Constructing a Multifactor Model for the Shanghai Stock Exchange

ABSTRACT

We examine the validity of five factor models for explaining the time-series and cross-sectional variations in stock returns in the Shanghai Stock Exchange. The factor models include four models proposed by previous literature. Moreover, we propose a four-factor model (comprising market, size, book-to-market, and sales-to-price factors) to explain variations of stock returns in the Shanghai Stock Exchange. The results show that the Shanghai stock market exhibits size, book-to-market, and sales-to-price effects. Both the adjusted coefficient of determination and regression model intercepts indicate that the proposed four-factor model explains variations of stock returns in the Shanghai Stock Exchange more effectively in comparison with other multifactor models.     

Size,value and momentum in stock returns: evidence from India

This paper examines the effects of size, value and momentum on the cross-sectional relation between expected returns and risk in the Indian stock market. We find that the conditional Carhart four-factor model empirically describes the variation of cross-section of return better than the unconditional model. When size, book-to-market and momentum effects are controlled in the conditional model, the positive relation of market beta, book-to-market and momentum with expected returns remains economically and statistically significant. However, this evidence is found to be subject to characteristics of test portfolios. The expected returns are sensitive to changes in predictive macroeconomic variables.     

Speculative capital and currency carry trades

In this paper, we study a two-country general equilibrium model with partially segmented financial markets, where hedge funds emerge endogenously. Empirically, we show that the hedge fund investment strategy predicted by our model, which we call the “risk-adjusted carry trade” strategy, explains more than 16% of the overall hedge fund index returns and more than 33% of the fixed income arbitrage sub-index returns. The flow of new money to hedge funds affects market interest rates, exchange rates, and both the hedge funds’ contemporaneous and expected future returns as predicted by the model.     

Housing Collateral, Consumption Insurance, and Risk Premia: An Empirical Perspective

In a model with housing collateral, the ratio of housing wealth to human wealth shifts the conditional distribution of asset prices and consumption growth. A decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. Using aggregate data for the United States, we find that a decrease in the ratio of housing wealth to human wealth predicts higher returns on stocks. Conditional on this ratio, the covariance of returns with aggregate risk factors explains 80% of the cross‐sectional variation in annual size and book‐to‐market portfolio returns.     

Overreaction after Controlling for Size and Book-to-Market Effects and its Mimicking Portfolio in Japan

In this paper we observe that firm size (SZ) and book-to-market (BM) cannot fully explain stock returns on prior-return- (PR-) based portfolios in the Japanese stock market. The overreaction effect after controlling for the SZ and BM effects is significant and persistent, and accounts for a large part of the zero-investment returns on the loser to the winner. We therefore propose a new mimicking portfolio whose returns mimic the common factor in returns related to overreaction. Our evidence shows that the proposed four-factor model captures common variation in returns on portfolios, based on stocks SZ, BM, and PR, better than the well-known three-factor model does.     

A comparison among various dimensions of illiquidity effect: A case study of Finland

We study different dimensions of the illiquidity effect on asset returns in the Finnish market. The market illiquidity is measured as unexpected rises and falls in average monthly zero returns across all stocks. We find that for the returns on the specific class of assets, a flight to the liquidity effect is the most important systematic risk among all dimensions of the illiquidity effect. In other words, higher returns for illiquid assets in good times compensate for a pronounced drop in those returns in bad times and vice versa. Furthermore, only one illiquidity-related factor has a similar pricing capacity as Fama and French's (1993) three-factor model and Carhart's (1997) four-factor model in the context of this study.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

Term premiums and default premiums in money markets

There are time-varying term and default premiums in the expected returns on money market securities. Default premiums decline with maturity and tend to be higher during recessions. Term premiums tend to increase with maturity during good times, but humps and inversions in the term structure of expected returns are common during recessions. Treasury bills produce positive average term premiums for the overall sample, but average term premiums for private-issuer securities are close to 0.0. A general conclusion is that variation in forward rates is primarily variation in current epected returns rather than in forecasts of changes in interest rates.     

Monetary Policy and the Cross-Section of Expected Stock Returns

Ample evidence shows that size and book-to-market equity explain significant cross-sectional variation in stock returns, whereas beta explains little or none of the variation. Recent studies also demonstrate that proxies for monetary stringency increase the explained variation in stock returns. We reexamine a three-factor model that includes beta, size, and book-to-market equity, while allowing monetary conditions to influence the relations between these risk factors and average stock returns. We find that ex-ante proxies for monetary stringency significantly influence the relations between stock returns and all three risk factors. Additionally, all three variables are found to contribute significantly to explaining cross-sectional returns in a three-factor model that includes the monetary sector.     

Cryptocurrency returns under empirical asset pricing

This study examines the predictability of cryptocurrency returns based on investors' risk premia. Prior studies that have examined the predictability of cryptocurrencies using various economic risk factors have reported mixed results. Our out-of-sample evidence identifies the existence of a significant return predictability of cryptocurrencies based on the cryptocurrency market risk premium. Consistent with capital asset pricing theory (CAPM), our results show that investors often require higher positive returns before taking on any additional risks, particularly in terms of riskier assets like cryptocurrencies. Tests involving the CAPM model demonstrates that the three largest cryptocurrencies have significant exposures to the proposed market factor with insignificant intercepts, demonstrating that the market factor explains average cryptocurrency returns very well.     

Return dispersion and expected returns

This paper proposes a two-factor asset-pricing model that incorporates market return and return dispersion. Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. The return dispersion model outperforms the CAPM, MVM, IVM, and FF-3M when using a set of 5×5 test portfolios constructed from NYSE and AMEX stock returns from August 1963 to December 2005. Return dispersion continues to play an important role in explaining the cross-sectional variation of expected returns, even when market volatility, idiosyncratic volatility, size, book-to-market factors, and a momentum factor are included. This study sheds some light on the ability of return dispersion to explain expected returns beyond the standard asset-pricing factors. Our finding suggests that return dispersion captures two dimensions of systematic risk: the business cycle and fundamental economic restructuring.     

A Market-based Risk Classification of Financial Institutions

In this article we derive, estimate, and analyze a multifactor model of the monthly holding period returns on the stocks of exchange-traded financial institutions. In addition to bond and equity returns, the factors include default, liquidity, and term structure risk premiums plus variables that measure changes in deposit demand. To ensure that our sample has a large number of firms, we use data from January 1981 through December 1988. The equity return explains a large share of time-series variation in financial institutions' returns. The additional factors implied by theory have little incremental explanatory power. The two-factor model regression coefficients have considerable cross-sectional variation. This permits us to group intermediaries into portfolios with similar risk exposures. These portfolios bear no relation to the SIC codes that group intermediaries by their charters and lines of business.     

Conditional Skewness in Asset Pricing Tests

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model that incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent per year. Our results suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.     

A Consumption-Based Explanation of Expected Stock Returns

When utility is nonseparable in nondurable and durable consumption and the elasticity of substitution between the two consumption goods is sufficiently high, marginal utility rises when durable consumption falls. The model explains both the cross‐sectional variation in expected stock returns and the time variation in the equity premium. Small stocks and value stocks deliver relatively low returns during recessions, when durable consumption falls, which explains their high average returns relative to big stocks and growth stocks. Stock returns are unexpectedly low at business cycle troughs, when durable consumption falls sharply, which explains the countercyclical variation in the equity premium.     

Can habit formation under complete market integration explain the cross-section of international equity risk premia?

In this article, we analyse whether the prominent habit formation model of Campbell and Cochrane (1999) can explain the cross-section of the G7 equity risk premia when formulated under the assumption of complete capital market integration. We test the conditional covariance representation of the model using a combined GARCH and GMM approach in the spirit of Bali (2008) and find that in comparison to the CAPM and the standard power utility CCAPM the habit model has superior explanatory power. It explains more than 90% of the cross-sectional variation in risk premia. Overall, our findings suggest that global consumption-based recession indicators and not returns of reference portfolios are key risk factors driving equity risk premia.     

Pairs trading in the UK equity market: risk and return

In this paper, we provide the first comprehensive UK evidence on the profitability of the pairs trading strategy. Evidence suggests that the strategy performs well in crisis periods, so we control for both risk and liquidity to assess performance. To evaluate the effect of market frictions on the strategy, we use several estimates of transaction costs. We also present evidence on the performance of the strategy in different economic and market states. Our results show that pairs trading portfolios typically have little exposure to known equity risk factors such as market, size, value, momentum and reversal. However, a model controlling for risk and liquidity explains a far larger proportion of returns. Incorporating different assumptions about bid-ask spreads leads to reductions in performance estimates. When we allow for time-varying risk exposures, conditioned on the contemporaneous equity market return, risk-adjusted returns are generally not significantly different from zero.     

Short-run money demand

The conventional wisdom holds that the short-run demand for money is unstable. This paper challenges the conventional view by finding a stable demand for M1 in U.S. data from 1959 through 1993. The approach follows previous work in interpreting long-run money demand as a cointegrating relation, and it uses Goldfeld's partial-adjustment model to interpret short-run dynamics. The key innovation is the choice of the interest rate in the money demand function. Most previous work uses a short-term market rate, but this paper uses the average return on “near monies”—the savings accounts and money market mutual funds that are close substitutes for M1. This choice helps rationalize the behavior of money demand; in particular, the increase in the volatility of velocity after 1980 is explained by increased volatility in the returns on near monies.     

Does investor recognition predict returns?

Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] shows that stocks about which not all investors are informed should yield a return premium. This premium depends on the shadow cost of incomplete information which in turn depends on the shareholder base, relative market size, and idiosyncratic risk. Utilizing a comprehensive database of Swedish shareholdings, we demonstrate that stock returns are positively related to the shadow cost. We also find that the shareholder base is negatively related to returns when controlling for size and idiosyncratic risk. Zero-cost portfolios based on the shadow cost/shareholder base yield substantial trading profits that are never positively correlated with the market and are only modestly explained by the four-factor model.     

The monetary origins of asymmetric information in international equity markets

Existing studies using low-frequency data have found that macroeconomic shocks contribute little to international stock market covariation. However, these papers have not accounted for the presence of asymmetric information where sophisticated investors generate private information about the fundamentals that drive returns in many countries. In this paper, we use a new microstructure data set to better identify the effects of private and public information shocks about U.S. interest rates and equity returns. High-frequency private and public information shocks help forecast domestic money and equity returns over daily and weekly intervals. In addition, these shocks are components of factors that are priced in a model of the cross-section of international returns. Linking private information to U.S. macroeconomic factors is useful for many domestic and international asset-pricing tests.     

On the validity of the augmented Fama and French’s (1993) model: evidence from the Hong Kong stock market

This paper investigates the performance of four-factor asset pricing model using Hong Kong stock returns. Our four-factor model is constructed by adding a momentum factor into the Fama and French’s (J Finance Econ 33(1):3–56, 1993) three-factor model. We find that the four-factor model may explain return variation using Hong Kong data. Our results show evidence that all the four factors are significant in the model and intercepts are not significant. In addition, the reasonably high values of adjusted R ² and the insignificance of an additional explanatory variable of residual standard deviation provide supportive evidence to the model. The robustness of the model is also checked for two effects: up- and down-market conditions and seasonal behavior.