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Cryptocurrency markets are notoriously noisy, but not all markets might behave in the exact same way. Therefore, the aim of this paper is to investigate which one of the cryptocurrency markets contributes the most to the common volatility component inherent in the market.

The paper extracts each of the cryptocurrency's markets' latent volatility using a stochastic volatility model and, subsequently, models their dynamics in a fractionally cointegrated vector autoregressive model. The authors use the refinement of Lien and Shrestha (2009, J. Futures Mark) to come up with unique Hasbrouck (1995, J. Finance) information shares.

The authors’ findings indicate that Bitfinex is the leading market for Bitcoin and Ripple, while Bitstamp dominates for Ethereum and Litecoin. Based on the dominant market for each cryptocurrency, the authors find that the volatility of Bitcoin explains most of the volatility among the different cryptocurrencies.

The authors’ findings are limited by the availability of the cryptocurrency data. Apart from Bitcoin, the data series for the other cryptocurrencies are not long enough to ensure the precision of the authors’ estimates.

To date, only price discovery in cryptocurrencies has been studied and identified. This paper extends the current literature into the realm of volatility discovery. In addition, the authors propose a discrete version for the evolution of a markets fundamental volatility, extending the work of Dias et al. (2018).

Financial theory suggests that with increasing labor income risk, the reluctance of households to hold stocks increases. Therefore, this paper aims to investigate the determinants of a household’s decision on whether to invest in risky financial assets.

Income risk is measured as the observed variation of household income over a five-year period. The authors use both the time and the cross-sectional dimension of the German socio-economic panel to control for unobserved heterogeneity.

The authors find that indeed higher variation, i.e. higher income risk, reduces the propensity to invest in risky assets. However, when controlling for household heterogeneity, as well as subjective measures of a household’s financial situation (income satisfaction, worries about financial situation), the impact of observed labor income variation vanishes. It is therefore concluded that in particular the perception of investment risk and of the riskiness of the environment determines the investment decision to a great extent.

The paper contributes to a better understanding of a household’s investment decision-making process. To the best of the authors’ knowledge, it is the first to fully exploit the panel structure of the data to control for unobserved heterogeneity which leads to novel conclusions with respect to the effect of labor income.

This comprehensive study aims to take a punctilious approach intended to present qualitative and quantitative knowledge on the emerging concept of noise trading and identify the emerging themes associated with noise trading.

This study combines bibliometric and content analysis to review 350 publications from top-ranked journals published from 1986 to 2020.

The bibliometric and content analysis identified three major themes: the impact of noise traders on the functioning of the stock market, traits of noise traders and different proxies used to measure the impact of noise trading.

This study undertakes research papers related to the field of finance, published in peer-reviewed journals and that too in the English language.

This study shall accommodate rational traders, portfolio consultants and other investors to gain deeper insights into the functioning of noise traders. This will further help them to formulate their trading/investment strategies accordingly.

The successful combination of the bibliometric and content analysis revealed major gaps in the literature and provided future research directions.

The emergence of social media as a corporate disclosure channel has caused significant changes in the production and dissemination of corporate information. This review identifies important themes in recent research on the impact of social media on the corporate information environment and provides suggestions for further explorations of this new but fast-growing area of research. Specifically, we first review the evolution of Internet-based corporate disclosure and related regulations, and then focus on three recent streams of research: 1) companies’ use of social media; 2) information produced by non-corporate users and its impact on capital markets; and 3) the credibility of corporate information on social media platforms.

This paper aims to investigate the relationship between sustainable investments and a series of uncertainties from January 2014 to December 2021, including many economic and political turbulences and the COVID-19 pandemic.

The authors use Rényi’s transfer entropy method, a nonparametric flexible tool that considers both the center distribution and lower quantiles, capturing extreme rare events that give additional insights to analysis.

The authors’ results indicate significant bidirectional information transmissions between the crude oil volatility and sustainability indices. The authors report information flows between the cryptocurrency uncertainty and sustainability indices considering tail events. The results are essential for market participants making decisions during turbulent times.

This paper is carried out for a variety of uncertainty measures and environmental, social and governance (ESG) portfolios of both developed and developing markets. It adds to literature in terms of methodology used. Rényi’s transfer entropy methodology is first used to measure the relationship between uncertainties and ESG investments.

This study examines the prediction power of investor sentiment on Bitcoin return.

We construct a Financial and Economic Attitudes Revealed by Search (FEARS) index using search volume from Google's search engine to reveal household-level (“bankruptcy”, “unemployment”, “job search”, etc.) and market-level sentiment (“bankruptcy”, “unemployment”, “job search”, etc.).

Using a variety of quantitative methodologies such as the transfer entropy model as well as threshold regression and OLS, GLS and 2SLS estimations, we find that (1) investor sentiment has strong predictive power on Bitcoin, (2) household-level sentiment has larger effects than market-level sentiment and (3) the impact of sentiment is greater in low sentiment regimes than in high sentiment regimes. Based on these information, we build a hypothetical trading strategy that outperforms a simple buy-and-hold strategy both on an absolute and risk-adjusted basis. The results are consistent across cryptocurrencies and regions.

The findings contribute to the ongoing debate in the literature on the efficiency of cryptocurrency markets. The results reveal that the Bitcoin market is not efficient in the sense of the efficient market hypothesis – asset prices do not fully reflect all available information and we were able to “beat the market”. In addition, it sheds further light on the debate whether Bitcoin can be considered a medium of exchange, i.e. a currency or an investment product. Because investors are reallocating their Bitcoin holdings during times of increased market sentiment due to liquidity needs, they obviously consider bitcoin an investment product rather than a currency.

This study is the first to examine the impact of investor sentiment measured by FEARS on Bitcoin return.

This paper aims to study the dynamics of bitcoin prices in Brazil, a large emerging economy with an unregulated bitcoin market.

First, this study tests if the Law of One Price (LOOP) is valid for bitcoin prices in Brazil, conducting tests with data from three Brazilian exchanges. Next, this study documents bitcoin price dynamics in the short run by studying the price discovery mechanism in these exchanges. This study uses Information Share and Component Share, combining the two measures to obtain an Information Leadership Share (ILS) measure.

This study finds a common trend within bitcoin prices among a set of exchanges, with cointegration tests between the price series indicating that LOOP is valid in Brazilian markets in the long run. ILS indicated that, for closing prices, the most liquid exchange (Foxbit) leads discovery, whereas the least liquid (Local Bitcoin) lags, with Mercado Bitcoin in the middle both in terms of discovery and liquidity. Finally, this study provides evidence that the price variation in the market that leads price discovery can be used to construct an arbitrage in another exchange.

This research brings the first evidence of a price discovery mechanism for exchanges in Brazilian Reais. Although LOOP is valid in the long run, price leadership in bitcoin markets potentially create arbitrage opportunities in the short run. This study contributes to the growing literature of bitcoin prices with novel evidence from a large emerging economy.

The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.

In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.

The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This study aims to examine the relationship between pandemic sentiment (PS) and stock market returns in an emerging order-driven stock market like India.

This study uses nonlinear causality and wavelet coherence techniques to analyze the sentiment-returns nexus. The analysis is conducted on the full sample period from January to December 2020 and further extended to two subperiods from January to June and July to December to investigate whether the associations between sentiment and market returns persist even several months after the outbreak.

This study constructs two novel measures of PS: one using Google Search Volume Intensity and the other using Textual Analysis of newspaper headlines. The empirical findings suggest a high degree of interrelationship between PS and stock returns in all time-frequency domains across the full sample period. This interrelationship is found to be further heightened during the initial months of the crisis but reduces significantly during the later months. This could be because a considerable amount of uncertainty regarding the crisis is already accounted for and priced into the markets in the initial months.

The ongoing coronavirus pandemic has resulted in sharp volatility and frequent crashes in the global equity indices. This study is an endeavor to shed light on the ongoing debate on the COVID-19 pandemic, investors’ sentiment and stock market behavior.

Motivated by the recent theoretical rehabilitation of mean-variance analysis, the authors revisit the question of whether minimum variance (MinVar) or maximum Sharpe ratio (MaxSR) investment weights are preferable in practical portfolio formation.

The authors answer this question with a focus on mainstream investors which can be modeled by a preference for simple portfolio optimization techniques, a tendency to cling to past asset characteristics and a strong interest in index products. Specifically, in a rolling-window approach, the study compares the out-of-sample performance of MinVar and MaxSR portfolios in two asset universes covering multiple asset classes (via investable indices and their subindices) and for two popular input estimation methods (full covariance and single-index model).

The authors find that, regardless of the setting, there is no statistically significant difference between MinVar and MaxSR portfolio performance. Thus, the choice of approach does not matter for mainstream investors. In addition, the analysis reveals that, contrary to previous research, using a single-index model does not necessarily improve out-of-sample Sharpe ratios.

The study is the first to provide an in-depth comparison of MinVar and MaxSR returns which considers (1) multiple asset classes, (2) a single-index model and (3) state-of-the-art bootstrap performance tests.