According to rational expectation models, uninformed or liquidity trading make market price volatility rise. This paper sets out to analyse the impact of herding, which may be interpreted as one of the components of uninformed trading, on the volatility of the Spanish stock market. Herding is examined at the intraday level, considered the most reliable sampling frequency for detecting this type of investor behavior, and measured using the Patterson and Sharma (Working Paper, University of Michigan–Dearborn, 2006) herding intensity measure. Different volatility measures (historical, realized and implied) are employed. The results confirm that herding has a direct linear impact on volatility for all of the volatility measures considered, although the corresponding intensity is not always the same. In fact, herding variables seem to be useful in volatility forecasting and therefore in decision making when volatility is considered a key factor.

Universities have a critical role in achieving the Sustainable Development Goals (SDGs), both for implementing active policies and for encouraging other actors to participate. This requires having the skills and mind-sets to contribute to these challenges. The relevance and the commitment of universities to sustainability has led to the inclusion of SDGs in the strategies and agendas of these institutions. This requires the involvement of all the actors and some structural and cultural changes that put SDGs at the core of the governance and management of the university, embracing all the stakeholders. Various internal and external factors may influence the impact and success of the policies and activities aiming at achieving the SDGs, both from an overall perspective and for individual SDGs. This paper assesses the influence of some internal factors, such as the presence of universities on the internet, the level of internationalization or the availability of financial resources. Through both regression analyses and the Gephi method, our results confirm the importance of the presence on the internet, the internationalization of the university and the financial resources for research and infrastructure received from regional governments for Spanish public universities to make a greater contribution to SDGs.

Herding among analysts emerges when analysts give priority to their peers’ opinions instead of their own beliefs or information. Some circumstances may enhance or restrain this type of behaviour. We postulate that market sentiment is one of them. This article analyses the effect that investor sentiment may have on analysts’ herding behaviour in the U.K. Our results suggest that ‘easy situations’ such as analysing easy-to-value securities and releasing optimistic information at times of high market sentiment clearly reduce herding practices, whereas herding clearly increases in difficult situations when analysts have to release negative information at moments of high investor sentiment.

This paper contributes to the literature about sustainability assessment and goes a step further by studying the effect on university performance. The aim is to analyze, from an external perspective, the relationships between the three dimensions of sustainability in universities (environmental, economic, and social), the similarities between universities, and the impact that it can have on performance. In order to carry out an empirical assessment for Spanish public universities, an index is proposed to measure sustainability through indicators for the three dimensions. The results show that there is a positive correlation among the three dimensions, but only the association between the environmental and the economic dimension is statistically significant, which evidences that there is not an integrated perspective of sustainability. Although there are no common patterns among universities, some similarities among them were found. Finally, the paper shows that the entities that integrate sustainability in their plans and activities have a positive impact on performance.

This paper analyses the herding behaviour among exchanges around the expiration of bitcoin futures traded on the Chicago Mercantile Exchange (CME). The database extends from December 2017 to October 2020, taking as a reference the main exchanges that trade bitcoin (Binance, Bitfinex, Bitstamp, Coinbase, itBit, Kraken, and Gemini) and using hourly closing prices and trading volumes in bitcoin and US dollars. Adapting the proposal of Chang, Cheng and Khorana (2000) (CCK) to test conditional herding, we obtain results that indicate that the herding effect is significant during the week before expiration. After expiration, the herding effect lasts for a few hours and disappears. Information overload originating, among other causes, from sophisticated investors’ strategies may generate this mimetic behaviour. The results show the relevance of intraday data applied to specific events such as expiration since the unconditional analysis shows, in general, anti-herding behaviour throughout the period of study.

The authors' aim was to analyze the influence of analysts' recommendations on the activity of informed and uninformed traders and whether such influence produces changes in the price discovery process. The analysis was carried out in the Spanish market, considered to be an ideal market given its characteristics. The authors' results indicate that although investors as a whole react to new information from analysts and their activity increases, this reaction is not independent of the type of stock. Informed traders do not increase their activity with small stocks to the same extent as uninformed investors do. Furthermore, the influence of these movements on price discovery is not significant. The results suggest that the interpretation role of analysts is more important for less accessible firms in terms of assessing their growth opportunities. This role may enhance the herding behavior of uninformed agents trading in those small titles for which they would otherwise need to invest extra time and extra money for taking profitable decisions.