Is the Market Portfolio a Dynamic Factor? Evidence from Individual Stock Returns

1997 ◽  
Vol 32 (3) ◽  
pp. 411-430 ◽  
Author(s):  
Gregory Koutmos
Keyword(s):  
Stock Returns ◽  
Dynamic Factor ◽  
Market Portfolio

scholarly journals Upper Bounds on Return Predictability

2017 ◽  
Vol 52 (2) ◽  
pp. 401-425 ◽  
Author(s):  
Dashan Huang ◽  
Guofu Zhou
Keyword(s):  
Stock Returns ◽  
Upper Bounds ◽  
Future Research ◽  
State Variables ◽  
Discount Factors ◽  
Predictive Regressions ◽  
Market Portfolio ◽  
Stochastic Discount Factors ◽  
Highly Correlated ◽  
Using Data

Can the degree of predictability found in data be explained by existing asset pricing models? We provide two theoretical upper bounds on theR2of predictive regressions. Using data on the market portfolio and component portfolios, we find that the empiricalR2s are significantly greater than the theoretical upper bounds. Our results suggest that the most promising direction for future research should aim to identify new state variables that are highly correlated with stock returns instead of seeking more elaborate stochastic discount factors.

scholarly journals Second-Order Stochastic Dominance, Reward-Risk Portfolio Selection, and the CAPM

2008 ◽  
Vol 43 (2) ◽  
pp. 525-546 ◽  
Author(s):  
Enrico De Giorgi ◽  
Thierry Post
Keyword(s):  
Stock Returns ◽  
Portfolio Selection ◽  
Stochastic Dominance ◽  
Risk Model ◽  
Second Order ◽  
Order Stochastic Dominance ◽  
Market Portfolio ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Mean Variance

AbstractStarting from the reward-risk model for portfolio selection introduced in De Giorgi (2005), we derive the reward-risk Capital Asset Pricing Model (CAPM) analogously to the classical mean-variance CAPM. In contrast to the mean-variance model, reward-risk portfolio selection arises from an axiomatic definition of reward and risk measures based on a few basic principles, including consistency with second-order stochastic dominance. With complete markets, we show that at any financial market equilibrium, reward-risk investors' optimal allocations are comonotonic and, therefore, our model reduces to a representative investor model. Moreover, the pricing kernel is an explicitly given, non-increasing function of the market portfolio return, reflecting the representative investor's risk attitude. Finally, an empirical application shows that the reward-risk CAPM captures the cross section of U.S. stock returns better than the mean-variance CAPM does.

scholarly journals The Systematic Risk and Leverage Effect in the Corporate Sector of Pakistan

2000 ◽  
Vol 39 (4II) ◽  
pp. 951-962
Author(s):  
Muhammad Nishat
Keyword(s):  
High Risk ◽  
Stock Returns ◽  
Risk Premium ◽  
Emerging Market ◽  
Systematic Risk ◽  
Institutional Development ◽  
Relative Price ◽  
Accounting Earnings ◽  
Stock Volatility ◽  
Market Portfolio

Poor corporate financing policies, non-competitive role of institutional development, a tendency towards the underpricing of initial offering resulted in high levered stocks in Karachi stock market (KSE). The KSE is termed as high risk high return emerging market where investors seek high risk premium Nishat (1999). The leverage is the most important factor which determines the firms risk premium [Zimmer (1990)]. Hamada (1969) and Bowman (1979) have demonstrated the theoretical relationship between leverage and systematic risk. Systematic risk of the leverage firm is equal to the without leverage systematic risk of the firm times one plus the leverage ratio (debt equity). Bowman (1979) established that systematic risk is directly related to leverage and the accounting beta (covariability of a firms’ accounting earnings with the accounting earnings of the market portfolio). One explanation of time-varying stock volatility is that leverage changes as the relative price of stocks and bonds change. Schwert (1989) demonstrated how a change in the leverage of the firm causes a change in the volatility of stock returns. Haugen and Wichern (1975) analysed the relationship between leverage and relative stability of stock value based on actuarial science1 and found that the duration of the debt is an important attribute in assessing the effect of leverage on stock volatility. If the leverage is persistent, or changing over time due to the issuance of additional debt, or if the firms are trying to return back the debt, this will change the risk of holding common stock. Kane, Marcus, and McDonald (1985) argued that a well defined metric for the advantage of debt financing is the difference in rates of return earned by optimally levered and unlevered firms, net of a return premium to compensate for potential bankruptcy costs.

scholarly journals A multi-dynamic-factor model for stock returns

1992 ◽  
Vol 52 (1-2) ◽  
pp. 245-266 ◽  
Author(s):  
Victor Ng ◽  
Robert F. Engle ◽  
Michael Rothschild
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Dynamic Factor ◽  
Dynamic Factor Model

Industries and Stock Return Reversals

2014 ◽  
Vol 50 (1-2) ◽  
pp. 89-117 ◽  
Author(s):  
Allaudeen Hameed ◽  
G. Mujtaba Mian
Keyword(s):  
Bearing Capacity ◽  
Stock Returns ◽  
Stock Return ◽  
Market Portfolio ◽  
Capital Constraints ◽  
Return Reversals ◽  
Over Time

AbstractThis paper documents pervasive evidence of intra-industry reversals in monthly returns. Unlike the conventional reversal strategy based on stock returns relative to the market portfolio, we document intra-industry return reversals that are larger in magnitude, consistently present over time, and prevalent across subgroups of stocks, including large and liquid stocks. These return reversals are driven by order imbalances and noninformational shocks. Consistent with reversals representing compensation for supplying liquidity, intra-industry reversals are stronger following aggregate market declines and volatile times, reflecting binding capital constraints and limited risk-bearing capacity of liquidity providers.

NONLINEAR RISK

2001 ◽  
Vol 5 (4) ◽  
pp. 621-646 ◽  
Author(s):  
Marcelle Chauvet ◽  
Simon Potter
Keyword(s):  
Business Cycle ◽  
Stock Returns ◽  
Conditional Expectation ◽  
Negative Relationship ◽  
Dynamic Factor ◽  
Expected Return ◽  
Conditional Moments ◽  
Risk Return ◽  
Excess Stock Returns ◽  
The Business Cycle

This paper analyzes the joint time-series properties of the level and volatility of expected excess stock returns. An unobservable dynamic factor is constructed as a nonlinear proxy for the market risk premia with its first moment and conditional volatility driven by a latent Markov variable. The model allows for the possibility that the risk–return relationship may not be constant across the Markov states or over time. We find an overall negative contemporaneous relationship between the conditional expectation and variance of the monthly value-weighted excess return. However, the sign of the correlation is not stable, but instead varies according to the stage of the business cycle. In particular, around the beginning of recessions, volatility rises substantially, reflecting great uncertainty associated with these periods, while expected return falls, anticipating a decline in earnings. Thus, around economic peaks there is a negative relationship between conditional expectation and variance. However, toward the end of a recession expected return is at its highest value as an anticipation of the economic recovery, and volatility is still very high in anticipation of the end of the contraction. That is, the risk–return relation is positive around business-cycle troughs. This time-varying behavior also holds for noncontemporaneous correlations of these two conditional moments.

scholarly journals Effects of Bank Specific Factors on Stock Returns of Listed Commercial Banks in Kenya

2021 ◽  
Vol 06 (08) ◽  
Author(s):  
Sheila Wamicwe ◽  
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Commercial Banks ◽  
The United States ◽  
Capital Adequacy ◽  
Market Portfolio ◽  
Specific Factors ◽  
Asset Quality ◽  
The Impact ◽  
Bank Specific Factors

The objective of this study was to establish the effects of bank specific factors on stock returns of listed commercial banks in Kenya, with four specific objectives; to determine the effect of capital adequacy on stock returns of listed commercial banks in Kenya, to determine the effect of asset quality on stock returns of listed commercial banks in Kenya, to determine the effect of earnings ability on stock returns of listed commercial banks in Kenya and to determine the effect of liquidity on stock returns of listed commercial banks in Kenya. The Kenyan banking sector instability within the stock market has been of great concern as depicted by continuous fluctuations in the stock prices of listed banks. Studies undertaken in other stock markets displayed mixed findings and much concentration has been on the United States, Turkey and Indonesian stock markets. Hence, a study providing a Kenyan perspective on the link between banks’ internal environment and stock returns of listed banks was crucial. The study was based on market portfolio theory, efficiency structure hypothesis and the buffer capital theory. The research targeted all the 11 listed commercial banks at the Nairobi Securities Exchange. Quarterly data was collected for the period 2010-2019. A pooled panel regression model was used in the estimation of the significance of the impact of the variables. Findings of the research established that capital adequacy and earnings had a significant effect on stock returns. The study recommends that commercial banks should improve their capital base and expand their asset quality through better loan management.

scholarly journals Quantile Risk–Return Trade-Off

2021 ◽  
Vol 14 (6) ◽  
pp. 249
Author(s):  
Nektarios Aslanidis ◽  
Charlotte Christiansen ◽  
Christos S. Savva
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Quantile Regressions ◽  
Lower Tail ◽  
Trade Off ◽  
Risk Return ◽  
Market Portfolio ◽  
Asset Pricing Models ◽  
The Us ◽  
European Stock Markets

We investigate the risk–return trade-off on the US and European stock markets. We investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market portfolio. We find that the risk–return trade-off is significantly positive at the upper tail (0.9 quantile), where the upper tail is large positive excess returns. The positive trade-off is as expected from asset pricing models. For the lower tail (0.1 quantile), that is for large negative stock returns, the trade-off is significantly negative. Additionally, for the median (0.5 quantile), the risk–return trade-off is insignificant. These results are recovered for the US industry portfolios and for Eurozone stock market portfolios.

scholarly journals A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment

2020 ◽  
Vol 12 (10) ◽  
pp. 3978 ◽  
Author(s):  
Yi Fu ◽  
Shuai Cao ◽  
Tao Pang
Keyword(s):  
Machine Learning ◽  
Stock Returns ◽  
Machine Learning Techniques ◽  
Selection Strategy ◽  
Dynamic Factor ◽  
Chinese Stock Market ◽  
Stock Selection ◽  
Bear Market ◽  
Back Testing ◽  
Current Period

In this paper, we consider a sustainable quantitative stock selection strategy using some machine learning techniques. In particular, we use a random forest model to dynamically select factors for the training set in each period to ensure that the factors that can be selected in each period are the optimal factors in the current period. At the same time, the classification probability prediction (CPP) of stock returns is performed. Historical back-testing using Chinese stock market data shows that the proposed CPP quantitative stock selection strategy performs better than the traditional machine learning stock selection methods, and it can outperform the market index over the same period in most back-testing periods. Moreover, this strategy is sustainable in all market conditions, such as a bull market, a bear market, or a volatile market.

An investor’s perspective on risk-models and characteristic-models

2016 ◽  
Vol 17 (3) ◽  
pp. 262-276 ◽  
Author(s):  
Christian Fieberg ◽  
Thorsten Poddig ◽  
Armin Varmaz
Keyword(s):  
Risk Factor ◽  
Portfolio Optimization ◽  
Stock Returns ◽  
Optimization Approach ◽  
Investment Strategies ◽  
Cross Sectional ◽  
Content Type ◽  
Factor Loadings ◽  
Market Portfolio ◽  
Out Of Sample

Purpose In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors. Design/methodology/approach To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum. Findings The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample. Originality/value The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.

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