In the empirical literature, it is hypothesized that the persistence of Business Groups (BGs) is linked to the capability of easing the financing constraints of participating firms through the implementation of an internal capital market (ICM). ICMs enable capital relocation, thus partially offsetting disparities in the access to bank credit. I offer three contributions. First, I formally ground this idea with a dynamic model in which an ICM is endogenously generated as a function of the bank's lending policy and firms' production incentives. A novelty in the literature, the model contains a trickle-down mechanism which allows bank credit to circulate across firms via inter-firm loans. Second, I study the model to understand how the ICM reacts to shocks to the following empirically critical channels: the BG's debt-to-equity ratio, the profitability of production markets and the bank's credit rationing policy. The model disentangles the contribution of each channel on the shape of the ICM, as measured in terms of the intensity of firms' cross-subsidization. In particular, I discover a non-monotonic relationship between the latter dimension and the intensity of cross-subidization. Third, I match stylized facts of the so called "Korean crisis" and relate the model implications to extant results of the empirical literature.

In this paper I investigate the nexus between buyer-seller dynamics, financial frictions and market efficiency in decentralized markets. To do so, I introduce financial frictions in a dynamic market with heterogeneous traders. In the model, heterogeneously constrained buyers sequentially enter the market to acquire units of a generic good from heterogeneously endowed sellers. I characterize two closely related classes of equilibria, respectively called homogeneous equilibrium with no entry (HEWNE) and homogeneous equilibrium with entry (HEWE). Both equilibria prescribe a market where only the efficiently endowed type of seller exists in the limit. However, the two equilibria diverge in the specification of agents' behavior subsequent to trade. In HEWNE, sellers and buyers exit the market upon successful trading. In HEWE, like in supply chains, in every period certain types of buyers replace exiting sellers, thus becoming potential sellers for subsequent waves of buyers. First, I identify the critical role of frictions in steering the complex evolution of market heterogeneity for both classes of equilibria. Secondly, I operationalize the combined study of HEWNE and HEWE to obtain sharp predictions on market efficiency for a range of empirically-relevant situations in which buyer-seller dynamics are decoupled, for example when entry of new sellers is delayed or stopped. Third, I test the theoretical findings against a simulated artificial market.

We explore how can we best predict violent attacks for a large data-set containing all crime records recorded in the Merseyside area between years 2015-2018 using a limited set of information on (a) previous violence, (b) previous knife and weapon carrying, and (c) violence-related behaviour of known associates. We show that network-based flag dominate non-network flags. We validate our findings with a host of Machine-learning techniques.

In this paper I develop a simple model of endogenous formation of Input-Output Economies to address the theoretical nexus between trade-credit, bank credit and credit contagion. I make two contributions. First, I show that competitive markets in which heterogeneous price-taker firms compete strategically by setting trade-credit maturities have a unique symmetric equilibrium in trade-terms and the equilibrium dictates the production flow along the supply chains. Secondly, I find that the network can have a role either as shock absorber or shock amplifier and this is determined by a testable condition which holds for a general class of trade-credit networks. On these grounds, I argue that the proportional credit rationing used by banks (i.e., richer borrowers obtain larger loans) may have ambiguous effects on systemic vulnerability. In fact, if for intermediate levels of bank-credit only a subset of firms substitute trade-credit in favor of bank-credit, the bank may worsen the quality of the inter-firm credit network, thus increasing the systemic vulnerability above the contagion threshold.

The surge of the COVID-19 pandemic urged regulators all over the world to deploy measures aimed at rarefying social contacts to contain the spread of the pandemic, the so called social distancing policies. Social distancing depresses employment and hinders economic activity, therefore regulators asymmetrically target sectors across the economy. As shocks unevenly spill through the network of sectors, social distancing has unclear aggregate implications. We adopt a multi-sector model to explore the effect of social distancing in an economy characterized by sectoral spillovers. We use as benchmark the Australian economy, as this allows to leverage very granular data-sets on historical sectoral fluctuations and COVID-19 contemporary employment variations. We do two exercises. First, we attribute the employment shock to a structural change in factor utilization and study the effect on GDP for varying temporal windows. We obtain a drop ranging between 6.6% (20 weeks of lockdown) and 20% (1 year of lockdown). Second, we evaluate the short-run effect of the observed employment shocks on sectoral value added growth. Several up-stream sectors are subject to larger losses in value added than predicated by the observed change in employment. In fact, for several of these sectors, employment change in the relevant period is actually positive. Hence, the result can be attributed to a compounded network effect.

In this paper we generalize a well-known model of diffusion of behaviors on networks by introducing market dynamics. More precisely, we study the relation between the structure of information networks, trading behaviour, and market dynamics in the context of a dynamic model with heterogeneous expectations. We start with an asset pricing model in which traders can choose between several forecasting rules. We then allow information about performance of these rules to be shared across traders by means of a network of local interactions. For general classes of networks, and in particular, fixed an average connectivity, for regular, random, and scale-free networks, we establish an ordered relation between network connectivity, agents' trading behaviour, price deviation from fundamentals and volatility. Our framework generalizes the existing literature in two dimensions. First, it permits a rigorous mapping of network features into the properties of market dynamics. Second, it allows for studying the effect of several classes of information transmission protocols.

The fastest growing segment of private equity deals is secondary buyouts sales from one private equity (PE) firm to another. We operationalize a novel FactSet database to map the network structures of secondary buyouts between PE firms. After controlling for economic covariates, we find that PE firms are almost four times more likely to transact if they share a common partner. Importantly, we find that profitability of such transactions is unambiguously higher only if these are the result of repeated interaction. Hence, we explore two complementary incentives which can resolve the puzzle of why PE firms sharing common partners decide to transact in first place. The first incentive is social premium: only repeated transactions are strongly profitable. The second incentive is access to information. In this regard, we show that information diffuses through the network, leading to "bursts" of transactions involving neighboring PE firms.