The ability to infer intentions of other agents, called theory of mind (ToM), confers strong advantages for individuals in social situations. Here, we show that ToM can also be maladaptive when people interact with complex modern institutions like financial markets. We tested participants who were investing in an experimental bubble market, a situation in which the price of an asset is much higher than its underlying fundamental value. We describe a mechanism by which social signals computed in the dorsomedial prefrontal cortex affect value computations in ventromedial prefrontal cortex, thereby increasing an individual’s propensity to ‘ride’ financial bubbles and lose money. These regions compute a financial metric that signals variations in order flow intensity, prompting inference about other traders' intentions. Our results suggest that incorporating inferences about the intentions of others when making value judgments in a complex financial market could lead to the formation of market bubbles.
Trust in others' honesty is a key component of the long-term performance of firms, industries, and even whole countries. However, in recent years, numerous scandals involving fraud have undermined confidence in the financial industry. Contemporary commentators have attributed these scandals to the financial sector’s business culture, but no scientific evidence supports this claim. Here we show that employees of a large, international bank behave, on average, honestly in a control condition. However, when their professional identity as bank employees is rendered salient, a significant proportion of them become dishonest. This effect is specific to bank employees because control experiments with employees from other industries and with students show that they do not become more dishonest when their professional identity or bank-related items are rendered salient. Our results thus suggest that the prevailing business culture in the banking industry weakens and undermines the honesty norm, implying that measures to re-establish an honest culture are very important.
The fringe banking industry, including payday lenders and check cashers, was nearly nonexistent three decades ago. Today it generates tens of billions of dollars in annual revenue. The industry’s growth accelerated in the 1980s with financial deregulation and the working class’s declining resources. With Current Population Survey data, we used propensity score matching to investigate the relationship between fringe loan use, unbanked status, and self-rated health, hypothesizing that the material and stress effects of exposure to these financial services would be harmful to health. We found that fringe loan use was associated with 38 percent higher prevalence of poor or fair health, while being unbanked (not having one’s own bank account) was associated with 17 percent higher prevalence. Although a variety of policies could mitigate the health consequences of these exposures, expanding social welfare programs and labor protections would address the root causes of the use of fringe services and advance health equity.
The financial crisis clearly illustrated the importance of characterizing the level of ‘systemic’ risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies.
The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only - but substantially - when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.
In financial markets, participants locally optimize their profit which can result in a globally unstable state leading to a catastrophic change. The largest crash in the past decades is the bankruptcy of Lehman Brothers which was followed by a trust-based crisis between banks due to high-risk trading in complex products. We introduce information dissipation length (IDL) as a leading indicator of global instability of dynamical systems based on the transmission of Shannon information, and apply it to the time series of USD and EUR interest rate swaps (IRS). We find in both markets that the IDL steadily increases toward the bankruptcy, then peaks at the time of bankruptcy, and decreases afterwards. Previously introduced indicators such as ‘critical slowing down’ do not provide a clear leading indicator. Our results suggest that the IDL may be used as an early-warning signal for critical transitions even in the absence of a predictive model.
The understanding of complex systems has become a central issue because such systems exist in a wide range of scientific disciplines. We here focus on financial markets as an example of a complex system. In particular we analyze financial data from the S&P 500 stocks in the 19-year period 1992-2010. We propose a definition of state for a financial market and use it to identify points of drastic change in the correlation structure. These points are mapped to occurrences of financial crises. We find that a wide variety of characteristic correlation structure patterns exist in the observation time window, and that these characteristic correlation structure patterns can be classified into several typical “market states”. Using this classification we recognize transitions between different market states. A similarity measure we develop thus affords means of understanding changes in states and of recognizing developments not previously seen.
The integration of ethnical minorities has been a hotly discussed topic in the political, societal, and economic debate. Persistent discrimination of ethnical minorities can hinder successful integration. Given that unequal access to investment and financing opportunities can cause social and economic disparities due to inferior economic prospects, we conducted a field experiment on ethnical discrimination in the finance sector with 1,218 banks in seven European countries. We contacted banks via e-mail, either with domestic or Arabic sounding names, asking for contact details only. We find pronounced discrimination in terms of a substantially lower response rate to e-mails from Arabic senders. Remarkably, the observed discrimination effect is robust for loan- and investment-related requests, across rural and urban locations of banks, and across countries.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 7 years ago
The birth and explosive growth of mobile money in Kenya has provided economists with an opportunity to study the evolution and impact of a new financial system. Mobile money is an innovation that allows individuals to store, send, and receive money on their mobile phone via text message. This system has opened up basic financial services to many who were previously excluded, and has had real and measurable impacts on the ability of households to protect themselves against health risks. Using a unique survey instrument covering nearly 2,300 households over 2008-2010, we first document the lightning-fast adoption of mobile money in Kenya, which was faster than most documented modern technologies in the United States. We then present evidence on how this innovation allows households to respond better to unexpected adverse health events. We find that in the face of these events, users of mobile money are better able to tap into remittances to finance additional health care costs without having to forego necessary expenditures on education, food, and other consumption needs.
We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.