Search Results (15)

This paper develops a dynamic asset allocation strategy for defined contribution pension funds using a stochastic control framework under the Heston stochastic volatility model. By solving the associated Hamilton–Jacobi–Bellman partial differential equation, we derive optimal equity allocations that adapt to changing market volatility and investor risk aversion using a constant relative risk aversion utility function (parameter $\gamma$). The strategy increases equity exposure during stable periods and reduces it during volatile regimes, capturing both myopic and intertemporal hedging demands. We test the model using historical U.S. data from 2006 to 2025 and benchmark its performance against a traditional static 60/40 stock–bond portfolio, as well as rule-based strategies such as volatility targeting and constant proportion portfolio insurance. Our results show that with moderate risk aversion, the dynamic strategy achieves long-term wealth comparable to the 60/40 benchmark while substantially reducing drawdown risk. As risk aversion increases, drawdown risk is further reduced and risk-adjusted returns remain competitive. Although higher aversion yields lower final wealth, certainty-equivalent returns are highest at moderate aversion levels. These results demonstrate that volatility responsive dynamic policies grounded in realistic stochastic volatility modeling can substantially enhance downside protection and risk-adjusted utility, especially for long-horizon, risk-averse pension participants. Full article

Cities face mounting pressures to deliver reliable, low-carbon services amid rapid urbanization and budget constraints. Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) are widely promoted to automate operations and strengthen decision-support across the built environment; however, it remains unclear whether these interventions are both effective and systemically integrated across domains. We conducted a Preferred Reporting Items for Systematic Reviews (PRISMA) aligned systematic review and meta-analysis (January 2015–July 2025) of empirical AI/ML/DL/IoT interventions in urban infrastructure. Searches across five open-access indices Multidisciplinary Digital Publishing Institute (MDPI), Directory of Open Access Journals (DOAJ), Connecting Repositories (CORE), Bielefeld Academic Search Engine (BASE), and Open Access Infrastructure for Research in Europe (OpenAIRE)returned 7432 records; after screening, 71 studies met the inclusion criteria for quantitative synthesis. A random-effects model shows a large, pooled effect (Hedges’ g = 0.92; 95% CI: 0.78–1.06; p < 0.001) for within-domain performance/sustainability outcomes. Yet 91.5% of implementations operate at integration Levels 0–1 (isolated or minimal data sharing), and only 1.4% achieve real-time multi-domain integration (Level 3). Publication bias is likely (Egger’s test p = 0.03); a conservative bias-adjusted estimate suggests a still-positive effect of g ≈ 0.68–0.70. Findings indicate a dual reality: high efficacy in silos but pervasive fragmentation that prevents cross-domain synergies. We outline actions, mandating open standards and APIs, establishing city-level data governance, funding Level-2/3 integration pilots, and adopting cross-domain evaluation metrics to translate local gains into system-wide value. Overall certainty of evidence is rated Moderate based on Grading of Recommendations Assessment, Development, and Evaluation (GRADE) due to heterogeneity and small-study effects, offset by the magnitude and consistency of benefits. Full article

This research explores the efficacy of rebalancing strategies in a diversified portfolio constructed exclusively with exchange-traded funds (ETFs). We selected five ETF types: short-term U.S. Treasury bonds, U.S. equities, global commodities, U.S. real estate investment trusts (REITs), and a multi-strategy hedge fund. Using a 10-year historical period, we applied a unique simulation model to generate random portfolios with varying asset weights and rebalancing tolerance bands, assessing the impact of rebalancing premiums on portfolio performance. Our study reveals a significant positive correlation (r = 0.6492, p < 0.001) between rebalancing-weighted returns and the Sharpe ratio, indicating that effective rebalancing enhances risk-adjusted returns. Support vector regression (SVR) analysis shows that rebalancing premiums have diverse effects. Specifically, equities and commodities benefit from rebalancing with improved risk-adjusted returns, while bonds and REITs demonstrate a negative relationship, suggesting that rebalancing might be less effective or even detrimental for these assets. Our findings also indicate that negative portfolio rebalancing returns combined with positive rebalancing-weighted returns yield the highest average Sharpe ratio of 0.4328, highlighting a distinct and reciprocal relationship between rebalancing effects at the asset and portfolio levels. This research highlights that while rebalancing can enhance portfolio performance, its effectiveness varies by asset class and market conditions. Full article

The hedge fund (HF) industry is known to be one of the most unequal professional fields when it comes to gender. This study quantifies and confirms this severe gender gap, which appears to be persistent or even widening in recent years. We assess whether performance and risk differences explain this gap by comparing samples of woman-managed HFs vs. man-managed HFs. Through analyzing their descriptive statistics first, examining their alphas using HF benchmark indices and a pricing model and, finally, comparing differences in wealth generation, we found no evidence of performance differences that could explain such an extreme gender gap. Furthermore, our results do not support the view that women are more risk-averse than men, or that this is translated into their investment decisions. Other sociocultural factors probably partly explain the existence and persistence of this gender gap in hedge funds. Full article

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets. Full article

This study contributes to the ongoing debate on the size effect and size-based investment styles by investigating the return and volatility spillovers and time-varying conditional correlations among Saudi large-, mid-, and small-cap indices. To this end, we utilize the weekly returns on the MSCI Saudi large-, mid-, and small-cap indices over a long sample period, spanning several crises. The econometric approach that we use is a VAR-asymmetric BEKK-GARCH model which accounts for structural breaks. On the basis of the VAR-asymmetric BEKK-GARCH model estimation results, we calculate portfolio weights and hedge ratios, and discuss their risk management implications. The empirical results confirm the presence of unilateral return spillovers running from mid- to small-cap stocks, while multilateral volatility spillovers are documented, albeit substantially weakened when accounting for structural breaks. The time-varying conditional correlations display clear spikes around crises, which translate to higher hedge ratios, increasing the cost of hedging during turbulent times. The optimal portfolio weights suggest that investors generally overweight large caps in their portfolios during uncertain times to minimize risk without lowering expected returns. The main takeaway from our results is that passively confining fund managers to a particular size category regardless of the prevailing market conditions may lead to suboptimal performance. Full article

This study proposes a structured product (SP) for hedging defined contribution pension fund members against capital market risk. Using Monte Carlo simulations on three different guaranteed returns to test the investment strategy of the SP against a balanced investment portfolio, we measure their performance across a wide variety of capital market returns and risk scenarios. The results show that the SP guarantees a minimal return on the pension savings portfolio and offers a higher portfolio return at a lower investment risk, compared with the balanced investment portfolio. We conclude that the SP may become popular among pension fund members, potentially leading to improved risk management, greater competition, and investment strategy innovations for defined contribution pension schemes. Full article

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic. Full article

Herein, we performed a meta-analysis evaluating the effects of treatment adherence enhancement programs on treatment adherence and secondary outcomes for hemodialysis patients. Twenty-five Korean and international articles published prior to 31 March 2022 were selected following the PRISMA and Cochrane Systematic Review guidelines. We calculated summary effect sizes, conducted homogeneity and heterogeneity testing, constructed a funnel plot, and performed Egger’s regression test, Begg’s test, trim-and-fill method, subgroup analyses, and univariate meta-regression. The overall effect of treatment adherence enhancement programs for hemodialysis patients was statistically significant (Hedges’ g = 1.10, 95% CI: 0.77, 1.43). On performing subgroup analysis to determine the cause of effect size heterogeneity, statistically significant moderating effects were found for a range of input variables (Asian countries, study centers, sample size, study design, intervention types, number of sessions, quality assessment scores, funding, and evidence-based interventions). On univariate meta-regression, larger synthesized effect sizes were found for a range of study characteristics (Asian populations, single-center studies, studies with <70 participants, quasi-experimental studies, educational interventions, studies with >12 sessions, studies with quality assessment scores above the mean, unfunded studies, and non-theory-based interventions). Our results provide evidence-based information for enhancing program efficacy when designing treatment adherence enhancement programs for hemodialysis patients. Full article

The paper’s prime objective is to understand the impact of Shareholder activism on firm performance. This study is conducted in a unique setup where traditional activist investors such as pension funds and hedge funds are not present. However, the activism cases are increasing yearly in an emerging economy like India. We have created a comprehensive shareholder activism index (sha index) using multiple activisms and corporate governance factors. To measure firm performance, we have used valuation (Tobin’s Q and Market capitalization), profitability (operating profit margin and net profit margin), and return ratios (Return on capital and return on equity). Panel data analysis (PDA) is employed for the current study as it overcomes the shortcomings of the time series analysis and cross-sectional studies. The sample comprises 37 listed firms’ data for FY2017 to FY2020. Chosen firms have experienced activism instances at least once during the 2017–2020 period. As per our analysis, shareholder activism has a significant negative impact on valuation measured in market capitalization and profitability estimated by operating profit margin. Activism primarily impacts the other four parameters negatively, but it is insignificant. India is in the nascent stage of activism, partly explaining the insignificance of the effects of shareholder activism on firm performance. Also, activist investors are targeting companies. These attacks are not fructifying desired outcomes as promoters own over 50% stake in the listed companies. The latest data for FY2021 has not been considered for the study as covid-19 impacted the businesses during the financial year. Also, we cannot capture activism instances that are not reported in regulatory filings. Unlike past research in this area, we have used a comprehensive activism index as a proxy of activism and have employed PDA instead of event studies to assess the impact on firm performance. Also, this is the first such empirical study conducted in an emerging economy setup where neither large hedge nor pension funds are present. Full article

Cryptocurrency literature is increasing rapidly nowadays. Particularly, the role of the cryptocurrency market as a risk management avenue has got the attention of researchers. However, it is an immature asset class and requires gaps in current literature for future research directions. This research provides a systematic review of the vast range empirical literature based on the cryptocurrency market as a risk management avenue against economic policy uncertainty (EPU). The review discovers that cryptocurrencies have mixed connectedness patterns with all national EPU therefore, the risk mitigation ability varies from country to country. The review finds that heterogeneous correlation patterns are due to the dependence of EPU on the policies and decisions usually taken by regulatory authorities of a particular country. Additionally, heterogeneous EPU requires heterogeneous solutions to deal with stock market volatility and economic policy uncertainty in different economies. Likewise, the divergent protocol and administration of currencies in the crypto market consequently vicissitudes the hedging and diversification performance against each economy. Many research lines can benefit investors, policymakers, fund managers, or portfolio managers. Therefore, the authors suggested future research avenues in terms of topics, data frequency, and methodologies. Full article

Protecting against unexpected yield curve, inflation, and longevity shifts are some of the most critical issues institutional and private investors must solve when managing post-retirement income benefits. This paper empirically investigates the performance of alternative immunization strategies for funding targeted multiple liabilities that are fixed in timing but random in size (inflation-linked), i.e., that change stochastically according to consumer price or wage level indexes. The immunization procedure is based on a targeted minimax strategy considering the M-Absolute as the interest rate risk measure. We investigate to what extent the inflation-hedging properties of ILBs in asset liability management strategies targeted to immunize multiple liabilities of random size are superior to that of nominal bonds. We use two alternative datasets comprising daily closing prices for U.S. Treasuries and U.S. inflation-linked bonds from 2000 to 2018. The immunization performance is tested over 3-year and 5-year investment horizons, uses real and not simulated bond data and takes into consideration the impact of transaction costs in the performance of immunization strategies and in the selection of optimal investment strategies. The results show that the multiple liability immunization strategy using inflation-linked bonds outperforms the equivalent strategy using nominal bonds and is robust even in a nearly zero interest rate scenario. These results have important implications in the design and structuring of ALM liability-driven investment strategies, particularly for retirement income providers such as pension schemes or life insurance companies. Full article

The performance of hedge funds is of interest to investors looking for ways of generating value over passive strategies, particularly in bad times. This study used the Hedge Index database with over 9500 hedge funds to analyse, in depth, the performance of ten major strategies, during and after the financial crisis (June 2007–January 2017). To the best of our knowledge, such a study covering the last ten years has not been published. Performance of the various strategies was analysed, using correlations, the Carhart’s four factor model, persistence of performance, and reward-risk ratios. The findings are that some hedge fund strategies which have persistent performances are also able to outperform the benchmark in some periods. In the crisis period, value-wise, all strategies did better than the S&P500, thereby, conserving value for investors, better than passive investment in the S&P500. Over the entire period of the research (June 2007–January 2017), seven strategies performed better than the S&P500: Global Macro, Multi Strategy, Emerging Markets, Long/Short Equity, Event Driven, Convertible Arbitrage, and Fixed Income Arbitrage. As hedge funds typically have skewed return distributions, performance was analysed in different periods, within conventional and downside risk frameworks. This research contributes to the advancement of knowledge on the outcomes of hedge fund strategies in different market conditions and the reliability of alternative risk frameworks in their evaluation. Apart from the theoretical implications, this research provides practical knowledge to managers and investors on which strategies hold better value and in what circumstances. Full article

The design and development of post-retirement income products require the assessment of longevity risk, as well as a basis for hedging these risks. Most indices for longevity risk are age-period based. We develop and assess a cohort-based value index for life insurers and pension funds to manage longevity risk. There are two innovations in the development of this index. Firstly, the underlying variables of most existing longevity indices are based on mortality experience only. The value index is based on the present value of future cash flow obligations, capturing all the risks in retirement income products. We use the index to manage both longevity risk and interest rate risk. Secondly, we capture historical dependencies between ages and cohorts with a cohort-based stochastic mortality model. We achieve this by introducing age-dependent model parameters. With our mortality model, we obtain realistic cohort correlation structures and improve the fitting performance, particularly for very old ages. Full article

We define a dynamic and self-adjusting mixture of Gaussian Graphical Models to cluster financial returns, and provide a new method for extraction of nonparametric estimates of dynamic alphas (excess return) and betas (to a choice set of explanatory factors) in a multivariate setting. This approach, as well as the outputs, has a dynamic, nonstationary and nonparametric form, which circumvents the problem of model risk and parametric assumptions that the Kalman filter and other widely used approaches rely on. The by-product of clusters, used for shrinkage and information borrowing, can be of use to determine relationships around specific events. This approach exhibits a smaller Root Mean Squared Error than traditionally used benchmarks in financial settings, which we illustrate through simulation. As an illustration, we use hedge fund index data, and find that our estimated alphas are, on average, 0.13% per month higher (1.6% per year) than alphas estimated through Ordinary Least Squares. The approach exhibits fast adaptation to abrupt changes in the parameters, as seen in our estimated alphas and betas, which exhibit high volatility, especially in periods which can be identified as times of stressful market events, a reflection of the dynamic positioning of hedge fund portfolio managers. Full article