Security Returns Research Articles

Diversified models for portfolio selection based on uncertain semivariance

ABSTRACTSince the financial markets are complex, sometimes the future security returns are represented mainly based on experts’ estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts’ estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

Market Timing and Agency Costs: Evidence from Private Equity

Private equity (PE) funds operate at the interface of private and public capital markets. This paper investigates whether PE fund managers have private information about the valuations of publicly traded securities. Using a dataset of cash flows from 941 buyout and venture funds, I show that PE funds’ distribution patterns predict returns of public securities in the industries of the funds’ specialization, but fund managers tend to sell at the market peaks only when they have performance fees to harvest. I find that the cost of this agency tension increases in the manager’s survival risk and that the managers’ knowledge pertains to the public firms’ future earnings rather than the discount rates. My tests distinguish market timing from reactions to the variation in risk premia and spillover effects of PE activity on public firms. The results help better understand PE performance and have strong implications for PE manager selection. It follows that PE activity embeds private information into the prices of public securities.

Identifying Broad and Narrow Financial Risk Factors with Convex Optimization

Factor analysis of security returns aims to decompose a return covariance matrix into systematic and specific risk components. To date, most commercially successful factor analysis has been based on fundamental models, although there is a large academic literature on statistical models. While successful in many respects, traditional statistical approaches like principal component analysis and maximum likelihood suffer from several drawbacks. These include a lack of robustness, strict assumptions on the underlying model of returns, and insensitivity to narrow factors such as industries and currencies, which affect only a small number of securities, but in an important way.We apply convex optimization methods to decompose a security return covariance matrix into low rank and sparse parts. The low rank component includes the market and other broad factors that affect most securities. The sparse component includes narrow factors and security specific effects.We measure the variance forecasting accuracy of a low rank plus sparse covariance matrix estimator on an equally weighted portfolio of 100 securities simulated from a model with two broad factors and 21 narrow factors. We find that the low rank plus sparse estimators are more accurate than estimates made with classical principal component analysis, in particular, at forecasting risk due to narrow factors. Finally, we illustrate a low rank plus sparse decomposition of an empirical covariance matrix of 100 equities drawn from 21 countries.

The volatility of a firm's assets and the leverage effect

We investigate the volatility of firms’ assets in contrast to existing studies that focus on equity volatility. We estimate asset volatility using a comprehensive data set on the market values of corporate security returns. We find significant differences between the properties of equity and asset volatilities with implications for several important areas of finance. First, financial leverage has a large influence on equity volatility. Second, leverage and asset volatility have permanent and transitory effects, respectively, on equity volatility, helping explain the short- and long-run dynamics of equity volatility. Third, we analyze and compare the cross-section of asset versus equity returns.

INVITED EDITORIAL COMMENT

1. Clifford S. Asness Although consensus might be too strong a word, modern financial researchers have mostly coalesced behind a set of “factors” that both explain security returns and deliver a positive return premium (not necessarily the same things). A “factor” is the spread between the

Response to Discussion of “Security Returns and Volume Responses around International Financial Reporting Standards (IFRS) Earnings Announcements”

This paper responds to Perkins' (2016) preferred methodology—association study rather than the use of an event study research design in Olibes' (2016) paper (Security Returns and Volume Responses around International Financial Reporting Standards (IFRS) Earnings Announcements). As noted by Collins and Kothari (1989), association studies recognize that investors likely learn “information about earnings and valuation relevant events from non-accounting information sources." As Callen et al (2009, 1364) note, event study analyses provide more direct evidence regarding the information conveyed of earnings disclosures. Most notably, in an empirical setting in which the focus is on testing the impact of new earnings information on stock returns and trading volume, it is vital to know when information arrives and the duration investors use that information in making investing and trading decisions. This reasoning justifies the methodology employed in Olibe (2016).

Discussion of “Security Returns and Volume Responses around International Financial Reporting Standards (IFRS) Earnings Announcements”

Olibe (2016) uses price and trading volume responses around IFRS earnings disclosures for U.K. cross-listed firms trading as American Depository Receipts in U.S. equity markets to investigate whether such disclosures provide information to market participants over and above U.S. GAAP earnings disclosures. He finds evidence of significant price and trading volume responses for IFRS earnings disclosures, leading to the conclusion that market participants find that those disclosures facilitate trading in U.S. equity markets. This discussion focuses on the paper's incremental contribution to the literature and offers suggestions for improvements in the research design and analyses.

Multiperiod Mean Absolute Deviation Uncertain Portfolio Selection

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

Analysts’ Forecasts and Asset Pricing: A Survey

This survey reviews the literature on sell-side analysts’ forecasts and their implications for asset pricing. We review the literature on the supply and demand forces shaping analysts’ forecasting decisions as well as on the implications of the information they produce for both the cash flow and the discount rate components of security returns. Analysts’ forecasts bring prices in line with the expectations they embody, consistent with the notion that they contain information about future cash flows. However, analysts’ forecasts exhibit predictable biases, and the market appears to underreact to the information in forecasts and to not fully filter the biases in forecasts. Analysts’ forecasts are also helpful in estimating expected returns on securities, but evidence on the relation between analysts’ forecasts and expected returns is still scarce. We conclude by identifying unanswered questions and offering suggestions for future research.

Dynamic multi-objective fuzzy portfolio model that considers corporate social responsibility and background risk

This study was conducted to examine the properties of a portfolio selection model when securities returns, background asset returns, and the corporate social responsibility index are all normal fuzzy numbers, and to propose a hybrid fruit fly optimisation algorithm (FOA) for solving a dynamic multi-objective optimisation model. A nondominated sorting hybrid FOA derived concurrently from NSGA-II and the FOA is presented here. The kurtosis of a portfolio without background risk was a constant value when risky asset returns were fuzzy normal numbers, and the kurtosis of a portfolio increased when the variance of back-ground risk increased. The main contribution of this study is the establishment of a dynamic multi-objective fuzzy portfolio model that considers corporate social responsibility and background risk. An analytic expression of portfolio kurtosis including background risk was deduced when the return of risky assets and background risk were fuzzy normal numbers.

Value of information in portfolio selection, with a Taiwan stock market application illustration

Despite many proposed alternatives, the predominant model in portfolio selection is still mean–variance. However, the main weakness of the mean–variance model is in the specification of the expected returns of the individual securities involved. If this process is not accurate, the allocations of capital to the different securities will in almost all certainty be incorrect. If, however, this process can be made accurate, then correct allocations can be made, and the additional expected return following from this is the value of information. This paper thus proposes a methodology to calculate the value of information. A related idea of a level of disappointment is also shown. How value of information calculations can be important in helping a mutual fund settle on how much to set aside for research is discussed in reference to a Taiwan Stock Exchange illustrative application in which the value of information appears to be substantial. Heavy use is made of parametric quadratic programming to keep computation times down for the methodology.

Uncertain portfolio selection with background risk

In real life, investors face background risk which may affect their portfolio selection decision. In addition, there are situations where background asset return and the security returns have to be given by experts’ evaluations because of occurrence of unexpected incidents in economic and social environment or lack of historical data. This paper discusses an uncertain portfolio selection problem in which background risk is considered and the returns of the securities and the background assets are given by experts’ evaluations instead of historical data. Using uncertainty theory, we propose a new uncertain portfolio selection model with background risk. To enable the users to solve the problem with currently available programing solvers, the crisp form of the model is provided. In addition, we discuss the optimal solution of the model when the returns of the securities and the background asset return obey normal uncertainty distributions, and compare the optimal portfolio with background risk with that without background risk. It is concluded that when everything else is same, the expected optimal portfolio return with background risk is smaller than that without background risk. Finally, a numerical example is given as an illustration.

Optimal Investment and Risk Control Strategy for an Insurer under the Framework of Expected Logarithmic Utility

In this paper, we consider an insurer who wants to maximize its expected utility of terminal wealth by selecting optimal investment and risk control strategies. The insurer’s risk process is modeled by a jump-diffusion process and is negatively correlated with the returns of securities and derivatives in the financial market. In the financial model, a part of insurers’ wealth is invested into the financial market. Using a martingale approach, we obtain an explicit solution of optimal strategy for the insurer under logarithmic utility function.

The Influence of Market States on Security Returns

Business and economic cycles have played a major factor in economies and people lives for centuries; indeed, it could be argued that the economic crises and economic good times affect all people’s wellbeing. As financial markets have developed in modern economies, researchers have observed that market cycles tend to be leading indicators of business cycles. While there is a long history of academic research into business cycles, academic research into how market states differentially influence security returns is a more recent phenomenon. The aim of this thesis is to explore not only new methods but also to create innovative schemes that enable the examination of how market states influence security returns in new and significant ways. The encompassing theme is the interplay between stock market anomalies and market states. The resulting empirical research in the thesis involves three separate but related empirical studies. The first study of the thesis examines whether market states (defined simply by past market performance) are able to provide information about the future performances of individual industries. In other words, can market states predict future industry returns? Specifically, I investigate whether an industry’s past relative performance in the same market state as the current one predicts the industry’s future performance. The findings show that market states can be used to construct profitable dynamic industry rotation strategies. Moreover, in support of market practitioners’ preferences for specific industries in different states of the market, certain industries are shown to have predictable returns in different market states.

Impact of Stock Splits and Rights Issue Announcements on Market Price: Evidence from India

The current paper studies the impact of two events i.e stock splits and rights issue announcement on the stock returns of companies listed on the Bombay Stock Exchange. The study consists of a sample of 90 announcements for stock splits and 29 announcements for rights issue during the period 2011-2014. Market model is used to calculate the abnormal returns of securities. Positive Average Abnormal Returns were observed for the two events on the day their announcements, however they are not statistically significant. The study concludes that the Indian stock market is efficient in its semi-strong form.

Is a larger equity market more information efficient? Evidence from intervalling effect

This paper investigates the impact of equity return autocorrelation on financial market efficiency via intervalling effect. A simple model is proposed to show that the degree of intervalling effect is related to the security return autocorrelation. A more general version of Levy and Levhari hypothesis is proposed to find that the degree of the autocorrelations of the security and the market returns determines the existence and the direction of the intervalling effect and the size of the intervalling effect are dependent on the degree of the security autocorrelations. Empirical evidence of the latter is presented.

Analystss Forecasts and Asset Pricing: A Survey

This survey reviews the literature on sell-side analysts’ forecasts and its implications for asset pricing. We review the literature on the supply and demand forces shaping analysts’ forecasting decisions as well as the implications of the information they produce for both the cash flow and the discount rate components of security returns. Analysts’ forecasts bring prices in line with the expectations they embody, consistent with the notion that analysts’ forecast contain information about future cash flows. However, analysts’ forecasts exhibit predictable biases and the market appears to underreact to the information in forecasts and not fully filter the biases in the forecasts. Analyst forecasts are also helpful in estimating expected returns on securities, but evidence on the relation between analysts’ forecasts and expected returns is still scarce. We conclude by identifying unanswered questions and offering suggestions for future research.

ANALISIS PENGARUH PENGUMUMAN DIVIDEN TUNAI TERHADAP ABNORMAL RETURN DAN VARIABILITAS TINGKAT KEUNTUNGAN SAHAM

The purpose of this research is to explain the reaction of markets to the dividend cash announcement on all companies share listed on the Sharia Effect List. The other goal is to explain the difference of abnormal return and security return variability (SRV) on the period before, during and after period ex-dividend date in 2014. This research used the event study, which was conducted observation of abnormal return and security return variability during the event period, i.e. before 10 days up to 10 days after the dividend cash announcement. The population in this research are all companies share listed on the Sharia Effect List (DES) in 2014. The sampling technique was conducted with a purposive sampling method and obtained a sample of 135. Based on the result of differential tests analysis showed that there are difference abnormal return on the period before-at the moment, after-at the moment and before-after and security return variability variable on the period before-after the dividend cash announcement. However, the security return variability (SRV) on the period before-after announcement showed that there are no difference.

Editorial introduction to the special issue on monetary conditions, political policy, and security returns

Editorial introduction to the special issue on monetary conditions, political policy, and security returns

Why Practitioners Should Use Bayesian Statistics

An article by Harris in 2014 argues that the distribution of returns for equity securities is a mixture distribution, the dominant distribution in the mixture is the truncated Cauchy distribution. An empirical test of this against mean-variance constructions was performed in a different study and confirms this argument. A consequence of this finding is that non-Bayesian methods are generally inadmissible for many standard problems. As such, practitioners using numerical methods in their work need to switch, as a general rule, to Bayesian methods.