How Much Do Expected Returns and Expected Dividend Growth Contribute to Movements in Stock Returns? Issues of Weak Identification Make Existing Estimates Unreliable

2011 ◽  
Author(s):  
Jun Ma ◽  
Mark E. Wohar
Keyword(s):  
Stock Returns ◽  
Expected Returns ◽  
Weak Identification ◽  
Dividend Growth

scholarly journals ANALISIS PORTOFOLIO SEBAGAI DASAR PENGAMBILAN KEPUTUSAN INVESTASI SAHAM YANG TERDAFTAR DI BEI

IQTISHODUNA ◽  
2013 ◽  
Author(s):  
Sri Yati
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Analytical Method ◽  
Stock Exchange ◽  
Investment Decisions ◽  
Portfolio Analysis ◽  
Rate Of Return ◽  
Expected Returns ◽  
Risk Level ◽  
Expected Return

This study aims to analyze rate of return and risk as the tools to form the portfolio analysis on 15 the most actives stocks listed in Indonesian Stock Exchange. Descriptive analytical method is used to describe the correlation between three variables: stock returns, expected returns of stock market, and beta in order to measure the risk of stocks to help the investors in making the investment decisions. The research materials are 15 the most actives stocks listed in Indonesian Stock Exchange during 2008-2009. The results show that PT. Astra International Tbk. has the highest average expected return of individual stock (Ri) of 308,3355685, while PT. Perusahaan Gas Negara Tbk. has the lowest of -477,0827847. The average expected return of stock market (Rm) is 0,00247163. PT. Astra International Tbk. has the highest systematic risk level of 20229,14205, while the lowest of -147,5793279 is PT. Kalbe Farma Tbk. Furthermore, the results also indicate that there are 9 stocks can be combined to form optimal portfolio because they have positive expected returns.

An Empirical Assessment of CAPM, Market Model and APT: Evidence from the Greek Stock Market

2006 ◽  
Vol 7 (1) ◽  
pp. 87-118
Author(s):  
Petros Messis ◽  
George Emmanuel Iatridis ◽  
George Blanas
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Market Model ◽  
Expected Returns ◽  
Capital Asset Pricing ◽  
Arbitrage Pricing Theory ◽  
Total Risk ◽  
Pricing Theory ◽  
Athens Stock Exchange ◽  
Better Than

This paper uses three models to estimate the financial performance of 33 securities traded on the Athens Stock Exchange (ASE). To estimate the expected returns, this study uses the Capital Asset Pricing Model (CAPM), the Market Model, and the Arbitrage Pricing Theory (APT). There is significant evidence that the APT performs better than the CAPM and the Market Model, while the differences between the CAPM and the Market Model appear not to be significant. The three models are tested for a five-year period from 2000 to 2005. Total risk is significantly negatively related to returns during down markets, while this relationship is positive but not significant in up markets. There is evidence that, apart from the market risk, other risk factors that influence the stock returns are the inflation rate and the exchange rate.

scholarly journals Pricing Liquidity Risk with Heterogeneous Investment Horizons

2020 ◽  
pp. 1-36 ◽  
Author(s):  
Alessandro Beber ◽  
Joost Driessen ◽  
Anthony Neuberger ◽  
Patrick Tuijp
Keyword(s):  
Stock Returns ◽  
Risk Premium ◽  
Liquidity Risk ◽  
Asset Returns ◽  
Spillover Effects ◽  
Expected Returns ◽  
Pricing Model ◽  
Liquidity Premium ◽  
Asset Pricing Model ◽  
Illiquid Asset

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small.

scholarly journals THE RELATIONSHIP BETWEEN STOCK RETURN SKEWNESS AND BANK FEATURES

2018 ◽  
Vol 06 (02) ◽  
pp. 1850010
Author(s):  
SILVIA BRESSAN ◽  
ALEX WEISSENSTEINER
Keyword(s):  
Stock Returns ◽  
Empirical Evidence ◽  
Stock Options ◽  
Stock Return ◽  
Expected Returns ◽  
The Past ◽  
Small Banks ◽  
The Relationship ◽  
Forward Looking

This paper studies to what extent bank-specific characteristics relate to stock return skewness. The main finding is that stock return skewness decreases significantly in bank size, measured in terms of total assets, i.e stocks of large banks are less skewed than those of small banks. This result holds for backward-looking skewness computed using the past stock returns, as well as for forward-looking skewness extracted from stock options. We interpret the empirical evidence by arguing that bank size increases the likelihood to have severe losses, to the point that investors expect to be compensated by receiving higher expected returns.

scholarly journals Expected Returns and Expected Dividend Growth

10.3386/w9605 ◽  
2003 ◽  
Author(s):  
Martin Lettau ◽  
Sydney Ludvigson
Keyword(s):  
Expected Returns ◽  
Dividend Growth

Factor Timing

2020 ◽  
Vol 33 (5) ◽  
pp. 1980-2018 ◽  
Author(s):  
Valentin Haddad ◽  
Serhiy Kozak ◽  
Shrihari Santosh
Keyword(s):  
Stock Returns ◽  
Substantial Improvement ◽  
Expected Returns ◽  
Stochastic Discount Factor ◽  
Portfolio Performance ◽  
Oxford University ◽  
Static Factor ◽  
Cyclical Behavior ◽  
New Challenges ◽  
Published Paper

Abstract The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF is larger, is more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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