Santa Claus Rally and firm size

2016 ◽  
Vol 42 (8) ◽  
pp. 817-829 ◽  
Author(s):  
Kenneth M Washer ◽  
Srinivas Nippani ◽  
Robert R Johnson
Keyword(s):  
Stock Market ◽  
Size Effect ◽  
Firm Size ◽  
Convincing Evidence ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
Santa Claus ◽  
The Usa ◽  
The Impact

Purpose – Several articles in the popular press have detailed an end-of-year anomaly known as the Santa Claus Rally, a period best defined as the last five trading days of December and the first two trading days of January. The purpose of this paper is to examine US stock market returns over this period from 1926 to 2014. Design/methodology/approach – The authors examine the Santa Claus Rally by relating it to firm size in the stock markets of the USA. The Santa Claus Rally consists of the last five trading days in December and the first two in January. The authors use t-tests, non-parametric test and regression analysis to determine if investors in small firms get superior returns over the period 1926-2014. Findings – The authors find that returns are generally higher during the period and that the effect is considerably stronger for small-firm portfolios relative to large capitalization portfolios. The authors also provide convincing evidence that the three most important trading days (especially for small stock portfolios) are the last trading day in December and the first two trading days in January. Research limitations/implications – The authors only check the markets in the USA. Market makers can use this to get significantly high returns during the Christmas-New Year period. The study shows for the first time that there is a size effect as part of the Santa Claus Rally. Practical implications – This is the first study to show that Santa Claus Rally exists for a long time in the USA. It is the first study to show that there is a size effect in Santa Claus Rally. Market participants could get significantly higher returns by investing or being invested in the stock market during this period. Social implications – The impact of the holiday season on stock market returns. Originality/value – This is the first major academic study to examine Santa Claus Rally in this much detail. The authors not only show that the rally exists, the authors show that it is based on firm size and has been in existence for nearly 90 years in the USA.

A quantile regression approach to evaluate the relative impact of global and local factors on the MENA stock markets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Slah Bahloul ◽  
Nawel Ben Amor
Keyword(s):  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Stock Markets ◽  
Financial Integration ◽  
Ordinary Least Squares ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

PurposeThis paper investigates the relative importance of local macroeconomic and global factors in the explanation of twelve MENA (Middle East and North Africa) stock market returns across the different quantiles in order to determine their degree of international financial integration.Design/methodology/approachThe authors use both ordinary least squares and quantile regressions from January 2007 to January 2018. Quantile regression permits to know how the effects of explanatory variables vary across the different states of the market.FindingsThe results of this paper indicate that the impact of local macroeconomic and global factors differs across the quantiles and markets. Generally, there are wide ranges in degree of international integration and most of MENA stock markets appear to be weakly integrated. This reveals that the portfolio diversification within the stock markets in this region is still beneficial.Originality/valueThis paper is original for two reasons. First, it emphasizes, over a fairly long period, the impact of a large number of macroeconomic and global variables on the MENA stock market returns. Second, it examines if the relative effects of these factors on MENA stock returns vary or not across the market states and MENA countries.

Cybercrime in Europe: surprising results of an expensive lapse

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Martin Roškot ◽  
Isaac Wanasika ◽  
Zuzana Kreckova Kroupova
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Abnormal Returns ◽  
Cyber Attacks ◽  
The European Union ◽  
Market Returns ◽  
Market Reactions ◽  
Stock Market Returns ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to investigate the impact of ransomware cyber-attacks “WannaCry” and “Petya” on stock prices of publicly traded companies in the European Union. The study analyses a set of case studies related to largest recent cybercrime events, which happened in the first half of 2017. The study answers two questions, what is the impact of cybercrime to public companies? How do cybercrime announcements and publications affect stock prices? Design/methodology/approach Using archival financial data, an event study methodology was used to assess the impact of cybercrime activity on market value of European companies affected during WannaCry and Petya ransomware attacks in 2017. Findings The results suggest that announcements of information breaches because of ransomware exploits have impact on stock market returns. There is evidence of positive investors` reactions to the announcements. Specifically, there was little impact of “Wannacry” ransomware attack on market returns. Although stock market reactions differ by the sector, the market was positively affected in general. Our analysis of the impact of the more aggressive “Petya attack,” aimed at destroying affected data found evidence that such information security breach leads to increased market returns. There were significant abnormal returns starting from the third day of the announcement. These findings contradict previous results and the literature related to the impact of cyber-attacks. Originality/value Contrary to previous findings, the results suggest that ransomware attacks lead to positive market returns. However, cybercrime and other types of cyber-attacks pose serious threats whose implications deserve further investigation. Different attacks may have different consequences and could be potentially damaging to a firm’s reputation. Thus, it is necessary for companies to avoid becoming victim of cybercrime. Information systems should be continuously monitored for vulnerabilities.

scholarly journals Predicting stock market returns in the US: evidence from an average correlation approach

2020 ◽  
Vol 33 (2) ◽  
pp. 411-433
Author(s):  
Xiyang Li ◽  
Bin Li ◽  
Tarlok Singh ◽  
Kan Shi
Keyword(s):  
Stock Market ◽  
Business Cycles ◽  
Stock Returns ◽  
Expected Returns ◽  
Market Returns ◽  
Average Correlation ◽  
Stock Market Returns ◽  
Content Type ◽  
Portfolio Returns ◽  
The Usa

Purpose This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA. Design/methodology/approach This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s). Findings The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner. Research limitations/implications The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models. Practical implications Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession. Social implications The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets. Originality/value The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

Weather, investor psychology and stock returns: evidence from Fiji's stock market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Janesh Sami
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Sunshine Duration ◽  
Market Returns ◽  
Modeling Framework ◽  
Stock Market Returns ◽  
Content Type ◽  
Asset Pricing Models ◽  
Weather Changes ◽  
Practical Implications

PurposeThis paper investigates whether weather affects stock market returns in Fiji's stock market.Design/methodology/approachThe author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.FindingsThe results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.Research limitations/implicationsIt is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).Practical implicationsInvestors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.Originality/valueThis is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

Islamic calendar and stock market behaviour in India

2018 ◽  
Vol 45 (11) ◽  
pp. 1550-1566
Author(s):  
Dharani Munusamy
Keyword(s):  
Stock Market ◽  
Least Square Method ◽  
Least Square ◽  
Stock Market Returns ◽  
Content Type ◽  
Ordinary Least Square ◽  
Average Return ◽  
Year Effect ◽  
The Common ◽  
The Impact

Purpose The purpose of this paper is to examine the behavior of the stock market returns in the different days of the week and different months of the year in accordance with the Islamic calendar. Further, the study estimates the risk-adjusted returns to test the performance of the indices during the Ramadan and non-Ramadan days. Finally, the study investigates the impact of Ramadan on the returns and the volatility of the stock market indices in India. Design/methodology/approach Initially, the study applies the Ordinary Least Square method to test the day-of-the-week and the month-of-the-year effect of the common and Shariah indices. Next, the study employs the risk-adjusted measurement to examine the underperformance and over-performance of the indices for both the periods. Finally, the study estimates the GARCH (1,1) and GJR-GARCH (1,1) models to observe the impact of Ramadan on the returns and the volatility of the Shariah indices in India. Findings The study finds that an average return of the indices during the Ramadan days are higher than non-Ramadan days. Further, the average returns of the Shariah indices are significantly higher on Wednesday than other days of the week. In addition, the highest and significant mean returns and mean risk-adjusted returns of the indices during the Ramadan days are observed. Finally, the study finds an evidence of the Ramadan effect on the returns and volatility of the indices in India. Originality/value The study observes evidence that the Ramadan effect influences the Shariah indices, but not the common indices in the stock market of the non-Muslim countries. It indicates that the Ramadan creates the positive mood and emotions in the investors buying and selling activities. The study suggests that investors can buy the shares before Ramadan period and sell them during the Ramadan days to get an abnormal return in the emerging markets.

Aggregate congressional trading and stock market returns

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Serkan Karadas ◽  
Minh Tam Tammy Schlosky ◽  
Joshua C. Hall
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
Macroeconomic Information ◽  
Corporate Insiders ◽  
The Media ◽  
Congressional Trading ◽  
The Relationship

Purpose What information do members of Congress (politicians) use when they trade stocks? The purpose of this paper is to attempt to answer this question by investigating the relationship between an aggregate measure of trading by members of Congress (aggregate congressional trading) and future stock market returns. Design/methodology/approach The authors follow the empirical framework used in academic work on corporate insiders. In particular, they aggregate 61,998 common stock transactions by politicians over the 2004–2010 period and estimate time series regressions at a monthly frequency with heteroskedasticity and autocorrelation robust t-statistics. Findings The authors find that aggregate congressional trading predicts future stock market returns, suggesting that politicians use economy-wide (i.e. macroeconomic) information in their stock trades. The authors also present evidence that aggregate congressional trading is related to the growth rate of industrial production, suggesting that industrial production serves as a potential channel through which aggregate congressional trading predicts future stock market returns. Originality/value To the best of the authors’ knowledge, this study is the first to document a relationship between aggregate congressional trading and stock market returns. The media and scholarly attention on politicians’ trades have mostly focused on the question of whether politicians have superior information on individual firms. The results from this study suggest that politicians’ informational advantage may go beyond individual firms such that they potentially have superior information on the overall trajectory of the economy as well.

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