SAFE working paper
https://safe-frankfurt.de/de/publikationen/working-papers.html
Refine
Year of publication
- 2021 (31) (remove)
Document Type
- Working Paper (31)
Language
- English (31) (remove)
Has Fulltext
- yes (31)
Is part of the Bibliography
- no (31)
Keywords
- COVID-19 (4)
- ESG (4)
- ETFs (2)
- ambiguity (2)
- climate change (2)
- green finance (2)
- volatility (2)
- Algorithmic transparency (1)
- Bank Bailout (1)
- Bank Recapitalization (1)
- Belief up-dating (1)
- Beliefs (1)
- Board Appointments (1)
- Bond risk premia (1)
- C corporations (1)
- Capital Purchase Program (1)
- Centrality (1)
- China (1)
- Corporate Social Responsibility (1)
- Covid-19 (1)
- DCC-GARCH (1)
- DSGE models (1)
- Dictionary (1)
- Digitalized Markets (1)
- Disposition Effect (1)
- Dividend Payments (1)
- ESG Rating Agencies (1)
- Equity Premium (1)
- Expectations (1)
- Explainable machine learning (1)
- Financial Crises (1)
- Financial Market Cycles (1)
- Fiscal theory of the price level (1)
- Formative experiences (1)
- Fund Flows (1)
- Government debt (1)
- Granger Causality (1)
- High Frequency Data (1)
- High-frequency event study (1)
- Hong test (1)
- Household Finance (1)
- Information processing (1)
- International Finance (1)
- Investor sentiment (1)
- LSTM neural networks (1)
- Machine learning (1)
- Market efficiency (1)
- Monetary Policy Surprises (1)
- NLP (1)
- Network theory (1)
- Nonlinear solution methods (1)
- Obfuscation (1)
- Oil market (1)
- Portfolio Rebalancing (1)
- Portfolio choice (1)
- Price Competition (1)
- Price Pressures (1)
- Public financial news (1)
- Rational Inattention (1)
- Responsible investment (1)
- Retail Investor (1)
- Risk taking (1)
- S corporations (1)
- S&P 500 (1)
- Social media (1)
- Socially responsible investing (1)
- Stock market (1)
- Sustainable Investments (1)
- TARP (1)
- Tax Cuts and Jobs Act (1)
- Twitter (1)
- XAI (1)
- age (1)
- ambiguity premium (1)
- anomalies (1)
- asset pricing (1)
- bank (1)
- banks (1)
- behavioral economics (1)
- belief effect (1)
- belief estimation (1)
- belief updating (1)
- benchmarks (1)
- bid-ask spread (1)
- bubbles (1)
- call auctions (1)
- climate risk (1)
- compliance behavior (1)
- confirmatory biases (1)
- consumer protection (1)
- corporate governance (1)
- corporate taxation (1)
- credence goods (1)
- designated market makers (1)
- discrimination (1)
- employees (1)
- endogenous information acquisition (1)
- erm structure of interest rates (1)
- factor timing (1)
- financial advice (1)
- financial market (1)
- financial risk-taking (1)
- financing (1)
- fintech (1)
- health (1)
- household finance (1)
- institutional investors (1)
- investment biases (1)
- investor behavior (1)
- labels (1)
- laboratory experiment (1)
- laboratory experiments (1)
- lending (1)
- lottery-type assets (1)
- mandatory disclosure (1)
- marginal propensity to consume (1)
- market discipline (1)
- market price (1)
- market-based (1)
- measure of ambiguity (1)
- media polarization (1)
- motivated beliefs (1)
- net zero transition (1)
- option prices (1)
- patents (1)
- persistence (1)
- saving (1)
- sentiment (1)
- source dependence (1)
- spillover effects (1)
- stock market crisis (1)
- sustainability (1)
- sustainable finance (1)
- tax cut (1)
- tax intervention (1)
- taxonomies (1)
- time series momentum (1)
- trading activity (1)
- trend chasing (1)
- valuation ratios (1)
- workforce (1)
- financial literacy (1)
Institute
- Center for Financial Studies (CFS) (31) (remove)
314
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.
322
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.
321
We present new statistical indicators of the structure and performance of US banks from 1990 to today, geographically disaggregated at the level of individual counties. The constructed data set (20 indicators for some 3150 counties over 31 years, for a total of about 2 million data points) conveys a detailed picture of how the geography of US banking has evolved in the last three decades. We consider the data as a stepping stone to understand the role banks and banking policies may have played in mitigating, or exacerbating, the rise of poverty and inequality in certain US regions.
318
Incentives, self-selection, and coordination of motivated agents for the production of social goods
(2021)
We study, theoretically and empirically, the effects of incentives on the self-selection and coordination of motivated agents to produce a social good. Agents join teams where they allocate effort to either generate individual monetary rewards (selfish effort) or contribute to the production of a social good with positive effort complementarities (social effort). Agents differ in their motivation to exert social effort. Our model predicts that lowering incentives for selfish effort in one team increases social good production by selectively attracting and coordinating motivated agents. We test this prediction in a lab experiment allowing us to cleanly separate the selection effect from other effects of low incentives. Results show that social good production more than doubles in the low- incentive team, but only if self-selection is possible. Our analysis highlights the important role of incentives in the matching of motivated agents engaged in social good production.
315
This paper explores the interplay of feature-based explainable AI (XAI) tech- niques, information processing, and human beliefs. Using a novel experimental protocol, we study the impact of providing users with explanations about how an AI system weighs inputted information to produce individual predictions (LIME) on users’ weighting of information and beliefs about the task-relevance of information. On the one hand, we find that feature-based explanations cause users to alter their mental weighting of available information according to observed explanations. On the other hand, explanations lead to asymmetric belief adjustments that we inter- pret as a manifestation of the confirmation bias. Trust in the prediction accuracy plays an important moderating role for XAI-enabled belief adjustments. Our results show that feature-based XAI does not only superficially influence decisions but re- ally change internal cognitive processes, bearing the potential to manipulate human beliefs and reinforce stereotypes. Hence, the current regulatory efforts that aim at enhancing algorithmic transparency may benefit from going hand in hand with measures ensuring the exclusion of sensitive personal information in XAI systems. Overall, our findings put assertions that XAI is the silver bullet solving all of AI systems’ (black box) problems into perspective.
305
The disposition effect is implicitly assumed to be constant over time. However, drivers of the disposition effect (preferences and beliefs) are rather countercyclical. We use individual investor trading data covering several boom and bust periods (2001-2015). We show that the disposition effect is countercyclical, i.e. is higher in bust than in boom periods. Our findings are driven by individuals being 25% more likely to realize gains in bust than in boom periods. These changes in investors’ selling behavior can be linked to changes in investors’ risk aversion and in their beliefs across financial market cycles.
304
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.
301
We study risk taking in a panel of subjects in Wuhan, China - before, during the COVID-19 crisis, and after the country reopened. Subjects in our sample traveled for semester break in January, generating variation in exposure to the virus and quarantine in Wuhan. Higher exposure leads subjects to reduce planned risk taking, risky investments, and optimism. Our findings help unify existing studies by showing that aggregate shocks affect general preferences for risk and economic expectations, while heterogeneity in experience further affect risk taking through beliefs about individuals’ own outcomes such as luck and sense of control.
JEL Classification: G50, G51, G11, D14, G41
309
We show that financial advisors recommend more costly products to female clients, based on minutes from about 27,000 real-world advisory meetings and client portfolio data. Funds recommended to women have higher expense ratios controlling for risk, and women less often receive rebates on upfront fees for any given fund. We develop a model relating these findings to client stereotyping, and empirically verify an additional prediction: Women (but not men) with higher financial aptitude reject recommendations more frequently. Women state a preference for delegating financial decisions, but appear unaware of associated higher costs. Evidence of stereotyping is stronger for male advisors.
324
Analysing causality among oil prices and, in general, among financial and economic variables is of central relevance in applied economics studies. The recent contribution of Lu et al. (2014) proposes a novel test for causality— the DCC-MGARCH Hong test. We show that the critical values of the test statistic must be evaluated through simulations, thereby challenging the evidence in papers adopting the DCC-MGARCH Hong test. We also note that rolling Hong tests represent a more viable solution in the presence of short-lived causality periods.