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To examine the familiar tradeoff between risk and return in financial investments, we use a rolling two-stage stochastic program to compare mean-risk optimization models with time series momentum strategies. In a backtest of allocating investment between a market index and a risk-free asset, we generate scenarios of future return according to a momentum-based stochastic process model. A new hybrid approach, time series momentum strategy controlling downside risk (TSMDR), frequently dominates traditional approaches by generating trading signals according to a modified momentum measure while setting the risky asset position to control the conditional value-at-risk (CVaR) of return. For insight into the outperformance of TSMDR, we decompose each strategy into two aspects, the trading signal and the asset allocation model that determines the risky asset position. We find that 1) weighted moving average can better capture the trend of the stock market than time series momentum computed as past 12-month excess return, 2) mean-risk strategies generally provide better returns whereas risk parity strategies have less investment risk and 3) controlling CVaR limits the investment risk better than controlling variance does.

The informativeness of financial analysts’ stock recommendations to investors has become the subject of much scrutiny and debate. This article investigates the investment value of target price (TP) forecasts under the specific angle of factor investing. It provides directions to quantitative portfolio managers for the integration of signals conveyed by those forecasts in systematic and rule-based investment decisions. Using TP data from 1999 to 2019 in Europe and North America, the author first documents a decline in analyst opinion on the performance of most equity factors and finds that their forecasts are style biased. He shows that a long-only strategy that picks best-ranked stocks according to consensus TP implied expected returns (CTPER) generates substantial alpha within large-cap, low-idiosyncratic-risk, and noncyclical stocks. Embedding the CTPER signal into the momentum factor helps materially improve its risk and return profile. Those findings support the idea of adequately using TP forecasts in the design of top-down multifactor portfolio construction frameworks. Key Findings ▪ Financial analyst opinion on the performance of most equity factors has declined over time. Their TP forecasts are style biased. ▪ Long-only strategies that pick the best-ranked stocks according to CTPER generate substantial alpha within large-cap, low-idiosyncratic-risk, and noncyclical stocks. ▪ Enriching the stock selection criterion of the momentum factor with the CTPER signal improves its risk and return profile.

Capital market assumptions (CMAs), which are long-term risk and return forecasts for asset classes, are important pillars of the investment industry. However, applying them reliably in portfolio construction has been (and still is) a challenge in the industry. This article demonstrates that, despite the difficulties, CMAs are useful for building an investment portfolio using a factor approach. Using a small set of macroeconomic factors, the authors detail a methodology for deriving a factor model from CMAs and then use it to show that (1) these factors price the expected returns from CMAs and (2) the mean–variance factor allocations are substantially more stable than the mean–variance asset portfolios. Furthermore, this article outlines a new approach to building an asset portfolio that respects a desired factor allocation. Overall, this article helps reduce the barrier to entry for factor-based portfolio construction by providing a recipe for building factor models and performing factor-based portfolio construction using publicly available CMAs. Key Findings ▪ This article presents a methodology to show that CMA returns can be cross-sectionally priced by a small set of underlying macroeconomic factors, which suggests that the CMA’s risk and return assumptions follow a factor structure. ▪ The mean–variance factor allocations generated by CMAs’ implied factors are intuitive and stable through time under unconstrained mean–variance optimization. ▪ This article presents a new approach to building an asset portfolio that respects a desired or target factor allocation with weights that are practical.

The aim of this study is to comparatively analyze the backtest performances of trading disciplines applied in various portfolio baskets (Bist 30, 50 and 100) for different investment periods (short term – ytd and long term). According to the results of the analysis, it has been determined that in all trading disciplines, the investor has a higher return than the benchmark indicator in a 5-year term, that is, they can earn abnormal returns. Also, the return in the 5-year term is much higher than the 1-year and YTD returns. In the P / E & MA model, the Bist - 50 index in the 5-year period and the Bist - 100 index in the 1-year period provide the maximum return, while according to the P / E model, the Bist-30 and Bist -50 indices provide optimum returns in all maturity options. Based on these findings, it can be expected that if the trading disciplines used in this study are applied in a long term such as 5 years and on the portfolio basket consisting of Bist-30 and Bist-50 industrial stocks, it will maximize returns. In terms of risk and return, in YTD period, the sharpe and treynor ratios of the model portfolio formed in all trading disciplines except M /B trading discipline were lower than in 1 year in the 5-year investment period. This situation arose due to the increased risk of the portfolio as a result of the extended maturity and is in line with our expectations.

This paper proposes a new strategy for portfolio selection in the Brazilian equity market with the use of multifractal detrended fluctuation analysis (MF-DFA) as a mechanism to select assets based on their efficiency levels. Empirical analysis uses daily prices to compose minimum variance (MVP) and maximum Sharpe ratio (MSR) long-only portfolios, and also includes their performances during the COVID-19 pandemic. MF-DFA indicated a multifractal nature for asset price returns, generally associated with long-term persistence. The strategy using the most efficient equities resulted in portfolios with lower levels of systematic risk (betas), indicating that the lack of efficiency is related to higher sensitivity to macroeconomic and conjuncture changes. The MVP portfolio produces higher performance than the alternatives in terms of risk and return. Finally, during the COVID-19 pandemic, besides its consistent negative impacts, MVP and MSR portfolios verified lower losses than the IBOVESPA.

Purpose - Increasing uncertainty in financial markets has rised risk perception and it has affected stock returns. In this context, the relationship between risk and stock return in the framework Modern Portfolio theory for Turkish football teams traded in financial markets. Design/methodology/approach - The purpose of this paper is to analyze The study consists of two stages. In the first stage, the most suitable GARCH models for the return series were estimated. In the second stage, the estimated GARCH models were used as risk indicators, and the causality relationship between risk and return was tested through the Fourier granger causality test. Return series were calculated by using FENER.IS, TSPOR.IS and GSRAY.IS’s daily closing maket prices. The study covers the periods 04.05.2005-10.03.2021. Findings - The best model for Fenerbahce is ACGARH (1,1,1) model, the best model Galatasaray is EGARCH model and the best model for Trabzonspor is APGARCH (1,1,1). In addition, there is causality relationship from risk to stock return. Discussion - The results obtained from the study indicate that the changes in risk perception can be used as a leading indicator for the stock returns of football teams.

In this paper, we examine the impact of including environmental, social and governance (ESG) criteria in the allocation of equity portfolios. We focus on the risk and return characteristics of the resulting ESG portfolios and investment strategies. Two specific measures are considered to quantify the ESG performance of a company; the ESG rating and the greenhouse gas (GHG) emission intensity. For both measures, we carry out empirical portfolio analyses with assets in either the STOXX Europe 600 or the Russell 1000 index. The ESG rating data analysis does not provide clear-cut evidence for enhanced performance of portfolios with either high or low ESG scores. We moreover illustrate that the choice of rating agency has a significant impact on the performance of the resulting ESG-constrained portfolios. Secondly, we study the impact of GHG emission reductions and increases. We show that emission reductions do not necessarily lead to increased risk or diminished returns, which gives confidence in a smooth transition towards the green economy pursued by the European Green Deal.

In this paper the concept of quantile-based optimal portfolio selection is introduced and a specific portfolio connected to it, the conditional value-of-return (CVoR) portfolio, is proposed. The CVoR is defined as the mean excess return or the conditional value-at-risk (CVaR) of the return distribution. The portfolio selection consists solely of quantile-based risk and return measures. Financial institutions that work in the context of Basel 4 use CVaR as a risk measure. In this regulatory framework sufficient and necessary conditions for optimality of the CVoR portfolio are provided under a general distributional assumption. Moreover, it is shown that the CVoR portfolio is mean-variance efficient when the returns are assumed to follow an elliptically contoured distribution. Under this assumption the closed-form expression for the weights and characteristics of the CVoR portfolio are obtained. Finally, the introduced methods are illustrated in an empirical study based on monthly data of returns on stocks included in the S&P index. It is shown that the new portfolio selection strategy outperforms several alternatives in terms of the final investor wealth.

The primary purpose of investors is maximizing the utility that is characterized by two essential criteria include risk and return. Regarding investors' uncertainty about the future, one of the main ways to reduce risk is to diversify the investment portfolio. In this research, we proposed an index conducted by Euclidean distance for assessing portfolio diversity. Besides, we designed a multi-objective model to select optimal stock portfolios with considering value at risk (VaR), which is one of the critical indicators of unacceptable risk, portfolio Beta as systematic risk, and portfolio variance as unsystematic risk simultaneously. The model presented in this paper aims to maximize diversification while minimizing value at risk and stock risks. Furthermore, maximizing returns are considered as a limitation of this model. Since the proposed model is nonlinear and concerning computational complexity, it is NP-hard; therefore, we utilized the PSO and the GE metaheuristic algorithms that are improved for solving multi-objective problems to solve the model. The results of the model implementation in multiple iterations showed that the average yield of selected portfolios by the model is higher than the desirable condition. The evaluation of stock performance indicators also shows the satisfactory performance of the multi-objective model.

High return correlations between assets have widely and plausibly been said to detract from active performance. The concept of Breadth supports this view. Buckle, however, reported that these correlations improve active performance. Based on three approaches, we report that Buckle is correct. First, we introduce a model of active investing that includes return correlations between assets. Design formulae give the time averages of active risk and return and other portfolio characteristics. Next, we show that Monte Carlo simulations confirm the conclusions of the model. Finally, we provide the investment intuition behind the conclusions. All else equal, active managers should be more aggressive when return correlations are expected to be high, not low. A related widely held belief is that return forecasts with lower correlations between assets provide better performance. The model also includes these correlations. The same approaches show that this consensus too is incorrect: all else equal, correlations of return forecasts between assets improve performance. The design formulae can be used for operational strategy design aimed at performance improvements. As examples, we use it to exploit differences in predictive power between sectors and to efficiently incorporate Environmental, Social and Governance controls into active security selection.

In this article, the authors propose a novel option strategy that is better (in terms of success rate, profitability, risk, and return) than the short straddle option strategy on the Indian stock market index, Nifty. By using the trigonometric ratio tan θ, an angle is formed between the monthly points earned/lost by the short straddle option strategy and the monthly change in the price of the Nifty index. Further, the angle for the next month is forecast by using the technique of the 14-month moving average. After calculating the forecasted angle by the 14-month moving—average technique, the related angle (of forecasted angle) in different quadrants is calculated. The angles of quadrants one and two are considered because short straddle is profitable in quadrants one and two and hence the angles of quadrants three and four have been ignored. By using the total straddle premium of the forecasting month, the upside and downside levels of Nifty of that month were calculated where this forecast angle will form. After finding the upper level and lower level of Nifty, the authors propose a strategy (angled short straddle strategy) that involves the selling of a call and put option. They compare the proposed strategy against the short straddle option strategies based on the success rate, overall profitability, risk, and return in the Indian stock market. The result indicates that the proposed strategy has an 18.06% higher success rate than the short straddle strategy. In terms of overall profitability, the proposed option strategy is more profitable by 57.22% than the short straddle strategy. They also compare the risk and return of both strategies, and their results show that the proposed strategy has lower risk and higher return than the short straddle option strategy; these results were verified by the regression analysis. In the end, the results of the success rate have been verified by the time-series plot of India VIX and realized volatility. Due to the higher success rate and better profitability, the proposed option strategy can be consistently applied by any trader on the Nifty for profit generation. TOPICS:Options, emerging markets, performance measurement, wealth management Key Findings ▪ The angled short straddle option strategy is more successful and profitable than the short straddle option strategy in the Indian stock Index (Nifty). ▪ The Nifty index has less bullish than bearish volatility behavior. ▪ The Nifty index has range-bound properties. ▪ This is the first article where trading results are based on angle tan θ.

Is the market portfolio consistent with the investors’ preferences for risk and return in the capital markets? The answer to this question is not so simple: on the one hand, the market portfolio (which is derived from a minimization of a coherent risk measurement) is an efficient portfolio in terms of risk and return and therefore should be consistent with the investor’s preference. On the other hand, since none of the current risk indices are considered to be coherent risk measurements, the market portfolio might not be consistent with the investors’ preference. This research attempts to fill this gap by invoking the Lorenz curve ranking method combined with compatible statistical tests, in order to rank the S&P 500 Index and its components in 2014–2017. We conclude that the S&P 500 Index is not considered to be the market portfolio from the investors’ point of view, but rather seen as another asset. In addition, we conclude that the investors exhibit a decreasing risk aversion behavior in ranking financial assets, which suggests that they are willing to take risks for higher rewards. This methodology presents a unique way to empirically examine the theoretical preference relation of von Neumann and Morgenstern.

The authors propose a framework for analyzing the risk and return impact of environmental, social, and governance (ESG) investing across regional equity markets. By examining the performance of ESG through passive index exposure via the MSCI ESG Leaders methodology, we seek to determine: 1) whether an ESG investment has generated meaningful, statistically significant alpha in the single-index model, 2) if any such alpha does exist, whether it is simply explained by known risk premia, 3) in regions where ESG has empirically added meaningful alpha, which of the three pillars, E, S, or G, has been the main determinant of this alpha, and whether the sensitivity to each of the three pillars has changed over time, and 4) the extent to which any ESG alphas are correlated across regions. TOPICS:ESG investing, portfolio theory, equity portfolio management, behavioral financial, theory, in portfolio management Key Findings • We propose a framework for analyzing the risk and return impact of environmental, social, and governance (ESG) investing across regional equity markets. • Our framework examines empirical ESG returns via MSCI ESG Leaders methodology to determine 1) statistical significance of alpha, 2) degree of exposure to other known risk premia, 3) sensitivity of alpha to E, S, or G pillars, and 4) interaction of regional ESG alphas for global investors. • We conclude through our three-step framework that 1) statistically significant empirical alpha exists in EM and Canada, 2) size and values factors do not explain much of this empirical alpha, 3) historical sensitivity to pillars is largely weighted toward governance, and 4) finally that regional MSCI ESG Leaders alphas are largely uncorrelated despite utilizing a consistent methodology.

A simple asset pricing model with both endogenous stock market participation and subjective risk can explain the negative cross-country correlation between participation rates and the volatility of excess returns, along with the time-varying participation rates in the data. Belief-driven learning dynamics can explain the interplay between participation rates, subjective risk, and stock price volatility. When agents adaptively learn about the risk and return, my model generates 25% of the excess volatility observed in US stock prices, while also matching key moments. With learning about risk, excess volatility of stock prices is driven by fluctuations in the participation rate that arise because agents’ risk estimates vary with prices. I find that learning about risk is quantitatively more important than learning about returns.

By viewing portfolio optimization as a cooperative game played by the assets minimizing risk for a given return, investors can compute the exact value each security adds to the common payoff of the game. This is known the Shapley value that imputes the contribution of each asset, by looking at all the possible portfolios in which securities might participate. In this paper I use the Shapley value to decompose the risk and return of optimal portfolios that result from minimizing ordinary least squares. These regression portfolios are identical to tangency portfolios obtained by maximizing the Sharpe ratio of holdings on the mean-variance efficient frontiers. The Shapley value of individual assets is computed using the statistics resulting from the regressions. The value imputation prices assets by their comprehensive contribution to portfolio risk and return. This procedure allows investors to make unbiased decisions when analyzing the inherent risk of their holdings. By running OLS regressions, the Shapley value is calculated for asset allocation using Ibbotson’s aggregate financial data for the years 1926–2019.

This study proposes an investment recommendation model for peer‐to‐peer (P2P) lending. P2P lenders usually are inexpert, so helping them to make the best decision for their investments is vital. In this study, while we aim to compare the performance of different artificial neural network (ANN) models, we evaluate loans from two perspectives: risk and return. The net present value (NPV) is considered as the return variable. To the best of our knowledge, NPV has been used in few studies in the P2P lending context. Considering the advantages of using NPV, we aim to improve decision‐making models in this market by the use of NPV and the integration of supervised learning and optimization algorithms that can be considered as one of our contributions. In order to predict NPV, three ANN models are compared concerning mean square error, mean absolute error, and root‐mean‐square error to find the optimal ANN model. Furthermore, for the risk evaluation, the probability of default of loans is computed using logistic regression. Investors in the P2P lending market can share their assets between different loans, so the procedure of P2P investment is similar to portfolio optimization. In this context, we minimize the risk of a portfolio for a minimum acceptable level of return. To analyse the effectiveness of our proposed model, we compare our decision‐making algorithm with the output of a traditional model. The experimental results on a real‐world data set show that our model leads to a better investment concerning both risk and return.

The theory of capital market is to deal with the equilibrium relationship between risk and expected return on risky assets. Based on the theory, the present study investigates the effects of sectoral size (sectoral capitalization) on risk and expected return for the period of 2000-2004 as monthly. Multifactor model is utilized in the study using the Arbitrage Pricing Theory in analyzing the effects of sectoral size on the risk and return by utilizing the ordinary least square estimation procedure. The findings indicate that the firm size or sector have insignificant effects on firm or sectoral return in the Brazilian stock market.

This paper examines cross-sectional relations between ex ante expected returns and betas. As a proxy for ex ante expected returns, we use implied returns obtained from the risk-adjusted option pric...

Managers are often evaluated and selected on the basis of their portfolio’s stand-alone risk and return properties. This is too narrowly focused. The basics of mean-variance optimization tells us that covariance and the relationship of a security’s returns within the context of the overall portfolio is important. We illustrate how the basics of portfolio construction with individual securities also applies to building a portfolio of managers. First, we show the statistical properties of different asset classes, as more and more managers are used to get exposure to the asset class. Depending on the asset class, tracking error, relative skewness, and relative excess kurtosis improve at different rates. This is important for considering how many managers may be necessary to get exposure to an asset class. We then illustrate an optimization method of combining the managers. Although the managers, on their own, may not have statistically positive alphas, they can be combined in a way to improve the likelihood that the portfolio of managers has a statistically positive alpha. The optimization method is not a substitute for traditional due diligence. It is just one additional tool useful for manager selection and portfolio construction. TOPICS:Wealth management, portfolio construction, manager selection Key Findings • Combining managers can reduce tracking error, but the relationship between tracking error reduction and manager addition depends on the asset class. Specifically, it depends on the average tracking error and average correlation of excess returns within an asset class. • A slightly modified Sharpe ratio is equivalent to a t-value. An information ratio (with Jensen’s alpha in the numerator) can also be interpreted as t-value. Maximizing these ratios is equivalent to maximizing confidence that the excess return or alpha will be positive. • Individual managers with no or even negative alpha can be combined to give positive alpha.

This article investigates the application of factor investing in corporate bonds. The authors analyze five different long-only factor investment strategies (Value, Equity Momentum, Carry, Quality, Size) within the USD investment grade and high yield market. These factors can explain a significant part of the cross-sectional variation in corporate bond excess returns. Combinations of the single factors turn out to be superior in risk-adjusted terms. Because the correlations between the single factors are low, a combined multi-factor signal benefits from diversification among the factors. A signal blending strategy is particularly suitable for active approaches targeting high alpha. This strategy leads to alphas up to 1.27% within investment grade and 5.90% within high yield. In contrast, a portfolio blending strategy is better aligned with more passive approaches, targeting low turnover and low tracking error. TOPICS:Factor-based models, style investing, performance measurement Key Findings • The authors find a strong positive relationship between Value, Equity Momentum, Size, Carry and Quality and future returns for USD denominated corporate bonds. • Due to the attractive correlation structure of the single factors, a multifactor strategy enhances the risk return profile even further. • The authors’ multifactor strategy leads to alphas up to 1.27% (5.90%) in IG (HY) even after transactions costs are taken into account.

Yazar, Evrim Erdogan/0000-0001-8689-4137 WOS: 000559811400007 This study focuses on the brand value-shareholder return relationship using the approach of Madden et al. (2006) based on Aaker (1991) and compares "Strong Brands Portfolio", created through brand values in "Turkey's Most Valuable Brands" annual report of Brand Finance published between 2007-, 2015, within alternative benchmark portfolio in terms of risk and return. in this context, although the analysis made by different weighting methods over asset pricing models has found that a portfolio of strong brands may provide significant abnormal returns with significantly lower market risk for shareholders, it is understood that the brand values published by Brand Finance are not fully priced in the Turkish stock market by considering the magnitude of that value. Copyright (c) 2019, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V.

This research study analyzes the variation in monthly returns of securities for companies listed in Pakistan Stock Exchange-PSX (Formerly known as KSE). The Capital Asset Pricing Model (CAPM) of SLB has provided a method for researchers and experts to forecast the risks and returns. The main purpose of CAPM is to estimate beta of security to explain how much security is aligned or sensitive with the movement or changes in the market return. The research is conducted by means of monthly market capitalization of companies; portfolios are formed and the role of idiosyncratic risk in explaining the variations in the stock returns have been studied. With the same portfolios, the relationship of risk and return relationship has also been analyzed. This empirical analysis is conducted for the period of May 2010 - April 2014. Data analysis reveal that the idiosyncratic risk is a significant factor in explaining the stock returns. Capital Asset Pricing model is rejected in this study context because of positive and significant intercept in all portfolios. The research findings strongly support Chan and Chui (1996) and Strong and Xu (1997), assertions that the relationship between beta and security returns is weak. Therefore, CAPM is an empirically anemic model to be used in the Pakistani market.

Investment is an important thing for everyone as an alternative in the storage of funds and to gain profit. Investors are faced with two things in making investments, namely risk and return. Diversification is one way to minimize risk by forming a portfolio of stocks. This study aims to determine and analyze optimal portfolio selection using single index model.The type of research used in this research is descriptive research with quantitative approach. The object of this research is using the company collected in BISNIS-27 index, with the research period from 2011 to 2015. The data collection method used is purposive sampling, where the company to be analyzed is only companies listed in BISNIS-27 stock index consistently during nine (9) periods from February 1, 2011 to July 31, 2015, and stocks are not suspended during the period February 1, 2011 to July 31, 2015. Data required in the analysis of data is historical price of stocks and IHSG (Indeks Harga Saham Gabungan). Analyzer using microsoft excel.

Purpose The purpose of this paper is to compare simple dynamic withdrawal strategies with the static withdrawal method, examining not only failure rates and ending wealth but also spending. All withdrawal strategies are adjusted for the Internal Revenue Service’s (IRS) required minimum distribution (RMD). In addition, this study investigates the use of small company stocks (SCS) in place of large company stocks (LCS). Results indicate SCS portfolios are superior to large. When returns are poor, some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies. Design/methodology/approach Using historical overlapping periods, different withdrawal strategies are examined. Previous studies focused on failure rates and ending wealth. As discussed in Milevsky (2016) different statistical distributions can have similar tail properties (prob of failure) but dissimilar risk and return profile. The detailed examination of both spending and use of small stocks advances the literature in this area. Findings Results indicate that use of small stocks is superior to using large stocks in the portfolios. When US historical stock returns are adjusted downward, there is the potential that some dynamic strategies will not ensure income for life. This study demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies. Originality/value This paper is the first to examine, in detail, annual spending results for the retiree. Second, it is shown that, overall, SCS are superior to LCS for all stock/bond allocations. Even though absolute downside risk increases slightly, this increase in downside risk is dominated by the upside potential. In other words, the positive skewness of small stock returns along with the cumulative effects of compounding at a higher rate increases both the available wealth for spending and ending wealth. Third, IRS’s RMDs are taken into account for every withdrawal strategy examined. Lastly, it demonstrates that the simplest dynamic strategy is superior to two popular dynamic strategies.

We propose a new modeling approach for the cross section of returns. Our method, Instrumented Principal Component Analysis (IPCA), allows for latent factors and time-varying loadings by introducing observable characteristics that instrument for the unobservable dynamic loadings. If the characteristics/expected return relationship is driven by compensation for exposure to latent risk factors, IPCA will identify the corresponding latent factors. If no such factors exist, IPCA infers that the characteristic effect is compensation without risk and allocates it to an “anomaly” intercept. Studying returns and characteristics at the stock-level, we find that five IPCA factors explain the cross section of average returns significantly more accurately than existing factor models and produce characteristic-associated anomaly intercepts that are small and statistically insignificant. Furthermore, among a large collection of characteristics explored in the literature, only ten are statistically significant at the 1% level in the IPCA specification and are responsible for nearly 100% of the model’s accuracy.

Purpose The purpose of this paper is to introduce an augmented high-order capital asset pricing model (AH-CAPM) as a new risk-based model to price stocks. Design/methodology/approach The AH-CAPM is defined as a linear model with high-order marginal moments and co-moments from the joint distributions of the sorted stock portfolio returns and the market return. Findings The performance of the AH-CAPM is tested in the Chinese and US stock markets. Empirical results show that the high-order marginal moments and co-moments from the joint distributions in AH-CAPM contain the risk and return information implied by the Fama–French factors, indicating it as a better risk measurement. Moreover, the AH-CAPM performs better than the Fama–French three-factor model and the Carhart four-factor model in both the Chinese and US stock markets. Originality/value Overall, this study introduces a new asset pricing model with better measurements to incorporate risk information in the stock market.

PurposeThis study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios.Design/methodology/approachThe mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios.FindingsThe authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios.Originality/valueThis is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.

One of the metrics that portfolio managers often use to gauge the success of factor investing strategies is the factor-to-specific risk ratio. There is much confusion, however, regarding its use and interpretation. Specifically, what is a reasonable level that investors might expect for long–short versus long-only factor portfolios, and how much deviation is normal? Are there limitations with this metric, and is a higher ratio necessarily always better? The authors find that even pure long–short factor mimicking portfolios do not have 100% of risk coming from factors. Using the value factor as an example, a long–short factor mimicking portfolio has an average ex ante factor-to-total-variance ratio of 80%. The long-only version has an even lower ratio of 61%. More importantly, factor-to-total-variance ratios are misleading in that they do not reflect how much risk and return come from the targeted factors versus the nontargeted factors. Counterintuitively, a simple quintile portfolio can have a very high ratio because of exposure to nontargeted factors. Other metrics such as exposure per unit of tracking error, percent of variance contribution, and the factor efficiency ratio are needed to understand how much exposure the portfolio has to the targeted factor and how much risk comes from the latter. TOPICS:Analysis of individual factors/risk premia, factor-based models, style investing Key Findings • Even long–short factor mimicking portfolios that exactly replicate the pure factor returns do not have 100% of risk coming from factors. Using the value factor, a long–short factor-mimicking portfolio has an average ex ante factor-to-total-variance ratio of 80%. • Different aspects of portfolio construction can affect all of these metrics. Portfolio tracking error, misalignment between the estimation and investment universes, and factor definition misalignment can significantly affect the factor-to-total-variance ratio. • Factor-to-total-variance ratios are misleading in that they do not tell us how much risk and return comes from targeted factors. Other metrics such as exposure per unit of tracking error and percent of variance contribution are needed.

The existing literature on DEA (Data Envelopment Analysis) for evaluating fuzzy portfolios usually takes risk as an input and return as an output. This assumption is actually not congruent with the real investment process, where the input is the initial wealth and the output is the corresponding terminal wealth. As for the risk and return, which are essentially two indicators derived from the terminal wealth, both should be regarded as outputs. In addition, few studies have employed the diversification model (nonlinear DEA) to estimate the fuzzy portfolio efficiency (PE), despite the fact that there are many studies available within the framework of classical probability theory. Further, the relationship between DEA and diversification models needs to be defined. In this paper, we take the initial wealth as an input, while the return and risk of terminal wealth are taken as desirable and undesirable outputs, respectively. We construct different evaluation models under the fuzzy portfolio framework. The relationships among the evaluation model based on a real frontier, the diversification model and the DEA model are investigated. We show the convergence of the diversification and DEA models under the fuzzy theory framework. Some simulations as well as empirical analysis are presented to further verify the effectiveness of the proposed models. Finally, we check the robustness of the evaluation results by using the bootstrap re-sampling approach.

In this article, the author provides an alternative to traditional portfolio re-balancing based on changes in asset market values, one informed by equity characteristics. The logic of policy portfolio re-balancing is applied to a framework that uses assets’ 12-month rolling average characteristic values and return volatilities as inputs and re-balances toward those assets that exhibit relatively high characteristic-value-to-volatility ratios. Three important practical constraints are applied to the author’s re-balanced portfolios. The first two constraints relate to asset weights and limit the degree to which a re-balanced portfolio’s individual asset class positions and portfolio-level asset allocation can deviate from a fixed-weight reference policy portfolio. The third constraint is that any re-balanced portfolio is required to have a volatility approximately equal to the representative fixed-weight policy portfolio. The author shows that the longer-term, full-sample performance, both risk and return, of even tightly constrained characteristic-driven policy portfolios is superior to the standard procedure of re-balancing to fixed weights. These results are further validated by measuring the performance of characteristic-driven policy portfolios over rolling sub-samples of the original dataset. In the final section of the article, a meta-optimization technique is introduced that allows investors to select one characteristic-driven policy portfolio among several as the best compromise asset allocation that comes closest to maximally satisfying each of their respective objectives. TOPICS:Portfolio theory, portfolio construction, equity portfolio management Key Findings • Policy portfolios based on re-balancing toward assets that exhibit relatively high characteristic-value-to-volatility ratios are shown to produce better performance relative to standard policy portfolios that re-balance towards fixed policy portfolio weights. • Characteristic-driven policy portfolios exhibit superior performance compared to standard policy portfolios even when the characteristic-driven policy portfolios are highly constrained with respect to their individual asset class positions, portfolio-level asset allocations and volatility profiles. • By means of a simple meta-optimization technique, it is possible to select one characteristic-driven policy portfolio among several as the one that comes closest to maximally satisfying each of their respective objectives.

Mutual funds syariah are other investment opportunities with measurable risk and return is high enough with enough capital affordable to the community. Mutual funds syariah have an Investment Manager with the ability and knowledge of the market it is or will happen. Therefore, mutual funds syariah selected by investors because it is cheap, easy and "managed by the experts". This research analysted do stock selection skill, market timing ability, Turnover ratio and Cashflow can influence the performance of equity mutual funds syariah in Indonesia. The data used in this research are data on financial statements , Net Asset Value (NAV), SBI, IHSG, yearly data and prospectus of 10 equity mutual fund syariah that were sampled during this research report from 2011-2014. As a research methodology, we used F test and t test to examine research’s hypothesis, also used assumption classic test there are normality test, autocorrelation test, heteroscedasticity test and multicolinearity test. These results can be viewed on multiple regression analysis and the coefficient of determination, the R value of 0.513 means the relation between the stock selection skill, market timing ability, Turnover ratio and Cashflow to profitability by 51.3%, meaning that the relationship between variables was most closely. Adjusted R Square value of 0.545 which means 54,5% achievement of profitability can be explained by the stock selection skill, market timing ability, Turnover ratio and Cashflow. The remaining 45,5% can be explained by other factors not examined in this research..

We propose a parsimonious statistical model of firm competition where structural differences in the strength of competitive pressure and the magnitude of return fluctuations above and below the system-wide benchmark translate into a skewed Subbotin or asymmetric exponential power (AEP) distribution of returns to capital. Empirical evidence from US data illustrates that the AEP distribution compares favorably to popular alternative models such as the symmetric or asymmetric Laplace density in terms of goodness of fit when entry and exit dynamics of markets are taken into account.

The association between risk and return is a significant concept in finance that has been studied in the past to a large extent. The stock market volatility is closely associated with the risk. The current study examines the intraday volatility pattern of stock market of Saudi Arabia by reviewing the stocks of Tadawul All Share Index (TASI). We obtain return data at 5-minute frequency from the SASEIDX starting on 25 October 2017 and ending on 9 May 2018. We examine the stock market volatility by using different symmetric and asymmetric GARCH models and observe that, the symmetric GARCH models showed a significant positive association between risk and return. Similarly, the asymmetric GARCH models show that the estimates were significant and the leverage estimate was negative and significant, indicating a no-leverage effect in the return series. Moreover, the asymmetric results suggest that negative shocks do not entail to future higher volatility than positive shocks. Therefore, the symmetric and asymmetric GARCH models are comfortable to capture the volatility of Saudi stock market from Intraday data.

This paper examines whether the intertemporal tradeoffs between risk and return explain mean reversion in sovereign CDS spreads. I test how mean reversion prevents the explosiveness of CDS spreads in Europe. The relationship between risk and return has been widely studied in finance. The results show that, during the pre-crisis period, sovereign CDS spread changes were more consistent with the mean reversion hypothesis for most European countries. I also find strong evidence that the intertemporal tradeoffs between volatility and return explain in part the mean reversion in the markets for European CDS. Moreover, I show that during the crisis period, CDS spreads’ changes are consistent with the random walk hypothesis and mean aversion is more important for large holding periods.

Research Question: The investigation of the optimal allocation of current assets. Motivation: Current assets investment is a decision process which affects firm value. In this paper, we develop a framework that encompasses these decisions by taking into consideration the trade-off between risk and return. Idea: We build up a model implemented in two stages. In the first stage, using random coefficient modeling on panel data, we obtain the estimates of the expected returns and standard deviations for cash holdings, inventories and receivables along with the correlations between them. Having these estimates on hand we move on to the second stage to determine the optimal allocation of current assets portfolio and construct the efficient frontier of the possible combinations of the current assets’ elements. Data: For the purposes of our study we use financial data from Greek manufacturing firms, drawn from their annual income statements and balance sheets. Firms are classified into the manufacturing industry for the years 2003 to 2014. Tools: In the first stage we use random coefficient modeling on panel data while in the second stage mean-variance analysis is employed. Findings: By applying the model in the Greek manufacturing sector we find that the minimum-variance portfolio of the average firm of our data set has an expected return of 10.00% with a 6.14% standard deviation (risk) and consists of 13% cash and cash equivalents, 29% inventories and 58% receivables. Contribution: Our model would be useful to assess and monitor firms’ current assets investments and may be used in the formulation of sound current assets policies and procedures.

Using monthly stock returns from 28 emerging market countries and a total sample period of 21 years, we investigate the predictive power of a broad set of factors. We document that the factor definitions of the Fama and French (2015) five-factor model are less robust compared to alternative factor definitions. In contrast, the anomalous returns associated with cash flow-to-price, gross profitability, composite equity issuance, and momentum are pervasive as they show up in equal- and value-weighted portfolio sorts as well as in cross-sectional regressions. In contrast to financial theory and in line with previous findings, we do not find a positive cross-sectional relationship between risk and return. Finally, return forecasts derived from the alternative factor definitions are superior in their out-of-sample predictive ability to the ones derived from the five-factor model.

PurposeThis paper aims to investigate patterns in UK stock returns related to downside risk, with particular focus on stock returns during financial crises.Design/methodology/approachFirst, stocks are sorted into five quintile portfolios based on the relevant beta values (classic beta, downside beta and upside beta, calculated by the moving window approach). Second, patterns of portfolio returns are examined during various sub-periods. Finally, predictive powers of beta and downside beta are examined.FindingsThe downside risk is observed to have a significant positive impact on contemporaneous stock returns and a negative impact on future returns in general. In contrast, an inverse relationship between risk and return is observed when stocks are sorted by beta, contrary to the classic literature. UK stock returns exhibit clear time sensitivity, especially during financial crises.Originality/valueThis paper focuses on the impact of the downside risk on UK stock returns, assessed via a comprehensive sub-period analysis. This paper fills the gap in the existing literature, in which very few studies examine the time sensitivity in relation to the downside risk and the risk-return anomaly in the UK stock market using a long sample period.

This paper explores the selection of optimal portfolio by replacing the standard Mean-Variance model by Mean-Minimum Return Level (MRL) framework and adding one important dimension—expectation of bounded First Passage Time (FPT) towards the MRL. To measure how much a given portfolio is exposed to risk, the new model can capture both, the amount of the largest possible loss at a certain confidence level and time to such an event occurring. The novelty of this paper is the introduction of bounded first passage time towards MRL and taking its expectation into consideration as an additional factor in portfolio selection decision making. Assuming that the asset price dynamics follow multi-dimensional Geometric Brownian Motion with drift, we obtain a portfolio wealth process for multiple assets and we evaluate the lowest possible value to which it can drop by a high confidence level. Then we extend our examination of the optimal portfolio selection by ultimately obtaining the efficient surface of risky portfolios. As a result, the paper shows that the third dimension can make a significant difference while choosing the asset weights compared to classical models ignoring the portfolio return paths as long as they achieve a desired combination of risk and return.

This paper explores the risk and return characteristics of a large and diverse cross section of 254 cryptocurrencies that differ in traded volume and main usage. First, we find long memory, volatility clustering, heavy tails, and negative leverage effects to be common features of cryptocurrencies’ return behavior. Second, GARCH models accounting for these features provide the best goodness-of-fit properties. About 80% of cryptocurrencies are well described by Student's t (stud) GARCH specifications with the TGARCH-stud chosen for about 20% of the sample. We then compare out-of-sample 1%-Value-at-Risk (VaR) forecasts under 48 specifications using standard backtesting procedures. Heavy-tailed VaR specifications outperform all normally distributed alternatives. Throughout the analysis, differences emerge when results are broken down by traded volume and usage categories. Overall, our findings have important implications for investors, policymakers, and regulators for the understanding and measuring of market risk in the cryptocurrency market.

We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness on the market price of risk. We derive the moment and equilibrium equations, specifying skewness price of risk as an additive component of the effect of variance on mean expected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index returns from January 1980 to mid-October 2020, our results show that the coronavirus crisis generated a deeply negative reaction in the skewness and total market price of risk, more negative even than the subprime and the October 1987 crises.

The fundamental research question associated with the asset pricing framework relates to the risk and return relationship in return predictability. We introduce the human capital component to the Fama–French five‐factor model and derive an equilibrium six‐factor asset pricing model in an intertemporal framework. The study comprises the Japanese monthly time‐series dataset for 24 years spanning November 1990 to December 2017. The Generalized method of moments estimation and Gibbons‐Ross‐Shanken test results confirm that the six‐factor model yields better estimates and equally outperforms the Fama–French three‐factor, Carhart four‐factor, and Fama–French five‐factor models in explaining the variation in excess return on Fama–French variant portfolios. The core results and findings hold when we use labor income growth as an alternate measure of human capital in the six‐factor asset pricing model. Full Text Available Here: https://doi.org/10.1002/cfp2.1109

This study was structured to see and determine the average difference in Risk and Return between conventional stocks in the LQ45 index and Islamic stocks I the Jakarta Islamic Index in 2015-2019. Risk and return is something that investors need to know, especially. This type of research is a descriptive study with a quantitative approach. The sample used in this study amounted to 8 conventional stocks and 8 islamic stocks which were taken from the monthly stock price data during the study period. The Kolmogorov-Smirnov normality test is used to determine whether the different test will use parametric or nonparametric statistics. The results of the analysis show that the risk in islamic stocks is higher than the risk of conventional stocks. As for return, conventional stocks have a higher return when compared to islamic stocks returns. In statistical tests using the Independent t-test with a significance level of 5%, the results obtained for risk and return are not the difference between conventional stocks and islamic stocks. Keyword : Risk; Return; Conventional Stocks; Sharia Stocks

PurposeThis paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables.Design/methodology/approachMonthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices.FindingsThe results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks.Research limitations/implicationsThe shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data.Practical implicationsAs the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies.Originality/valueFirst, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.

An empirical puzzle in financial markets, known as the low-risk anomaly, is that riskier assets earn lower risk-adjusted returns than less risky assets. Theories for this phenomenon focus either on market frictions, such as leverage costs, or non-traditional preferences for lottery-like payoffs. We relate the low risk anomaly to the Favorite-Longshot Bias in betting markets, where returns to betting on riskier "longshots" are lower than returns to betting on "favorites," and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs. We find that a model with reference-dependent preferences, featuring probability weighting and diminishing sensitivity, two features of Cumulative Prospect Theory, best fits the data, and is able to capture the choice and the amount to bet. Calibrated parameter values for probability weighting and diminishing sensitivity that capture financial markets facts can simultaneously explain the betting market facts. However, explaining the choice to bet is at odds with loss-aversion, a feature of Cumulative Prospect Theory often used in financial markets applications.

Optimal Design of Combined Contingent Claims: Theory and Applications. In “Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management”, Paolo Guiotto and Andrea Roncoroni develop a normative framework for the optimal design, value assessment, and operations management integration of financial derivatives. Most business and operating revenues entail a mix of financially insurable and noninsurable risk. A risk-averse firm may face them by positioning in a pair of financial derivatives with optimal bespoke payoff functions; one claim is written on the insurable term, and the other claim is written on any observable index exhibiting correlation to the noninsurable term. On a theoretical ground, the authors 1) state the problem in a general setup and prove existence and uniqueness of the optimal pair of combined claims, 2) show that the optimal payoff functions satisfy a Fredholm integral equation, and 3) assess the incremental benefit the firm obtains by switching from the optimal single-claim custom hedge to the optimal combined custom hedge they propose. On an experimental ground, they show that 1) the optimal combined custom hedge would be empirically relevant for a highly risk-averse firm facing a market shock shown during the first period of the COVID-19 pandemic in 2020, 2) integration with the optimal procurement in a generalized newsvendor model leads to a significant improvement in both risk and return, and: 3) this gain can be traded off for a substantial enhancement in operational flexibility.

Portfolio diversification has long been known and prevalent as a risk minimization method. The goal of this work is to develop a model to use Grey Wolf Optimization (GWO) algorithm to create an investment portfolio with optimal risk-return ratio. The initial set of equities we propose to be selected using Self-Organizing Maps (SOM). We have highlighted the set of factors to use as input variables for SOM. When the equities in portfolio is defined, we need to decide the proportion of our capital distributed among the portfolio equities. For this task we employ the GWO algorithm, which let us find the optimal weights assignment along the portfolio shares based on Mean-Variance portfolio minimization condition. As we know the Variance and Mean respectively express the Risk and Return rating of portfolio. Our research investigates the sensitivity of the GWO algorithm to number of iterations, wolf herd size and shares weight limits. Just knowing these parameters best values, we can expect for optimal portfolio diversification. The model verification was performed on stocks from S&P500 index. The GWO was applied for determining the weights for portfolio equities to get optimal risk-return ratio. The comparison of GWO balanced portfolio performance with the direct investment to S&P500 index, let us conclude the advantage of our proposed model.

We utilize one of the earliest and largest NFT collections to investigate the pricing and the risk-return profile of NFTs. In general, we find that NFTs have higher returns than traditional financial assets. Yet, investing in NFTs comes along with extremely high volatility. The average monthly returns on NFTs range from 6.10% to 44.11%. But their standard deviations fluctuate between 44.35% and 74.57%, leading to a Sharpe ratio comparable to the NASDAQ index. NFT prices surge when there is a drastic increase in demand for alternative investments and a search for yield, especially in a low interest rate environment. We also find that the pricing of NFT largely depends on a token’s scarceness and an investor’s aesthetic preference. Hence, conventional asset-pricing models are unlikely to explain NFT returns. Overall, we provide the first comprehensive analysis that NFTs serve as a novel investment vessel in this Fintech era.

Previous studies document discretionary income smoothing in banks via the association between loan loss provisions and pre-provision earnings. In this study, we argue that there is a large non-discretionary component to the association that is driven by the positive relationship between risk and return. Including loan interest as a control for risk significantly attenuates the coefficient on pre- provision earnings in models explaining loan loss provisions. We draw upon the non-bank literature on earnings smoothing to construct a new measure of discretionary income smoothing. We validate our measure using an exogeneous shock to income smoothing in a short window surrounding the SEC's intervention in bank accounting in the late 1990's. Employing this new measure, we provide evidence that discretionary smoothing is associated with more informative loan loss provisions, inconsistent with the notion prevalent in recent studies that smoothing is associated with lower transparency.