scholarly journals Time-varying relationship between corporate governance and expected stock returns

2019 ◽  
Vol 9 (1) ◽  
pp. 64-74
Author(s):  
Yosuke Kakinuma
Keyword(s):  
Corporate Governance ◽  
Stock Returns ◽  
Investment Strategy ◽  
Expected Returns ◽  
Time Varying ◽  
Markov Switching Model ◽  
Switching Model ◽  
Expected Stock Returns ◽  
Macroeconomic Determinants ◽  
Style Switching

This paper aims to analyze a time-varying relationship between corporate governance and expected stock returns in Thailand. The time variation of corporate governance premium is estimated by macroeconomic determinants using a two-state Markov switching model. The results indicate the presence of asymmetries in the variations of corporate governance premium over the Thai economic cycles. Investors can take advantage of the time-varying characteristics with the adaptation of switching investment strategy. Incorporation of style switching strategy with value premium in recessions and momentum premium in expansions improves expected returns of corporate governance-sorted portfolios.

scholarly journals Time-series evidence on corporate governance in Thailand: the effect on expected stock returns

2019 ◽  
Vol 16 (3) ◽  
pp. 332-340
Author(s):  
Yosuke Kakinuma
Keyword(s):  
Corporate Governance ◽  
Stock Returns ◽  
Stock Prices ◽  
Good Governance ◽  
Explanatory Power ◽  
Expected Returns ◽  
Long Run ◽  
Expected Stock Returns ◽  
Effect Regression ◽  
Governance Practice

This paper presents an empirical evidence of a time-varying relationship between corporate governance and its impacts on stock returns in Thailand. The governance grades assessed by the Thai Institute of Directors are used as governance measurement for the analysis. The parameters estimated by Fama-Macbeth regression indicate that firms with higher governance ratings generate greater expected stock returns in a long run. However, on yearly basis, the positive relationship deteriorates and loses explanatory power in the most of the tested years. The coefficients of governance ratings estimated by fixed effect regression are examined for statistical difference, which confirms that effect of corporate governance on stock returns differs year by year. While there are some distinct years that governance ratings affect stock prices positively, higher governance ratings lead to lower returns in other particular years. The both positive and negative magnitudes of corporate governance’s impact on expected returns do not stay the same over time. Good governance practice at a firm does not always yield positive returns to investors.

scholarly journals Corporate Governance and Expected Stock Returns: Evidence from Germany

2004 ◽  
Vol 10 (2) ◽  
pp. 267-293 ◽  
Author(s):  
Wolfgang Drobetz ◽  
Andreas Schillhofer ◽  
Heinz Zimmermann
Keyword(s):  
Corporate Governance ◽  
Stock Returns ◽  
Expected Stock Returns

scholarly journals Value versus Growth: Time-Varying Expected Stock Returns

2011 ◽  
Vol 40 (2) ◽  
pp. 381-407 ◽  
Author(s):  
Huseyin Gulen ◽  
Yuhang Xing ◽  
Lu Zhang
Keyword(s):  
Stock Returns ◽  
Growth Time ◽  
Time Varying ◽  
Expected Stock Returns

The predictive ability of stock market factors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Mohammed Elgammal ◽  
Fatma Ehab Ahmed ◽  
David Gordon McMillan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Five Factor Model ◽  
Trading Strategy ◽  
Expected Returns ◽  
Time Varying ◽  
Content Type ◽  
Varying Coefficients ◽  
One Step ◽  
Market Factors

Purpose This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions. Design/methodology/approach Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns. Findings Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns. Research limitations/implications The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy. Practical implications The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement. Originality/value The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

Can the Cross-Sectional Variation in Expected Stock Returns Explain Momentum?

2009 ◽  
Vol 44 (4) ◽  
pp. 777-794 ◽  
Author(s):  
George Bulkley ◽  
Vivekanand Nawosah
Keyword(s):  
Stock Returns ◽  
Expected Returns ◽  
Sample Period ◽  
Expected Return ◽  
Cross Sectional ◽  
Holding Period ◽  
Expected Stock Returns ◽  
The Cross

AbstractIt has been hypothesized that momentum might be rationally explained as a consequence of the cross-sectional variation of unconditional expected returns. Stocks with relatively high unconditional expected returns will on average outperform in both the portfolio formation period and in the subsequent holding period. We evaluate this explanation by first removing unconditional expected returns for each stock from raw returns and then testing for momentum in the resulting series. We measure the unconditional expected return on each stock as its mean return in the whole sample period. We find momentum effects vanish in demeaned returns.

scholarly journals A Comparison Study of Copula Models for European Financial Index Returns

2017 ◽  
Vol 9 (10) ◽  
pp. 155
Author(s):  
Paula V. Tofoli ◽  
Flavio A. Ziegelmann ◽  
Osvaldo Candido
Keyword(s):  
Subprime Crisis ◽  
Time Varying ◽  
Comparison Study ◽  
Markov Switching Model ◽  
Copula Models ◽  
New Approach ◽  
Switching Model ◽  
Long Period ◽  
Financial Index ◽  
Over Time

In this paper, we introduce a new approach to modeling dependence between international financial returns over time, combining time-varying copulas and the Markov switching model. We apply these copula models and also those proposed by Patton (2006), Jondeau and Rockinger (2006) and Silva Filho, Ziegelmann, and Dueker (2012) to the return data of the FTSE-100, CAC-40 and DAX indexes. We are particularly interested in comparing these methodologies in terms of the resulting dynamics of dependence and the models’ abilities to forecast possible capital losses. Because risks related to extreme events are important for risk management, we compare and select the models based on VaR forecasts. Interestingly, all the models identify a long period of high dependence between the returns beginning in 2007, when the subprime crisis was evolving. Surprisingly, the elliptical copulas perform best in forecasting the extreme quantiles of the portfolios returns.

Idiosyncratic Volatility and the Cross Section of Expected Returns

2008 ◽  
Vol 43 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Turan G. Bali ◽  
Nusret Cakici
Keyword(s):  
Stock Returns ◽  
Cross Section ◽  
Idiosyncratic Volatility ◽  
Expected Returns ◽  
Cross Sectional ◽  
Monthly Data ◽  
Portfolio Returns ◽  
Expected Stock Returns ◽  
The Cross ◽  
Data Frequency

AbstractThis paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that i) the data frequency used to estimate idiosyncratic volatility, ii) the weighting scheme used to compute average portfolio returns, iii) the breakpoints utilized to sort stocks into quintile portfolios, and iv) using a screen for size, price, and liquidity play critical roles in determining the existence and significance of a relation between idiosyncratic risk and the cross section of expected returns. Portfoliolevel analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse volatility-weighted), three breakpoints (CRSP, NYSE, equal market share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that no robustly significant relation exists between idiosyncratic volatility and expected returns.

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