Crime opportunities in decentralized finance: how actor attributes shape target attractiveness

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of the impact of social media events on stock price volatility. The main purpose of the research is to examine the impact of the Reddit posts from January 2022 through July 2023 on the price volatility of the six U.S. mega-cap technology stocks. Unlike most of the previous studies that focus on Twitter, this study focuses on Reddit. This study not only examines how Reddit posts relate to volatility but also how trading volume and stock price relate to volatility. Therefore, while focusing on the impact of social media events on volatility, the study controls for the effects of trading volume and price. Based on the previous research on social media events on different platforms, it is expected that Reddit events significantly affect stock price volatility. Again, based on the previous research on social media events on different platforms, higher trading volume and higher stock prices are expected to have a positive relationship with stock price volatility (i.e. higher volumes and higher prices are associated with higher volatility). Overall, the findings in this paper support these expectations. First, the ANOVA test results reject the null hypothesis of no predictive relationship between the three independent variables (i.e. “Socialmedia”, “Price”, and “Volume”) and the stock price volatility of the six mega-cap stocks. For the whole group of firms, the regression analyses show that the positive Reddit events are associated with lower volatility when compared to negative Reddit events, and that higher trading volumes and prices are associated with higher volatility. Therefore, for the group of six mega-cap stocks, the results support our hypothesis. When individual regressions are performed for each stock, the results are mixed. The results for Alphabet (i.e. Google), Tesla, Meta, and Microsoft are more in line with the expectations, while the results for Apple and Nvidia are not. For Google and Tesla stocks, when there is a positive social media event, the volatility is lower. This finding indicates that a positive event calms the investors of these stocks. For Meta and Microsoft stocks, when there is a positive social media event, the volatility is higher. This finding may imply that increased volatility due to a positive event possibly stems from the extra demand for these stocks in a very short period. For Apple and Nvidia stocks, there is no significant relationship between social media events and volatility. Overall, we conclude that, a prospective investor who wants to invest in a pool of “mega-cap technology stocks”, social media events should be a factor when making an investment decision. On the other hand, a prospective investor who is a “stock picker”, needs to evaluate each individual regression result when making an investment decision.

The main goal of this paper is an examination of the interdependence stuctures of stock returns, volatility and trading volumes of companies listed on the CAC40 and FTSE100. The authors establish that the mean values of respective measures are different on the markets under study. In general, they are larger for equities from CAC40 than from FTSE100. The Mixture of Distributions Hypothesis with long memory is rejected for about 70 % of stocks from both markets. Additionally fractional cointegration was tested. The lack of fractional cointegration, suggests a rejection of the last variant of MDH in all cases, i.e. the time series under study do not exhibit common long-run dependence. The analyzed time series are not driven by a common information arrival process with long memory. Correlation between volatility and trading volume is present for all the stocks of companies from these markets. The mixtures of rotated copulas and Kendall correlation coefficient allowed the checking of extreme return-volume dependence structures. The empirical results reflect significant dependencies between high volatility and high trading volume. In general, the dependence structures of stock returns and trading volume are different. In the case of CAC40 companies high trading volume is not correlated as frequently with high stock returns as with low stock returns. For companies listed on the FTSE100 high stock returns are mostly related with high trading volume.

The concept of high trade-volume may be approached in two ways: high volume may be considered in absolute terms (i.e.large numbers), or in relative terms (i.e.large numbers in relation to the population and biology of the species).Absolute high trade-volume does not alone have any bearing on whether a species is threatened by trade.However, species traded in high absolute numbers are likely to be of considerable ecological significance.Relative high trade-volume is of direct relevance to the survival of the species involved, but there is no evidence that this is correlated with absolute high trade-volume.By virtue of their designation on the iv Appendices, trade in all CITES-listed species is of concern, and should be monitored.-Consideration of absolute high trade-volume as a major criterion for selecting species for special attention is thus not only irrelevant in terms of species conservation, but may divert attention from more important cases.The Technical Committee Working Group on Significant Trade in Appendix II Species produced a paper, based on its meeting in Switzerland in December 1984, which aimed to formulate a procedure or course of action to enable the Technical Committee to fulfil the recommendations of Resolution Conf.4.7. Itwas decided that the Group should restrict its attention to fauna, as a Plant Working Group was already in existence.The conclusions of the WTMU report on high trade-volume were endorsed, in that the Working Group agreed that it was not possible to identify those Appendix II taxa of greatest concern on the basis of trade data alone.Information on biological status, population trends and a whole range of other factors was needed in order to assess properly the impact of the trade in those taxa.A five-part procedure was established as the most appropriate mechanism for implementing Resolution Conf.4.7.This plan was presented to the fifth meeting of the Conference of the Parties which was. held at Buenos Aires, Argentina in 1985 (Doc. 5.26).Steps 1-3 have already been carried out.Step 1 : Production of list "A" It was acknowledged that, with a very few exceptions, all taxa listed in Appendix II should be able to withstand some degree of exploitation for international trade.The Working Group chose an arbitrary "safe" level of trade for any such taxon of an average of 100 individuals taken from the wild (globally) and entering trade per year.By eliminating all taxa traded at a level within that considered "safe", a list of "potential candidate" taxa could be produced (List "A").These taxa were defined as those that might be the subject of significant international trade.the importing Parties should assist in urgent clarification of identification and taxonomic uncertainties.List 3 (27 taxa)It was agreed that available information indicated that these taxa were essentially unaffected by international trade.Vll METHODS This report comprises the review of the biological and trade status of species included in list "C".It was carried out by the lUCN Conservation Monitoring Centre under contract to the CITES Secretariat over the period September 1985 to April 1986.As a first step, the CITES Secretariat circulated a request for information to all of the countries in which the species occurred, contacting the CITES Management Authorities in the countries party to CITES and designated wildlife management or equivalent authorities in others.The responses to this request were passed to CMC and are referenced in the following format: Name of country CITES MA, 1987.Comments received from wildlife management authorities in non-Party states are referenced by the name of the government department involved.Information was also solicited from relevant specialists (

The efficient market hypothesis (EMH) assumes that investors are rational and value securities rationally. A rational investor would value a security by its net present value; the price of a stock in this framework is based on the discounted cash flow or the present value model. Although the EMH-based model is partially successful in computing fundamental stock prices, other anomalies such as high trading volume, high volatility, and stock market bubbles remain unexplained. These models assume rational investors who are utility maximizers. But some investors behave irrationally or against the predictions, and in the aggregate they become irrelevant. In this paper, we relax the assumption of investor rationality, and attempt to explain high volatility, high trading volume, and stock market bubbles by incorporating investor sentiment into the already existing asset pricing model.

With the continuous growth of the economy, the residents' demand for preserving and appreciating family wealth has gradually become prominent. The stock market has become the primary choice for investors pursuing asset appreciation. Investors expect to achieve higher returns through scientific methods, and factor models, as well as mispricing, have become the focus of attention, aiming to obtain excess returns through market anomalies. However, with technological advances, the complexity of the market is increasing, making it increasingly difficult to find excess returns through pricing models. As trading volume is the most fundamental indicator in the market, containing many effective market insights and capturing investor disagreements, it is necessary to delve into the role of trading volume in the impact of mispricing on expected returns. This research selects all A-share stocks from 2000 to 2022, excluding the Sci-Tech Innovation Board, measuring mispricing through the anomaly in pricing models. Then, by independently double-sorting mispricing and trading volume into 5x5 portfolios, constructing market-weighted investment portfolios, and holding for one month, we observe the predictive ability of mispricing for future returns at different levels of trading volume. The results reveal that in stocks with high trading volume, the predictive ability of mispricing is stronger. Specifically, in stocks with high trading volume, the difference in future returns between the undervalued stock portfolio and the overvalued stock portfolio is greater than the difference in returns in low trading volume stocks. In other words, trading volume plays a moderating role in the impact of mispricing on expected returns, enhancing the predictive ability of mispricing on returns.

Can high trading volume and volatility switch boost momentum to show greater inefficiency and avoid crashes in emerging markets? The economic relationship in factor investing in emerging markets

In this Heckscher-Ohlin-Samuelson model, production of a pollutive good damages production of another good within the same country. Unilateral and bilateral shifts from laissez faire to Pigouvian policy are numerically simulated for cases of low emissions and a high volume of trade, high emissions and a high volume of trade, and finally, high emissions and a low volume of trade. A country can be worse off when it becomes Pigouvian and it can be worse off when its trading partner becomes Pigouvian. Nevertheless, a simple game theory version of the model suggests a “race to the top”, in which all countries become Pigouvian.

Sustainable economic growth is one of three essential components of the United Nations sustainable development concept together with the society and environment. The up going urbanization force human and economic development. The human capital is one of the important components for growing industrialization, innovations, scientific research, education and formation of the higher skilled workers. The main aim of the study is to provide an analysis of the human capital impact on sustainable economic growth. We used the methods of econometric modelling and cluster analysis on the base of data collections: United Nations Development Programme and the World Bank World Development Indicators (WDI). Cluster analysis was done for the Human Development Index (HDI) and WDI for countries with different levels of social and economic development together with ecological situations. With the help of spatial data analysis of population density and urbanization for the studied countries, we can separate them into different groups. As a result of the analysis, the countries with high HDI and Gross national income (GNI) per capita has a clusterization in different groups due to the role of scientific research and industrialization as in the case with a high volume of trade (exports and imports) per capita and high urbanization. The main difference between them is that the role of innovation in economic growth can generate the same or higher HDI as in the case of a high volume of trade (exports and imports) per capita. But it is possible to separate them with the help of population density and urbanization. It looks like countries with a high volume of trade (exports and imports) per capita have the highest level of urbanization. Based on this approach it is possible to separate the countries with the same HDI value and different income or similar income and different HDI values.

Heterogeneity Through Market Entrance: An Overlapping Generations Model with Non-Bayesian Learning

Ample empirical and experimental evidence documents that individuals place greater weight on information gained through personal experience—a phenomenon that Tversky and Kahneman call availability bias. I embed this bias in an overlapping generations equilibrium model in which the period that investors first enter the market establishes the starting point of their experience history. The difference in the individuals’ experience leads to heterogeneity among agents and perceived noise trading. The model captures several empirical findings. It explains why returns on high-volume trading days tend to revert. Furthermore, it provides explanations for a high trading volume, a connection between trading volume and volatility, excess volatility, and overreaction and reversal patterns. Consistent with empirical evidence, young investors buy high and sell low, trade frequently, and obtain lower returns. For intraday trading, it predicts a high trading volume around the opening hours, especially for cross-listed stocks.

Although the majority of emerging infectious diseases can be linked to wildlife sources, most pathogen spillover events to people could likely be avoided if transmission was better understood and practices adjusted to mitigate risk. Wildlife trade can facilitate zoonotic disease transmission and represents a threat to human health and economies in Asia, highlighted by the 2003 SARS coronavirus outbreak, where a Chinese wildlife market facilitated pathogen transmission. Additionally, wildlife trade poses a serious threat to biodiversity. Therefore, the combined impacts of Asian wildlife trade, sometimes termed bush meat trade, on public health and biodiversity need assessing. From 2010 to 2013, observational data were collected in Lao PDR from markets selling wildlife, including information on volume, form, species and price of wildlife; market biosafety and visitor origin. The potential for traded wildlife to host zoonotic diseases that pose a serious threat to human health was then evaluated at seven markets identified as having high volumes of trade. At the seven markets, during 21 observational surveys, 1,937 alive or fresh dead mammals (approximately 1,009 kg) were observed for sale, including mammals from 12 taxonomic families previously documented to be capable of hosting 36 zoonotic pathogens. In these seven markets, the combination of high wildlife volumes, high risk taxa for zoonoses and poor biosafety increases the potential for pathogen presence and transmission. To examine the potential conservation impact of trade in markets, we assessed the status of 33,752 animals observed during 375 visits to 93 markets, under the Lao PDR Wildlife and Aquatic Law. We observed 6,452 animals listed by Lao PDR as near extinct or threatened with extinction. The combined risks of wildlife trade in Lao PDR to human health and biodiversity highlight the need for a multi-sector approach to effectively protect public health, economic interests and biodiversity.

This study investigates the joint effect of trade volume and report timing on earnings‐announcement premiums. We find that high trading volume effect adds to early announcement effect but not vice versa. After controlling for firm characteristics, late timing and high trade volume have a positive joint effect; stocks with late announcements and low trade volume earn the largest but short‐lived premium. We cannot find evidence to support the notion that early announcements result in superior premiums; the unusual volume effect is much greater in magnitude, longevity, and significance than the timing effect.

The herding behavior of equity investors in the Pakistan Stock Exchange (PSX) is examined in this study. There are two ways to look at herding behavior: first, in different market conditions, like bull and bear, extreme and less extreme, high and low trading volume, and high and low volatility; second, in different events, like the month of Ramadan, the financial crisis of 2007–2008, and the Covid-19 pandemic. Financial herding behaviour is when equity investors follow the actions of other equity investors without making their own decisions because they lack information or they think others have more trustworthy information. Regression analysis using Newey West Consistent Estimators is performed on Pakistan Stock Exchange (PSX) daily stock market data ranging from January 2001 to June 2021. The study found that the herding behavior among equity investors is absent during extreme market conditions, less extreme market conditions, high trading volume, high market volatility, and low market volatility; however, it is present during low trading volume, the month of Ramadan, financial crisis (2007-2008) and during Covid-19 pandemic as well.

Underreaction, Trading Volume and Post earnings announcement drift

This paper examines the reactions of investors to the arrival of unexpected information in five major US equity markets from 1990 to 2001, a period characterized by high daily trading volume and the increasing presence of noise-traders. Market surprises are identified using a strictly quantitative approach, cumulative abnormal returns are calculated and tracked for a period of 30 days after each favourable or unfavourable event. The empirical results provide evidence that investors’ reactions during the sample period are consistent with the prediction of the Uncertain Information Hypothesis in all markets except NASDAQ. One major implication of these results for investors is that implementing a contrarian strategy of buying current losers and selling current winners will not generate superior returns.