Determinants of Investor Demand for Cross‐Listed Firms

By focusing on the decisions of investors to invest in cross‐listed stocks, this paper presents new evidence on why we observe striking differences in the percentage of trade in foreign markets for cross‐listed stocks. With a large sample of Toronto Stock Exchange (TSX) stocks cross‐listed in the U.S. and Canada, we document the effect of investor recognition and risk characteristics on the distribution of trading volume. Firms that are more visible to American investors are traded more heavily in the U.S. At the same time, firms that offer diverse risk characteristics are attractive to Americans. While investors understand the benefits of international diversification, as they are attracted to stocks that are different (e.g., the stock of small firms with few assets in the U.S.), they also seek stocks that provide them with high returns.     

Internet search intensity,liquidity and returns in emerging markets

Using a sample of listed companies in the Vietnam stock market from 2013 to 2018, this paper investigates the linkage between Internet search intenseness and stock returns and trading volume. The empirical results confirm the “price pressure hypothesis” that search intensity is positively associated with subsequent stock returns and trading volume. It also finds that the positive effects on stock returns are not temporary but remain for the long term although some reversals occur. The results show that the effects of search intensity on stock returns are higher for large stocks than for small stocks. The findings also reveal that stocks that attract more attention from the public are exposed to higher market risk. These findings have not been documented in the literature so they enrich the information on the relationship between Internet search intenseness and stock market returns, especially for emerging markets where Internet user numbers are sharply increasing.     

O/S: The relative trading activity in options and stock

Relatively little is known about the trading volume in derivatives relative to the volume in underlying stocks. We study the time-series properties and the determinants of the options/stock trading volume ratio (O/S) using a comprehensive cross-section and time-series of data on equities and their listed options. O/S is related to many intuitive determinants such as delta and trading costs, and it also varies with institutional holdings, analyst following, and analyst forecast dispersion. O/S is higher around earnings announcements, suggesting increased trading in the options market. Further, post-announcement absolute returns are positively related to pre-announcement O/S, which suggests that at least part of the pre-announcement options trading is informed.     

Investor response to online value line rank changes: Foreign versus local stocks

We find that the Value Line effect is confined to U.S. stocks. U.S. listed stocks significantly outperform their benchmarks long after Value Line Timeliness rank change announcements. In contrast, we find no evidence of a Value Line effect for recommendations made for foreign stocks that list on U.S. exchanges, nor for those that list outside the U.S. For days surrounding rank change announcements, trading volume is abnormally high for U.S. listed stocks, but remains unchanged for the foreign stock sample. Our findings are unchanged after controlling for unique valuation challenges, varying market conditions, beta, firm size, book-to-market, momentum, and post earnings announcement effects.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Trading Volume: Implications of an Intertemporal Capital Asset Pricing Model

We derive an intertemporal asset pricing model and explore its implications for trading volume and asset returns. We show that investors trade in only two portfolios: the market portfolio, and a hedging portfolio that is used to hedge the risk of changing market conditions. We empirically identify the hedging portfolio using weekly volume and returns data for U.S. stocks, and then test two of its properties implied by the theory: Its return should be an additional risk factor in explaining the cross section of asset returns, and should also be the best predictor of future market returns.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

On the Stability of the Cross‐Section of Expected Stock Returns in the Cross‐Section: Understanding the Curious Role of Share Turnover

In this paper, we shed further light on cross‐sectional predictors of stock return performance. Specifically, we explore whether the cross‐section of expected stock returns is robust within stock groups sorted by past monthly return. We find that the book/market and momentum effects are remarkably robust to sorting on past returns. However, share turnover is negatively related to future returns for stocks with abnormally low stock price performance in the recent past, but postively related to returns for well‐performing stocks. This casts doubt on the use of turnover as a liquidity proxy, but is consistent with turnover being a proxy for momentum trading which pushes prices in the direction of past price movements. Our results are robust to both NYSE/AMEX and Nasdaq stocks, and also robust to stratifying the sample by time period.     

THE EFFECT OF CROSS‐LISTING ON TRADING VOLUME: REDUCING SEGMENTATION VERSUS SIGNALING INVESTOR PROTECTION

We examine the relation between cross‐listing on the U.S. and UK regulated and unregulated exchanges and trading volume for a sample of 500 foreign firms from 34 countries. We find that the increase in trading volume is a function of both reducing segmentation and signaling investor protection. In addition, we find that home market trading volume, firm size, firm returns, and analyst forecast accuracy are the major determinants of a firm's trading volume. We also show that U.S. and UK investors trade foreign securities that originate from low‐investor‐protection countries more than they trade those from high‐investor‐protection countries, which is consistent with the bonding hypothesis.     

Speculate against speculative demand

Measuring individual investors' speculative demand for stocks using the Google search volume index (hereafter “SVI”) on penny stocks, we examine how it relates to the return dynamics of U.S. stock indices. Speculative demand leads to a short-term return reversal. A simple trading strategy that sells a stock index when SVI is high and buys it otherwise generates annual excess returns of up to 20% over the buy-and-hold strategy. Applying the trading strategy to the corresponding ETFs and index futures yields similar results. Transaction costs, liquidity risk and strong time variation of the excess returns can potentially limit the exploitation of arbitrage opportunities.     

Empirical Identification of Non-Informational Trades Using Trading Volume Data

This paper empirically identifies non-informational and informational trades using stock returns and trading volume data of the U.S., Japanese, and U.K. stock markets and five individual firms. We achieve the identification by imposing a restriction from theoretical considerations. Our results show that trading volume is mainly driven by non-informational trades, while stock price movements are primarily driven by informational trades. We also find that, around the 1987 stock market crash, trading volumes due to non-informational trades increased dramatically, while the decline in stock market prices was due mainly to informational trades. Increases in volatilities both in returns and in trading volumes during and after the crash are mainly due to non-informational trades. Regarding the trading volume-serial correlation in the stock returns relationship, we find evidence that is consistent with theoretical predictions that non-informational components can account for high trading volume accompanied by a low serial correlation of stock returns.     

The Impact of Cross‐Listing on the Home Market's Information Environment and Stock Price Efficiency

We empirically examine changes in information asymmetry and informational efficiency of cross‐listed stocks in their home market around a cross‐listing in the United States. We estimate intraday market microstructure measures of information asymmetry and price efficiency, and find that a U.S. cross‐listing significantly improves the quality of a firm's information environment and stock price efficiency in the home market. This improvement is stronger for cross‐listings that take place after the adoption of Sarbanes‐Oxley Act. Our results demonstrate that stricter disclosure from a U.S. cross‐listing is beneficial, in line with the legal and reputational bonding hypotheses.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

Aggregate Jump and Volatility Risk in the Cross‐Section of Stock Returns

We examine the pricing of both aggregate jump and volatility risk in the cross‐section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected returns. Consistent with theory, stocks with high sensitivities to jump and volatility risk have low expected returns. Both can be measured separately and are important economically, with a two‐standard‐deviation increase in jump (volatility) factor loadings associated with a 3.5% to 5.1% (2.7% to 2.9%) drop in expected annual stock returns.     

THE CHOICE OF TRADING VENUE AND RELATIVE PRICE IMPACT OF INSTITUTIONAL TRADING: ADRs VERSUS THE UNDERLYING SECURITIES IN THEIR LOCAL MARKETS

We address two important themes associated with institutions’ trading in foreign markets: (1) the choice of trading venues (between a company's listing in its home market and that in the United States as an American Depositary Receipt [ADR]) and (2) the comparison of trading costs across the two venues. We identify institutional trading in both venues using proprietary institutional trading data. Overall, our research underscores the intuition that the choice of institutional trading in a stock's local market or as an ADR is a complex process that embodies variables that measure the relative adverse selection and liquidity at order, stock, and country levels. Institutions route a higher percentage of trades to more liquid markets, and these trades are associated with higher cumulative abnormal returns. We also find that institutional trading costs are generally lower for trading cross‐listed stocks on home exchanges even after controlling for selection bias.     

Communication and Comovement: Evidence from Online Stock Forums

We study investor communication and stock comovement using a novel data set from an active online stock forum in China. We find substantial comovement among the returns of a stock and its “related stocks,” which are frequently discussed in the subforum dedicated to the given stock. Comovement is greater when the discussion of related stocks is more intensive. Further, the effect of communication on comovement is stronger for stocks associated with higher information uncertainty. Codiscussed stocks are more actively traded and experience more correlated trading. A trading strategy that exploits communication‐driven comovement generates abnormal returns. Our findings highlight the impact of investor communication on asset comovement.     

Market Segmentation and Cross‐predictability of Returns

We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value‐relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross‐predict each other's returns, (ii) the magnitude of return cross‐predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.     

Evidence and Implications of Increases in Trading Volume Around Exchange Listings

After controlling for market volume trends and differences in volume measurement between the Nasdaq and the exchanges, we find that mean trading volumes increase significantly for Nasdaq stocks that list on the Amex or the NYSE. Furthermore, stocks with low (high) pre‐listing volume tend to realize the largest volume increases (decreases) as well as the best (worst) post‐listing performance. Our results support the hypothesis that stocks with high past trading volumes tend to experience lower future returns, and shed new light on the nature and possible causes of poor post‐listing stock performance.     

Is Gold a Hedge or a Safe Haven? An Analysis of Stocks,Bonds and Gold

Is gold a hedge, defined as a security that is uncorrelated with stocks or bonds on average, or is it a safe haven, defined as a security that is uncorrelated with stocks and bonds in a market crash? We study constant and time‐varying relations between U.S., U.K. and German stock and bond returns and gold returns to investigate gold as a hedge and a safe haven. We find that gold is a hedge against stocks on average and a safe haven in extreme stock market conditions. A portfolio analysis further shows that the safe haven property is short‐lived.