Open research questions in Financial Markets and Investment Strategies

What the literature leaves open

• These thresholds should be validated against the existing crisis chronology literature (Reinhart and Rogoff 2009; BIS crisis database) when applied to real data.

  A Methodology for Identifying Crisis Mechanisms in Equity Markets: Externally Grounded Labeling, Gradient Boosting, and Synthetic Validation · 2026 · DOI

•  Generate R30 and R7 metrics for every individual hour. Freeze historical best hours and test sign and rank retention.   Run walk-forward best-hour versus full-window comparisons.  Measure monthly and weekly consistency by constituent hour. Compare historical and recent contribution concentration.  Test residual-window robustness after dominant-hour weakening.  Condition hour evidence on market regimes and feature states.   Replicate across other instruments and timeframes.  Investigate session and macroeconomic-announcement mechanisms.

  Individual Hours Versus Contiguous Intraday Windows Across Bullish Engulfing Risk-Reward Configurations in XAUUSD M15 · 2026 · DOI

•  Recover the complete event-level binary sequence for all 50 configurations. Calculate exact WW, WL, LW, and LL transition matrices.   Run 100,000-or-more Bernoulli simulations per sequence.  Run fixed-count permutation benchmarks preserving exact wins and losses.       Estimate exact observed maximum-streak percentile ranks. Compare selected-boundary and all-hours streak structures. Construct close-order sequences using verified close timestamps. Test streak stability through walk-forward and common-OOS segments. Condition streaks on feature-state regimes only after the unfiltered series is complete. Evaluate streak-based pause logic separately from Daily Accumulated Loss. A particularly important extension is to compare signal-order and close-order adverse sequences. That analysis would determine whether overlapping trades create account-level loss concentration that is not visible in signal-order streaks.

  Win and Loss Streak Dynamics Across Bullish Engulfing Risk-Reward Configurations and Intraday Boundaries in XAUUSD M15 · 2026 · DOI

• This paper uses public disclosures, accessible media coverage, and daily market data. It does not use minute-level trading data, a licensed full-text news archive, a formal market model, or a complete comparator-series design. Those limits are intentional. The paper is an event inventory, not a formal event study. Daily OHLC data are enough to identify preliminary price-window behavior and intraday stress, but not enough to establish within-day sequencing. Reuters and selected mining/financial press are enough to identify public narrative frames, but not enough to claim a complete media corpus. BHP official disclosures are enough to identify formal event language, but not enough to claim that the public record contains all company knowledge. Future work could use this inventory to define event windows, estimate abnormal returns, collect intraday data, build comparator models, and code a larger media corpus. The present paper's narrower contribution is to specify the event record before those methods are applied. 13. Conclusion: Before the Event Study BHP Jansen is a useful public case because its disclosures did not move through one channel. They moved through company language, market tape, and media narrative. The company channel says what BHP formally changed: sanction, acceleration, Stage 2 approval, cost reset, schedule movement, omitted execution quantities, impairment language. The tape channel shows how BHP.AX traded through selected windows: positive 13 divergence in July 2025 and strong next-session stress in June 2026. The media channel shows what the events became in public: capital-allocation concern, portfolio logic, progress, cost pressure, execution burden, impairment, market reaction, and long-term strategy maintained. The channels do not always agree. July 2025 is the key divergence event: project disclosure worsened, media coverage mixed Jansen cost pressure with strong group operating performance, and the stock closed higher. June 2026 is the lead aligned event: disclosure, tape, and media converged around cost, delay, impairment, and market stress. That is enough to justify an event-study inventory. It is not enough to claim causality. Before asking whether Jansen disclosures caused abnormal returns, the event record itself has to be built. For BHP Jansen, that record has at least three channels: what BHP disclosed, how the stock traded, and what the media narrative made of the disclosure.

  When Megaprojects Move the Tape: Disclosure, Market Tape, and Media Narrative Around BHP Jansen · 2026 · DOI

• Conclusion This empirical investigation concludes that mutual funds are rapidly capturing a central position within the financial planning architecture of contemporary Indian retail investors, serving as a practical and democratized instrument for longterm household wealth accumulation. While basic superficial awareness has achieved a moderate-to-high threshold— particularly amplified among younger, digitally connected working professional cohorts the substantive, functional depth of investment literacy remains profoundly fragmented. SIPs have decisively emerged as the premier mechanism for retail market integration due to their capacity to democratize capital access and enforce saving discipline. Equity schemes lead asset preferences, yet debt and hybrid instruments maintain an important stabilizing presence for risk-averse demographics. Ultimately, the chasm separating basic product recognition from active financial participation is driven by informational gaps (lack of actionable knowledge) and psychological frictions (fear of market risk). To successfully sustain the financialization of Indian household savings, the national strategy must pivot from driving generic product awareness toward institutionalizing functional, executionoriented financial literacy. 7.2 Strategic Recommendations  For Individual Investors: Retail market participants should shift completely to formal goal-based investing models, directly mapping distinct SIP instruments to specific longterm lifecycle objectives such as retirement, higher education, or real estate acquisition. Investors must cultivate psychological resilience against short-term macroeconomic volatility, consciously avoiding panic redemptions and strictly adhering to structural asset allocation across equity and fixed-income categories to insulate their long-term portfolios.  For Mutual Fund Asset Management Companies (AMCs): Asset management firms must urgently expand their educational and operational infrastructure into Tier-2 and Tier-3 geographic markets. AMCs should aggressively deconstruct financial jargon, adopting simplified, localized vernacular communication tools. Marketing strategies must pivot away from aggressive product pushing toward structured, fee-transparent, trust- based financial advisory frameworks that support first-time investors through their initial onboarding phase.  For Regulatory Bodies (SEBI and AMFI): Regulators must continue to harden investor protection mechanisms while fundamentally redesigning structured financial literacy campaigns.

  Investor Awareness and Preference Toward Mutual Funds in India: · 2026 · DOI

• This study incorporates specific operational limitations that must be acknowledged to provide a transparent context for its conclusions. The empirical analysis is strictly limited to a sample size of 100 respondents selected via non- probability convenience sampling within a specific geographic zone; consequently, the findings cannot be automatically generalized to represent the highly heterogeneous broader Indian retail investor population. Furthermore, the methodology relies primarily on descriptive percentage analysis rather than rigorous multi- variable inferential statistical models. Future research can significantly build upon this framework by dramatically expanding the sample size, utilizing rigorous stratified probability sampling methods, conducting formal comparative analyses between urban and rural retail investor cohorts, and employing advanced structural equation modeling (SEM) and regression techniques to formally test and validate the proposed research hypotheses ($H_1$ through $H_6$). REFERENCES 1. Association of Mutual Funds in India. (2024). Mutual fund industry data and investor awareness resources. AMFI Publications. 2. Bhatia, S., & Sethi, R. (2022). Age-based preferences in mutual fund investment. Journal of Emerging Markets, 14(2),112–126. 3. Das, T. (2023). Digital platforms and mutual fund accessibility in India. Indian Journal of Digital Finance, 8(1), 45–59. 4. Gupta, R., & Jain, P. (2018). Investor awareness and mutual fund participation in India. Journal of Financial Studies, 22(4), 201–215. © 2026 IJSRET 6 International Journal of Scientific Research & Engineering Trends Volume 12, Issue 2, March-April -2026, ISSN (Online): 2395-566X 5. Iyer, A. (2024). Portfolio review behavior and long-term investment discipline. Review of Financial Planning, 19(3), 88–102. 6. Nair, R. (2022). Evaluating AMFI's "Mutual Fund Sahi Hai" campaign on retail investor awareness. Indian Journal of Financial Literacy, 6(2), 134–149. 7. Patel, M., & Mehta, K. (2021). Tax-saving motives and investor preference in mutual funds. MBA Research Review, 11(3), 76–89. 8. Reddy, S., & Kumar, A. (2020). Role of financial advisors in mutual fund selection. Journal of Wealth Management Studies, 15(1), 92–105. 9. Securities and Exchange Board of India. (2024). Mutual fund regulations and investor protection framework. SEBI Regulatory Reports. 10. Sharma, V. (2019). Risk perception among retail investors in equity mutual funds. Indian Finance Review, 27(2), 118– 132. 11. Singh, H. (2020). Financial literacy and mutual fund investment behaviour.

  Investor Awareness and Preference Toward Mutual Funds in India: · 2026 · DOI

• Journal of Business Economics and Management, 2026, 27(1), 74–93 89 The main contribution of the study resides in the use of established financial models to analyze smart manufacturing investments, as very few studies have examined the relevance of these specific types of investments for the risk-adjusted return of portfolios.

  Investment implications of Industry 4.0: evidence from smart manufacturing ETFs · 2026 · DOI

• fundamental and behavioral approaches. EMH and MPT continue to provide the benchmark for pricing and allocation and clarify diversification benefits (Fama, 1970; Markowitz, 1952; Malkiel, 1973). Behavioral finance explains where and why observed choices depart from those benchmarks through preferences such as loss aversion, beliefs such as miscalibration and anchoring, and attention mechanisms such as availability and salience. In an emerging market, for example Nepalese capital market, where retail participation is substantial frictions are (Amgain, 2024) and nontrivial, a behaviorally informed approach can improve investment quality and investor welfare. Policy-relevant include bias-aware financial-literacy programs that explicitly teach recognition of loss aversion and overconfidence, platform choice architecture that discourages overtrading and nudges toward diversification by default, and disclosure design that counters anchoring and availability effects through clearer reference metrics and scenario analysis. Recognizing these forces is and essential intermediaries, implications information investors, for SocioEconomic Challenges (SEC) Volume 10, Issue 1, 2026 ISSN: 2520-6214 https://armgpublishing.com/journals/sec/ regulators who seek to align decision processes with long-run risk–return objectives and to enhance the contribution of capital markets to the real economy.

  Investor Behavioral Bias as a Socio-Economic Challenge in Capital Markets: Trends and Future Directions · 2026 · DOI

• finance proposes that certain financial outcomes are best explained by models allowing for investors who do not behave with full rationality (Barberis & Thaler, 2003). In the same line, Shiller (2000) evidenced that asset prices can diverge from fundamentals when waves of investor psychology, such as herding, narrative contagion, and feedback trading, fuel speculative booms and subsequent busts. Behavioral finance and systematic expected-utility maximization. Prospect Theory shows that utility is reference-dependent and that losses weigh more heavily than equivalent gains, a property termed loss aversion. Decision weights are nonlinear, which produces risk aversion in gains and risk seeking in introduced bounded from rationality deviations SocioEconomic Challenges (SEC) Volume 10, Issue 1, 2026 ISSN: 2520-6214 https://armgpublishing.com/journals/sec/ losses (Kahneman & Tversky, 1979). These features explain widely observed patterns such as holding losing positions in the hope of breaking even and realizing gains prematurely, commonly referred to as the disposition effect. and availability that overconfident overconfidence, anchoring, The literature also documents robust biases including herding, mental and accounting, representativeness in judgment. Early influential evidence shows individual investors trade excessively and, after costs, the stocks they purchase tend to underperform those they sell, which indicates miscalibrated beliefs and noise- driven rebalancing (Odean, 1998). Mental accounting leads investors to treat money differently across accounts or time frames and thereby distorts risk- taking and sell-or-hold thresholds (Mubaraq et al., 2021). Prospect-theoretic preferences further imply asymmetric risk attitudes around reference points, which helps to reconcile clustered selling near round numbers, reluctance to realize losses, and volatility bursts around salient news (Kahneman & Tversky, 1979). The heuristics program clarifies why these biases persist. Heuristics are rules of thumb that simplify complex judgements under constraints of time and information (Ritter, 2003). Although such rules can be adaptive, they also produce predictable errors. and anchoring are core shortcuts in judgment (Kahneman & Tversky, 1974). In market settings, gambler’s fallacy and overconfidence additionally shape trading intensity, risk perception, and herd behavior (Waweru et al., 2008). In practice, heuristics economize on attention; however, when combined with noisy or salient signals, they can amplify turnover and mispricing, especially in retail- dominated markets. Representativeness, availability, trading, The post-2020 expansion of dematerialized accounts and online together with heightened attention to hydropower and banking scrips, created new decision environments for retail investors (Vaidya, 2021). Survey-based studies report and overconfidence among investors, which align with behavioral predictions and with broader emerging- market experience (Risal & Khatiwada, 2019). For example, investors reportedly hold losing stocks longer than winning stocks in order to avoid realizing loss-averse herding, holding, https://doi.org/10.61093/sec.10(1).120-139.2026 123 losses, which is consistent with loss aversion and the disposition effect (Vaidya, 2021). Trend-following during rallies and synchronized exits during downturns indicate herd behavior that heightens short-run volatility and weakens the link between prices and fundamentals (Risal & Khatiwada, 2019). Anchoring on prior peaks or initial purchase prices also appears markets, which biases reference points and distorts perceived downside risk (Gurung et al., 2024). These patterns mirror cross-market findings: availability and representativeness shape reactions to salient news and recent returns, while overconfidence and gambler’s fallacy push traders toward higher turnover and extrapolative beliefs (Waweru et al., 2008). Account-level evidence in other jurisdictions further shows that excess confidence is associated with excess trading and lower net performance (Odean, 1998). Although heuristics can be usefully simplifying under time pressure or sparse data (Ritter, 2003), environments with social amplification, such as online forums and influencer commentary, can propagate cascades, create echo chambers, and generate attention-driven flows.

  Investor Behavioral Bias as a Socio-Economic Challenge in Capital Markets: Trends and Future Directions · 2026 · DOI

• Future research could focus on identifying effective policies and measures to mitigate herding behaviour in financial markets. Additionally, the study’s geographical focus on South Africa may limit the generalizability of its results to other markets, thus future research could address this limitation by examining herding behaviour across multiple countries or regions to identify common patterns and differences, especially in the global south.

  The effect of herding behaviour on JSE returns: A comparative analysis of before and during COVID-19 · 2026 · DOI

• The results further support the argument by Lobe and Walkshäusl (2014) that the superior performance of sin stocks was largely limited to the 1960s and 1970s.

  The Myth of the Sin Premium: Evidence from U.S. Sin Stocks During Periods of Economic Uncertainty · 2026 · DOI

• There are several limitations that should be taken into account when interpreting our results. To begin with, the statistical significance rate of individual DAF betas (14.7% low) indicates that the effect might not be preva- lent among all securities. Second, the small R2 increases, although regular, suggest that dividend announcement effects account for a small percentage of movement in returns. Third, we study only one emerging market and, therefore, this work should not be extrapolated to other markets or timeframes. Fourth, we lack control over other corporate activities or announcements that could overlap dividend announcements, which could influence the explanation of abnormal returns.

  From market signals to investor surges—unveiling the fallacy of bird-in-hand in a volatile emerging market · 2026 · DOI

• The results from this research highlighted how behaviourial biases can have a huge effect on the decisions that investors make when selecting mutual funds. Although traditional recommendations tend to focus on financial education and literacy of investors, future solutions should extend beyond these conventional approaches to also include the psychological roots of behaviourial decision-making when it comes to investing. The following recommendations include forward-thinking ideas that will aid in reducing behaviourial biases associated with investing in mutual funds. 8.1 Behavioural Cooling Off Mechanisms Related to Investment Decision Making Investment platforms that are available today could implement mandatory “cooling-off” periods before certain investment actions are taken such as switching funds after having just experienced a very positive return for a short period or redeeming funds during periods of poor market performance. This period would give the investor time (a few minutes) to access historical returns of the fund or any other relevant market cycle/period data before making their final decision to switch funds or to redeem their investment. Therefore, the implementation of this type of mechanism should reduce behaviourial biases that are associated with changing one's investment behaviour based on recent performance (recency bias) and/or making rash decisions based on fear or panic due to the lack of clarity surrounding market volatility. 8.2 Financial Risk Score and Behavioural Risk Score Disclosures of risks in Mutual Funds typically show the amount of risk in financial terms (e.g. Volatility or Credit Risk); it is possible to include a behavioural risk score which highlights how likely it is for any given category of fund to trigger a number of behavioural biases. For example: Sector or Thematic funds will have a higher behavioural risk score because these funds tend to lead to more significant Herding Behaviours than Diversified Funds. © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution.

  Behavioural Biases in Mutual Fund Investment Decisions · 2026 · DOI

• The bounds in equation (36) require careful selection of the exponent p < q < 1 with p/q < α < 1, but the paper provides no systematic procedure for choosing these parameters given specific transaction cost levels (θ₁, θ₂) and initial conditions (ẑ, ˆx, ŷ, ˆν) in practical applications.

  Nonconcave Utility Maximization with Transaction Costs · 2026 · DOI

• The analysis uses Lemma 3 (Bernoulli inequality bounds) to extend results from short time intervals to slightly longer periods, but the paper does not characterize the precise relationship between the integer n, the time horizon T-t, and the achievable convergence rates for the nonconcave utility maximization problem across different utility function classes.

  Nonconcave Utility Maximization with Transaction Costs · 2026 · DOI

• Lemma 2 establishes large deviation bounds for log stock price ratios under the assumption that Assumption 2 holds uniformly, but the paper does not investigate how the exponential decay rate dᵥ varies with respect to the regime variable ν or demonstrate whether dᵥ remains uniformly bounded away from zero across the entire state space of the Markov chain.

  Nonconcave Utility Maximization with Transaction Costs · 2026 · DOI

• The proof of Proposition 2 requires the constraint T - t ≤ min{1, (w-z)⁴/(16Cᵥ(1-θ₁)wᵅ)⁴, (ln2/(4Cᵥ))²}, but the paper does not provide explicit formulas or numerical guidance for computing the constant Cᵥ and its dependence on the volatility coefficient σ(ν) across different regime-switching states.

  Nonconcave Utility Maximization with Transaction Costs · 2026 · DOI

• The analysis in Proposition 2 establishes bounds on wealth probability when the transaction cost parameters θ₁ and θ₂ are held fixed, but the dependence of the convergence rate on the magnitude of transaction costs—particularly how small transaction costs must be relative to portfolio size—is not explicitly characterized.

  Nonconcave Utility Maximization with Transaction Costs · 2026 · DOI

• While previous research mainly explored this in Asian and American markets, our study addresses this gap in understanding Northern European reactions, particularly in rising and falling markets, and aims to explore the existence of herding during the COVID-19 pandemic and to further investigate its occurrence and intensity during the periods of upward and downward movements.

  Do Global Disruptive Events Induce Herding Behaviour during Upward and Downward Market Movements? The Evidence from Nordic and Baltic Stock Markets · 2024 · DOI

• Originality / value / implications / recommendations – The results of the paper fill the existent gap in the literature and complemented the ongoing discussions on the topic of cross-index comparison in the domain of ESG-investing in the European market.

  Comparison of Risk-Adjusted Relative Returns of MSCI ESG Thematic Indexes on the European Market · 2024 · DOI

• Emerging market financial development indices (market access, depth, efficiency) show mixed relationships with equity price indices, but no unified econometric model explains why efficiency improvements sometimes reduce stock prices or how these relationships vary by emerging market type and development stage.

  The Relationship Between Financial Development and the Composite Stock Price Index in Emerging Market Countries: A Panel Data Evidence · 2023 · DOI

• This research contributes to the debate on the importance of socially responsible investment guided by ESG criteria, filling a gap in the literature regarding Latin America and confirming the better performance by calculating a wide range of portfolio evaluation indicators.

  Socially responsible portfolios,<scp>environmental, social, corporate governance (ESG)</scp>efficient frontiers, and psychic dividends · 2023 · DOI

• We argue that this measure fills a gap in the literature and show in a simulation study that it strikes a good balance between robustness and sensitivity.

  Assessing skewness in financial markets · 2022 · DOI

• The COVID-19 outbreak gives financial economists an example of health risk underestimation, and of an unexpectedly slow response during a stress period; issues that should be examined in the future under a behavioral view.

  Health risk and the efficient market hypothesis in the time of COVID-19 · 2020 · DOI

• Finally, although breakeven transaction costs are considerably larger than actual transaction costs in UK, other variables measuring trading behaviour under MA strategy provide mixed results when seen in relation to volatility.

  Assesing Performance of Moving Average Investment Timing Strategy Over the UK Stock Market · 2017 · DOI

Most-cited papers in Financial Markets and Investment Strategies

• Differences of Opinion and the Cross Section of Stock Returns · The Journal of Finance · 2002 · 1,721 citations
• Differential Interpretation of Public Signals and Trade in Speculative Markets · Journal of Political Economy · 1995 · 911 citations
• Taming the Factor Zoo: A Test of New Factors · The Journal of Finance · 2020 · 655 citations
• Investor Sophistication and Patterns in Stock Returns after Earnings Announcements · The Accounting Review · 2000 · 625 citations
• Good News for Value Stocks: Further Evidence on Market Efficiency · The Journal of Finance · 1997 · 591 citations
• Tracking Retail Investor Activity · The Journal of Finance · 2021 · 534 citations
• A Further Investigation of the Weekend Effect in Stock Returns · The Journal of Finance · 1984 · 506 citations
• Climate Change, Firm Performance, and Investor Surprises · Management Science · 2023 · 497 citations
• The Delisting Bias in CRSP's Nasdaq Data and Its Implications for the Size Effect · The Journal of Finance · 1999 · 470 citations
• Is There a Replication Crisis in Finance? · The Journal of Finance · 2023 · 470 citations

Most recent work

• Which Stock Screens Actually Work? A Survivorship-Free Audit of 16 Fundamental Metrics — ROIC, Value, Momentum, and Quality — on the S&P 500 (2010–2024) · Zenodo (CERN European Organization for Nuclear Research) · 2026
• Risk spillovers and portfolio diversification: Empirical evidence from emerging and traditional assets · Emerging Markets Review · 2026
• Investment implications of Industry 4.0: evidence from smart manufacturing ETFs · Journal of Business Economics and Management · 2026
• Short Shares for a Short Month · The Journal of Continuing Education in Nursing · 2026
• The Impact of Negative Public Sentiment on Stock Price Volatility: A Case Study of Qiaqia Food · Exploring Science Academic Conference Series · 2026
• Changepoint detection in the cross-section of stock returns · Annals of Operations Research · 2026
• Are Exchange-Traded Funds (ETFs) Replacing Mutual Funds as the Preferred Investment Choice Among Young Investors in India? · International Scientific Journal of Engineering and Management · 2026
• Nonconcave Utility Maximization with Transaction Costs · SIAM Journal on Financial Mathematics · 2026
• A STUDY ON ACTIVE AND PASSIVE MUTUAL FUND AND THEIR IMPLICATION FOR RETAIL INVESTORS AT RICCHIE RICH INVESTMENTS · International Scientific Journal of Engineering and Management · 2026
• A STUDY ON SPECIAL REFERENCE TO NSE AND BSE BASED ON RETURN AND RISK EVALUATION OF INDEX FUND PORTFOLIO · International Scientific Journal of Engineering and Management · 2026

Related topics in Economics, Econometrics and Finance