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Investment Strategies and Market Dynamics
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Investment Strategies and Market Dynamics

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5071

Special Issue Editors

Prof. Dr. Wayne Y. Lee

E-Mail Website
Guest Editor
Sam M. Walton College of Business, University of Arkansas, WCOB 475, Fayetteville, AR 72701, USA
Interests: banking; corporate finance; international finance and investments
Dr. Craig G. Rennie

E-Mail Website
Guest Editor
Sam M. Walton College of Business, University of Arkansas, WCOB 475, Fayetteville, AR 72701, USA
Interests: empirical investments; risk management; energy finance; corporate finance

Special Issue Information

Dear Colleagues,

We seek manuscripts for a Special Issue on “Investment Strategies and Market Dynamics” in the Journal of Risk and Financial Management. The submission deadline is December 31, 2025. The focus of the Special Issue will be on papers covering all aspects of investments and financial market dynamics. Topics of particular interest include the following: portfolio management strategies and tactics; diversification in managing risk; currency speculation and hedging; sector diversification (specifically including technology and energy); Exchange Traded Funds (ETFs); mutual funds, pension funds, and sovereign wealth funds; equities, fixed income securities, and alternative investments (including private equity, private debt, hedge funds, commodities, precious metals, and real estate); passive vs. active investing; behavioral finance; volatility; dynamic asset allocation; ESG-related market disruptions; fundamental and technical analysis; algorithmic trading; use of derivatives alone or in combination with underlying assets for hedging and/or speculation; cryptocurrencies; utility tokens (including potential applications of smart contracts); robotics; artificial intelligence; tariffs; and investment effects of tax regime change.

The primary purpose of this Special Issue is to provide innovative research that builds on and extends the current literature on investment strategy and market dynamics for readers and researchers alike. The Special Issue also seeks to provide contributing authors with timely meaningful feedback on their submissions. Empirical research is strongly encouraged, but well-argued theoretical research will also be considered. Methodological contributions and creative thought are welcome.

Prof. Dr. Wayne Y. Lee
Dr. Craig G. Rennie
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

  • investments
  • risk
  • financial markets
  • market dynamics

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

Published Papers (4 papers)

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Research

25 pages, 1318 KB  
Article
Optimal and Non-Optimal MACD Parameter Ranges with Stop-Loss and Take-Profit Rules: Evidence from the Gold Market
by Byung-Kook Kang
J. Risk Financial Manag. 2026, 19(3), 192; https://doi.org/10.3390/jrfm19030192 - 5 Mar 2026
Viewed by 915
Abstract
This study investigates optimal and non-optimal MACD parameter ranges in the gold market using a simulation-based framework and examines their implications for trading strategy design and risk-adjusted performance. By systematically identifying optimal and non-optimal MACD parameter ranges together with appropriate stop-loss and take-profit [...] Read more.
This study investigates optimal and non-optimal MACD parameter ranges in the gold market using a simulation-based framework and examines their implications for trading strategy design and risk-adjusted performance. By systematically identifying optimal and non-optimal MACD parameter ranges together with appropriate stop-loss and take-profit levels, this study addresses an issue that has not been explored in the existing literature on gold markets. The empirical results reveal a clear contrast between the optimal and non-optimal groups. Importantly, the superior performance of the optimal strategies emerges at the group level, rather than being driven by isolated exceptional models. Annual analysis further shows that models in the optimal groups respond effectively to overall market direction, taking long (short) positions under upward- (downward-) biased market conditions. Additional analyses examine fixed-ratio stop-loss and take-profit rules, identifying parameter–strategy combinations that balance risk control and profit realization. A cross-market comparison between the gold and stock markets highlights significant heterogeneity in optimal parameter ranges and investment horizons, underscoring the market-specific nature of MACD-based trading rules and the limits of cross-asset parameter transferability. Overall, these findings provide deeper insights into market-specific trading dynamics, going beyond the provision of an empirical benchmark and a methodological reference for MACD-based trading strategy design in the gold market. Full article
(This article belongs to the Special Issue Investment Strategies and Market Dynamics)
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25 pages, 622 KB  
Article
Bond vs. Equity Mutual Fund Performance Using False Discovery Rate (FDR)
by Lifa Huang, Wayne Y. Lee and Craig G. Rennie
J. Risk Financial Manag. 2026, 19(1), 89; https://doi.org/10.3390/jrfm19010089 - 21 Jan 2026
Viewed by 462
Abstract
This paper compares actively managed bond vs. equity mutual fund performance using modified False Discovery Rate (q∗) and percent simulated t(α) < Actual t(α). Bond funds are more likely to outperform than equity funds: q∗(%Sim < Act) shows [...] Read more.
This paper compares actively managed bond vs. equity mutual fund performance using modified False Discovery Rate (q∗) and percent simulated t(α) < Actual t(α). Bond funds are more likely to outperform than equity funds: q∗(%Sim < Act) shows 33.9% (30.0%) of bond funds generate positive t(α) on net excess returns vs. 1.8% (0.0%) for equity funds. q∗ shows percent simulated t(α) < Actual t(α)results are sensitive to Type II error. Bond fund outperformance is associated with long-term holdings, and corporate bond fund excess returns tend to decline with fund size. Full article
(This article belongs to the Special Issue Investment Strategies and Market Dynamics)
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24 pages, 288 KB  
Article
Regulations and the “Too-Big-to-Fail” Problem: Evidence from the Dodd–Frank Act
by Jenny Gu, Yingying Shao and Pu Liu
J. Risk Financial Manag. 2026, 19(1), 78; https://doi.org/10.3390/jrfm19010078 - 19 Jan 2026
Viewed by 826
Abstract
Before the enactment of the Dodd–Frank Act, firm size was taken into account by rating agencies in determining the credit ratings of banks. Therefore, the “too-big-to-fail” problem was, at least partially, reflected in big banks’ elevated ratings, which are more than justified by [...] Read more.
Before the enactment of the Dodd–Frank Act, firm size was taken into account by rating agencies in determining the credit ratings of banks. Therefore, the “too-big-to-fail” problem was, at least partially, reflected in big banks’ elevated ratings, which are more than justified by intrinsic creditworthiness. What is unclear is whether the bond market still gives an additional discount in yield to big banks over and above the lower yield spread that is already reflected in the elevated credit ratings due to their size. In this study, we examine this question and document a significant incremental yield discount for large banks even after controlling for credit ratings. Furthermore, we find that big banks with lower ratings pay lower borrowing costs than non-big banks with higher ratings. This additional discount, however, mostly disappeared after the Dodd–Frank Act. Full article
(This article belongs to the Special Issue Investment Strategies and Market Dynamics)
12 pages, 891 KB  
Communication
The GT-Score: A Robust Objective Function for Reducing Overfitting in Data-Driven Trading Strategies
by Alexander Pearson Sheppert
J. Risk Financial Manag. 2026, 19(1), 60; https://doi.org/10.3390/jrfm19010060 - 12 Jan 2026
Viewed by 2218
Abstract
Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, [...] Read more.
Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010–2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions (p < 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research. Full article
(This article belongs to the Special Issue Investment Strategies and Market Dynamics)
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