Abstract: Does the rise of intangible capital create financial instability? Firms hoard liquidity in the form of bank debt (e.g., deposits) for non-pledgeable intangible investments. This liquidity demand pushes down interest rate, giving banks a funding cost advantage, so banks bid up asset prices in booms as they grow. Higher asset prices induce firms to invest more in intangibles and hoard more liquidity, leading to an even lower interest rate and enabling banks to bid up asset prices even further. This paper models corporate savings glut that arises endogenously from the interaction between firms and banks in asset and money markets. The feedback mechanism explains several concurrent phenomena in the run-up to the Great Recession, and how endogenous risk accumulates in booms and materializes into severe and stagnant crises.
Presentations: European Winter Finance Summit (Sudipto Bhattacharya Memorial Prize); WFA (Western Finance Association), EFA (European Finance Association), SED (Society for Economic Dynamics), Econometric Society (North America), 7th HKUST Macro Workshop, CMU/OSU/Pittsburgh/Penn State conference; earlier version at:
Columbia University, Federal Reserve Bank of New York, Finance Theory
Group (parallel session), LBS Trans-Atlantic Doctoral Conference
We provide a dynamic asset-pricing model of (crypto-) tokens on (blockchain-based) platforms. Tokens intermediate peer-to-peer transactions, and their trading creates inter-temporal complementarity among users and generates a feedback loop between token valuation and adoption. Consequently, tokens capitalize future platform growth, accelerate adoption, and reduce user-base volatility. Equilibrium token price increases non-linearly in platform productivity, user heterogeneity, and endogenous network size. Consistent with evidence, the model produces explosive growth of user base after an initial period of dormant adoption, accompanied by a run-up of token price volatility.
Finance Theory Group,
City U of Hong Kong International Finance Conf,
2nd Private Markets Research Conference, 3rd Rome Junior Finance Conf, Emerging Trends in Entrepreneurial Finance (
Best Paper Award ), Finance UC International Conference, JOIM Conf on FinTech, Norwegian School of Economics, LeBow/GIC/FRB Conf on Cryptocurrency, Shanghai Forum
with Chen Wang, Jun 2018
Abstract: Taking advantage of big data, professional asset managers increasingly use nonlinear techniques, such as machine learning, to form the full probability distribution of asset returns rather than a simple point estimate. We provide the first theoretical framework that features this most general form of asset managers' skill - the knowledge of true return distribution. In contrast, investors face model uncertainty. The equilibrium level of delegation depends on model uncertainty, and the cross-section variation of expected asset returns are driven by a CAPM component and an alpha, which constitutes of a model-uncertainty beta and a price of uncertainty depending on delegation. We show that even when delegation approaches 100% (e.g., driven by declining costs of asset management), the alpha of certain assets never disappears because of investors' model-hedging motive that increases in delegation and is unique to our setup. Our model also resolves several puzzles in the literature of asset management, such as delegation in spite of underperformance. We provide supporting evidence on both delegation and asset pricing.
We estimate the liquidity multiplier and individual banks’
contribution to systemic risk in an interbank network using a structural model. Banks borrow liquidity
from neighbors and update their valuation based on neighbors' actions. When
the former (latter) motive dominates, the equilibrium exhibits strategic substitution (complementarity) of liquidity holdings, and a reduced (increased) liquidity
multiplier dampening (amplifying) shocks. Empirically, we find substantial and
procyclical network-generated risks driven mostly by changes of equilibrium
type rather than network topology. We identify the banks that generate most systemic risk and solve the
planner's problem, providing guidance to macro-prudential policies.
NBER SI 2018, Macro Finance Society, Bank of England, Cass Business School, Duisenberg School of Finance, Koc University, London School of Economics, Stockholm School of Economics, OSU Fisher
with Chen Wang, Jun 2018
Abstract: The prices of dividends at alternative horizons contain critical information on the behavior of aggregate stock market. The ratio between prices of long- and short-term dividends, "price ratio" (pr), predicts annual market return with an out-of-sample R2 of 19%. pr subsumes the predictive power of traditional price-dividend ratio (pd). After orthogonalized to pr, the residuals of pd strongly predicts dividend growth. Using an exponential-affine model, we show a one-to-one mapping between pr and the expected market return when the expectation of future cash flow is transient. Moreover, we find that return predictability is stronger after market downturns, and holds outside the U.S. As an economic test, shocks to pr are priced in the cross-section of stocks, consistent with ICAPM. Our measure of expected return declines during monetary expansions, and varies strongly with the conditions of macroeconomy, financial intermediaries, and sentiment.
Presentations: Econometric Society (North America), FMA Asia, Northern Finance Association (NFA)