-19 crisis was remarkable because no global recession model could predict or provide early notice of when the coronavirus pandemic would happen and damage the global economy. Resilience to financial shocks is crucial for households as future crises like COVID-19 are inevitable. [...] Read more.
The COVID-19 crisis was remarkable because no global recession model could predict or provide early notice of when the coronavirus pandemic would happen and damage the global economy. Resilience to financial shocks is crucial for households as future crises like COVID-19 are inevitable. Therefore, the current study aims to examine the effects of financial literacy and accessibility to financial information on the financial resilience of Vietnamese households through the lens of an information-processing perspective. The Bayesian Mindsponge Framework (BMF) analytics was employed on a dataset of 839 samples for the investigation. We found that households of respondents with better financial knowledge and investment skills are less likely to be financially affected during the peak of the COVID-19 crisis, but the effect of investment skills is weakly reliable. Accessibility to financial information through informal sources (having a household member working in the financial sector) and formal sources (participating in a financial course) is positively associated with the respondents’ financial knowledge and investment skills. This finding suggests that the spillover effect of financial knowledge and skills among residents exists, leading to better resilience toward financial shocks. However, if the financial information is inaccurate, it might lead to misinformation, false beliefs, and poor economic decisions on a large scale. Full article
(This article belongs to the Special Issue Advances in Financial Decisions Modeling and Analytics)
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22 pages, 1846 KiB  
Article
The Methodology Matters: What Influences Market Reaction, and Post-Issue Returns in Seasoned Equity Offerings?
by C. N. V. Krishnan and Minghao Wu
J. Risk Financial Manag. 2022, 15(10), 473; https://doi.org/10.3390/jrfm15100473 - 18 Oct 2022
Viewed by 1516
Abstract
Using a large database of U.S. seasoned equity offering (SEO) announcements from 2010 to 2015, we examine the effects of several explanatory variables—firm specific, macroeconomic, fixed income, and stock market variables—on the announcement period abnormal stock returns and on the longer-run post-issue abnormal [...] Read more.
Using a large database of U.S. seasoned equity offering (SEO) announcements from 2010 to 2015, we examine the effects of several explanatory variables—firm specific, macroeconomic, fixed income, and stock market variables—on the announcement period abnormal stock returns and on the longer-run post-issue abnormal returns. We use five different statistical methods—multivariate linear regression, regression on a reduced model using principal components analysis, year-by-year regression on a reduced model using principal components analysis, random forest regression on the whole sample, and year-by-year random forest regression. In general, across the methods, we find that firm’s profitability in the recent past is an important explanatory factor in both short-term and long-term abnormal stock returns, but several other significant explanatory factors change based on the statistical method used. Therefore, the statistical method used affects the results reported. Full article
(This article belongs to the Special Issue Advances in Financial Decisions Modeling and Analytics)
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