Digitalization led to a rapid expansion of loyalty tokens typically bundled as part of product price. An open question is whether issuers are incentivized to make loyalty tokens tradable, raising regulation issues for monetary and banking authorities. This paper argues that an issuer earns more revenue by making tokens non-tradable even though consumers would pay a higher price for tradable tokens. We further show that an issuer with stronger market power makes its revenue more token-dependent. We test the model's predictions with data on airline mileage and hotel reward programs and document consistent empirical results that align with our theory.

Trust fundamentally shapes financial activities, and the persistent variation in trust across societies may lead to huge financial development gap in the globe. We model trust as the collective reputation of borrowers and study how trust exhibits persistent variation across societies, consequently leading to divergent credit and economic growth. Our theory demonstrates distrust to be a self-fulfilling prophecy: lenders believe borrowers are more likely to default and raise interest rates, reducing borrowers' incentive to repay loans, thus fulfilling the expected low trust environment. This shows that high-trust and low-trust equilibria can both exist even in the same society. Financial deregulation benefits high-trust countries more as trust enforces good behaviors in equilibrium. The informal credit market amplifies the equilibrium divergence. Empirically, distrust persistently predicts less formal borrowing, lower credit card ownership, and lower GDP growth with 50% predictability attributed to slower credit expansion. Micro-level evidence explains how trust affects individuals’ switching to informal credit sources. Financial liberalization fosters higher GDP growth in high-trust countries but the opposite in low-trust countries.

Blockchain-based platforms and DeFi prominently feature ``staking'': Besides offering transaction convenience, tokens are staked for generating consensus or incentivizing network development/economic activities, and consequently earn staking rewards. We model a continuous-time economy where agents heterogeneous in wealth and preference dynamically allocate wealth among consumption, onchain transaction, and staking. The aggregate staking ratio endogenously affects the dynamic of platform productivity. These interactions lead to the aggregate staking ratio positively correlate with reward rate, negatively predicts future reward rate, and importantly, positively predicts future token excess returns. We further characterize using the master equation the wealth redistribution due to staking and aggregate shocks. Long-run wealth distribution is Pareto-like, where platform growth and welfare improvements accompany with increasing concentration. Empirical evidence from all major stakable tokens corroborate the model predictions, including how staking explains the UIP violations and crypto premia by influencing convenience wedges of consumption and transaction.

We document a novel high-volume overnight premium (intraday discount). Specifically, stocks with high trading volume exhibit remarkable future outperformance (underperformance) during overnight (intraday) period. The results are pervasive across global equity markets, robust to alternative measures of trading volume, and persistent for at least three years after portfolio formation. Aligned with a model of heterogeneous investors, the overnight premium and intraday discount are more pronounced when (uninformed) investors are more optimistic or certain about the economy. The predictability of trading volume mainly stems from its retail activity component.

It is growing rapidly that platforms serve as intermediaries in delegated asset management, especially featuring algorithms delivering personalized recommendations to a large user base. We develop a model to study the platform’s investor-optimal algorithm design when investors are unaware of their risk aversion and contract with fund managers conditional on recommendation status. We show algorithms can mitigate the manager’s moral hazard in over risk-taking inherited from linear limited-liability contracts. In contrast with consumption platforms, the algorithm observes noisy historical performance, reduces unsure recommendations under ambiguous signals, and potentially compensates clear signals, generating information rent paid by investors. Ultimately, the algorithm serves as an information gatekeeper with commitment power from leveraging the user base. We provide an approach to solve general algorithm design problems and discuss the properties of algorithms, including their inequality concerns, interactions with contract design, comparisons with other delegated investment scenarios, and co-existence with fund rating systems.

Transaction records on blockchains are considered highly transparent, yet widely used for fraud and illegal deals. This is largely due to the low cost of creating new on-chain agents, making the economy fail to develop an effective agent reputation system to distinguish good counterparties. I study the potential solution to build a reputation system based on the medium of exchange, i.e. evaluating the reputation of a token instead of an agent. The logic is, agents (nodes) and exchanges (edges) constitute the transaction network. The exchange of tokens carries the reputation of agents, whereas several agents for one criminal activity invariably need to be connected through a series of transactions. The economy faces a trilemma: it is impossible to achieve an effective token reputation system with both zero entry costs and fungible tokens. I further show that token reputation systems based on non-fungible tokens and non-uniform transaction fees successfully discharge fraudulent agents, respectively. The reputation-embedded token can also be viewed as an overlay of money memory and signalling. The token reputation system can also be used as a complement to traditional real-world participant reputation systems, generating a complete reputation network.

We examine the performance difference between A-share-listed companies in Southern and Northern China. Based on the data from 1996 to 2019, we find that Southern China outperforms Northern in terms of the number of listed companies, aggregate market value, revenue and profit growth rates. We provide a mechanism to explain the observed performance difference between the listed companies in Southern and Northern China. Further analysis suggests that this difference is closely related to the regional business environment, financial resource allocation, and economic policies, and is one of the important reasons for the widening of the economic gap between Southern and Northern China. We provide policy recommendations based on our findings. 

We evaluate the cross-country convergence of financial development and its relationship with GDP growth. Financial inclusion variables have been widely converged across countries, and the catch-up effect of countries with poor financial coverage mainly drives the convergence. In contrast, financial development measures — including domestic credit, liability, mutual fund size, and stock market capitalization — have diverged since 1985 despite the absolute convergence in GDP and financial inclusion. The GDP growth rates strongly correlate with the change in financial development but not the improvement in financial inclusion.