Research

Networks in finance shape how value is created and risks emerge, both systemic and systematic. We survey how different networks, including production, banking, directors, and information networks, propagate micro shocks and transmit them to broader levels of aggregation. First, we synthesize the literature on the efficiency-stability trade-off governing systemic risk: interconnections transmit gains, liquidity, and knowledge, but create channels for contagion. Beyond the foundational focus on within-network transmission, we extend the discussion to across-networks spillovers. Second, moving from systemic to systematic risk, we show how network topology drives the stochastic discount factor, affecting factors and expected returns. We also trace how networks form and evolve, dynamically generating new sources of risk. By adopting a macro perspective that unifies distinct networks into an overarching network-of-networks (meta-network), we focus on shared propagation mechanisms and outline directions for future research, including how networks may evolve with the rise of AI, intangibles, and deglobalization.

We find that high productivity-based macroeconomic uncertainty is associated with greater accumulation of physical capital despite a reduction in investment and valuations. To reconcile this puzzling evidence, we show that uncertainty predicts lower aggregate depreciation of existing capital, which dominates the investment slowdown. We explain these findings by developing a quantitative production-based model in which firms implement precautionary savings through reducing utilization rather than raising investment. Through this novel intensive-margin mechanism, uncertainty shocks command a quarter of the equity premium in general equilibrium. Flexibility in utilization adjustments also helps explain uncertainty risk exposures in the cross-section of industry returns.

The impact of uncertainty shocks on firm-level economic activity depends on their origin in supply-chains. Upstream (downstream) uncertainty from suppliers (customers) is associated with variability over future input (output) prices. Consequently, a real-option production model with time-to-build suggests that only upstream uncertainty suppresses investment since upstream (downstream) uncertainty affects the shorter-run (longer-run). Consistently, production-network data show that upstream uncertainty affects firm-level outcomes negatively. Conversely, downstream uncertainty affects firm-level outcomes more weakly but positively. At the macro-level, these two uncertainties oppositely predict aggregate growth and asset prices. Overall, downstream uncertainty has an expansionary effect, in contrast to other facets of uncertainty.

We study the interaction of flexible capital utilization and depreciation for expected returns and investment of firms. Empirically, an investment strategy that buys (sells) equities with low (high) utilization rates earns 5% p.a. Utilization predicts excess returns beyond other production-based variables. We reconcile this novel utilization premium quantitatively using a production model. The model suggests that flexible utilization is important for matching the cross-sectional distribution of investment and stock prices jointly. A model without flexible utilization yields many counterfactuals that flexible utilization addresses by making depreciation fluctuate endogenously. Overall, utilization tightens the link between firms' production and valuations.

The data-generating process of productivity growth includes both trend and business-cycle shocks, generating many counterfactuals for prices under full-information. In practice, agents cannot immediately distinguish between the two shocks, leading to ``rational confusion’’: each shock inherits properties of its counterpart. This confusion magnifies the perceived share of permanent shocks and implies that, in contrast to canonical frameworks, transitory shocks are the main driver of long-run risk through trendy business-cycles. With learning, the equity premium turns positive, and both investment and valuation ratios become procyclical, in-line with the data. Consequently, rational confusion is key for bridging disciplined macro-dynamics with equilibrium asset-prices.

This paper studies the implications of trade credit for asset prices, the dynamics of production-network linkages, and the quality of firms’ customers. We document that firms that extend more trade credit earn 7% p.a. lower risk premia, maintain longer relationships with their customers, and trade with more productive customers. Moreover, suppliers with longer-duration links to customers command lower expected returns. Using a production-based model, we quantitatively explain these facts. Trade credit reduces the departure probability of high-quality customers, thereby reducing firms' exposures to systematic costs incurred in finding new customers. Overall, trade credit is informative for micro- and macro-level fundamentals.

We examine empirically and theoretically the relation between firms' risk and their distance to consumers in a production network. We document two novel facts: firms that are further away from consumers have higher risk premia and higher exposures to aggregate productivity. We quantitatively explain these findings using a general equilibrium model featuring a multi-layer production process. The economic force is "vertical creative destruction" -- positive productivity shocks to suppliers devalue customers’ assets-in-place, which lowers the cyclicality of downstream firms' values. We show that vertical creative destruction varies with competition and firm characteristics, and generates sizable cross-sectional differences in risk premia.

What is the impact of higher technological volatility on asset-prices and macroeconomic aggregates? I find the answer hinges on its sectoral origin. I document several novel empirical facts: Volatility that originates from the consumption sector plays the ``traditional'' role of depressing the real economy and stock prices, whereas volatility that originates from the investment sector boosts prices and growth; Investment (consumption) sector's technological volatility has a positive (negative) market-price of risk; Investment sector's technological volatility helps explain cross-sectional spreads beyond first-moment shocks. I show that a quantitative two-sector DSGE model, that features monopolistic power for firms and sticky prices, as well as early resolution of uncertainty, can explain the differential impact of sectoral volatilities on real and financial variables. In all, the sectoral decomposition of volatility can overturn the typical negative interaction of volatility with economic activity.

Does macroeconomic uncertainty increase or decrease aggregate growth and asset prices? To address this question, we decompose aggregate uncertainty into ‘good’ and ‘bad’ volatility components, associated with positive and negative innovations to macroeconomic growth. We document that in line with our theoretical framework, these two uncertainties have opposite impact on aggregate growth and asset prices. Good uncertainty predicts an increase in future economic activity, such as consumption, output, and investment, and is positively related to valuation ratios, while bad uncertainty forecasts a decline in economic growth and depresses asset prices. Further, the market price of risk and equity beta of good uncertainty are positive, while negative for bad uncertainty. Hence, both uncertainty risks contribute positively to risk premia, and help explain the cross-section of expected returns beyond cash flow risk.