Sustainable Architecture for Finance in Europe (SAFE)
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We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
With the second wave of the Covid-19 pandemic in full swing, banks face a challenging environment. They will need to address disappointing results and adverse balance sheet restatements, the intensity of which depends on the evolution of the euro area economies. At the same time, vulnerable banks reinforce real economy deficiencies. The contribution of this paper is to provide a comparative assessment of the various policy responses to address a looming banking crisis. Such a crisis will fully materialize when non-performing assets drag down banks simultaneously, raising the specter of a full-blown systemic crisis. The policy responses available range from forbearance, recapitalization (with public or private resources), asset separation (bad banks, at national or EU level), to debt conversion schemes. We evaluate these responses according to a set of five criteria that define the efficacy of each. These responses are not mutually exclusive, in practice, as they have never been. They may also go hand in hand with other restructuring initiatives, including potential consolidation in the banking sector. Although we do not make a specific recommendation, we provide a framework for policymakers to guide them in their decision making.
The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also affect sustainable investments leading to the identification of different investment universes and consequently to the creation of different benchmarks. This implies that in the asset management industry it is extremely difficult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the effect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.
This paper discusses policy implications of a potential surge in NPLs due to COVID-19. The study provides an empirical assessment of potential scenarios and draws lessons from previous crises for effective NPL treatment. The paper highlights the importance of early and realistic assessment of loan losses to avoid adverse incentives for banks. Secondary loan markets would help in this process and further facilitate bank resolution as laid down in the BRRD, which should be uphold even in extreme scenarios.