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This paper aims to examine the extent of price clustering in a selection of Islamic stocks listed in Indonesia, Malaysia and Pakistan and also investigates the determinants of the phenomenon at the firm level.

The author test the uniformity of price distribution in the selected securities. Then, the determinants of price clustering were investigated through multivariate analysis based on a binary logistic regression model. Following the arguments of Narayan et al. (2011), who emphasize the importance of considering firm heterogeneity when studying the phenomenon, the author conducts the empirical study at the firm level.

The evidence indicates that Islamic stocks show a mild level of price clustering. Only half of the stocks under analysis rejected the uniformity test in the distribution of prices. In these cases, investors exhibited a preference for prices ending at zero and five. The evidence does not confirm the cultural clustering theories. Price clustering is found to be positively associated with price level and relative bid-ask spread. Overall, the negotiation hypothesis, which predicts that investors prefer round prices to minimize the costs associated with negotiations, best explains most of our results.

The existence of price clustering is difficult to reconcile with the prediction of the efficient market hypothesis that prices should follow a random walk. Moreover, the evidence indicates that Muslim investors share a preference for round prices in some settings, under the assumption that Islamic stocks are mostly traded by Muslim investors.

To the author’s best knowledge, this is the first study to address the subject of price clustering in Islamic stocks.

This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

The purpose of this paper is to study the existence of psychological barriers in cryptocurrencies.

To detect psychological barriers, the authors perform a uniformity test, a barrier hump test, a barrier proximity test and conditional effects test to a sample comprised by the daily closing quotes of six of the most liquid cryptocurrencies.

The results evidence the existence of psychological barriers in four of the cryptocurrencies under scrutiny, namely, Bitcoin, Dash, NEM and Ripple.

The fact that the cryptocurrency market has a high share of unexperienced investors and presents several cases of psychological barriers is consistent with the hypothesis that that class of investors is particularly prone to the behavioral biases which cause psychological barriers.

This paper studies, for the first time, the existence of psychological barriers in the market of cryptocurrencies.

This paper aims to evaluate the influence of economic policy uncertainty (EPU) on the momentum effect, analysing its influence depending on the economic cycle and in different quantiles.

To determine the influence of EPU in the momentum effect taking into account the economic cycle and the level of the quantile, linear regression and quantile regression have been applied for the period from 2 January 1985 to 30 April 2019 for the US stock market.

It is shown that an increased feeling of insecurity associated with EPU reduces the momentum effect, especially in times of recession. Distinguishing by quantiles, an asymmetry in the impact of EPU in the momentum effect is discovered, finding that EPU reduces (increases) the profits of momentum strategies in the lowest (highest) quantiles. In the highest quantiles, an investor can obtain higher extraordinary returns with this strategy. For example, in the highest quantile, a one-point increase in the EPU levels would have increased the daily profitability by 12.7 basis points. These findings have important implications for investors and policymakers.

To the best of the authors’ knowledge, this is the first paper that evaluates the influence of EPU on the momentum effect by conducting an analysis based on the economic cycle and different quantiles, demonstrating how these factors are relevant in the influence of this uncertainty in the momentum anomaly.

This study aims to examine the deterrent effect of the Market Abuse Directive (MAD) introduced in the European Union in 2003. The purpose is to evaluate whether the Directive has resulted in significant changes in pre-bid stock price run-ups observed in mergers and acquisitions within the Portuguese, Spanish and Greek stock markets.

The study analyzes a sample of 199 mergers and acquisitions in the aforementioned stock markets. The magnitude of pre-bid stock price run-ups is investigated as an indicator of illegal insider trading. The effects of the MAD, toehold positions of bidders and industry similarity between firms involved in the deals are assessed using statistical analysis.

The study’s findings indicate that the MAD has been ineffective in deterring investors from trading on non-public information. Pre-announcement price run-ups remain significant, suggesting ongoing illegal insider trading practices. Additionally, the research reveals that pre-bid stock price run-ups tend to be lower when bidders have established a larger toehold position in the target and when the firms involved in the deal belong to the same industry.

This study contributes to the existing literature by providing empirical evidence on the ineffectiveness of the MAD in deterring illegal insider trading. The findings highlight the limitations of increasing penalties without an effective monitoring system in place. Furthermore, the study identifies additional factors, such as toehold positions and industry similarity, that influence the magnitude of pre-announcement price run-ups in mergers and acquisitions.

This paper aims to examine the influence of religious backgrounds and religiosity on three dimensions of household finance (the decision to hold secured debt, the likelihood of being in a state of financial distress and the likelihood of being in a state of financial well-being) across a large sample of European countries.

The study uses data from the European Union Statistics on Income and Living Conditions (EU-SILC) data set, spanning from 2004 to 2018. The authors conduct regression analysis to examine the relationship between religion and household financial choices.

The study finds that belonging to a predominantly Catholic or Orthodox (Protestant) country is negatively (positively) associated with the likelihood of holding a mortgage. Belonging to a mostly Catholic (Protestant) country is negatively (positively) associated with the likelihood of being in a state of financial distress. Belonging to a predominantly Catholic (Protestant) country is positively (negatively) associated with the likelihood of being in a state of financial well-being. These relationships remain robust after controlling for a large number of demographic and economic variables.

In this paper, the authors analyze for the first time the impact of religion on household finance in a wide range of European countries. It is also the first time that the EU-SILC database, which aggregates data on more than three million European households, is used for the study of this topic.

This paper aims to explore the effects of a surge in tourism short-term rentals (STR) on housing prices in municipalities within Portugal’s two largest Metropolitan Statistical Areas.

This study applies the difference-in-differences (DiD) methodology by using a feasible generalized least squares (FGLS) estimator in a seemingly unrelated regression (SUR) equation model.

The results show that the liberalization of STR had a significant impact on housing prices in municipalities where a higher percentage of housing was transferred to tourism. This transfer led to a leftward shift in the housing supply and a consequent increase in housing prices. These price increases are much higher than those found in previous studies on the same subject. The authors also found that municipalities with more STR had low housing elasticities, which indicates that adjustments to the transfer of real estate from housing to tourism were made by increasing house prices, and not by increasing supply quantities.

The study suggests that an unforeseen consequence of allowing property owners to transfer the use of real estate from housing to other services (namely, tourism) was extreme housing price increases due to inelastic housing supply.

This is the first time that the DiD methodology has been applied in real estate markets using FGLS in a SUR equation model and the authors show that it produces more precise estimates than the baseline OLS FE. The authors also find evidence of a supply shock provoked by STR.

This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).

The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.

The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.

Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.

To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe.

In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe.

The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month.

The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies.

Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic.

The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.

The literature provides extensive evidence for seasonality in stock market returns, but is almost non-existent concerning the potential seasonality in American depository receipts (ADRs). To fill this gap, this paper aims to examine a number of seasonal effects in the market for ADRs.

The paper examines four ADRs for the period from April 1999 to March 2017 to look for signs of eight important seasonal anomalies. The authors follow the standard methodology of using dummy variables for the time period of interest to capture excess returns. For comparison, the same analysis on two US stock market indices is conducted.

The results show the presence of a highly significant pre-holiday effect in all return series, which does not seem to be justified by risk. Moreover, turn-of-the-month effects, monthly effects and day-of-the-week effects were detected in some of the ADRs. The seasonality patterns under analysis tended to be stronger in emerging market-based ADRs.

Overall, the results show that significant seasonal patterns were present in the price dynamics of ADRs. Moreover, the findings lend support to the idea that emerging markets are less efficient than developed stock markets.

This is the most comprehensive study to date for indication of seasonal anomalies in the market for ADRs. The authors use an extensive sample that includes recent significant financial events such as the 2007/2008 financial crisis and consider ADRs with different characteristics, which allows to draw comparisons between the differential price dynamics arising in developed market-based ADRs and in the ADRs whose underlying securities are traded in emerging markets.