Superior Risk-adjusted Returns Research Articles

Navigating median and extreme volatility in stock markets: Implications for portfolio strategies

This study explores the interdependencies among developed stock markets using the LASSO technique with quantile regression within a network analysis framework. Traditional forecasting methods often fail during volatile market conditions, necessitating innovative approaches that blend interconnectedness and factor modeling. By employing quantile regression, which examines financial assets across various distribution quantiles, this study addresses tail risk, a critical aspect of market behavior during crises. The network analysis framework provides insights into the relationships between stock markets, highlighting how variables interact within a complex system. The study assesses market behaviors at different quantile levels, considering clustering coefficients to analyze cycles, middlemen, ins, and outs. Additionally, it examines the impact of several factors on market interconnectedness, offering insights into the interplay of individual stocks with broader market conditions. Key findings demonstrate that incorporating interconnectedness factors into forecasting models enhances accuracy and informs better decision-making, leading to portfolios that can withstand extreme market conditions and provide superior risk-adjusted returns.

Optimizing stock market volatility predictions based on the SMVF-ANP approach

The stock market is considered one of the most complicated financial systems, comprising several components or inventories, whose prices vary substantially over time. Bursaries include revealing market tendencies over time. All investors in the stock market aim to maximize profits and reduce the associated risks. Stock currency forecasts are a significant financial concern that is being handled even more closely. In recent years, various neural network and hybrid models have surpassed classic linear and non-linear techniques to produce reliable prediction outcomes. This study investigates the efficacy of dynamic and effective stock market forecasting using neural network models. This study examines market transmission mechanisms and assesses the predicted links between multiple financial and economic factors. A stock market volatility and artificial network prediction (SMVF–ANP) approach is presented. The models analyzed included a multi-layer perceptron (MLP), dynamic artificial neural network, and generalized autoregressive conditional heteroscedasticity to extract additional input variables. The results reveal that the trade strategies led by the classification models yield superior risk-adjusted returns compared to the buy-and-hold approach and those led by the neural network and linear regression model-level estimate predictions. The numerical results show that the proposed SMVF-ANP method achieved a high performance ratio of 94.1%, an enhanced prediction ratio of 98.4%, a high stock market volatility rate of 96.7%, a reduced mean square percentage error ratio of 16.3%, a probability rate of 32.7%, an increased efficiency rate of 96.9%, and an accuracy ratio of 97.2% compared to other methods.

The Diversification Benefits of Foreign Real Estate: Evidence from 40 Years of Data

We investigate the potential of global real estate to improve the long-term performance of a US equity portfolio, utilizing a recent dataset of 40 years’ worth of US stocks, US real estate, 13 foreign stock markets, and 13 foreign real estate markets across diverse regions. Despite a modest performance in terms of risk and return, foreign real estate has consistently lower correlations with US stocks compared to foreign equities. Rolling correlation analysis indicates that foreign real estate markets remain relatively segmented compared to foreign equity, despite increasing financial market correlations over time. Efficient frontier analysis demonstrates that portfolios including foreign real estate consistently outperform those limited to US stocks and US real estate or those including foreign stocks, indicating the importance of foreign real estate in optimizing portfolio performance. Subperiod analysis reveals that foreign real estate retains its diversification benefits even in the latter, more integrated period. Our results are robust when using Conditional Value-at-Risk as a measure of risk. Overall, our findings highlight the persistent diversification benefits and superior risk-adjusted returns from incorporating foreign real estate into US equity portfolios.

Determining Drivers of Private Equity Return with Computational Approaches

AbstractPrivate equity (PE) represents the acquisition of stakes in non-listed companies, often long-term, with the objective of improving the performance and value of the company to obtain significant benefits at time of disinvestment. PE has gained particular importance in the global financial system for delivering superior risk-adjusted returns. Knowing the PE return drivers has been of great interest among researchers and academics, and some studies have developed statistical models to determine PE return drivers. Still, the explanatory capacity of these models has certain limitations related to their precision levels and exclusive focus on groups of countries located in Europe and the EE.UU. Therefore, in the current literature, new models of analysis of the PE return drivers are demanded to provide a better fit in worldwide scenarios. This study contributes to the accuracy of the models that identify the PE return drivers using computational methods and a sample of 1606 PE funds with a geographical focus on the world’s five regions. The results have provided a unique set of PE return drivers with a precision level above 86%. The conclusions obtained present important theoretical and practical implications, expanding knowledge about PE and financial forecasting from a global perspective.

Sustainable Investing and Islamic Finance: Evidence from the Organisation of Islamic Cooperation (OIC) Countries

Sustainable investing and Islamic finance have been two of the fastest-growing areas of finance. In Islamic finance, equity markets play an important role. In the literature on Islamic equities, there are relatively few studies that have integrated sustainability factors into Islamic finance. In this paper, our objective is to contribute to the line of research on the integration of sustainable investing into Islamic finance. To that end, we first examine the comparative performance of investing in the sustainability indices from those Organization of Islamic Cooperation (OIC) countries that are partners of the Sustainable Stock Exchanges (SSE) initiative. We then conduct a case study of Borsa Istanbul in Turkey, which has the best-performing sustainability index from OIC countries. Overall, our findings reveal the heterogeneity in sustainable investment performance, and suggest the potential of obtaining superior risk-adjusted returns in certain economies from OIC countries. This study draws implication on how sustainable investing can help bridge the gap between Islamic and conventional financial markets.

Bitcoin replication using machine learning

Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.

Isolating the female agency-driven development factor in external sovereign emerging market debt

The underlying literature hypothesises and provides randomised evidence for the positive impact of promoting the broad-based inclusion, empowerment, and representation of women on regional ecosystems. This study seeks to isolate a female agency-driven development factor in external sovereign emerging market debt and finds evidence for superior risk-adjusted returns from tilting towards female agency leaders. We propose the female agency factor as an additional scope in the modern investor’s toolbox of holistic credit assessment, allowing investors to isolate the issuers which are the most effective sovereign transmission mechanisms of sustainable development capital. This contribution to the corpus supports the notion of integrating sustainability factors into portfolio construction and reinforces the argument for supporting female-led development from a financial markets’ perspective.

Performance of Risk Parity Strategy in Developed Markets

This study examines the performance and effectiveness of the Risk Parity (RP) strategy in strategic asset allocation specifically utilizing the inverse volatility (IV) approach. The study utilizes data from 13 developed countries, encompassing stocks and government data, to analyze the performance of RP portfolio. A comparative analysis is conducted between the RP portfolio, traditional 60/40 and equal-weighted portfolios across significant market crises, including the dot-com crisis, financial crisis, sovereign debt crisis, and COVID-19 pandemic. This study reveals that inverse volatility approach or a portfolio with a higher weight towards safer assets compared to riskier assets delivers superior risk-adjusted returns. It presents evidence that challenges the notion of the market or 60/40 portfolio as an efficient portfolio and suggests that an alternative portfolio such as RP may offer superior efficiency in terms of risk and return. Moreover, the study examines the role of leveraging in RP portfolios, highlighting how investors can align risk levels with benchmark portfolios while achieving higher risk-adjusted returns. The findings highlight the substantial impact of leveraging on RP portfolio performance, demonstrating their resilience and superiority even in challenging market conditions.

Model Smart Beta dalam Menyusun Portofolio Optimal a Literature Review

The Theory of Capital Asset Pricing Model (CAPM) states that market risk (beta) can generate risk premium for investors. So investors only need to invest in a market portfolio or portfolio based on market capitalization weighting. Smart Beta portfolio is an index-based investment strategy that seeks to obtain superior risk-adjusted returns through transparent techniques and rules-based criteria based on specific factors or attributes that generate investment returns. Combining the Fama French Five Model, Variables Tracking Error and Active Share, this study proposes a Smart Beta model that will be selected from Issuers on the LQ 45 Index to 20 Issuers and compare which Return is better between IDX High Dividend Return 20 and Return from Issuer smart Beta portfolio results.

Performance of ESG-integrated smart beta strategies in Asia-Pacific stock markets

Environmental, Social, and Governance (ESG) investing is about ethical investing. While ESG investing has garnered heightened attention, the research has not settled on whether ESG investing can “do well while doing good”. Using a proprietary ESG rating database of monthly firm-specific data, we examine the performance of ESG-incorporated investing strategies in Australia, Mainland China, Hong Kong, Malaysia, and Singapore. Specifically, we combine positive screening and the smart beta approach to evaluate the performance of ESG-based and non-ESG-based (traditional) equity portfolios. Our key findings reveal that high-ESG-based portfolios do not offer superior risk-adjusted returns compared to the low-rated portfolios. While a high-ESG-rated portfolio generally outperforms the market index, the ESG and traditional smart beta alphas differ little. Our results also indicate that the minimum-volatility portfolio achieves the best performance of all the factors. We use data from Refinitiv ESG and Bloomberg ESG to substantiate and support the results. Our findings add to the growing ESG literature that answers whether investors risk sacrificing returns while investing ethically.

Exploring the merits of five-factor investing: A critical literature review

The dynamics of investing in stock markets have changed considerably over the past twenty years. The value and growth investing concept has drawn much attention and continues to dominate portfolio management. Factor investing is an approach that aims at quantifying particular characteristics through matrixes of quality, value, momentum, and growth to improve the returns of a portfolio. Although the three-factor style of investing has gained widespread acceptance, the five-factor continues to be a source of disagreement among academics and industry practitioners. This study aimed to review the merits of five-factor investing critically. A critical literature review was used to review prior literature. The narratives from the literature reveal that the five-factor investing style will result in multiple sources of expected return. Hence, using five factors in portfolio formation has merits and will significantly improve the returns and most likely outperform the market portfolio. Factor premiums have proven to be drivers of asset return. Therefore, market participants and investment managers can intentionally assume additional exposures to deliver superior risk-adjusted returns compared to the market with the five-factor investing style. According to the author's knowledge, this is the first study to objectively analyze the literature on five-factor investing and propose that active managers can earn positive alphas on a risk-adjusted basis through factor exposures.

Foreign Ties That Bind: Cross-Border Firm Expansions and Fund Portfolio Allocation Around the World

AbstractWe investigate whether international operations enhance information links between firms and foreign investors. Exploiting novel subsidiary-level data and within-location variations, we show that, after expanding into another country, a firm attracts greater investment allocation from funds from that country than from other foreign funds. This increase is economically significant, equivalent to one-fifth of the average firm weight in a country-specific portfolio. The observed effect cannot be attributed to funds’ influence, persists even when funds are already familiar with the firm, and helps them generate superior risk-adjusted returns. Our results suggest that firms’ cross-border economic activities contribute to global financial interconnectedness.

A stochastic programming model for dynamic portfolio management with financial derivatives

Stochastic optimization models have been extensively applied to financial portfolios and have proven their effectiveness in asset and asset-liability management. Occasionally, however, they have been applied to dynamic portfolio problems including not only assets traded in secondary markets but also derivative contracts such as options or futures with their dedicated payoff functions. Such extension allows the construction of asymmetric payoffs for hedging or speculative purposes but also leads to several mathematical issues. Derivatives-based nonlinear portfolios in a discrete multistage stochastic programming (MSP) framework can be potentially very beneficial to shape dynamically a portfolio return distribution and attain superior performance. In this article we present a portfolio model with equity options, which extends significantly previous efforts in this area, and analyse the potential of such extension from a modeling and methodological viewpoints. We consider an asset universe and model portfolio set-up including equity, bonds, money market, a volatility-based exchange-traded-fund (ETF) and over-the-counter (OTC) option contracts on the equity. Relying on this market structure we formulate and analyse, to the best of our knowledge, for the first time, a comprehensive set of optimal option strategies in a discrete framework, including canonical protective puts, covered calls and straddles, as well as more advanced combined strategies based on equity options and the volatility index. The problem formulation relies on a data-driven scenario generation method for asset returns and option prices consistent with arbitrage-free conditions and incomplete market assumptions. The joint inclusion of option contracts and the VIX as asset class in a dynamic portfolio problem extends previous efforts in the domain of volatility-driven optimal policies. By introducing an optimal trade-off problem based on expected wealth and Conditional Value-at-Risk (CVaR), we formulate the problem as a stochastic linear program and present an extended set of numerical results across different market phases, to discuss the interplay among asset classes and options, relevant to financial engineers and fund managers. We find that options’ portfolios and trading in options strengthen an effective tail risk control, and help shaping portfolios returns’ distributions, consistently with an investor’s risk attitude. Furthermore the introduction of a volatility index in the asset universe, jointly with equity options, leads to superior risk-adjusted returns, both in- and out-of-sample, as shown in the final case-study.

Does performance persistence exist in mutual and pension funds? Evidence from Turkey

The objective of this study is to investigate the performance persistence of Turkish mutual and pension funds. 310 mutual and 259 pension funds were analyzed between the period of 2010–2019 in order to determine if there is an evidence of performance persistence. In this study, a persistence rate is developed, and the skill ratio is used to crosscheck the results of the persistence rate. Furthermore, six different risk-adjusted return measures, such as Sharpe, Treynor, Information, Jensen’s alpha, Sortino, and Omega ratios are calculated to analyze whether funds also exhibit superior risk-adjusted returns. The results indicate that only 2% of funds demonstrate persistence above 50%, and 15 out of 20 fund categories do not have any funds that show persistence in 10 years. Most of the persistent funds have positive skill ratios, and it is observed that the persistence rate is effective. However, it cannot be stated that there is performance persistence in the Turkish fund management industry, since performance persistence is not evident for various fund types, so investors do not need to invest in the best funds of the previous year. Additionally, the empirical results associated with risk-adjusted performance analysis indicate that persistent funds also do not generally yield higher risk-adjusted returns. The lack of persistence in funds’ performance is a significant result for investors in their investment decisions, for fund managers in their human resource policies and bonus schemes, and for regulators in their policy decisions.

Do stop-loss rules add value in international equity market allocation?

ABSTRACT We consider the performance of stop-loss rules in international equity market allocation. Diversifying internationally gives the potential of larger returns but often involved higher risks, so it is a natural setting to consider these rules. Our results indicate that stop-loss rules, which involve closing positions that decline by a pre-specified percentage, are an important determinant of asset allocation in a parametric portfolio policy setting. They generate portfolios that have superior mean and risk-adjusted returns for investors. This result holds in general but is economically stronger in declining markets. The outperformance is robust to the inclusion of transaction costs.

Millionaires speak: What drives their personal investment decisions?

We survey 2484 U.S. individuals with at least $1 million of investable assets about how well leading academic theories describe their financial beliefs and personal investment decisions. The wealthy's beliefs about financial markets and the economy are surprisingly similar to those of the average U.S. household, but the wealthy are less driven by discomfort with the market, financial constraints, and labor income considerations. Portfolio equity share is most affected by professional advice, time until retirement, personal experiences, rare disaster risk, and health risk. Concentrated equity holding is most often motivated by belief that the stock has superior risk-adjusted returns. Beliefs about how expected returns vary with stock characteristics frequently differ from historical relationships, and more risk is not always associated with higher expected returns. Active equity fund investment is most motivated by professional advice and the expectation of higher average returns. Berk and Green (2004) rationalize return chasing in the absence of fund performance persistence by positing that past returns reveal managerial skill but there are diminishing returns to scale in active management. Forty-two percent of respondents agree with the first assumption, 33% with the second, and 19% with both.

Mutual funds behavior and risk-adjusted performance in Nigeria

The paper investigates the behavior of mutual funds and their risk-adjusted performance in the financial markets of Nigeria between April 2016 and May 31, 2019, using descriptive statistics, as well as CAPM, Jensen’s alpha, and other risk-adjusted portfolio performance measures such as Sharpe and Treynor ratios, as well as Fama decomposition of return. The descriptive tests revealed that 80.77% of the funds were superior to market returns, while 13.46% were riskier. The market and the fund returns behaved abnormally with asymptotic and leptokurtic characteristics as their skewness and kurtosis varied from the normal requirements. Diagnostically, the normality test by Jacque-Berra showed that the return was not normally distributed at a 1% significance level. The market was more aggressive relative to the funds. The average risk-free rate was 6.75% above the market’s return. The risk-adjusted portfolio returns measured by Sharpe and Treynor ratios showed that 67.31% of the funds underperformed the market compared to 40.38% that outperformed the market using Jensen’s alpha. Fama decomposition of return revealed that the fund managers are risk-averse with 48% superior selection ability and rationally invested over 85% of investors’ funds in schemes with fixed income securities at a given risk-free return that cushioned the negative effects of the systematic and idiosyncratic risks and consequently threw the total returns into positive territories. Overall, the fund managers possessed 52% of inferior selection abilities that only earned 33% of superior risk-adjusted returns and hence, failed to achieve the desired diversification in the relevant period.

Equity Factors: To Short or Not to Short, That Is the Question

What is the best market-neutral implementation of classical equity factors? Should one use the specific predictability of the short leg to build a zero beta long–short portfolio, in spite of the specific costs associated to shorting, or is it preferable to ban the shorts and hedge the long leg with—say—an index future? The authors revisit this question by focusing on the relative predictability of the two legs, the issue of diversification, and various sources of costs. Their conclusion is that, using the same factors, a long–short implementation leads to superior risk-adjusted returns than its hedged long-only counterpart, at least when assets under management are not too large. <b>Key Findings</b> ▪ A toy model gives key insights on the long hedged investment versus long–short investment dilemma. ▪ The authors provide an in-depth analysis and revisiting of “When Equity Factors Drop Their Shorts” by Blitz, Baltussen, and van Vliet (2020). ▪ Realistic implementations of long hedged factor investing and long–short factor investing lead to different conclusions than those of Blitz, Baltussen, and van Vliet (2020).

A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs

Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional return distribution is multivariate generalized hyperbolic and the dispersion matrix dynamics are driven by the leading factors in a principal component decomposition. Each of these leading factors is endowed with a univariate GARCH structure, while the remaining eigenvalues are kept constant over time. Joint maximum likelihood estimation of all model parameters is performed via an expectation maximization algorithm, and is applicable in high dimensions. The new model generates realistic correlation forecasts even for large asset universes and captures rising pairwise correlations in periods of market distress better than numerous competing models. When applied to portfolio optimization, it generates strategies with lower turnover and maximum drawdown, and superior risk-adjusted returns net of transaction costs. Moreover, unlike its competitors, it performs well in the sudden market downturn triggered by the global COVID-19 pandemic.

Low-Risk Benchmarking Transcends Rebalancing Methods

The dismal performance of managed investment and the success of equal allocation and minimum variance models prompt these questions: Can rebalancing driven by minimum variance and maximum diversification rebalancing outperform naive models? Can a minimum variance model produce higher risk-adjusted returns than a maximum diversification model when security selection favors low-correlated assets? This study uses expected shortfall to measure risk, bootstrapping to transform fat-tailed distributions so they are suitable for t-tests, and factor analysis to help explain the relative performance of the models. The minimum variance and maximum diversification models outperformed naive models, and the minimum variance models produced higher risk-adjusted returns than the maximum diversification model. Market factors adequately explained differences in returns between rebalancing models, but adding a factor for the effect of rebalancing procedures improved all models. All models constrained by the lower risk benchmark produced higher risk-adjusted returns than corresponding models constrained by the higher risk benchmark. This outcome suggests many rebalancing models—perhaps even return-based models—could produce superior risk-adjusted returns if lower risk benchmarks constrain risk. TOPICS:Portfolio theory, portfolio construction, risk management, factor-based models, exchange-traded funds and applications Key Findings ▪ The two minimum variance models produced statistically significant superior risk-adjusted returns. ▪ All models constrained by the lower risk benchmark produced higher risk-adjusted returns than corresponding models constrained by the higher risk benchmark. ▪ Although market factors explained differences in model returns, adjusted R2 values increased when regression models included a rebalancing factor.