Better to stay apart: asset commonality, bipartite network centrality, and investment strategies

By exploiting a bipartite network representation of the relationships between mutual funds and portfolio holdings, we propose an indicator that we derive from the analysis of the network, labelled the Average Commonality Coefficient (ACC), which measures how frequently the assets in the fund portfolio are present in the portfolios of the other funds of the market. This indicator reflects the investment behavior of funds' managers as a function of the popularity of the assets they held. We show that $ACC$ provides useful information to discriminate between funds investing in niche markets and those investing in more popular assets. More importantly, we find that $ACC$ is able to provide indication on the performance of the funds. In particular, we find that funds investing in less popular assets generally outperform those investing in more popular financial instruments, even when correcting for standard factors. Moreover, funds with a low $ACC$ have been less affected by the 2007-08 global financial crisis, likely because less exposed to fire sales spillovers.Comment: 38 pages, 6 figure

The Accounting Network: how financial institutions react to systemic crisis

The role of Network Theory in the study of the financial crisis has been widely spotted in the latest years. It has been shown how the network topology and the dynamics running on top of it can trigger the outbreak of large systemic crisis. Following this methodological perspective we introduce here the Accounting Network, i.e. the network we can extract through vector similarities techniques from companies' financial statements. We build the Accounting Network on a large database of worldwide banks in the period 2001-2013, covering the onset of the global financial crisis of mid-2007. After a careful data cleaning, we apply a quality check in the construction of the network, introducing a parameter (the Quality Ratio) capable of trading off the size of the sample (coverage) and the representativeness of the financial statements (accuracy). We compute several basic network statistics and check, with the Louvain community detection algorithm, for emerging communities of banks. Remarkably enough sensible regional aggregations show up with the Japanese and the US clusters dominating the community structure, although the presence of a geographically mixed community points to a gradual convergence of banks into similar supranational practices. Finally, a Principal Component Analysis procedure reveals the main economic components that influence communities' heterogeneity. Even using the most basic vector similarity hypotheses on the composition of the financial statements, the signature of the financial crisis clearly arises across the years around 2008. We finally discuss how the Accounting Networks can be improved to reflect the best practices in the financial statement analysis

Responsiveness of open innovation to COVID-19 pandemic: The case of data for good

Due to the COVID-19 pandemic, countries around the world are facing one of the most severe health and economic crises of recent history and human society is called to figure out effective responses. However, as current measures have not produced valuable solutions, a multidisciplinary and open approach, enabling collaborations across private and public organizations, is crucial to unleash successful contributions against the disease. Indeed, the COVID-19 represents a Grand Challenge to which joint forces and extension of disciplinary boundaries have been recognized as main imperatives. As a consequence, Open Innovation represents a promising solution to provide a fast recovery. In this paper we present a practical application of this approach, showing how knowledge sharing constitutes one of the main drivers to tackle pressing social needs. To demonstrate this, we propose a case study regarding a data sharing initiative promoted by Facebook, the Data For Good program. We leverage a large-scale dataset provided by Facebook to the research community to offer a representation of the evolution of the Italian mobility during the lockdown. We show that this repository allows to capture different patterns of movements on the territory with increasing levels of detail. We integrate this information with Open Data provided by the Lombardy region to illustrate how data sharing can also provide insights for private businesses and local authorities. Finally, we show how to interpret Data For Good initiatives in light of the Open Innovation Framework and discuss the barriers to adoption faced by public administrations regarding these practices

Systemic importance of financial institutions: from a global to a local perspective? A network theory approach

After the systemic effects of bank defaults during the recent financial crisis, and despite a huge amount of literature over the last years to detect systemic risk, no standard methodologies have been set up until now. We aim to build a concise but comprehensive picture of the state of the art, illustrating the open issues, and outlining pathways for future research. In particular, we propose the analysis of some examples of local systems that attract the attention of the financial sector. This work is directed to both academic researchers and practitioners

Systemic risk and banking regulation: some facts on the new regulatory framework

The recent financial crisis highlighted the relevant role of the systemic effects of banks’ defaults on the stability of the whole financial system. In this work we draw an organic picture of the current regulations, moving from the definitions of systemic risk to the issues concerning data availability. We show how a more detailed flow of data on traded deals might shed light on some systemic risk features taken into account only partially in the past. In particular, we analyse how the new regulatory framework allows regulators to describe OTC derivatives markets according to more detailed partitions, thus depicting a more realistic picture of the system. Finally, we suggest to study sub-markets illiquidity conditions to consider possible spill over effects which might lead to a worsening for the entire system

Assessing financial distress dependencies in OTC markets: a new approach by Trade Repositories data

After the recent financial crisis, it is undoubtedly recognized the importance of assessing not only the risk of distress for a single \financial entity", but also the distress dependencies between the different \entities", where by \entities" we mean in a broad sense any relevant cluster of products, risk factors, counterparties. In this paper, we focus on the Interest Rate Swap (IRS) segment as a significant fraction of the OTC market. We define a distress indicator by combining some distress drivers, such as averaged volumes, liquidity, volatility and bid-ask proxies. Hence, we analyse the distress dependencies among sub-markets identified by the segmentation of the IRS market according to contractual and financial features. We try to combine in an innovative way some new ingredients, namely the more granular data on OTC derivatives available from the trade repositories along with the classical JPoD approach introduced in the recent years by the IMF for studying the distress interdependence structure among financial institutions. The proposed technique seems to be quite promising. Indeed, the results are quite close to the practical intuition. At the best of our knowledge, this work is the first empirical study based on trade repositories' data for assessing systemic risk

Communities and regularities in the behavior of investment fund managers

We analyze a large microlevel dataset on the full daily portfolio holdings and exposures of 22 complex investment funds to shed light on the behavior of professional investment fund managers. We introduce a set of quantitative attributes that capture essential distinctive features of manager allocation strategies and behaviors. These characteristics include turnover, attitude toward hedging, portfolio concentration, and reaction to external events, such as changes in market conditions and flows of funds. We find the existence and stability of three main investment attitude profiles: conservative, reactive, and proactive. The conservative profile shows low turnover and resilience against external shocks; the reactive one is more prone to respond to market condition changes; and members of the proactive profile frequently adjust their portfolio allocations, but their behavior is less affected by market conditions. We find that exogenous shocks temporarily alter this configuration, but communities return to their original state once these external shocks have been absorbed and their effects vanish

Peer-Group Detection of Banks and Resilience to Distress

The paper looks at the importance of the true business model in shaping the risk profile of financial institutions. We adopt a novel indirect clustering approach to enrich the classic bank business model classification on a global data set including about 11,000 banks, both listed and non-listed representing more than 180 countries over the period 2005-2014. A comprehensive list of global distress events, which combines bankruptcies, liquidations, defaults, distressed mergers, and public bailouts, is regressed against financial statement ratios (i.e. proxies for CAMELS) and controlling for macro and sectoral effects using a rare-event logit model. Our findings suggest that individual characteristics along with macro and sectoral factors contribute differently, sometimes with opposite sign, to the likelihood of distress and to the volatility of business models with the exception of liquidity whose contribution appears exogenous to business model choice. By capturing the switching behaviour across groups, we find that business model volatility exacerbates vulnerability and distress, especially if moving from wholesale-oriented to deposit oriented groups

Banks' business strategies on the edge of distress

The paper investigates the importance of banks’ business classification in shaping the risk profile of financial institutions on a global scale. We employ a rare-event logit model based on a state-of-the-art list of major global distress events from the global financial crisis. When clustering banks by their business strategies using a community detection approach, we show that (i) capital enhanced resilience only for traditional banks that were on average less capitalized than other banks; (ii) boosting ROE, usually associated with riskier exposures, improved resilience for stable funded and asset diversified banks; (iii) conversely, higher levels of ROA exacerbated banks’ vulnerability when associated with concentrated (not-diversified) investment structures; (iv) size in terms of total assets contributed to instability only for wholesale-funded institutions due to their high levels of unstable funding. Liquidity, on the contrary, reduced the institution likelihood of being in distress, regardless of its business classification. Although our findings refer to the recent financial crisis, they provide evidence that a tailored risk monitoring based on a proper peer group identification can facilitate banks’ distresses prediction