A generalised seasonality test and applications for stock market seasonality. [Working paper]

This study develops a novel generalised seasonality test that utilises sequential dummy variable regressions for seasonality periodicity equal to prime numbers. It allows both to test for existence of any seasonal patterns against the broad null hypothesis of no seasonality and to isolate most prominent seasonal cycles while using harmonic mean p-values to control for multiple testing. The proposed test has numerous applications in time series analysis. As an example, it is applied to identify seasonal patterns in 76 national stock markets to detect trading cycles, determine their length, and test the weak-form efficient market hypothesis

New Evidence on the Institutional Causes of Economic Growth: Using Peer Pressure to Unbundle Institutions across Countries

This study presents a new framework of assessing the causal effect of institutions on economic growth via exploiting the exogenous variation in institutions triggered by “peer pressure” exerted on governments by other states, affecting policy decisions and the environment national and international businesses operate in. The applied method reinforces the importance of institutional factors for economic outcomes and allows to effectively address the data quality and instrument validity concerns surrounding earlier studies. Most importantly, the “peer pressure” method allows to distinguish between influences of various institutions, answering not only the question whether, but also which institutions do matter for growth. The study shows that the proposed method has significant power to “unbundle institutions” and finds that property rights protection and financial freedom are more important for growth than democracy, constraint on the executive, legal origins, or business freedom, even when controlled for human capital. The developed method has significant applicability in future research of institutional and cultural impact on international business and economic outcomes

Efficient scholars: academic attention and the disappearance of anomalies

This study examines the dynamics of ten most notable stock market anomalies through 1926–2018 and assesses the joint impact of academic attention, post-publication decay, data-snooping bias, institutional trading, and time trend on their disappearance. It proposes new and simple measures of academic attention attracted by stock market anomalies using the number of articles published on the relevant topic available via Google Scholar or respective citation counts. The study finds that academic attention is the most dominant factor explaining the diminishing abnormal returns of anomaly-exploiting strategies. The approach developed by this study can also be useful in determining whether a stock return regularity is a behavioural anomaly or a systematic risk factor

A fitting return to fitting returns: cryptocurrency distributions revisited. [Working paper]

This study fits 22 theoretical distribution functions, four of them originally derived, onto 772 cryptocurrency daily returns with goodness-of-fit evaluated using Cramer-von Mises, Anderson-Darling, Kuiper, Kolmogorov-Smirnov, and Chi-squared tests, as well as a harmonic mean p-value synthetic criterion. Most cryptocurrency return distributions can be sufficiently approximated with a Johnson SU function or an asymmetric power function. Johnson SU, asymmetric Student, and asymmetric Laplace distributions have better fit for larger cryptocurrencies, while error, generalised Cauchy, and Hampel (a Gaussian-Cauchy mixture) distributions are more characteristic of smaller cryptocurrencies, with larger coins demonstrating better overall fit. Less than 8% of sample coins and less than 4% of the top quartile by size do not fit into any of the investigated distributions, three largest “misbehaving” cryptocurrencies being Litecoin, Dogecoin, and Decred. Bitcoin and Ethereum are best modelled with error and asymmetric power law distributions, respectively, with asymmetric power law distributions stable through time. More than 30% of sample cryptocurrencies, and 26% from the top quartile, have infinite theoretical variance, severely limiting the diversification potential with such cryptoassets. Three most prominent infinite-variance coins are Bitcoin SV, Tezos, and ZCash. This study has substantial implications for risk management, portfolio management, and cryptocurrency derivative pricing

To float or to sink? Revisiting the causal effects of exchange rate regimes. [Working paper]

This study proposes a novel instrumental variable construction procedure based on international trade concentration that has a sufficiently strong first stage for exchange rate policy choice globally and applies it to revisit the causal effects of exchange rate regimes on macroeconomic outcomes. Fixed exchange rates are shown to cause lower economic growth rates, higher volatility of output and inflation, and higher unemployment, without reducing average inflation. These effects persist even when monetary unions are excluded from the sample, across subsamples with varying levels of per capita income and institutional quality and is robust to alternative regime classifications as well as to property rights, human capital, and trade openness controls

When bitcoin is high: cryptocurrency value, illicit markets and US marijuana bills.

The purpose of this paper is to estimate the implications of illicit market use for the value of Bitcoin in an event studies framework. This study uses a data set of 58 state-level marijuana decriminalisation and legalisation bills and referenda in the USA in 2010–2022. Decriminalisation is associated with a strong and consistent positive Bitcoin price response around the event, recreational legalisation induces a more ambiguous reaction and medical legalisation is found to have a negative albeit small impact on Bitcoin value. This suggests decriminalisation enhances shadow economy use value of Bitcoin, whereas recreational and medical legalisation are not consistently reducing illicit drug cryptomarket activity. The effects are robust to various estimation windows, in subsamples, and also when outliers, heavy tails, conditional heteroskedasticity and state size are accounted for. New to the literature, the choice of US marijuana bills, specifically as sample events, is based on both theoretical and empirical grounds

Analyst herding-whether, why, and when? Two new tests for herding detection in target forecast prices.

This study proposes two novel tests for security analyst herding based on binomial correlation and forecast error volatility scaling and applies it to investigate herding patterns in analyst target prices in 2008-2020 in the UK. Analysts robustly herd in their valuations, with results consistent across years, sectors, in panel fixed effect, quantile, instrumental variable regressions, and when controlled for optimism and conservatism. Herding becomes prominent for stocks followed by at least five analysts and towards the long sides of Fama-French sorts, reinforcing its non-spurious and behavioral nature. Analyst herd more strongly subject to low volatility and uncertainty

The financial pandemic: COVID-19 and policy interventions on rational and irrational markets. [Working paper]

Financial markets are useful indicators of public beliefs and dispersed knowledge on future outcomes and policy efficiency, especially in periods of uncertainty. 51 national stock markets successfully absorb publicly available information regarding COVID-19 and anticipate policy measures being taken to address the pandemic. The financial markets imply national lockdown policies, as well as monetary or fiscal stimuli, are counterproductive measures while targeted regional lockdowns can be effective. The fundamental effect of the pandemic is relatively low, sentiment and irrational panic play a greater role, while the most significant drivers of negative stock returns are policy interventions

Taming the blockchain beast? Regulatory implications for the cryptocurrency market. [Working paper]

This paper uses a unique dataset of 120 regulatory events from five classes to test the relevance of the regulatory framework for cryptocurrency value. Time-series market-wide estimates and panel estimates for 300 individual coins and tokens show statistically and economically significant impact of anti-money laundering and issuance regulation. Tighter regulation and more active role of government decrease cryptocurrency prices, evidencing that potentially lower risks and wider adoption commonly attributed to the establishment of the regulatory framework do not compensate for respective efficiency and consumer utility losses. The market is generally efficient in reflecting regulatory information in cryptocurrency prices

The marginal cost of mining, Metcalfe's law and cryptocurrency value formation: causal inferences from the instrumental variable approach. [Working paper]

This paper is the first to rigorously test commonly cited simplistic theories of cryptocurrency pricing, namely, cost-based model and Metcalfe's law, using causal inferences from the instrumental variables approach on block-level data for six proof-of-work coins. Positive effects of hashrate and transaction count implied by cost-based pricing and Metcalfe's law, respectively, are non-existent for any of the coins investigated. Negative and insignificant estimators cannot be explained by weak instruments, suggesting previously reported strong positive relationships are spurious due to autocorrelation and endogeneity. The study reinforces the need for a more sophisticated cryptocurrency valuation framework