Distribution Of Returns Research Articles

Collaborative Green Innovation of Livestock Product Three-Level Supply Chain Traceability System: A Value Co-Creation Perspective

To enhance the health and stability of livestock product supply chains, it is imperative to augment product sustainability and satisfy food safety requirements through collaborative green innovation. Digital traceability technology amalgamates information and resources from stakeholders in the supply chain, facilitating knowledge sharing and product tracking throughout the entire process to purify the supply chain environment. Augmenting communication and trust among supply chains paves the way for green innovation, thereby yielding value-added benefits. Consequently, this paper formulates a simulation model for manufacturers, retailers, and consumers—three pivotal stakeholders in the traceability process of livestock products—from a value co-creation standpoint. It also incorporates a contract penalty mechanism to probe the collaborative green innovation process among various entities involved in the livestock product supply chain. System simulation is employed to emulate the evolution path of collaborative green innovation in the livestock product supply chain under the value co-creation perspective. Subsequently, a stepwise penalty mechanism, green incentive mechanism, and fair distribution mechanism are proposed for stakeholders to actualize collaborative green innovation and value creation. The findings reveal that: (1) the collaborative green behavior between suppliers, retailers, and consumers is influenced by factors such as value co-creation excess returns, innovative technology costs like traceability, cost sharing among stakeholders, and a certain threshold of government penalties. (2) A balanced distribution of excess returns and cost-sharing among stakeholders fosters the evolution of a green collaborative state in the supply chain, thereby achieving sustainable development with value chain enhancement and ecological optimization.

Peer-to-peer loan returns: heterogeneous effects across quantiles

ABSTRACT In this study, we examine how loan and borrowers’ characteristics have a different impact on profitable and non-performing loans. Using a quantile regression profit-scoring model estimated with 472 , 106 loans from the U.S. P2P lending platform Lending Club, we show that higher loan amounts, loan term, interest rate and lower income are associated with lower returns for less creditworthy borrowers, i.e. for under-performed loans. Conversely, for performing loans, higher loan amounts, loan term, interest rates and lower income are associated with higher returns. We also find that borrowers’ credit (debt-to-income and FICO score) matters mostly for the tails of the return distribution, to mitigate losses for non-performing loans and improve profits for highest-performing loans. The results have broader implications for the design of credit risk models.

Validation of Stock Price Prediction Models in the Conditions of Financial Crisis

The distribution laws of various natural and anthropogenic processes in the world around us are stochastic in nature. The development of mathematics and, in particular, of stochastic modeling allows us to study regularities in such processes. In practice, stochastic modeling finds a huge number of applications in various fields, including finance and economics. In this work, some particular applications of stochastic processes in finance are examined in the conditions of financial crisis, aiming to provide a solid approach for stock price forecasting. More specifically, autoregressive integrated moving average (ARIMA) models and modified ordinary differential equation (ODE) models, previously developed by some of the authors to predict the asset prices of four Bulgarian companies, are validated against a time period during the crisis. Estimated rates of return are calculated from the models for one period ahead. The errors are estimated and the models are compared. The return values predicted with each of the two approaches are used to derive optimal risk portfolios based on the Markowitz model, which is the second major aim of this study. The third aim is to compare the resulting portfolios in terms of distribution (i.e., weights of the stocks), risk, and rate of return.

Measuring systemic risk contribution: A higher-order moment augmented approach

Marginal contributions of individual institutions to the systemic risk contain predictive power for potential future exposures and provide early warning signals to regulators and the public. In this paper, the higher-order co-skewness and co-kurtosis are used to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity

From the aspect of behavioral finance, which is an emerging area integrating human behavior into finance, this work studies a robust portfolio problem for loss-averse investors under distribution and mean return ambiguity. A loss-aversion distributionally-robust optimization model is constructed if the return distribution of risky assets is unknown. Then, under the premise that the mean returns of risky assets belong to an ellipsoidal uncertainty set, a model under joint ambiguity in distribution and mean returns is constructed. This study solves both robust models and derives their analytical solutions, respectively. Moreover, the effect of ambiguity aversion and loss aversion on robust optimal portfolio returns is studied. The results show that ambiguity-neutral investors who do not know the return distribution obtain more robust optimal portfolio returns than ambiguity-averse investors who are unaware of both the distribution and mean return. The difference between them decreases with the increase of loss aversion coefficients and increases with ambiguity aversion coefficients. Both loss aversion and ambiguity aversion play important roles in investors’ behavioral portfolio selection.

OPTIMAL HEDGE RATIO OF SUKUK AND ISLAMIC EQUITY: A NOVEL APPROACH

This research applies a novel model to compute a hedge ratio. Specifically, the model modifies volatility forecasts of an exponentially weighted moving average method to account for the fat-tailed distribution of returns. This simpler model aims to overcome the widely-known drawback of the complex GARCH models that a long daily return period is required to ensure the model’s convergence. The data are Islamic exchange-traded funds: SP Funds Dow Jones Global Sukuk ETF, Wahed FTSE USA Shariah ETF, and iShares MSCI EM Islamic UCITS ETF. Sukuk act as a diversifier over the turmoil period since they are positively correlated with Islamic equity and their volatility is less than that of Islamic equity. This work also implements widely-used methods such as Dynamic Equicorrelation-GARCH, GO-GARCH, asymmetric DCC-GARCH, naïve approach, and linear regression. Two forms of data splitting and a rolling-window analysis are carried out to reduce data mining bias. All models generate one-step ahead forecasts of hedge ratios. Applying wavelet-transformed returns and utility analysis incorporating third and fourth moments, the proposed models produce better performance than the competing models. The results remain the same irrespective of different hedging instruments (precious metals) and asset classes.

Cooperation maintenance in dynamic discrete-time multicriteria games with application to bioresource management problem

We consider a dynamic, discrete-time, game model where the players use a common resource and seek to optimize different criteria. The center (referee) shares the resource available for exploitation between the competitors. To construct a multicriteria Nash equilibrium the bargaining solution is adopted. To obtain a multicriteria cooperative equilibrium, a modified bargaining scheme that guarantees the fulfillment of individual rationality conditions is applied. Two approaches to maintain cooperative behavior, namely, the cooperative incentive equilibrium and the time-consistent payoff distribution procedure, are considered. Moreover, the cooperative incentive equilibrium is constructed under the assumption that the center punishes players for deviations. A dynamic bi-criteria bioresource management problem is investigated to illustrate the solution concepts proposed.

A Decomposition of Conditional Risk Premia and Implications for Representative Agent Models

We develop a methodology to decompose the conditional market risk premium and risk premia on higher-order moments of excess market returns into risk premia related to contingent claims on down, up, and moderate market returns. The decomposition exploits information about the risk-neutral market return distribution embedded in option prices, but does not depend on assumptions about the functional form of investor preferences or about the market return distribution. The total market risk premium is highly time-varying, as are the contributions from downside, upside, and central risk. Time-series variation in risk premia associated with each region is primarily driven by variation in risk prices associated with the probability of entering each region at short horizons, but it is primarily driven by variation in risk quantities at longer horizons. Analogous decompositions implied by prominent representative agent models generally fail to match the dynamic risk premium behavior implied by the data. Our results provide a set of new empirical facts regarding the drivers of conditional risk premia and identify new challenges for representative agent models. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01663 .

A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures

We study large-scale portfolio optimization problems in which the aim is to maximize a multi-moment performance measure extending the Sharpe ratio. More specifically, we consider the adjusted for skewness Sharpe ratio, which incorporates the third moment of the returns distribution, and the adjusted for skewness and kurtosis Sharpe ratio, which exploits in addition the fourth moment. Further, we account for two types of real-world trading constraints. On the one hand, we impose stock market restrictions through cardinality, buy-in thresholds, and budget constraints. On the other hand, a turnover threshold restricts the total allowed amount of trades in the rebalancing phases. To deal with these asset allocation models, we embed a novel hybrid constraint-handling procedure into an improved dynamic level-based learning swarm optimizer. A repair operator maps candidate solutions onto the set characterized by the first type of constraints. Then, an adaptive ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document}-exact penalty function manages turnover violations. The focus of the paper is to highlight the importance of including higher-order moments in the performance measures for long-run investments, in particular when the market is turbulent. We carry out empirical tests on two worldwide sets of assets to illustrate the scalability and effectiveness of the proposed strategies, and to evaluate the performance of our investments compared to the strategy maximizing the Sharpe ratio.

Dynamic robust portfolio selection under market distress

This article proposes a dynamic robust portfolio selection model that is based on minimizing portfolio’s worst case scenarios using the Conditional Value at Risk as relevant risk measure. Our proposed empirical model for the dynamics of portfolio constituents has three main features: i) accommodates tail dependence between assets by means of a mixture of copula functions; ii) conditional heteroscedasticity and leverage effects are considered through the implementation of a GJR-GARCH model; and iii) extreme events are taken into account by considering parametric and semiparametric hybrid models for the marginal distribution of asset returns. We illustrate the performance of this portfolio before and during the COVID-19 pandemic using statistical measures such as the Sharpe ratio, cumulative returns, and volatility. The results show the outperformance of our WCVaR portfolio during the turmoil period against benchmark portfolios commonly used by practitioners. The method also exhibits good performance during calm periods.

How does a change in downside risk affect optimal demand for a risky asset?: Comparative statics on Tail Conditional Expectation

This paper explains how changes in the return distribution of a risky asset, which alters Tail Conditional Expectation (TCE), impact the agent’s portfolio selection. We establish sufficient conditions for distribution changes, leading to a TCE reduction, to increase optimal demand for the risky asset. These results imply that risk-averse agents may increase their optimal demand for the risky asset when the downside risk premium increases. Furthermore, evaluating TCE, or downside risk premium, is compatible with second-degree stochastic dominance. This suggests that such an order of downside risk measures can provide additional insight into an agent’s portfolio selection, as identified by the stochastic dominance criteria.

Asymmetric rate of returns and wealth distribution influenced by the introduction of technical analysis into a behavioral agent-based model

Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent-based model to study quantitatively this relationship. In order to approximate the simulated market to the complexity of real markets, we consider that the investors are connected among them through a Scale-Free network; each one of the investors has his own psychological profile (Imitation, Anti-Imitation, Random); two different strategies for decision making: one of them is based on the trust neighborhood of the investor and the other one considers a technical analysis, the momentum of the market index technique. We analyze the market index fluctuations, the wealth distribution of the investors according to their psychological profiles and the rate of returns distribution. Moreover, we analyze the influence of changing the psychological profile of the hub of the network and report interesting results which show how and when anti-imitation becomes the most profitable strategy for investment. Besides this, an intriguing asymmetry of the rate of returns distribution is explained considering the behavioral aspect of the investors. This asymmetry is quite robust, being observed even when a completely different algorithm to calculate the decision making of the investors was applied to it, a remarkable result which, up to our knowledge, has never been reported before.

On solving robust log-optimal portfolio: A supporting hyperplane approximation approach

A log-optimal portfolio is any portfolio that maximizes the expected logarithmic growth (ELG) of an investor’s wealth, which typically assumes prior knowledge of the true return distribution. However, in practice, return distributions are often ambiguous; i.e., the true distribution is unknown, making this problem challenging to solve. This paper proposes a supporting hyperplane approximation approach, reformulating a class of distributional robust log-optimal portfolio problems with polyhedron ambiguity sets into tractable robust linear programs. An efficient algorithm is presented to determine the optimal number of hyperplanes. Additionally, to adapt to the constantly changing market, we propose an online trading algorithm using a sliding window approach to solve a sequence of robust linear programs, offering significant computational advantages. The effectiveness of the proposed approach is supported by empirical studies using historical stock price data.

Application of descriptive statistics methods to Kazakhstan stock market

The article discusses the methods of descriptive statistics and its application for the most liquid shares of the Kazakhstan stock market KASE (trading codes: HSBK, KZTK, CCBN, KZTO, KCEL, KEGC, KZKAK) from 2007 to 2022. Descriptive statistics are calculated for each stock, such as average return, variance, sample range, standard deviation, coefficient of variation, kurtosis, and skewness. The coefficients of the regression equation and the coefficient of determination R2 are estimated, which show the relationship between the market return and the return on an individual stock. The comparative analysis of index growth of stocks was carried out from 2013 to 2022. The histogram of the frequency distribution of KASE market returns based on annual and monthly periods and its empirical distribution function have been constructed. The hypothesis about the normal distribution of returns according to the Pearson criterion was tested. The distributions of returns for many stocks do not fully correspond to the normal distribution and have a positive skewness, i.e. have a long tail on the right side. The market curve has been constructed that characterizes the dependence of stock returns on the level of risk. According to this curve, it is established that HSBK stocks are the most risky of all the considered stocks, undervalued and attractive investments for investors using an aggressive investment strategy. KEGC stocks are less risky and very attractive investments for investors who use a more conservative investment strategy in the Kazakhstani stock market.

Climate Risk, Stock Crash Risk, and Greenhouse Gas Emission Trading Scheme: Evidence From Korea

This study examines the relationship between climate risk and stock crash risk and the moderating effect of greenhouse gas emission trading scheme (ETS). Numerous studies have tested stock price crash risk measured by the negative skewness of return distributions in the areas of internal governance mechanism. While interest in environmental performance for sustainable growth of firms has increased, there are no studies on the effect of climate risk on stock crash risk. Thus, we find that climate risk (a firm’s greenhouse gas emissions and energy consumption) increases stock crash risk, and the positive effect of climate risk on stock crash risk is weakened after introduction of the emission trading scheme. This study theoretically extends the literature on the empirical determinants of stock crash risk, and practically provides investors with proven information about whether climate risk is worth considering during investment decisions. Furthermore, at the level of government policy, we can suggest that a monitoring system is needed to mitigate potential information asymmetry problems in firm with high climate risk.

Modelization and Calibration of the Power-Law Distribution in Stock Market by Maximization of Varma Entropy

Proper description of the return distribution is crucial for investment practitioners. The underestimation of the tail risk may lead to severe consequences, even for assets with moderate fluctuations. However, many empirical studies found that the distribution tails of many financial assets drop off more slowly than the Gaussian distributions. Therefore, we intend to model and calibrate the heavy tails observed in financial fluctuations in this study. By maximizing the Varma entropy with value-at-risk and expected shortfall constraints, we obtain the probability distribution of stock return and observe that the tail of stock return distribution is a power law. Since the variance of the real stock portfolio may be a random variable, using the mean-VaR-ES constraints to maximize the Varma entropy effectively avoids the problem of assuming that the variance is a constant value under the traditional mean-variance constraint. Therefore, the deduced theoretical model would be more consistent with the real market. Using high-frequency data from China’s stock markets, we calibrate our theoretical model and give the concrete form of probability density distribution p(x) for different time intervals. The calibration results show that the tail of the stock return distribution is a power law with most of the power-law orders between −2 and −7. We prove the robustness of our results by calibrating the Varma entropy for S&P 500 of the USA stock market and different stock market indices in China’s A-share market. Our research’s findings not only offer a theoretical perspective for researchers but also give investing professionals a theoretical foundation on which to base their decisions.

On the core of a patent licensing game

This note revisits the impossibility result shown by Watanabe and Muto (2008) that almost all outcomes cannot be in the core of a patent licensing game. To overcome this difficulty, we consider a weaker stability concept by restricting objections against payoff distributions to the ones made under an exogenously fixed payment scheme and a payment rate. In contrast to their result, we demonstrate that the coalition structures in which licensee firms are the majority can be stable in this sense. However, such a possibility result breaks down if we allow negotiations on the payment rate.

On the prediction of stock price crash risk using textual sentiment of management statement

PurposeSince stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.Design/methodology/approachSpecific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.FindingsThe experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).Research limitations/implicationsIt should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.Originality/valueThe authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.

Bayesian predictive distributions of oil returns using mixed data sampling volatility models

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV-type models. Notably, GAS-family models exhibit superior performance in terms of in-sample fit, out-of-sample forecast accuracy, as well as Value-at-Risk and Expected Shortfall prediction.

A further examination of equity indexed annuities

Equity indexed annuities (EIAs) are deferred annuities that credit interest according to a formula tied to the performance of an underlying equity index. This research expands previous research, particularly that of Reichenstein (2009, 2011), by examining the distribution of returns that could have been created on a rolling monthly basis since 1928 for 11 through 15-year investment horizons.Second, we examine investment alternatives that include the options imbedded in EIAs. Third, rather than assuming constant cap rates we allow cap rates to vary with interest rates. We find that for long time horizons the opportunity costs of investing in EIAs is high.