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Using real-time data from the University of Luxembourg’s COME-HERE nationally representative panel survey, covering more than 8,000 individuals across France, Germany, Italy, Spain, and Sweden, the author investigates how income distributions and poverty rates have changed from January to September 2020. The author finds that poverty rates increased on average in all countries from January to May and partially recovered in September. The increase in poverty is heterogeneous across countries, with Italy being the most affected and France the least; within countries, COVID-19 contributed to exacerbating poverty differences across regions in Italy and Spain. With a set of poverty measures from the Foster–Greer–Thorbecke family, the author then explores the role of individual characteristics in shaping different poverty profiles across countries. Results suggest that poverty increased disproportionately more for young individuals, women, and respondents who had a job in January 2020 – with different intensities across countries.

This paper tests the degree to which Sunstein's law of group polarization predicts the increase or decrease in polarization among individuals in an out-group during a polarizing event. The authors use the discourse on Parler surrounding the events of January 6th as a case study.

The study includes an overall sentiment analysis, a statistical analysis of emotions, along with eight other feelings, including anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Specifically, the authors measure the differences in feelings related language used in posts as they pertain to Donald Trump and the Make America Great Again (MAGA) movement vs. Trump's Vice President Mike Pence both before and after January 6, 2021. The authors use this empirical analysis to show whether polarization in the Parler community increased or decreased after January 6th.

The authors find evidence that there is more complexity to polarization than Sunstein's theory would predict. The authors would expect a very polarized outed group to become more polarized relative to the general public after a central event; however, the authors see two extremes emerging within the Parler community (both strongly positive and strongly negative feelings toward Trump). The authors do not see unanimous consent across the Parler platform as Sunstein's theory would suggest; the out-group is becoming more polarized relative to the rest of the population. Instead, the authors observe a wide mix in reactions. The results of this study demonstrate that there is dissent even among the Parler echo chamber. For many themes surrounding the January 6th riots, Parler users express strong disagreement with each other and a lack of unity in their feelings for former President Trump.

The results suggest further research into polarization of outed groups and the policy implications of their polarization changes over time.

Increases in group polarization are often a motivator for public policy and are further becoming a major focus for research. Brookings' authors Stephanie Forrest and Joshua Daymude point to polarization as a substantial threat to American society, claiming “reducing extreme polarization is key to stabilizing democracy” (2022). Researchers Diana Epstein and John D. Graham demonstrate that polarized politics has impacted the “substance of rulemaking, judicial decisions, and legislation” along with “complicating long-term policy changes” (2007). The authors study how entrepreneurs have responded to this increase in polarization and its implications for public policy.

Not only does group polarization impact all types of groups, from the social to the economic, but also it has “particular implications for insulated ‘outgroups’” (Sunstein, 1999, p. 21). Groups that are excluded by either coercion or choice from dialog with other groups become even more polarized and extreme (Sunstein, 1999; Turner et al., 1989).

The authors have engaged in an empirical analysis that no other paper has addressed. This paper summarized the Parler sample data set and analyzed various themes associated with the events of January 6th, namely President Trump and MAGA themes and Vice President Pence. The analysis demonstrated a dramatic increase in negative sentiment and emotion related to Vice President Mike Pence after January 6th as well as mixed support for President Trump and an increase in disgust before and after the Capitol riot.

The purpose of this paper is to examine the presence of the day-of-the-week (DOW) and January effect in the Indian currency market for selected currency pairs; USD-(Indian rupee) INR, EUR-INR, GBP-INR and JPY-INR, from January, 1999 to December, 2014.

Ordinary least square regression analysis is used to examine the presence of DOW and January effect to test the efficiency of the Indian currency market. The sample period is later divided into two sub-periods, that is, pre- and post-2008 to capture the behavior of returns before and after the 2008 financial crisis. Further, the authors also use the non-parametric technique, the Kruskal-Wallis test, to provide robustness check for the results.

The results indicate that the returns during Monday to Wednesday are positive and higher than the returns on Thursday and Friday which show negative returns. The returns during January are found to be higher than the returns during rest of the year. Further, all currencies exhibit significant DOW and January effects in pre-crisis period, however, post-crisis; these effects disappear for all currencies indicating that the markets have become more efficient in the later time. The findings can be further attributed to the increased intervention in the forex markets by the Reserve Bank of India after the crisis.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW or January effect which in turn indicates that the currency markets have become more efficient with time. The results are in conformity with those reported for the developed markets. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision making.

The authors provide the first study to examine the calendar anomalies (DOW and January effect) across a range of emerging currencies using 16 years of data from January, 1999 to December, 2014. To the best of the authors’ knowledge, no study has yet examined these calendar anomalies in the currency markets using data which covers two important periods, pre-2008 and post-2008.

This study aims to examine the presence of the day-of-the-week (DOW), January and turn-of-month (TOM) effect in 20 currency pairs against the US dollar, from January, 1995 to December, 2014.

Ordinary least square with GARCH (1,1) framework is used to examine the presence of DOW, January and TOM effect to test the efficiency of the currency markets. The sample period is later divided into two sub-periods of equal length, that is, from 1995 to 2004 and 2005 to 2014, to explore the time-varying behavior of the calendar anomalies. Further, the authors also use the non-parametric technique, the Kruskal–Wallis test, to provide robustness check for the results.

For the DOW effect, the results indicate that the returns on Monday and Wednesday are negative and lower than the returns on Thursday and Friday which show positive and higher returns. The returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). However, these calendar anomalies seem to have disappeared for almost all currencies during 2005 to 2014 and indicate that the markets have achieved a higher degree of efficiency in the later part of the sample.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW, January or TOM effect, which in turn indicates that the currency markets have become more efficient with time. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision-making.

The findings of lower returns on Monday and Wednesday and high returns during Thursday and Friday for all the currencies indicate that the foreign investors can take the advantage by going short on Monday and Wednesday and long on Thursday and Friday. Similarly, the returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). During this period, investors in the currency markets could benefit themselves by taking long (short) positions in January (TOM trading days) and short (long) positions during rest of the year (non-TOM trading days).

The author provides a pioneer study on the presence of calendar anomalies (DOW, TOM and the January effect) across a wide range of currencies using 20 years of data from January 1995 to December 2014. To the best of the author’s knowledge, no study has examined the presence of January effect in the currency market; therefore, the author provides the first study in which January effect in a number of currencies is investigated.

The purpose of this paper is to examine the January effect, a well-documented capital markets pricing anomaly in which January return premiums are observed to be on average higher than in other months of the year. Extant literature focusses primarily on investor trading behaviors and incentives. This study is different in that it investigates the link between the unusually high returns characteristic of the January effect and accounting earnings, a popular measure that investors use to judge firm value.

The empirical model used in this study is derived from the analytical framework of Ohlson (1995) and Feltham and Ohlson (1995), which explains returns as a function of current and future accounting earnings. Isolating firms that exhibit January effect return premiums from those that do not offers a deeper look at the characteristics of the anomaly. Regression analyses are carried out using a modified Fama-MacBeth (1973) methodology. Quarterly earnings and returns data are drawn from Compustat and CRSP.

The main finding is that the association between January returns and first quarter earnings is unexpectedly and significantly negative, not positive as predicted by the model. Coefficient signs for the other three quarters behave as expected. Additional analyses highlight a difference in the returns-earnings association between firms affected by the anomaly and those that are not. Robustness checks indicate that the findings are not spurious.

Rather than applying trading or multifactor economic models that rely on some level of market inefficiency or irrational investor behavior, this study uses an accounting valuation approach that relies on neither. The unexpected negative association between January effect returns and earnings suggests that other factor(s) besides earnings may play into valuation judgments for investors in such firms, and invites further research.

The Revenue Reconciliation Act of 1993, implemented since 1 January 1994, facilitates the increase of institutional investment in the real estate investment trust (REIT) market. Utilizing this particular feature in the market, we examine the time‐series effect of institutional holdings to distinguish the tax‐loss‐selling hypothesis and the window‐dressing hypothesis for REITs. Consistent with the tax‐loss‐selling hypothesis, we have evidence that the January premiums decreased with the level of institutional involvement for REITs. Furthermore the January premiums declined significantly for equity REITs only that attracted more institutional investors than mortgage REITs. On the other hand, the January premiums did not decrease significantly for mortgage REITs. Overall the results suggest that trading strategies to profit the higher January returns may work only when institutional investors exit and leave the market.

The purpose of this paper is to investigate the impact of January sentiment on investors’ asset allocation decisions in the US corporate bond market during the rest of the year. Specifically, the study evaluates if the shift in January sentiment is a predictor of corporate bond spreads from February to December.

Using corporate bond trades reported in TRACE between 2005 and 2014, the authors examine the ability of the Index of Consumer Sentiment and the Index of Investor Sentiment to predict bond spreads over the 11 months following January. The study evaluates both the sign of the change in sentiment and the magnitude of the change in sentiment using two generalized linear models, controlling for industry, bond and firm fixed effects. Portfolios are analyzed based on yield, firm size and firm leverage. Additional analysis is performed to ensure results are robust to the impacts of the subprime financial crisis.

This paper finds that the changes in the sentiment measures in January predict bond spreads associated with bond trades in the subsequent 11 months, and this phenomenon, which the authors label as the “January sentiment effect,” has opposing impacts on risky and less risky bond portfolios.

This paper adds to the literature on the relationship between sentiment and investor’s allocation decisions. The evidence documented in this study is the first known to find that investors’ allocation decisions in a year are driven by their sentiment in January.

The house money effect is proposed to describe that people appear to consider large or unexpected wealth gains to be distinct from the rest of their wealth, and are thus more willing to gamble with such gains than they ordinarily would be. On the other hand, the availability heuristic describes that people tend to have a cognitive and systematic bias due to their reliance on easily available or associational information. The purpose of this paper is to employ these behavioral perspectives in an empirical model regarding the January anomaly to explore investor behavior in Taiwanese stock market with bonus culture and well-known electronics industry.

This study uses the conventional and standard dummy variable regression model, as employed in prior studies, and further includes some control variables for firm, industry and macro-economic level factors. Moreover, 19 industrial indices for Taiwanese stock market over the period January 1990 to December 2014 are included in this study to examine the hypotheses, except for the 1997 Asian financial crisis and the global financial crisis period of 2007-2009 to avoid the potential effect. On the other hand, the authors also use the entire sample period of 1990-2014 for understanding whether the magnitude of January effect is different.

The empirical results indicate that Chinese bonus payments in January induce a strong January effect in the Taiwanese stock market, especially when most listed firms have positive earnings growth in the preceding year, suggesting a house money effect. Moreover, this study further provides some preliminary evidence that the higher January returns due to bonus culture are apparent only in the electronics industry when both Chinese New Year and bonus payments are in January, implying the role of availability heuristic based on the electronics stocks in investor behavior before the impending stock exchange holidays. Some robust tests show qualitative support.

The major contribution of this study is to extend the existing research by incorporating cultural and industrial factors with behavioral finance, thus enriching the literature on the causes of seasonality for Asian stock markets.

This study also has behavioral implications of investments for investors in the Taiwanese stock market, especially for foreign institutional investors which pay close attention to this market.

This study first applies and examines the culture bonus hypothesis with regard to how employees who receive culture bonuses in January can change their attitudes toward risk and induce the January effect from the concept of mental accounting. Moreover, this study further proposes and examines the extended culture bonus hypothesis related to how the January effect due to culture bonus is different for the electronics and non-electronics industries when taking into account the stock market holidays from the concept of availability heuristic.