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Different momentum investors use different time horizons, or formation periods, to evaluate prior stock performance. We show that this heterogeneity has important consequences on asset returns. We provide evidence that heightened trading pressure due to the concurrence of the heterogeneous momentum investors contributes to enhanced return autocorrelation. In particular, U.S. "overlapping" momentum stocks - stocks that are in the intersection of the 6-month and 12- month momentum portfolios - display superior medium-term returns to the momentum strategy that neglects the overlap. The differential returns of the "overlapping" momentum portfolio are robust to the inclusion of a broad set of risk factors and interpretations. The results provide insights on the underlying mechanisms of under and over-reaction in financial markets as the result of limited information investment rules and slow information diffusion.

We propose a model that allows for conditional heteroskedasticity in the volatility of asset returns and incorporates current return information into the volatility nowcast and forecast. Our model can capture all stylised facts of asset returns even with Gaussian innovations and is simple to implement. Moreover, we show that our model converges weakly to the GARCH-type diffusion as the length of the discrete time intervals between observations goes to zero. Empirical evidence shows that our model has a better fit, a more efficient parameter estimator as well as more accurate volatility and VaR forecasts than other common GARCH-type models.

This paper shows how one of the most popular savings products in Europe -- life insurance financial products -- shares market risk across investor cohorts. Insurers smooth returns by varying reserves that offset fluctuations in asset returns. Reserves are passed on between successive investor cohorts, causing redistribution across cohorts. Using regulatory and survey data on the 1.4 trillion euro French market, we estimate this redistribution to be quantitatively large: 1.4% of savings value per year on average, or 0.8% of GDP. These findings challenge a large theoretical literature that assumes inter-cohort risk sharing is impossible. We develop and provide evidence for a model in which the elasticity of investor demand to predictable returns determines the amount of risk sharing that is possible. The evidence is consistent with low elasticity, sustaining inter-cohort risk sharing despite predictable returns. Demand elasticity is higher for investors with a larger investment amount, suggesting that low investor sophistication enables inter-cohort risk sharing.

We show that, in the presence of a risk-free asset, the return distribution of every portfolio is determined by its mean and variance if and only if asset returns follow a specific skew-elliptical distribution. Thus, contrary to common belief among academics and practitioners, skewed returns do not allow a rejection of mean-variance analysis. Our work differs from Chamberlain's [Chamberlain G (1983) A characterization of the distributions that imply mean-variance utility functions. J. Econom. Theory 29(1):185–201.] by focusing on the returns of portfolios, where the weights over the risk-free asset and the risky assets sum to unity. Furthermore, it extends Meyer's [Meyer J, Rasche RH (1992) Sufficient conditions for expected utility to imply mean-standard deviation rankings: Empirical evidence concerning the location and scale condition. Econom. J. (London) 102(410):91–106.] by introducing elliptical noise into their generalized location-scale framework. To emphasize the relevance of our skew-elliptical model, we additionally provide empirical evidence that it cannot be rejected for the returns of typical portfolios of common stocks or popular alternative investments. This paper was accepted by Kay Giesecke, finance.

We introduce the concept of risk entanglement in a preference-free setting to jointly explain the exchange rate volatility, cyclicality, and currency risk premia in the data. Risk entanglement specifies a subset of incomplete market models, in which nondiffusive or nonlog-normal shocks to exchange rates are not fully spanned by asset returns. When risks are entangled, there exist multiple pricing-consistent exchange rates, but none of them are equal to the ratio of the stochastic discount factors (SDFs) or their projections. Decoupling the exchange rate from the SDFs allows us to address key FX market patterns that are puzzling in international finance.

How can we optimize for the Sharpe ratio if we only have limited training data? Estimates of mean asset returns are noisy, and this noise hurts the out-of-sample Sharpe ratio of current methods. The minimum-variance portfolio, which ignores mean returns, often has a better Sharpe ratio. We develop a parameter-free and scalable method called AlphaRob for this problem. AlphaRob ’s portfolio is a convex combination of two prespecified portfolios. To select the best combination, AlphaRob fuses robust optimization with a new notion of a portfolio’s regret that accounts for the training data’s size. Our analysis only needs mild assumptions on the distribution of asset returns. AlphaRob significantly outperforms competing methods on several simulated and real-world datasets, even after adjusting for transaction costs. AlphaRob is 7.5 % better on average than the nearest competitor, and 28 % better than the next-best combination portfolio method. Using our regret of regret, we are also able to explain the performance of the minimum-variance portfolio.

This paper explores a channel whereby asset-pricing anomalies can appear as investors alter portfolios according to findings in academic research. In particular, I find that assets with low realized capital asset pricing model (CAPM) alphas outperform those with high alphas, but this finding only appears after the CAPM’s publication in the 1960s. I find evidence consistent with the widespread application of the CAPM generating incentives to tilt portfolios systematically away from low CAPM alpha assets, causing such assets to be undervalued. This paper was accepted by Kay Giesecke, finance.

Interest in impact investing is growing, but investors typically allocate to impact funds and traditional assets independently from one another. We develop an optimizer that brings both types of assets together. Doing so allows investors to maximize impact for a certain level of risk-adjusted returns. For example, we find that investors can allocate 18% more to impact funds on average while keeping risk-adjusted returns constant. Intuitively, analyzing how impact funds and traditional assets interact should allow for better portfolio diversification. As interest in impact investing broadens, tools integrating impact funds into mainstream finance should become more widespread. TOPICS:ESG investing, portfolio construction, performance measurement, wealth management Key Findings ▪ We design a portfolio optimizer that brings together impact investments and traditional assets. Our optimizer has three dimensions: risk, return, and impact. Intuitively, returns from impact funds correlate with traditional asset returns. Taking such correlations into account allows for better portfolio diversification. That is, investors can achieve higher impact without hurting risk-adjusted returns. ▪ Diversification benefits can be large. Our optimizer allows an investor to increase impact by 18% without financial sacrifice. ▪ Demand for impact investing is rising. Tools that help to integrate impact funds into traditional portfolios are therefore useful and likely will become more widespread.

According to modern finance theory and increasing need for efficient investments, we evaluate the portfolio performance based on the data envelopment analysis method. By the fact that stock market’s return distributions usually exhibit skewness, kurtosis and heavy-tails, we consider some appropriate underlying distributions that affect the input and output of the model. In this regard, the multivariate skewed t and the multivariate generalized hyperbolic as the heavy-tailed distributions of Normal mean-variance mixture are applied. The models are inspired by the Range Directional Measure (RDM) model to deal with negative values. The value-at-risk (VaR) and conditional VaR (CVaR) as risk measures are used in these optimization problems. We estimate the parameters of such distributions by Expectation Maximization algorithm. Then we present an empirical investigation to measure the relative efficiency of two sets of seven groups of companies from different industries of Iran stock exchange market. By comparing the results of introduced models with previous RDM approach, we show that how well the distribution of assets affect the performance evaluation.

In this paper, the comparison of using garch (1, 1) and intergrated garch, igarch (1, 1) models on petroleum prices will be examined. This time-varying variation of asset returns as the horizon widens about kurtosis and volatility persistence are calculated and the results shows that petroleum prices dynamics submits more to igarch (1, 1) than garch (1, 1) model.

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.

The work aims at investigating and establishing if Aggregational Gaussianity, (AG) is in the dynamics of petroleum prices. This AG aspect is the phenomenon in which the empirical distribution of log-returns tends to normality (or as the time scale over which the returns are calculated increases). In order to achieve this, the petroleum price series was tested for arch effects. In addition, tests for Aggregational Gaussianity, (AG) were carried out using qualitative (graphical) approach and inferential approach, (involving statistical inference). The study shows that the presence of arch effects does not guarantee existence of AG. It is also observed that qualitative (graphical) approach may suggest normality and hence, presence of AG, on the other hand, inferential approach (involving statistical tests) gives a better picture of the actual conclusion, of the presence (or otherwise) of AG in the data set, with a 99.97% rejection from normality by the three tests-Kolmogorov-simonorv,Shapiro-Wilks, and Anderson-darling. In the circumstance, there is no evidence to confirm a discernible presence of AG in the dynamics of petroleum prices. The non-existence of AG in the study shows the instability in the dynamics of petroleum prices, since one cannot invoke normality as an invariant property this, among other factors, make the economy unstable as it is oil- driven. However, since the highest percentage of the budget for the country is based on the petroleum sales, which as this study reveals is unstable, hence, diversification of the economy is proposed. The softwares used in the work are Eviews 10, Minitab 18, Spss 17, Easy-fit 5.6 professional, and R 3.2.2.

This paper provides details on a classroom experiment that focuses on returns to foreign assets given uncertain future exchange rates. Students are assigned the role of foreign analysts and decide ...

One of the main challenges facing researchers and industry professionals for decades is the successful prediction of asset returns. This paper enriches this endeavor by applying topological metrics of correlation networks to the challenge of financial forecasting. These network‐based metrics are retrieved with the help of graph theory and quantify the interconnectedness of financial assets. In this paper, we show that this network‐based information statistically significantly predicts future asset returns. Because industry professionals are more interested in the economic value‐added of competing forecasting approaches, we also devote our attention to an economic analysis. Considering economic performance metrics, network‐based predictors generate a clear value‐added, which also applies to the multi‐asset allocation case.

We propose a new method to tighten the restrictions on the admissible pricing kernel space. Our framework improves information-based pricing kernel bounds and can be used to test various asset pricing models. This method incorporates higher moments of asset returns and conditional information based on the possible risk-free rate fluctuations. The variation in the risk-free rate is attributed to two possible states of the economy, and the proposed approach identifies these states and improves the pricing kernel bound.

Purpose This paper aims to examine the impact of the Brexit referendum on the risk structure of financial asset prices. Co-movements are analysed using daily price returns of major stock and bond indices as well as commodities and exchange rates from June 2014 to June 2018. The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Design/methodology/approach The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. Findings It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. Research limitations/implications The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies. Originality/value To the best of the authors’ knowledge, research studying the underlying asset co-movements around Brexit does not exist.

PurposeReal estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?Design/methodology/approachThis study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.FindingsThe empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.Practical implicationsThe sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.Originality/valueThis study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert information is available in terms of bivariate marginals. Such expert information allows for one to choose from among the many possible parametric families of bivariate copulas. By considering a neighborhood of distance $\rho$ around the bivariate marginals with the Kullback-Leibler divergence measure, we model the trade-off between conservatism in the worst-case risk measure and confidence in the expert information. Our bound is developed when the only information available on the bivariate marginals forms a tree structure in which case it is efficiently computable using convex optimization. For consistent marginals, as $\rho$ approaches $\infty$, the bound reduces to the comonotonic upper bound and as $\rho$ approaches $0$, the bound reduces to the worst-case bound with bivariates known exactly. We also discuss extensions to inconsistent marginals and instances where the expert information which might be captured using other parameters such as correlations., Comment: 23 pages, 6 figures

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.

This paper introduces time-inconsistent preferences into the standard model of capacity choice and investigates its influences on entrepreneurial firms' investment decisions and asset returns. With...

Based on the survey results for the last 3 years, data on food companies and the minimum turnover of accounts receivable and debt has increased and this has an impact on taking profits in the form of assets. These things can have an impact on the investment invested in shareholders. The purpose of this study is to assess the effect of short-term debt and accounts receivable turnover on asset returns. The samples in this study were 8 companies in the 2015-2019 period. The results of t-test analysis partially show that short-term debt does not have a significant effect on profitability, receivables turnover has a significant effect on profitability. Keywords: short-term debt, accounts receivable turnover, profitability

A reduced-form model is proposed to disentangle the dynamics of positive-jump, diffusion, and long-term risks in VIX assets. A dissection of the three component risks in volatility is undertaken to determine their distinctive dynamics, risk prices, and market information. The dynamics of three component volatility risks play important and distinctive roles in the VIX derivatives market. The risk dissection substantially improves VIX option valuation. The component risks considerably explain VIX asset returns with a distinct exposure pattern and require separate risk premia. The risks are priced distinctively in the volatility market and are sensitive to different macroeconomic conditions. A comparison of the VIX derivatives market with the S&P 500 market reveals incremental information from the component risks beyond that provided by the traditional S&P 500 and its option market. TOPICS:Derivatives, options, factors, risk premia Key Findings ▪ A reduced-form model is used to disentangle the jump risk dynamic from the diffusion and long-term risks in the volatility market. The dynamic volatility jump risk is shown to be critical in the VIX derivatives market. ▪ The authors find unique properties in the different volatility risks. The three component risks are separately priced in the VIX market, the risk premia compose the total variance premium by different patterns, and they have unique responding for the macroeconomic conditions. ▪ They compare the pricing information in the S&P 500 and volatility markets and show that component risk dynamics in the VIX derivatives market contain incremental and critical pricing information beyond what is embedded in the S&P 500 and its option market.

In the financial markets, asset returns exhibit collective dynamics masking individual impacts on the rest of the market. Hence, it is still an open problem to identify how shocks originating from ...

Identifying efficient portfolio diversification strategies subject to stochastic dominance (SD) criteria usually assumes that the state-space of future asset returns can be captured by a fixed sample of equally probable historical returns. This paper relaxes this assumption by developing SD criteria under incomplete information on state probabilities. Specifically, we identify portfolios that dominate a given benchmark for any state probabilities in a given set. The proposed approach is applied to analyze if industrial diversification can be utilized to outperform the market portfolio. The results from this application demonstrate that the use of set-valued state probabilities can help to improve out-of-sample performance of SD-based portfolio optimization.

This article proposes a general framework of a Markov Switching GARCH model with a mixture of truncated Gaussian to model asset returns with price limits in China. Theoretically, while retaining ma...

This article studies the relationship between freedom of press and equity returns in emerging markets. While research has shown that asymmetric information is one reason for high volatility and one type of risk in financial markets, the role of press in reducing that risk has not yet been studied thoroughly. The article attempts to bridge that gap and investigates how a free and unbiased press affects excess returns in stock market. Using the International Capital Asset Pricing Model (ICAPM), Fama-MacBeth’s methodology, and dataset from16 emerging economies from 1986 to 2000, this research is able to show that cross-country excess returns are significantly related to the degree of press freedom.

The price of discount rate risk reveals whether increases in equity risk premia represent good or bad news to rational investors. Employing a new empirical methodology, we find that the price is negative, which suggests that discount rates are high during times of high marginal utility of wealth. Our approach relies on using future realized market returns to consistently estimate covariances of asset returns with the market risk premium. Covariances drive observed patterns in a broad cross section of stock and bond expected returns.

We propose a two-dimensional fast Fourier transform (FFT) network to retrieve the prices of options that depend on two Levy processes. Applications include, but are not limited to, the valuation of options on two stocks under the Levy processes, and options on a single stock under a random time-change Levy process. The proposed numerical scheme can be applied to different multivariate Levy constructions such as subordination and linear combination provided that the joint characteristic function is available. The proposed FFT-network can be thought of as a lattice approach implemented through the characteristic function. With the prevalent implementation of FFT, the network approach results in significant computational time reduction while maintaining satisfactory accuracy. Furthermore, we investigate option pricing on a single asset where the asset return and its volatility are driven by a pair of dependent Levy processes. Such a model is also called the random time-changed Levy process. Numerical examples are given to demonstrate the efficiency and accuracy of FFT-network applied to exotic and American-style options.

Although there is substantial literature linking news to the asset return volatility of a single asset, little attention has been paid to how news influences the relationships between firms. This paper addresses this issue by examining how firm-specific scheduled and unscheduled news arrivals influence the systemic risk of individual firms based on a sample of 47 US financial institutions. Whereas negative surprises from scheduled news announcements and a higher rate of unscheduled news both increase the systemic risk of a firm, positive news surprises decrease this systemic risk. In addition, negative scheduled news and a higher rate of unscheduled news across the sector increases the total connectedness or systemic risk across the sector as a whole. These effects are magnified when the market is already in distress. The results indicate that regulators should consider more than volatility and pay attention to the news flow when monitoring systemic risk.

In the presence of non-normally distributed asset returns, an optimal portfolio selection should consider higher-order (co-)moments when no sampling errors exist. However, the curse of dimensionali...

PurposeThis paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.Design/methodology/approachThe authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.FindingsThe authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.Originality/valueThis study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

Risk-based portfolio construction methods focus on optimally extracting information from the covariance matrix of asset returns, as opposed to utilising forecasts of expected returns, in determinin...

In this paper, we propose to use the mixture of truncated Gaussian distribution in modelling the financial asset return distribution under price limits. Theoretically, while retaining many convenie...

We provide a closed-form solution to an optimal investment and consumption problem for a constant absolute risk aversion (CARA) agent, who faces execution costs when trading correlated risky assets with return predictability. The optimal investment strategy indicates that the agent should trade gradually toward a dynamic aim portfolio, which is an adjusted Merton portfolio with modifications to account for the persistence of the return-predicting signals and the execution costs. The optimal consumption strategy is quadratic in the return-predicting signals and linear in the agent's wealth. Our numerical studies show that the execution costs diminish the importance of asset return predictability on the agent's optimal investment strategy, thereby confirming the conjecture raised by Liu (2004). In addition, the presence of the intermediate consumption leads to a more aggressive aim portfolio than the case without consumption.

We explore whether and how liquidity factors influence risk transfers between commodity and stock markets using a composite liquidity index and five different types of liquidity measures. We find that liquidity shocks, including both funding liquidity and market liquidity, are positively associated with comovements between commodity and stock markets after 2000, although the relationship is insignificant before 2000. The structural change indicates that financialization creates a role for adverse liquidity shocks to increase cross‐market correlations. Further evidence shows that the effect of liquidity on cross‐market correlations is state‐dependent and intensifies when liquidity conditions deteriorate and asset returns sustain substantial declines. Our findings are not explained by business cycles.

Asset returns incorporate new information via the effects of independent and possibly identically distributed random shocks. They may also incorporate long memory effects related to the concept of self‐similarity. The two approaches are here combined. In addition, methods are proposed for estimating the contribution of each component and evidence supporting the presence of both components in both the physical and risk‐neutral distributions is presented. Furthermore, it is shown that long‐horizon returns may be nonnormal when there is a self‐similar component. The presence of a self‐similar component also questions positive equity biases over the longer term.

The optimal rebalance interval that maximizes the expected geometric return of passive strategies decreases in relation to increasing asset volatility, increasing dispersion in asset volatility, and increasing negative 1-month autocorrelation of asset returns. Conversely, the optimal rebalance interval increases in relation to increasing positive 1-month return autocorrelation and increasing dispersion in expected return of the underlying assets. Much of the literature on the rebalancing premium has focused on the effect of asset volatility. But empirically, the positive autocorrelation of asset returns and the dispersion in asset expected returns have a much larger impact on the rebalancing premium. Depending on the set of assets chosen, the optimal rebalance frequency can vary between 6 months for an equal-weighted portfolio of multiple asset classes and 10 years for an equal-weighted portfolio of regional indices. The large variation in optimal rebalance frequency challenges the notion of a universal rebalancing premium. TOPICS:Portfolio construction, wealth management, pension funds Key Findings • The empirical and theoretical term structure of the rebalancing premium is driven primarily by return dispersion in asset classes and the autocorrelation of return. • The optimal rebalancing frequency to maximize return varies between 6 months and at least 10 years, depending on the particular set of asset classes in the portfolio.

Forecasting the covolatility of asset return series is becoming the subject of extensive research among academics, practitioners, and portfolio managers. This paper estimates a variety of multivariate GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of Coffee Arabica and compares the forecasting performance of these models based on high-frequency intraday data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of MGARCH models to pay particular attention to the measurement of realized volatility and employ high-frequency data whenever feasible.

A stopped clock is right twice a day. Similarly, any portfolio allocation is likely to be optimal at least at some point. Risk parity is no stopped clock. The authors derive a general result regarding when and why risk parity is Sharpe ratio optimal, even with negative-Sharpe-ratio assets. This derivation goes beyond the simple observation that risk parity is Sharpe ratio optimal when asset correlations and Sharpe ratios are identical. Based on the analytical result, the authors develop an indicator to describe when risk-parity strategies are likely to be more or less optimal. They also explain how negative-Sharpe-ratio assets can still be an important part of a portfolio—whether it is a risk-parity portfolio or not. Although risk parity and risk balancing in general do not require assumptions about returns, the authors provide guidance regarding how to infer returns that are consistent with the portfolios built from targeting risk. TOPICS:Statistical methods, portfolio theory, portfolio construction Key Findings • Conditions that are generally sufficient for risk-parity portfolios to be Sharpe ratio optimal are provided, beyond the special case of identical cross-asset correlations and Sharpe ratios. From this, an indicator of how likely risk parity is to be Sharpe ratio optimal can be derived. • Based on diversification properties of assets, the generally sufficient conditions for risk-parity portfolios to be Sharpe ratio optimal explain why even negative-return assets can be included in a diversified portfolio. • A method is provided to translate risk targets into a rank ordering of asset returns. With an additional set of assumptions, these rank orderings can be translated into returns for a coherent set of risk targets, risks, and returns.

El objetivo del presente trabajo de investigación es determinar la relación que existe entre capital de trabajo y rentabilidad de las empresas industriales que cotizan en la Bolsa de Valores de Lima durante los periodos 2010 al 2015. El estudio es de tipo correlacional, obedece a un diseño no experimental, transversal y retrospectivo. Se recopiló información de los estados financieros secuenciales declarados en los años de estudio. Para el análisis de datos se utilizó el coeficiente de Pearson. Los resultados muestran que tres indicadores de capital de trabajo tienen una relación inversa significativamente negativa con los indicadores de rentabilidad, excepto con días de cuentas por pagar y resultado bruto de explotación; es decir estos indicadores no tienen relación significativa. Esto explica que al reducir los días de inventario, días de cuentas por cobrar, días de cuentas por pagar y ciclo de conversión de efectivo, las empresas industriales invierten menos capital de trabajo y mejoran su rentabilidad de activos y resultado bruto de explotación; a excepción los días de cuentas por pagar que no guardan una relación inversa significativa con el resultado bruto de explotación.

Testing for predictability of asset returns has been a long history in economics and finance. Recently, based on a simple predictive regression, Kostakis, Magdalinos, and Stamatogiannis derived a W...

In this article, a rule-based definition of market corrections that depends on price volatility is proposed. This enables consistent comparison of corrections in different markets. Statistics of corrections in several US equity indexes and major US equity sector exchange-traded funds are compiled. According to the definition proposed in this work, the bear market of 2007–2009 had five distinct corrections, three of which exceeded 20%. It is shown that binary variables that account for market corrections improve accuracy of the ARMA-GARCH model for asset returns. Among other findings, volatility during market corrections has increased in recent years. The losses of the SP however, this is not the case since 2007. Corrections in the US equity sector exchange-traded funds are determined by the sector-specific trends rather than by their volatility. The results obtained in this article may be useful for a better understanding of business cycles and optimal portfolio rebalancing among various equity sectors. TOPICS:Exchange-traded funds and applications, mutual funds/passive investing/indexing Key Findings • A rule-based definition of market corrections that depends on price volatility is proposed. • The statistics of corrections in several major US equity indexes and sector exchange-traded funds is compiled. • It is found that the binary variables that account for market corrections improve accuracy of the ARMA-GARCH model for asset returns.

In asset return predictability, realized returns and future expected returns tend to move in opposite directions. This generates a tension between tax- and market-timing incentives. In this study, a portfolio choice problem in the presence of both return predictability and capital gains tax is examined. We characterize various features of the optimal trading strategy, and demonstrate that the optimal strategy helps mitigate the tension between market- and tax-timing. The calibrated model suggests that return predictability can significantly increase both the utility loss due to capital gains tax and the value of deferring capital gains realization. Overall, our results suggest that the nature of the asset return process can have important implications for the welfare effects of capital gains tax. This paper was accepted by Karl Diether, finance.

During the process of portfolio decision, the investor usually assumes that the distribution of asset return is known in order to identify the optimal investment strategy. In fact, the distribution of asset return is unknown and the investor often uses the historical data to estimate its distribution, which is uncertainty and should be taken into account during the process of portfolio decision. In this paper, we use the relative entropy to measure the uncertainty degree of risk premia and covariance matrix, build the model of mean-variance portfolio, and obtain the portfolio’s risk premia and variance under the worst scenario. At last, a comparative empirical study based on the portfolio’s effective frontier is performed. Results show: parameter uncertainty has significant effect on portfolio decision, and the effect of risk premium uncertainty is more than that of covariance matrix uncertainty; for the investor of risk-aversion, the higher the degree of parameter uncertainty is, the lower the portfolio performance is, and the stronger the relationship of assets in portfolio is, the weaker the effect of parameter uncertainty on portfolio is.

In this paper, we theoretically and empirically study the intrahorizon value at risk (iVaR) in a general jump-diffusion setting. We propose a new class of models of asset returns, the displaced mixed exponential model, which can arbitrarily closely approximate finite and infinite activity Lévy processes. We then derive analytical results for the iVaR and disentangle, in a theoretically consistent way, the jump and diffusion contributions to the intrahorizon risk. We estimate historical and option-implied value at risk and iVaR for several popular jump models using the Standard & Poor’s (S&P) 100 Index and American options. Empirically disentangling the contribution of the jumps from the contribution of the diffusion, we conclude that jumps account for about 90% of the iVaR on average. Our back-testing results indicate that the option-implied estimates are much more responsive to market changes than their historical counterparts, which perform poorly. This paper was accepted by Gustavo Manso, finance.

We examine whether there is common behavior in limit order cancellation activity, that is, commonality in cancellation activity, on U.S. exchanges. We then examine whether this commonality in cancellation activity is associated with increased levels of return comovement and commonality in liquidity. We document strong evidence of limit order traders exhibiting exchange, industry, marketwide, and stock‐level commonality with regard to cancellation activity, which is consistent with limit order traders exhibiting correlated trading behavior. We also find that this correlated behavior in cancellation activity is associated with increased levels of return comovement and commonality in liquidity.

Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.

Covariance matrix of the asset returns plays an important role in the portfolio selection. A number of papers is focused on the case when the covariance matrix is positive definite. In this paper, we consider portfolio selection with a singular covariance matrix. We describe an iterative method based on a second order damped dynamical systems that solves the linear rank-deficient problem approximately. Since the solution is not unique, we suggest one numerical solution that can be chosen from the iterates that balances the size of portfolio and the risk. The numerical study confirms that the method has good convergence properties and gives a solution as good as or better than the solutions that are based on constrained least norm Moore–Penrose, Lasso, and naive equal-weighted approaches. Finally, we complement our result with an empirical study where we analyze a portfolio with actual returns listed in S&P 500 index.

Purpose The purpose of this paper is to investigate investor behavior under two broad categories, market-wide sentiment and herding. Design/methodology/approach Using a dynamic factor model, that extracts distinct latent factors representing fluctuations in asset returns due to changes in fundamentals as well as investors’ sentiments, the paper investigates the impact of investor behavior on asset pricing. Findings Consistent with the literature, the results suggest that the behavioral factors play a significant role in explaining variation in the asset prices. However, the degree of influence depends on the nature of the stocks or portfolios. The findings conform to the hypothesis that behavioral factors play a more important role in explaining the price movements of high and medium valued stocks than those of smaller valued stocks. Further, the behavioral factors also exhibit high auto-correlation, depicting the pervasive nature of such factors, and proving that information cascades and other behavioral mechanisms propagate over a period of time leading to bubbles and market crashes. Finally, since herding is often associated with market volatility, the authors test the hypothesis using two measures of volatility and the result shows positive significant associations between them as suggested in the literature. Originality/value The paper presents a dynamic factor model to study the impact of investor behavior on asset returns using a conventional three factors model with behavioral factors. A factor model is proposed to extract distinct latent factors representing fluctuations in asset returns due to changes in fundamentals as well as investors’ sentiments. The study investigates investor behavior under two broad categories, market-wide sentiment and herding. Consistent with the literature, the results suggest that the behavioral factors play a significant role in explaining variation in the asset prices. However, the degree of influence depends on the nature of the stocks or portfolios. The findings conform to the hypothesis that behavioral factors play a more important role in explaining the price movements of high and medium valued stocks than those of smaller valued stocks. Further, the behavioral factors also exhibit high auto-correlation, depicting the pervasive nature of such factors, and proving that information cascades and other behavioral mechanisms propagate over a period of time leading to bubbles and market crashes.