中国金融学术研究网
CHINA FINANCIAL RESEARCH NETWORK

资本市场--资产定价
工作论文
2011-03-31 第3卷 第13期

编: 麻省理工学院斯隆管理学院金融学讲席教授,清华大学经管学院特聘教授。

执行主编: 杨之曙清华大学经济管理学院金融学副教授。


本期目录

No News Is Not Good News: Evidence from the Intraday Return Volatility- Volume Relationship in Shanghai Stock Exchange

Chandrasekhar Krishnamurti University of Southern QueenslandSchool of Accounting,Economics & Finance
Gary Tian University of WollongongSchool of Accounting and Finance
Min Xu Beihang UniversitySchool of Economics and Management
Guangchuan Li Beihang UniversitySchool of Economics and Management

How Predictable Is the Chinese Stock Market?

Fuwei Jiang Singapore Management UniversityDepartment of Finance
David E. Rapach Saint Louis UniversityDepartment of Finance
Jack K. Strauss Saint Louis UniversityDepartment of Finance
Jun Tu Singapore Management UniversityDepartment of Finance
Guofu Zhou Washington University in St. LouisDepartment of Finance

Asset Growth and Stock Returns: Evidence from Asian Financial Markets

Shaw Chen University of Rhode Island College of Busines
Tong Yu University of Rhode IslandDepartment of Finance
Tong Yao University of IowaDepartment of Finance
Ting Zhang University of DaytonDepartment of Finance

A Long-run Risks Model with Long- and Short-run Volatilities:Explaining Predictability and Volatility Risk Premium

Guofu Zhou Washington UniversityOlin School of Business
Yingzi Zhu Tsinghua UniversityDepartment of Finance

Valuation under the criterion of required payback period

Sanguo Luo Tsinghua UniversityDepartment of Finance


论文摘要

No News Is Not Good News: Evidence from the Intraday Return Volatility- Volume Relationship in Shanghai Stock Exchange

Chandrasekhar Krishnamurti University of Southern QueenslandSchool of Accounting,Economics & Finance
Gary Tian University of WollongongSchool of Accounting and Finance
Min Xu Beihang UniversitySchool of Economics and Management
Guangchuan Li Beihang UniversitySchool of Economics and Management

We find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets (Wu 2001, and Bae, Kim and Nelson 2007). Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by return chasing behaviour of investors in large stocks during bull markets. We also find that volatility increases after stock price declines in bear markets especially for small stocks. This increase in volatility of small stocks after bad news in bear markets is partly driven by liquidity. After controlling for liquidity shifts, there are no significant patterns in the volatility of small stocks during bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in Chinese stock market.

How Predictable Is the Chinese Stock Market?

Fuwei Jiang Singapore Management UniversityDepartment of Finance
David E. Rapach Saint Louis UniversityDepartment of Finance
Jack K. Strauss Saint Louis UniversityDepartment of Finance
Jun Tu Singapore Management UniversityDepartment of Finance
Guofu Zhou Washington University in St. LouisDepartment of Finance

We analyze return predictability for the Chinese stock market, including the aggregate market portfolio and the components of the aggregate market, such as portfolios sorted on industry, size, book-to-market and ownership concentration. Considering a variety of economic variables as predictors, both in-sample and out-of-sample tests highlight significant predictability in the aggregate market portfolio of the Chinese stock market and substantial differences in return predictability across components. Among industry portfolios, Finance and insurance, Real estate, and Service exhibit the most predictability, while portfolios of small-cap and low ownership concentration firms also display considerable predictability. Two key findings provide economic explanations for component predictability: (i) based on a novel out-of-sample decomposition, time-varying macroeconomic risk premiums captured by the conditional CAPM model largely account for component predictability; (ii) industry concentration and market capitalization significantly explain differences in return predictability across industries, consistent with the information-flow frictions emphasized by Hong, Torous, and Valkanov (2007).

Asset Growth and Stock Returns: Evidence from Asian Financial Markets

Shaw Chen University of Rhode Island College of Busines
Tong Yu University of Rhode IslandDepartment of Finance
Tong Yao University of IowaDepartment of Finance
Ting Zhang University of DaytonDepartment of Finance

This study examines the effect of corporate asset growth on stock returns using data on nine equity markets in Asia. For the period from 1981 to 2007, we find a pervasive negative relation between asset growth and subsequent stock returns. We further examine the determinants of this asset growth effect across markets. The negative relation between asset growth and stock returns is weaker in markets where firms’ assets growth rates are more homogeneous, and in markets where firms rely more on internal financing and bank financing for growth. On the other hand, corporate governance, investor protection, and legal origin do not influence the magnitude of the asset growth effect in the Asian markets.

A Long-run Risks Model with Long- and Short-run Volatilities:Explaining Predictability and Volatility Risk Premium

Guofu Zhou Washington UniversityOlin School of Business
Yingzi Zhu Tsinghua UniversityDepartment of Finance

In this paper, we extend the long-run risks model of Bansal and Yaron (BY, 2004) to allow both a long- and a short-run volatility component in consumption growth, long-run risks, and dividend growth. Our two volatility model better captures macroeconomic volatility than a single volatility model, and can reconcile simultaneously the large negative market variance risk premium, di?ering predictability in excess returns, consumption, dividends, and stock market volatility, all of which are di±cult to explain previously by the BY model.

Valuation under the criterion of required payback period

Sanguo Luo Tsinghua UniversityDepartment of Finance

Stock valuation is fundamentally important to finance. The current absolute and relative valuations do not wok in some common circumstances. This paper finds a new valuation method with the criterion of required payback period. The new method is a brand new way in valuation paralleling to the discounted cash flow method. This paper further derives the models of theoretical P/E, P/B and P/S based on the new method. These new valuation models are theoretical sound and flexible for valuing various stocks and market bubbles. They can also bridge the gap between the relative and absolute valuations.


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