Cannabis Stocks Returns: The Role of Liquidity and Investors’ Attention via Google Metrics

This paper studies one of the most popular investment themes over recent years, investing in the cannabis industry. In particular, it investigates relationships between investor attention, as proxied by Google Trends, and stock market activities, i.e., return, volatility, and liquidity. To this end, in the empirical analysis we study how liquidity and investors’ attention affect the return dynamics of an investment in cannabis stocks by augmenting the three-factor Fama–French model. In addition, we use a vector autoregressive approach and the impulse response function to measure shock transmission between the variables under consideration. Our empirical findings show that there is a statistically positive relationship between cannabis stock returns and liquidity. We also find that increased investors’ attention results in higher returns.

The dynamic interaction between investor attention and green security market: an empirical study based on Baidu index

PurposeThis paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.Design/methodology/approachThis study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.FindingsThe empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.Originality/valueThe conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

Daily Cryptocurrency Returns Forecasting and Trading via Machine Learning

We execute a comparative analysis of machine learning models for the time-series forecasting of the sign of next-day cryptocurrency returns. We begin by compiling a proprietary dataset that encompasses a wide array of potential cryptocurrency valuation factors (price trends, liquidity, volatility, network, production, investor attention), subsequently identifying and evaluating the most significant factors. We apply eight machine learning models to the dataset, utilizing them as classifiers to predict the sign of next day price returns for the three largest cryptocurrencies by market capitalization: bitcoin, ethereum, and ripple. We show that the most significant valuation factors for cryptocurrency returns are price trend variables, seven and thirty-day reversal, to be specific. We conclude that support vector machines result in the most accurate classifications for all three cryptocurrencies. Additionally, we find that boosted models like AdaBoost and XGBoost have the poorest classification accuracy. At length, we construct a probability-based trading strategy that secures either a daily long or short position on one of the three examined cryptocurrencies. Ultimately, the strategy yields a Sharpe of 2.8 and a cumulative log return of 3.72. On average, the strategy’s log returns outperformed standalone investments in all three cryptocurrencies by a factor of 5.64, and Sharpe ratios more than threefold.

Impact of information risk and cost of equity: moderating role of investor attention

PurposeThe purpose of this study is to finds out how investor attention plays the moderating role between the relation of information risk and COE by considering the effect of three different types of information risk, that is private information, lack of quality and transparent information.Design/methodology/approachFor that purpose, data is collected from all the non-financial firms listed on PSX from 2007 to 2019. Two-step system GMM dynamic panel estimators are applied to test the dynamic nature of the proposed model.FindingsThe findings of the study show that investor attention reduces these three information risks by increasing the stock liquidity and decreasing the crash risk which ultimately decreases the COE. Also, this study examined the role of investor attention between the relations of information risk and corporate investment in the dynamic panel model, where the two-step system generalized method of the moment has been applied. The finding of the study shows that investor attention stimulates the innovative investment by increasing investor confidence and decreasing the agency conflict.Originality/valueThis study contributes to the literature by providing the novel findings by considering the role of investor attention in reducing the effect of three different types of information risk, that is private information, less quality as well as less transparency of information and further their effect on the cost of equity.

Smart collectibles; use case of NFT tokens

Non-Fungible Tokens (NFTs) have garnered remarkable investor attention recently, with some NFTs securing selling prices that may have seemed unthinkable for a non-fungible virtual asset. This raises fascinating questions about “value” and “scarcity” with respect to blockchain technology, through a prism of non-fungibility of a digital asset, and this paper aims to draw attention to these questions insofar as they may shape an alternative space of blockchain development and exchange going forward. We find that NFT submarkets are cointegrated and feature various causal short-run connections between them. The success or adoption of younger NFT projects is influenced by that of more established markets. At the same time, the success of newer markets has an impact on the more established projects. The results contribute to the overall understanding of the NFT phenomenon and suggest that NFT markets are immature or even inefficient. This article will tackle these questions from a UK perspective, specifically looking at cases from England and Wales and Scotland, while also covering a few relevant CJEU decisions. This is a relatively recent technology, which will require a lengthier technical explanation to analyse the legal issues that are raised. In some instances, the public perception will be dealt with as well, as it has become evident that there is considerable misunderstanding not only about what an NFT really is, but about the ownership and copyright issues that surround the technology. While NFTs are not entirely related to copyright, and in some way they’re trying to bypass legal transactions in favour of technical solutions, this paper will concentrate on the copyright questions, but it will also tackle some of the emerging issues about the technology. A quick note about balance. This work will take a generally neutral approach to the study of NFTs, but this is a subject that is not devoid of controversy. There have been concerns raised about the viability of this model from various perspectives, but it is not the remit of the work to tackle these, and the approach will be to view non-fungible tokens at face value. The concerns range from the environmental cost of running blockchain technology,9 to the use of tokens for money laundering,10 to the existence of often crippling transaction fees that could make it difficult for artists to profit from their work.11 It is important to highlight these here, although they will not be the subject of further analysis.

A new look at the meeting clustering effect

PurposeThe study aims to test the existence of a meeting clustering effect in the Spanish Stock Exchange (SSE).Design/methodology/approachThis paper studies the relationship between the clustering of annual general meetings and stock returns in the SSE. A multivariate analysis is carried out in order to analyse the relationship between monthly returns and the clustering of general meetings in the SSE.FindingsThe authors show that meeting clustering exists and that some months exhibit significant and positive additional returns related to the holding of ordinary or extraordinary general meetings.Research limitations/implicationsThe authors have explored some possible explanations for the meeting clustering effect, such as a potential link with the “Halloween” effect or the presence of higher-than-normal levels of volatility, trading volumes or investor attention. However, none of these can explain the meeting clustering effect that emerges as a new anomaly in the SSE.Practical implicationsThe authors have documented significant and positive abnormal returns in some months that coincide with the holding of general meetings. Therefore, the holding of ordinary and/or extraordinary meetings in some months involves the release of relevant information for investors.Originality/valueThis study complements the financial literature because it is focused on the clustering of meetings and its effect on a stock market whose legal order is based on civil law. This fact allows us to shed new light on meeting clustering and its effect on other types of markets.