Economics - Finance IV

Social distancing measures adopted to combat the COVID-19 pandemic have created severe disruptions to economic output. During lockdown firms are required to shut down or substantially reduce their economic activity if they cannot comply with social distancing rules and are located in non-essential industries. Another source of negative direct shocks arises from changed consumption behavior of individuals to avoid infectious exposure. The shocks to the economy are highly industry-specific and therefore affect firms in heterogeneous ways [1].

We analyse the cycling of mass in over 200 real-world aquatic weighted food webs, that quantify feeding relationships between groups of species. The majority of matter cycling described through Finn Cycling Index (FCI) can be traced back to microorganisms that assimilate dead organic matter. This microbial loop recycles a significant fraction of primary production. We show how the very skew distribution of FCI at the network level originates from approximately lognormal distributions of distinct groups of nodes.

Cryptocurrencies have become an important dual entity (asset and currency) of the global financial system. Investing in cryptocurrencies as well as using them as a payment method is exponentially expanding. Prediction of cryptocurrency prices is a challenging task mainly because they represent a relatively new phenomenon, exhibiting high volatility. To better understand cryptocurrency trends and to improve price predictability, we propose to use sentiments of cryptocurrency-related news and micro-blogs.

The aim of this paper is to investigate the influence of investors’ confidence in their portfolio holding with respect to their social group and of various social network topologies on the dynamics of an artificial stock exchange. An investor’s confidence depends on the growth rate of her wealth relative to her social group average wealth. If the investor’s confidence is low, the agent will change her asset allocation, otherwise she will maintain it. We consider three types of social networks: Barabási, small-world, and random.