Qiongbing Wu

Bank stock returns and economic growth

Previous research has established (i) that a countrys financial sector influence future economic growth and (ii) that stock market index returns affect future economic growth. We extend and tie together these two strands of the growth literature by analyzing the relationship between banking industry stock returns and future economic growth. Using dynamic panel techniques to analyze panel data from 18 developed and 18 emerging markets, we find a positive and significant relationship between bank stock returns and future GDP growth that is independent of the previously documented relationship between market index returns and economic growth. We also find that much of the informational content of bank stock returns is captured by country-specific and institutional characteristics, such as bank-accounting-disclosure standards, banking crises, enforcement of insider trading law and government ownership of banks.

Bank Stock Returns and Economic Growth

Previous research has established (i) that a countrys financial sector influence future economic growth and (ii) that stock market index returns affect future economic growth. We extend and tie together these two strands of the growth literature by analyzing the relationship between banking industry stock returns and future economic growth. Using dynamic panel techniques to analyze panel data from 18 developed and 18 emerging markets, we find a positive and significant relationship between bank stock returns and future GDP growth that is independent of the previously documented relationship between market index returns and economic growth. We also find that much of the informational content of bank stock returns is captured by country-specific and institutional characteristics, such as bank-accounting-disclosure standards, banking crises, enforcement of insider trading law and government ownership of banks.

Hazard versus Probit in Predicting U.S. Bank Failures: A Regulatory Perspective over Two Crises

We compare the out-of-sample forecasting accuracy of the time-varying hazard model developed by Shumway (2001) and the one-period probit model used by Cole and Gunther (1998). Using data on U.S. bank failures from 1985 – 1992, we find that, from an econometric perspective, the hazard model is more accurate than the probit model in predicting bank failures, but this improvement in accuracy results from incorporating more recent information in the hazard, but not the probit, model. When we limit both models to the same information set, we find that the one-period probit model is slightly more accurate than the time-varying hazard model. We also find that a parsimonious specification of the one-period probit model fit to data from the 1980s performs surprisingly well in forecasting bank failures during 2009 – 2010.We compare the out-of-sample accuracy of two methodologies—the time-varying hazard model of Shumway (2001) and the static probit model used by Cole and Gunther (1998)—in forecasting U.S. bank failures from both academic and regulatory perspectives. When we limit both models to financial data available at the time of prediction, we find that the probit model slightly outperforms the hazard model, indicating that the superior performance of hazard models documented in previous empirical research is attributable to use of more timely financial data rather than to incorporation of time-varying covariates. We also find that a parsimonious specification fit to data over 1985-1993 performs well in forecasting bank failures during 2009-2010—evidence that the characteristics of “distressed banks�? have experienced little change over the past two decades despite substantial changes in structure and regulation of the U.S. banking industry. Our findings support supervision focusing on banks’ traditional CAMELS risk ratios.

The high-volume return premium and changes in investor recognition

The phenomenon of high-volume return premium is generally attributed to the visibility hypothesis proposed by Gervais et al. (2001) based on the theoretical framework of Miller (1977) and Mertons (1987) investor recognition hypothesis. However, no existing empirical study has directly tested the visibility hypothesis due to the lack of high-frequency shareholding data. In this paper, we utilize the unique daily shareholding data for stocks listed on the Australian Stock Exchange to directly test this hypothesis by examining the relationship between the high-volume return premium and changes in investor recognition. We find that high-volume shocks do attract more investor attention and increase the investor base on the date of, and following, the shocks. This provides direct empirical evidence in support of the visibility explanation for the high-volume return premium. We also find that institutional and individual investors attend to different kinds of stocks; stocks attracting more institutional (individual) investors outperform (underperform) subsequent the volume shocks and exhibit a higher (lower) high-volume return premium. Our findings shed new light on the visibility hypothesis by showing that recognition/attention from institutional or individual investor is also crucial in determining the extent of the high-volume return premium and may help to reconcile the existing mixed empirical evidence across international markets.

Informed trade, uninformed trade and stock price delay

ABSTRACT The probability of informed trading (PIN), a measure of information-based trading risk, has been broadly applied to empirical studies on asset pricing. However, it is still controversial whether PIN measures exclusively the risk of firm-specific private information or it also captures the private interpretation of market wide public information. This article examines the relevance of PIN to the delayed response of stock prices to market-wide information. We find that PIN significantly explains individual stock price delay even controlling for size, liquidity and risk, and low-PIN stock prices adjust to market information more rapidly not only because of a notably high level of informed trading but also an even much higher level of uninformed trading. Our findings support the notion that PIN also captures the private skilled interpretation of public common factor information by sophisticated investors, and provide new empirical evidence on how information-based trading affects the speed at which stock prices adjust to information.

Who trades during the extreme swings in Chinese stock market

We investigate who are the investors that buy or sell on the days when the absolute value of market returns or the daily range of market index prices exceeds 5% in the Chinese stock market. Unlike Dennis and Strickland (2002) who find that institutional investors are buying (selling) more when there is a large market increase (decline) in U.S. markets, we find that institutional investors in China are systematically buying more than the less sophisticated individual investors during extreme market swings, particularly on extreme market-down days. Instead, we find strong evidence that institutional investors, primarily pension funds, provide a stabilizing influence during market downturn days. However, there is some evidence to suggest that institutional investors in aggregate do contribute towards pushing stock prices far beyond their fundamental values.

Informed Trade, Uninformed Trade, and Stock Price Delay

This paper examines how the probability of informed trading (PIN), a measure of information-based trading risk developed by Easley et al (1996), affects the speed at which stock prices adjust to market-wide information. We find that in all but the least active stock portfolios, prices of low PIN stocks are faster to impound market-wide news than those of high PIN stocks. PIN’s significance in explaining individual stock price delay is robust to the inclusion of size, liquidity and risk controls, but is subsumed by the level of uninformed trade. Our results suggest that low-PIN stock prices adjust to market information more rapidly as a result of a notably high level of informed trading as well as an even much higher level of uninformed trading, and the delayed response of high-PIN stock prices is primarily driven by the lack of uninformed trading. Our findings provide new empirical evidence regarding the channel through which trading affects the speed at which stock prices adjust to information, and support the notion that at least part of the informed trading captured by PIN relates to the skilled interpretation of public common factor information by sophisticated investors (Vega, 2006).

The High Volume Return Premium and Changes in Investor Recognition

Utilising unique shareholding data for Australian equities we examine whether the high volume return premium (‘HVRP’) is associated with changes in investor recognition as has been posited in various empirical studies. We confirm the existence of the premium in Australia as stocks which experience unusually high volume over a day significantly outperform stocks which experience unusually low volume. The premium is strongest in the first two weeks following the extreme volume event and among stocks with recent poor return performance. However, we show the HVRP is more likely attributable to divergent opinions than to the changes in risk-sharing that underline the pricing-effects of Merton’s (1987) investor recognition hypothesis. Specifically, the premium exists irrespective of increases or decreases in the breadth of ownership of a stock around the extreme volume event, and irrespective of shifts in investor numbers between individuals and institutions. Evidence that high volume stocks which attract more institutional (individual) investors but fewer individuals (institutions), show the highest (lowest) returns following the extreme volume event, suggests the premium in part may relate to superior stock selection or a private information advantage among institutional investors.

Information Asymmetry, Information Attributes and Industry Sector Returns

We examine whether the probability of informed trading (‘PIN’) is a determinant of stock returns in Australia, an alternative market with considerably different information attributes to the U.S. Uniquely, we contrast PIN’s price effect for the country’s historically dichotomous sectors, resources and industrials. Using data for the period from 1996 to 2010, we find a significantly positive relationship between PIN and expected returns among industrials sector stocks, providing evidence in support of Easley and O’Hara (2004). We observe no PIN premium among resources sector stocks and among those with no record of operating revenues, both notable for their speculative nature and uncertainty about true asset values. Our results are consistent with previous empirical evidence that documents strong investor behavioural biases in valuing extremely uncertain stocks or hard-to-value stocks (Kumar, 2009). Our findings shed light on the existing mixed evidence that a strong PIN premium exists in NYSE and AMEX but not in NASDAQ where high-tech stocks are prevalent, and suggest that caution is needed when applying PIN in the pricing of highly speculative stocks.