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JRFM
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Advances in Financial Modeling and Innovation
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Advances in Financial Modeling and Innovation

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

Deadline for manuscript submissions: 1 July 2026 | Viewed by 5143

Special Issue Editors

Dr. Bill Hu

E-Mail Website
Guest Editor
Finance & International Business, School of Business, Howard University, Washington, DC 20059, USA
Interests: fintech; financial modeling; market microstructure; behavioural finance
Prof. Dr. Joon Ho Hwang

E-Mail Website
Guest Editor
Business Administration, Korea University Business School, Seoul 02841, Republic of Korea
Interests: fintech; corporate finance; investments
Special Issues, Collections and Topics in MDPI journals
Dr. Ying Johnson

E-Mail Website
Guest Editor
Department of Economics and Finance, Mitchell School of Business, University of South Alabama, Mobile, AL 36604, USA
Interests: cash; corporate governance; M&A; valuation; pricing strategy; microstructure research; property valuation

Special Issue Information

Dear Colleagues,

This Special Issue (SI) of Journal of Risk and Financial Management (JRFM) is seeking contributions that explore the intersection of financial modeling with modern technologies, including spreadsheets, Python, AI, and other related tools and techniques. This SI invites researchers, practitioners, and experts in the fields of finance, data science, artificial intelligence, and spreadsheet modeling to submit their original research papers, case studies, and innovative solutions. This SI welcomes submissions on a wide range of topics related to financial modeling, including but not limited to:

  • Innovations in Financial Modeling: Present novel approaches and methodologies for creating and optimizing financial models using contemporary tools.
  • Risk Management and Scenario Analysis: Present research on risk modeling, stress testing, and scenario analysis in financial decision making. Address how financial modeling and technology can aid in compliance with evolving financial regulations.
  • Modeling Financial Decisions: Explore the choice and impact of discount rates, time horizon, and cash flows.
  • Financial Forecasting and Budgeting: Explore the latest trends and technologies in financial forecasting and budgeting processes.
  • AI and Machine Learning in Finance: Explore the application of AI and machine learning algorithms for predictive modeling, risk assessment, and trading strategies. Investigate the influence of blockchain technology and cryptocurrencies on financial modeling and analysis.
  • Integration of Python in Financial Analysis: Share experiences and insights into using Python or Python-in-Excel for financial data analysis, visualization, and automation.

Dr. Bill Hu
Prof. Dr. Joon Ho Hwang
Dr. Ying Johnson
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 250 words) can be sent to the Editorial Office for assessment.

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 1600 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

  • financial modeling
  • compliance modeling
  • retirement modeling
  • project finance
  • risk management
  • financial forecasting
  • artificial intelligence
  • data science
  • Python
  • Excel

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

31 pages, 12864 KB  
Article
Evaluating Simple Strategies with Mutual Funds and ETFs to Outperform the China’s Shanghai Composite Index (SCI)
by Minfei Liang, Yuanyuan Tang, Saiteja Puppala and Eugene Pinsky
J. Risk Financial Manag. 2026, 19(4), 246; https://doi.org/10.3390/jrfm19040246 - 28 Mar 2026
Viewed by 538
Abstract
This paper examines several portfolio rules for comparing performance against the Shanghai Composite Index. The investor can use mutual funds or sector-based Exchange-Traded funds (ETFs). Five different approaches are applied for analysis. Two core approaches are discussed in detail and compared to passive [...] Read more.
This paper examines several portfolio rules for comparing performance against the Shanghai Composite Index. The investor can use mutual funds or sector-based Exchange-Traded funds (ETFs). Five different approaches are applied for analysis. Two core approaches are discussed in detail and compared to passive investing: The top-N strategy and the sector rotation strategy. The Top-N strategy shifts capital each period into the last period rank-N fund, and the sector rotation strategy ranks funds based on their performance in the preceding investment period, forming three baskets: “Winners”, “Median”, and “Losers”. Extensive statistical tests on more than 300 equity mutual funds are performed for the top-N strategy to evaluate performance persistence using quintile sorts, winner–loser spreads, and transition tests. In contrast, the sector-rotation strategy and a holdings-based replication strategy (constructed from annual reports and sector funds) are implemented as case studies using the ten largest funds. Their performance is evaluated using multiple return and risk metrics. Full article
(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)
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21 pages, 1408 KB  
Article
Asset Pricing in the Presence of Market Friction Noise
by Peter Yegon, W. Brent Lindquist and Svetlozar T. Rachev
J. Risk Financial Manag. 2026, 19(4), 243; https://doi.org/10.3390/jrfm19040243 - 26 Mar 2026
Viewed by 247
Abstract
We present two models for incorporating the total effect of market friction noise into the dynamic pricing of assets and European options. The first model is developed under a continuous-time Black–Scholes–Merton framework. The second model is a discrete, binomial tree model developed as [...] Read more.
We present two models for incorporating the total effect of market friction noise into the dynamic pricing of assets and European options. The first model is developed under a continuous-time Black–Scholes–Merton framework. The second model is a discrete, binomial tree model developed as an extension of the static Grossman–Stiglitz model. Both models are market-complete and provide a unique equivalent martingale measure that establishes a unique map between parameters governing the risk-neutral and real-world price dynamics. We provide empirical examples to extract the coefficients of the model, in particular those coefficients characterizing the influence of the frictions on prices. In addition to isolating the impact of noise on the volatility, the discrete model enables us to extract the noise impact on the drift coefficient. We provide evidence for the primary market friction that we believe our empirical examples capture. Full article
(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)
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21 pages, 2392 KB  
Article
Sector Rotation Strategies in the TSX 60: A Comprehensive Analysis of Risk-Adjusted Returns, Machine Learning Applications, and Out-of-Sample Validation (2000–2025)
by Gourav Salotra and Eugene Pinsky
J. Risk Financial Manag. 2026, 19(1), 70; https://doi.org/10.3390/jrfm19010070 - 15 Jan 2026
Viewed by 1944
Abstract
We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing [...] Read more.
We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing generates statistically significant risk-adjusted returns (Sharpe ratio 0.922 versus 0.624 for equal-weighted buy-and-hold). Our primary contributions include rigorous out-of-sample validation, demonstrating performance persistence from 2020 to 2025, machine learning regime classification with 72.7% accuracy, and a comprehensive transaction cost analysis. Results support intermediate-horizon mean reversion in sector returns and challenge strict efficient market hypothesis interpretations in concentrated markets. Findings inform tactical asset allocation practices and contribute to the momentum-reversal literature by documenting conditions under which rotation strategies generate economically meaningful alpha. Full article
(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)
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13 pages, 265 KB  
Article
An Investigation of Trades That Move the BBO Using Strings
by Ying Huang, Bill Hu, Hong Chao Zeng and Matthew D. Hill
J. Risk Financial Manag. 2025, 18(1), 15; https://doi.org/10.3390/jrfm18010015 - 2 Jan 2025
Viewed by 1343
Abstract
We investigate the common movement and information content of trades at steps away from the best bid and offer (BBO) using Tokyo Stock Exchange data. We create strings, a series of trades at the same or at an inferior price. The number of [...] Read more.
We investigate the common movement and information content of trades at steps away from the best bid and offer (BBO) using Tokyo Stock Exchange data. We create strings, a series of trades at the same or at an inferior price. The number of the strings is invariant for securities across trading days. The number of shares traded during a string and the time needed for the completion of a string are also significantly related across days for a given stock. The strings represent liquidity beyond the BBO. In addition, the strings characterize the price adjustment process in which we relate to the information on the underlying asset value. The strings measure order aggressiveness beyond the BBO. Finally, we show that the return for the strings is significantly related to the state of the limit order book at the start of the string. Thus, traders can infer information using strings to achieve higher returns. Full article
(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)
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