Size-related anomalies and stock return seasonality: Further empirical evidence

This study examines, month-by-month, the empirical relation between abnormal returns and market value of NYSE and AMEX common stocks. Evidence is provided that daily abnormal return distributions in January have large means relative to the remaining eleven months, and that the relation between abnormal returns and size is always negative and more pronounced in January than in any other month — even in years when, on average, large firms earn larger risk-adjusted returns than small firms. In particular, nearly fifty percent of the average magnitude of the ‘size effect’ over the period 1963–1979 is due to January abnormal returns. Further, more than fifty percent of the January premium is attributable to large abnormal returns during the first week of trading in the year, particularly on the first trading day.     

The impact of Saturday trading on stock returns: Evidence from the Tokyo stock exchange January 1976 to January 1989

This paper examines the impact of TSE Saturday trading on daily TOPIX returns and TSE trading volume over the January 1976 to January 1989 period. Saturday trading is shown to have no significant impact on mean stock returns for the other days of the week. However, a significant shift in the pattern of Monday and Tuesday TOPIX returns is documented in the post-August 1986 period. This shift does not appear to be related to Saturday trading. TSE Saturday trading is found to have a significant impact on the variance of stock returns on surrounding days. In addition, trading volume is significantly lower on trading days surrounding Saturday trading. These findings are relevant to the timing of portfolio adjustment decisions.     

The impact of company responses to exchange queries on the Australian equity market

This study examines the impact of company responses to trading‐induced queries made by the Australian Securities Exchange over the period January 2007–December 2008, inclusive. We utilise event study methodology and a matched sample approach to assess the impact of trading query announcements. We use multivariate analysis to investigate any cross‐sectional determinants affecting abnormal returns and volume, and find significant positive shareholder wealth and volume effects associated with query announcements. Further, the unexplained abnormal returns observed prior to the announcement of the trading query persist post‐announcement. Subsequent analysis reveals the industry effect reported in the literature loses significance after accounting for sample selection bias.     

Volume and skewness in international equity markets

We examine the relation between trading volume and skewness in 11 international stock markets using daily and monthly data from January 1980 to August 2004. We construct single equation and VAR models of the relation between the first three moments of market returns and trading volumes. Our results show hitherto unrecognised channels of influence, and support the investor heterogeneity approach to explaining return asymmetries.     

Legal insider trading and market efficiency

Does legal insider trading contribute to market efficiency? Using refinements proposed in the recent microstructure literature, we analyzed the information content of legal insider trading. We used data on 2110 companies subject to 59,244 aggregated daily insider trades between January 1995 and the end of September 1999. Our main finding is that, even though financial markets do not respond strongly in terms of abnormal returns to insider trading activities, the significant change in price sensitivity to relative order imbalance due to abnormal insider trades reveals that price discovery is hastened on insider trading days.     

The January Effect and Aggregate Insider Trading

This study investigates the seasonal pattern of aggregate insider trading to help distinguish between two competing explanations for the seasonal pattern of security returns. The first potential explanation examined is that the January effect arises from predictable changes in turn-of-the-year demand for securities. The second potential explanation examined is that the January effect represents compensation for the higher risk of trading against informed traders at the turn of the year.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

How to detect illegal corporate insider trading? A data mining approach for detecting suspicious insider transactions

Only in the U.S. Stock Exchanges, the daily average trading volume is about 7 billion shares. This vast amount of trading shows the necessity of understanding the hidden insights in the data sets. In this study, a data mining technique, clustering based outlier analysis is applied to detect suspicious insider transactions. 1,244,815 transactions of 61,780 insiders are analysed, which are acquired from Thomson Financial, covering a period of January 2010–April 2017. In order to detect outliers, similar transactions are grouped into the same clusters by using a two‐step clustering based outlier detection technique, which is an integration of k‐means and hierarchical clustering. Then, it is shown that outlying transactions earn higher abnormal returns than non‐outlying transactions by using event study methodology.     

The UK equity market around the ex-split date

Using UK stock market data this study unveils positive abnormal returns on and around the ex-split date. These excess returns are partially predictable using the publicly available information prior to the ex-split date. There is also a persistent increase in the post-split volatility of these stocks with the results being robust to the choice of the volatility proxy. Post-split volatility is found to be positively related to trading activity. Contrary to the US findings, volatility dynamics following the stock split are better captured by changes in the daily trading volume rather than by the number of trades.     

Digital Tulips? Returns to investors in initial coin offerings

We analyze a dataset of 2390 completed ICOs, which raised a total of $12 billion in capital, nearly all since January 2017. We find evidence of significant ICO underpricing, with average returns of 179% from the ICO price to the first day's opening market price, over a holding period that averages just 16 days. After trading begins, tokens continue to appreciate in price, generating average buy-and-hold abnormal returns of 48% in the first 30 trading days. We also study the determinants of ICO underpricing and relate cryptocurrency prices to Twitter activity.     

TAX-LOSS SELLING,INSTITUTIONAL INVESTORS,AND THE JANUARY EFFECT: A NOTE

In this note we test the hypothesis that trading by tax-motivated individual investors is responsible for the January effect. We examine the ownership structure of a large sample of firms over a four-year period and find that the small firms that usually exhibit high January returns have low institutional ownership. After controlling for firm size, we still find that institutional ownership is significantly related to January abnormal returns. These results suggest that one reason the January effect may concentrate in small firms is because these firms are held by tax-motivated individual investors.     

Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects

This paper provides empirical support for the notion that Autoregressive Conditional Heteroskedasticity (ARCH) in daily stock return data reflects time dependence in the process generating information flow to the market. Daily trading volume, used as a proxy for information arrival time, is shown to have significant explanatory power regarding the variance of daily returns, which is an implication of the assumption that daily returns are subordinated to intraday equilibrium returns. Furthermore, ARCH effects tend to disappear when volume is included in the variance equation.     

The dynamics of individual and institutional trading on the Shanghai Stock Exchange

We investigate the daily dynamic relation between returns and institutional and individual trades in the emerging Chinese stock market. Consistent with the hypotheses of trend-chasing and attention-grabbing trading, we find that the response of individual trading to return shocks is much stronger than that of institutional trading, and individuals are net buyers following return shocks. Second, we find that past individual buys and sells have predictive power, whereas past institutional buys and sells have predictive power for market returns in longer horizons. However, both institutional and individual trading activities are more strongly related to past trades than past returns, and individual trading is also influenced by institutional trading. Moreover, we find that institutional trading in the largest quintile leads the trading in the smallest quintile, but no such lead–lag relation is found for individual trades. Finally, we find that the average cumulative abnormal trading volume of individuals is much larger than that of institutions around the firms' earnings announcement, suggesting that less-informed individual investors are more heavily influenced by firm-specific information disclosures and attention-grabbing events.     

The anomalous stock market behavior of small firms in January: Empirical tests for tax-loss selling effects

Small firms experience large returns in January and exceptionally large returns during the first few trading days of January. The empirical tests indicate that the abnormally high returns witnessed at the very beginning of January appear to be consistent with tax-loss selling. However, tax-loss selling cannot explain the entire January seasonal effect. The small firms least likely to be sold for tax reasons (prior year ‘winners’) also exhibit large average January returns, although not unusually large returns during the first few days of January.     

Shorting flows,public disclosure,and market efficiency

Shorting flows remain a significant predictor of negative future stock returns during 2010–2015, when daily short-sale volume data are published in real time. This predictability decays slowly and lasts for a year. Long-term shorting flows are more informative than short-term shorting flows. Indeed, abnormal short-term shorting flows do not predict future returns or anticipate bad news. We find that short sellers exploit prominent anomalies. A comparison with the Regulation SHO data indicates that the predictability is much shorter-term during 2005–2007. Short sellers appear to have shifted from trading on short-term private information to trading on long-term public information that is gradually incorporated into prices.     

The effect of US holidays on the European markets: when the cat’s away…

This paper presents evidence of the existence of a return effect on European stock markets coinciding with New York Stock Exchange (NYSE) holidays, which is particularly marked after positive closing returns on the NYSE the previous day. The effect is large enough to be exploited by trading index futures. This anomaly cannot be explained by seasonal effects, such as the day of the week effect, the January effect or the pre‐holiday effect, nor is it consistent with behavioural finance models that predict positive correlation between trading volume and returns. However, examination of factors such as information volume or investor mix provides a reasonable explanation.     

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.     

Social media bots and stock markets

This study examines the link between information spread by social media bots and stock trading. Based on a large sample of tweets mentioning 55 companies in the FTSE 100 composites, we find significant relations between bot tweets and stock returns, volatility, and trading volume at both daily and intraday levels. These results are also confirmed by an event study of stock response following abnormal increases in the volume of tweets. The findings are robust to various specifications, including controlling for traditional news channel, alternative measures of volatility, information flows in pretrading hours, and different measures of sentiment.     

WHAT'S IN A NICKNAME? PRICE AND VOLUME EFFECTS OF A PURE TICKER SYMBOL CHANGE

We examine the effect of a voluntary change in ticker symbol without other contemporaneous corporate events such as a name change. We find significant declines in trading volume and prices on the effective date of the ticker change. We segment the sample by exchange listing, share turnover activity, and subperiod. We observe declines in trading volume in all subsamples and negative abnormal returns in recent years.     

The turn-of-the-year effect and tax-loss-selling by institutional investors

Prior studies attribute the turn-of-the-year effect whereby small capitalization stocks earn unusually high returns in early January to tax-loss-selling by individual investors and window-dressing by institutional investors. My results suggest that a significant portion of the effect on turn-of-the-year returns that prior studies attribute to window-dressing is actually attributable to tax-loss-selling by institutional investors. Among small capitalization stocks, I find that institutional investors with strong tax incentives and weak window-dressing incentives realize significantly more losses in the fourth quarter than in the first three quarters of the calendar year, and that their fourth quarter realized losses have a significant impact on turn-of-the-year returns. A one percentage point change in these institutional investors' fourth quarter realized losses scaled by a firm's market capitalization results in an increase of 47 basis points in the firm's average daily return over the first three trading days of January, which represents a 46 percent change for the mean firm.