Risk-adjusted Portfolio Performance Research Articles

STUDY ON PORTFOLIO MODEL UNDER BACKGROUND RISK AND FRACTAL MARKET

In view of the necessity of optimizing portfolio models under a combination of the fractal market and the background risk, this paper incorporates the fractal correlations and background risk into the research framework of portfolio model optimization. Then the portfolio model under background risk and fractal market (PBF) is constructed, and the analytical formula of the model is given. Based on this, the empirical results not only illustrate the efficiency of PBF but also show that PBF can improve the yield and risk-adjusted portfolio performance. According to the research results, PBF can provide a more efficient reference for people to make decisions on the investment portfolio.

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.

How do Artificial Intelligence and Robotics Stocks co-move with traditional and alternative assets in the age of the 4th industrial revolution? Implications and Insights for the COVID-19 period

This study investigates the interdependence between AI & Robotics stocks and traditional (including stocks and bonds) and alternative (commodities and cryptocurrencies) assets, employing wavelet coherence analysis in time-frequency space. We further provide a fresh perspective on potential hedging and diversification benefits of AI & Robotics stocks. Overall, our results suggest that co-movements between AI & Robotics stocks and other assets significantly depend on the wavelet decomposition levels, suggesting time-scale-dependent investment benefits. Wavelet coherences and correlations have substantially increased, mostly in the low frequencies, during the COVID-19 pandemic. Government securities exhibit safe haven properties for investors at the highest and lowest scales. Even if cryptocurrencies can provide hedging benefits over the full sample, these benefits seem to be diminished during the COVID-19 period. We observe substantially higher co-movements of AI stocks with the composite stock index, corporate bonds, and commodities at all scales after March 2020, implying that inclusion of these assets in AI & Robotics stock portfolios may not enhance risk-adjusted portfolio performance in times of market turbulence. These results offer potential implications for investors and portfolio managers in terms of hedging/diversification benefits as well as for authorities and policy makers regarding the development of strategies to mitigate financial risk.

Are pension funds actively decarbonizing their portfolios?

We study whether investors are actively decarbonizing their portfolios. With the adoption of the Paris Agreement in December 2015, a better understanding of portfolio related carbon dioxide (CO2) exposures has become increasingly important for investors, regulators and society at large. Carbon-intensive stocks still carry a substantial weight in common market benchmarks. We analyze if investors are actively divesting by deviating from market benchmark allocations to reduce carbon exposures. We utilize a stock-level holdings dataset of Dutch pension funds over the period 2009–2017 and combine this with firm-level CO2 emissions information to measure the portfolio carbon footprint and active portfolio management. We find that pension funds that deviate from market benchmark weights have substantially lower carbon footprints. This effect is mostly driven by reduced exposures to carbon-intensive industries and is larger for pension funds that measure and report their carbon footprints. We find no evidence that deviations from the market benchmark impair risk-adjusted portfolio performance.

Sukuk As a Diversifier in Traditional Mixed Asset Portfolios

During the last decade, Islamic securities named Sukuk recorded an exceptional course in number and size of new issuances. However, apart from religious motivations there is not much known on its economic merits compared to traditional asset classes, such as stocks, bonds and cash. We empirically analyse if Sukuk can serve as a portfolio diversifier, when adding to conventional mixed asset portfolios. Tests are conducted based on a minimum-variance portfolio and a naive 1/N portfolio strategy. We show that Sukuk could be used by asset managers to enhance a risk-adjusted portfolio performance.

Testing Rebalancing Strategies for Stock-Bond Portfolios Across Different Asset Allocations

We compare the risk-adjusted performance of stock-bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and in-corporates the financial markets of the United States, the United Kingdom, and Germany. To draw reasonable recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. If the portfolio weight of stocks exceeds 30%, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analyzed countries and all risk-adjusted performance measures.

Testing rebalancing strategies for stock-bond portfolios across different asset allocations

ABSTRACTWe compare the risk-adjusted performance of stock–bond portfolios between rebalancing and buy-and-hold across different asset allocations by reporting statistical significance levels. Our investigation is based on a 30-year dataset and incorporates the financial markets of the United States, the United Kingdom and Germany. To draw useful recommendations to investment management, we implement a history-based simulation approach which enables us to mimic realistic market conditions. Even if the portfolio weight of stocks is very low, our empirical results show that a frequent rebalancing significantly enhances risk-adjusted portfolio performance for all analysed countries and all risk-adjusted performance measures.

The Effective Combination of Risk-Based Strategies with Momentum and Trend Following

The Efficient Market Hypothesis (EMH) has been widely called into question in the investment literature, through two main anomalies: timing and low-volatility anomalies. In this paper, we aim to combine the predictive power of timing and low-volatility strategies to deliver better risk-adjusted portfolio performance. We adopt a two-step approach for a constant dataset composed of 18 country MSCI stock market indices over the 1975-2014 period. First, we use different timing strategies: moving averages and momentum. We select stock market indices based on different moving averages (6, 8, 10, and 12 months), while the momentum strategy ranks the different stock market indices into momentum subsets (low, medium, and high momentum). After the first step using the different timing strategies, the second step consists in building risk-based portfolios (MV, ERC, and MD) as well as 1/N benchmark portfolios for each of these timing strategies. Our results highlight the effectiveness, the relevance and the robustness of our approach. First, risk-based portfolios using relevant timing strategy indeed provide better returns, lower volatilities, higher Sharpe ratios, and lower Value-at-Risk (VaR) and Expected Shortfall (ES) than traditional risk-based portfolios. The second contribution of our approach features that risk-based strategies provide better risk-adjusted returns and lower VaR and ES than the 1/N portfolio within a context in which the first step is dedicated to the application of a relevant timing strategy. Finally, among these risk-based portfolios using relevant timing strategies, the MD and MV portfolios usually obtain the best risk-adjusted performance.

Active Portfolio Management on the Romanian Stock Market

The homogeneity of expectations and rationality of decisions in the neo-classical portfolio theory imply the existence of an efficient market – meaning a market where assets’ prices coincide with their fundamental value. The efficient market hypothesis, stating that efforts to find over/under valued assets are unnecessary and will not produce results superior to a passive strategy, is clearly contrary to the active portfolio management strategy and its proponents sustain that investing in a tracking portfolio (that is a portfolio that closely tracks a composite stock index) will bring the maximum possible return. In this research we aim to assess the individual Romanian portfolios performance (real individual investment accounts on Bucharest Stock Exchange) in comparison with the international stock markets’ evolution (proxied by the multinational equity index MSCI World Index) and also with the overall evolution of the Romanian Stock Market (proxied by its composite index BET-C). We construct a so-called “Individual Portfolios Index” or IPI from the average daily returns weighted with the market value for 30 real individual portfolios. The comparative analysis reveals that the most efficient portfolio is the one tracking the multinational MSCI World Index, and also that IPI has a slightly lower risk and return than the Romanian composite index. Next, we evaluate the risk-adjusted portfolio performance for the index IPI, the composite Romanian index BET-C, and for each of the 30 individual accounts. We conclude that the overall value of an active portfolio management strategy on the Romanian equity market is inferior to the value of a passive strategy tracking the market index over the analyzed period.

Dynamic hedge fund portfolio construction: A semi-parametric approach

In this article, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. We compare the standard mean–variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, which is based on extreme value theory, copula and Monte Carlo simulation. We compare the semi-parametric approach with the standard, non-parametric approach, used to compute CVaR, CDaR and Omega, and the benchmark parametric approach, based on both static and dynamic mean–variance optimization. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the parametric mean–variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.

Risk-Factor Diversification and Portfolio Selection

Traditionally, investment portfolios have been constructed with a focus on what asset classes to invest in and how much to invest in each. Recent research, however, has shown that focusing on risk-factor allocations, rather than asset class allocations, can result in better risk-adjusted portfolio performance. The existing literature has focused on simple allocation strategies such as equal-weighted and equal-risk-weighted portfolios. In addition to these simple allocation techniques, this paper compares the performance using mean-variance analysis, and presents evidence that the outperformance of risk-factor diversification may not be as conclusive as has been previously presented in the literature. While confirming some of the prior findings on risk-factor diversification, the research shows that previous findings may be subject to strong caveats. Specifically, the evidence suggests that the selection of risk-factors, portfolio selection techniques and time-period have a large impact on performance outcomes.

Dynamic Hedge Fund Portfolio Construction: A Semi-Parametric Approach

In this paper, we evaluate alternative optimization frameworks for constructing portfolios of hedge funds. Using monthly hedge fund index returns for the period 1990 to 2011, we compare the standard mean-variance optimization model with models based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investment strategies. In order to implement the CVaR, CDaR and Omega optimization models, we propose a semi-parametric methodology, in which we first model the marginal density of each hedge fund index using extreme value theory and construct the joint density of hedge fund index returns using a copula-based approach. We then simulate hedge fund returns from this joint density in order to compute CVaR, CDaR and Omega, which are used in the optimization process. We compare the semi-parametric approach with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. We report two main findings. The first is that the CVaR, CDaR and Omega models offer a significant improvement in terms of risk-adjusted portfolio performance over the mean-variance model. The second is that semi-parametric estimation of the CVaR, CDaR and Omega models offers a very substantial improvement over non-parametric estimation. Our results are robust to the choice of target return, risk limit and estimation sample size.

Distributing weights under hierarchical clustering: A way in reducing performance breakdown

This paper proposes a clustering asset allocation scheme which provides better risk-adjusted portfolio performance than those obtained from traditional asset allocation approaches such as the equal weight strategy and the Markowitz minimum variance allocation. The clustering criterion used, which involves maximization of the in-sample Sharpe ratio (SR), is different from traditional clustering criteria reported in the literature. Two evolutionary methods, namely Differential Evolution and Genetic Algorithm, are employed to search for such an optimal clustering structure given a cluster number. To explore the clustering impact on the SR, the in-sample and the out-of-sample SR distributions of the portfolios are studied using bootstrapped data as well as simulated paths from the single index market model. It was found that the SR distributions of the portfolios under the clustering asset allocation structure have higher mean values and skewness but approximately the same standard deviation and kurtosis than those in the non-clustered case. Genetic Algorithm is suggested as a more efficient approach than Differential Evolution for the purpose of solving the clustering problem.

Os dividendos como estratégia de investimentos em ações

Na Teoria de Finanças, o papel dos dividendos nas cotações é um tema controverso, uma vez que há diferentes teorias que abordam sua relevância para o valor das ações e, em conseqüência, para a riqueza dos acionistas. Além disso, as descobertas empíricas que originaram a Hipótese de Mercado Eficiente de Capitais (HME) não apenas contradizem a Análise Técnica, mas também impõem um desafio à declarada habilidade superior da Análise Fundamentalista em gerar retornos superiores, com base em fatores ou variáveis contábeis, financeiros e econômicos, tais como o dividend yield (retorno em dividendos). A fim de se verificar a relação entre dividend yields e as taxas de retornos das ações, bem como a viabilidade de uma estratégia baseada em dividend yields históricos de "bater" o mercado, foram construídas, mensalmente, durante o período que vai do Plano Real em julho de 1994 a dezembro de 1999, três diferentes carteiras (alto, baixo e zero). Seus riscos, retornos e indicadores de desempenho ajustados ao risco foram calculados e comparados entre si e com seu paradigma (benchmark), o Índice da Bolsa de Valores de São Paulo - Ibovespa. A evidência empírica é incapaz de sugerir que as ações de altos dividend yield tendem a possuir maiores ou menores taxas de retorno do que as ações de baixo ou zero yield. Ademais, as evidências sugerem que não é possível demonstrar, usando o método empírico aplicado, uma clara associação entre dividend yield e taxas de retorno das ações.

Life insurance companies as investment managers: New implications for consumers

This paper examines the attractiveness of the equity portfolios of life insurance companies as an alternative investment to mutual funds. In particular, this study analyzes the risk-adjusted investment performance of the stock portfolios of life insurance companies, attributable to their stock selection and market timing abilities. Using conventional measures of risk-adjusted portfolio performance, we find that life insurance companies exhibit performance similar to mutual funds. The evidence suggests that the life insurance companies, like their mutual fund counterparts, fail to exhibit differential stock selection or market timing abilities that are statistically significant. While the risk-adjusted investment performance of the two investment vehicles is similar, the variable annuity contracts of life insurance companies may offer an edge over mutual funds due to their ability to defer taxes.