Probability of Information-Based Trading as a Pricing Factor in Taiwan Stock Market

Lu, Ralph; Wong, Woon K.

doi:10.2139/ssrn.1115419

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…According to Easley et al (2002), the average PIN in the USA from 1983 to 1998 is 0.191. According to Lu and Wong (2008), the average PIN in Taiwan from 1997 to 2005 is 0.20. Our estimate of PIN is larger than those in the relatively more developed economies, which indicates that Chinese firms have higher levels of information asymmetry.…”

Section: Information Asymmetrymentioning

confidence: 99%

Information Asymmetry and Leverage Adjustments: A Semiparametric Varying-coefficient Approach

Jin

Zhao

Kumbhakar

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Information asymmetry reflects the risk and uncertainty faced by investors and is a measure of a firm's transparency. High information asymmetry could increase the cost of external financing, which in turn impedes a firm's leverage (debt–asset ratio) adjustment. The paper studies the adjustment speed towards the target leverage in the presence of information asymmetry by using microlevel data from China. In contrast with previous studies, we allow heterogeneity in the adjustment speed coefficient by modelling it as a non‐parametric function of information asymmetry and other firm characteristics. This refinement not only allows for more flexibility in the model, but it also facilitates further exploration into the differences and determinants of firms’ financing behaviour. We uniquely build the firm level measure of information asymmetry into the traditional partial leverage adjustment framework. Based on our firm level measure of the adjustment speed, our paper explores why the leverage adjustment speed matters by examining its association with corporate performance indicators. We find that China's firms do have leverage targets and they slowly adjust towards these targets. We also find that the adjustment speed decreases with an increase in information asymmetry. Overall, firms which converge towards their targets faster perform better in value, profitability, investment and costs.

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Section: Information Asymmetrymentioning

confidence: 99%

Information Asymmetry and Leverage Adjustments: A Semiparametric Varying-coefficient Approach

Jin

Zhao

Kumbhakar

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…In addition, we want to test for the relation of liquidity with stock returns. Finally, inspired by Easley et al [15] and Lu and Wong [22], we introduce the effect of momentum and volatility too. The calculations of these asset pricing factors are given in the following paragraphs.…”

Section: Data and Preliminariesmentioning

confidence: 99%

“…It is well-known that the historical stock prices contain information about the future stock returns, so we also involve two additional factors, momentum and volatility, which have been widely used in the previous asset pricing literature including Easley et al [15] and Lu and Wong [22]. In our paper, momentum is calculated as the average percentage of weekly returns over the preceding two years, and volatility is the standard deviation of the weekly returns over the preceding two years.…”

Section: Momentum and Volatilitymentioning

confidence: 99%

The Risk of Individual Stocks’ Tail Dependence with the Market and Its Effect on Stock Returns

Fan

Girardin

Wong

et al. 2015

Discrete Dynamics in Nature and Society

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Traditional beta is only a linear measure of overall market risk and places equal emphasis on upside and downside risks, but actually the latter is always much stronger probably due to the trading mechanism like short-sale constraints. Therefore, this paper employs the nonlinear measure, tail dependence, to measure the extreme downside risks that individual stocks crash together with the whole market and investigates whether such tail dependence risks will affect stock returns. Our empirical evidence based on Shanghai A shares confirms that most stocks display nonnegligible tail dependence with the whole market, and, more importantly, such tail dependence risks can indeed provide additional information beyond beta and other factors for asset pricing. In cross-sectional regression, it is proved that this tail dependence does help to explain monthly returns on Shanghai A shares, whereas the time-series regression further indicates that mimicking portfolio returns for tail dependence can capture strong common variation of Shanghai A stock returns.

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How to survive and compete: the impact of information asymmetry on productivity

2019

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Probability of Information-Based Trading as a Pricing Factor in Taiwan Stock Market

Cited by 7 publications

References 21 publications

Information Asymmetry and Leverage Adjustments: A Semiparametric Varying-coefficient Approach

Information Asymmetry and Leverage Adjustments: A Semiparametric Varying-coefficient Approach

The Risk of Individual Stocks’ Tail Dependence with the Market and Its Effect on Stock Returns

How to survive and compete: the impact of information asymmetry on productivity

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