Modeling non-normality using multivariate t: implications for asset pricing

2017 ◽  
Vol 7 (1) ◽  
pp. 2-32 ◽  
Author(s):  
Raymond Kan ◽  
Guofu Zhou
Keyword(s):  
Asset Pricing ◽  
Portfolio Management ◽  
Stock Return ◽  
Multivariate Normality ◽  
Expected Returns ◽  
Content Type ◽  
Normality Assumption ◽  
Statistical Estimation Problem ◽  
Distribution Assumption ◽  
Return Data

Purpose The purpose of this paper is to show that multivariate t-distribution assumption provides a better description of stock return data than multivariate normality assumption. Design/methodology/approach The EM algorithm is applied to solve the statistical estimation problem almost analytically, and the asymptotic theory is provided for inference. Findings The authors find that the multivariate normality assumption is almost always rejected by real stock return data, while the multivariate t-distribution assumption can often be adequate. Conclusions under normality vs under t can be drastically different for estimating expected returns and Jensen’s αs, and for testing asset pricing models. Practical implications The results provide improved estimates of cost of capital and asset moment parameters that are useful for corporate project evaluation and portfolio management. Originality/value The authors proposed new procedures that makes it easy to use a multivariate t-distribution, which models well the data, as a simple and viable alternative in practice to examine the robustness of many existing results.

Is value premium sector-specific? Evidence from India

2020 ◽  
Vol 46 (12) ◽  
pp. 1605-1628
Author(s):  
Vanita Tripathi ◽  
Priti Aggarwal
Keyword(s):  
Asset Pricing ◽  
Portfolio Management ◽  
Factor Model ◽  
Emerging Market ◽  
Value Premium ◽  
Market Model ◽  
Top Down ◽  
Content Type ◽  
The Difference ◽  
The Relationship

PurposeThis paper is an attempt to explore the fact that whether the literature-promised value premium has any sector orientation. The paper tests the relationship between the value premium and Indian sectors: fast-moving consumer goods (FMCG), financials, healthcare, information technology (IT), manufacturing and miscellaneous.Design/methodology/approachThe paper analyses around 210–480 companies listed on BSE-500 for the period of the recent 18 years ranging from March 1999 to March 2017. The paper employed Welch's ANOVA to examine whether the price-to-book market ratio is significantly different across sectors. Two prominent asset pricing models – single factor market model and Fama–French three-factor model – were used to examine the existence of value premium within sectors for full period and two sub-periods.FindingsThe empirical results of the paper suggest that the difference in the P/B ratio both between sectors and within sectors is statistically significant. The results further suggest that the value premium exists within the sectors irrespective of their value-growth orientation.Research limitations/implicationsThe paper is not free from certain limitations. Firstly, due to the non-availability of data in the public domain, the time period before 1999 could not be considered. Secondly, the study has used data pertaining to the Indian stock market only. To add to it, our study has concentrated on BSE-500 companies only; however, the future researchers can include both NSE and BSE companies.Practical implicationsThe paper has important implications for portfolio managers and retail investors following a top-down approach of investing. The portfolio manager can strategically build up the portfolios to concentrate more on the companies belonging to sectors like healthcare, manufacturing and FMCG. Investors following the top-down approach should avoid the underperforming growth stocks belonging to the growth sectors and allocate their funds to value stocks in the growth sector.Originality/valueThe paper is first of its kind to study the relationship between the value premium and Indian sectors. The paper contributes to portfolio management and asset pricing literature for an emerging market.

Analyzed relationship between risks and expected returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Doan Van Dinh
Keyword(s):  
Standard Deviation ◽  
Portfolio Management ◽  
Management Policy ◽  
Expected Returns ◽  
Expected Return ◽  
Content Type ◽  
Return On Equity ◽  
Financial Investment ◽  
Risk Levels ◽  
The Relationship

PurposeThis study aims to investigate the relationship between risks and the expected return of financial investment because the relationship between them is negative; if the investors agree to the higher level of risk, they have the greater the expected return; therefore, investors always require a degree of proportionality between the risks and returns.Design/methodology/approachThis study applied the standard deviation, variance, coefficient of variation methods and matrix function to measure risks. Besides, the dataset is a return on equity ROE, which is collected in three companies at time series from 2005 to 2020.FindingsWhen the variance or the standard deviation is higher, the return on the securities is higher, but the securities are a higher risk and vice versa. The results showed risk levels of stocks that are 2.509%, 0.367%, 3.666% and the corresponding return mean of 38.68%, 23.99% and 14.02%.Originality/valueThe results support the portfolio management policy appropriately. This study identifies issues for managers, investors and readers to consider: have a comprehensive solution among microcosmic policies, finance policy, investment policy and other policies to control and balance the relationship between risks and returns; have appropriate policies to regulate funds to stimulate investment in the long term.

Cross section of stock returns on Shari’ah-compliant stocks: evidence from Pakistan

2019 ◽  
Vol 12 (2) ◽  
pp. 282-302 ◽  
Author(s):  
Salman Ahmed Shaikh ◽  
Mohd Adib Ismail ◽  
Abdul Ghafar Ismail ◽  
Shahida Shahimi ◽  
Muhammad Hakimi Mohd. Shafiai
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Cross Section ◽  
Factor Model ◽  
Five Factor Model ◽  
Value Premium ◽  
Expected Returns ◽  
Content Type ◽  
The Cross ◽  
Three Factor

Purpose This paper aims to study the cross section of expected returns on Shari’ah-compliant stocks in Pakistan by using single- and multi-factor asset pricing models. Design/methodology/approach To estimate cross section of expected returns of Shari’ah-compliant stocks, the study uses capital asset pricing model (CAPM), Fama-French three-factor model and Fama-French five-factor model. Data for the period 2001-2015 on 217 companies are used. For the market portfolio, PSX-100 and Dow Jones Islamic Index for Pakistan are used. Findings The study could not find empirical support for CAPM using Lintner (1965), Black et al. (1972) and Fama and Macbeth (1973) approach. Nonetheless, the relation between beta and returns is positive in up-market and negative in down-market. The results of Fama-French three-factor and five-factor models suggest that size premium is positive and significant for explaining the cross section of stock returns of small size stocks, whereas value premium is positive and significant for explaining the cross section of returns of high value stocks. Practical implications The results suggest that fund managers can use Shari’ah-compliant stocks for portfolio diversification and for offering specialized investments given the positive market excess returns and the existence of size and value premium on Shari’ah-compliant stocks. Originality/value This is the first study on Fama-French (2015) five-factor model for Islamic capital markets in Pakistan.

Investor sentiment and stock return volatility: evidence from the Indian Stock Exchange

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sreenu N ◽  
Suresh Naik
Keyword(s):  
Asset Pricing ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Return ◽  
Stock Exchange ◽  
Investor Sentiment ◽  
Return Volatility ◽  
Stock Market Volatility ◽  
Content Type ◽  
Stock Return Volatility

PurposeIn any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.Design/methodology/approachThe paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.FindingsFinally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.Practical implicationsThe investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.Originality/valueThe outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

Risk Factors of Collateralized Loan Obligations and Corporate Bonds

2020 ◽  
Vol 32 (6) ◽  
pp. 347-355
Author(s):  
Mark Wahrenburg ◽  
Andreas Barth ◽  
Mohammad Izadi ◽  
Anas Rahhal
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Asset Pricing ◽  
Systemic Risk ◽  
Corporate Bonds ◽  
Risk Exposure ◽  
Expected Returns ◽  
Yield Spreads ◽  
Pricing Factors ◽  
Return Difference

AbstractStructured products like collateralized loan obligations (CLOs) tend to offer significantly higher yield spreads than corporate bonds (CBs) with the same rating. At the same time, empirical evidence does not indicate that this higher yield is reduced by higher default losses of CLOs. The evidence thus suggests that CLOs offer higher expected returns compared to CB with similar credit risk. This study aims to analyze whether this return difference is captured by asset pricing factors. We show that market risk is the predominant risk factor for both CBs and CLOs. CLO investors, however, additionally demand a premium for their risk exposure towards systemic risk. This premium is inversely related to the rating class of the CLO.

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

2019 ◽  
Vol 15 (2) ◽  
pp. 647-659 ◽  
Author(s):  
Zahra Moeini Najafabadi ◽  
Mehdi Bijari ◽  
Mehdi Khashei
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Markets ◽  
Time Series Prediction ◽  
Investment Decisions ◽  
Rate Of Return ◽  
Expected Returns ◽  
Prediction Methods ◽  
Content Type ◽  
Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

New multivariate process capability indices for measuring the performance of multivariate processes subject to non-normal distributions

2015 ◽  
Vol 33 (1) ◽  
pp. 42-61 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Chung-I Li ◽  
Wei-Chen Shih
Keyword(s):  
Process Capability ◽  
Process Capability Indices ◽  
Process Data ◽  
Multivariate Process ◽  
Multivariate Processes ◽  
Content Type ◽  
Normal Distributions ◽  
Normality Assumption ◽  
Capability Indices ◽  
Multivariate Manufacturing

Purpose – In the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions. Design/methodology/approach – In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC p , RNMC pm and RNMC pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC p and NMC pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case. Findings – A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices. Practical implications – Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system. Originality/value – Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

scholarly journals Using project demand profiling to improve the effectiveness and efficiency of infrastructure projects

2018 ◽  
Vol 38 (6) ◽  
pp. 1422-1442 ◽  
Author(s):  
Janet Godsell ◽  
Donato Masi ◽  
Antonios Karatzas ◽  
Timothy Mark Brady
Keyword(s):  
Portfolio Management ◽  
Infrastructure Projects ◽  
Content Type ◽  
Utility Company ◽  
Efficient Delivery ◽  
Management Literature ◽  
Effectiveness And Efficiency ◽  
Stage 1 ◽  
Demand Profile ◽  
Theoretical Saturation

Purpose The purpose of this paper is to explore the applicability and utility of supply chain (SC) segmentation through demand profiling to improve the effectiveness and efficiency of infrastructure projects by identifying different types of project demand profiles. Design/methodology/approach A three-stage abductive research design was adopted. Stage 1 explored the applicability of SC segmentation, through demand profiling, to the portfolio of infrastructure projects in a utility company. Stage 2 was an iterative process of “theory matching”, to the portfolio, programme and project management literature. In stage 3, theoretical saturation was reached and “theory suggestions” were made through four propositions. Findings Four propositions outline how SC segmentation through project demand profiling could improve the effectiveness and efficiency of infrastructure projects. P1: the ability to recognise the different demand profiles of individual projects, and groups thereof, is a portfolio management necessity. P2: projects that contribute to the strategic upgrade of a capital asset should be considered a potential programme of inter-related repeatable projects whose delivery would benefit from economies of repetition. P3: the greater the ability to identify different demand profiles of individual/groups of projects, the greater the delivery efficiency. P4: economies of repetition developed through efficient delivery of programmes of repeatable projects can foster greater efficiency in the delivery of innovative projects through economies of recombination. Originality/value This work fills a gap in the portfolio management literature, suggesting that the initial screening, selection and prioritisation of project proposals should be expanded to recognise not only the project type, but also each project’s demand profile.

Predictive view of the value relevance of earnings in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hajam Abid Bashir ◽  
Manish Bansal ◽  
Dilip Kumar
Keyword(s):  
Capital Investment ◽  
Stock Return ◽  
Value Relevance ◽  
Stock Exchange ◽  
Predictive Ability ◽  
Cash Flows ◽  
Data Set ◽  
Content Type ◽  
Uniform Sample ◽  
Reported Earnings

Purpose This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings. Design/methodology/approach The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables. Findings Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results. Originality/value This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

Market overreaction, firm-specific information and macroeconomic variables in US and Chinese markets during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ooi Kok Loang ◽  
Zamri Ahmad
Keyword(s):  
Unemployment Rate ◽  
Stock Return ◽  
Design Methodology ◽  
Specific Information ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Real Gdp ◽  
Chinese Markets ◽  
The Impact ◽  
Loser Portfolio

PurposeThis study examines the impact of firm-specific information and macroeconomic variables on market overreaction of US and Chinese winner and loser portfolio before and during COVID-19.Design/methodology/approachThe firm-specific information includes firm size, volume, volatility, return of asset (ROA), return of equity (ROE), earning per share (EPS) and quick ratio while the macroeconomic variables are export rate, import rate, real GDP, nominal GDP, FDI, IPI and unemployment rate. Besides, one-third of the top performance stocks are categorized as winner portfolio while one-third of lowest performance stocks are categorized as loser portfolio. This study uses AECR to indicate stock return and measure market overreaction. GAECR is used to determine contrarian profit. The data range of pre-COVID-19 is from 1-Jan-2015 to 31-Dec-2019 while the period of COVID-19 is from 1-Jan-2020 to 31-Dec-2020.FindingsIn pre-COVID-19, firm-specific information (volatility, ROA, ROE and EPS) and macroeconomic variables are found to be correlated to stock return in US and Chinese portfolios except Chinese winner portfolio. Nonetheless, the impact of firm-specific information has vanished and macroeconomic variables are significant to stock return in COVID-19. It shows that investors rely on the economic indicators to trade in turbulent period due to emergence of COVID-19 as a disruption in market. Furthermore, US and Chinese portfolios are overreacted during COVID-19. Chinese loser portfolio has higher tendency of overreaction than US loser portfolio while US winner portfolio has higher tendency of overreaction than Chinese winner portfolio.Originality/valueThe results of this study assists academician, practitioners and investors on understanding and create awareness to the existence of market overreaction and the determinants that can cause the phenomenon.

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