Knowledge that Transforms

This study employs a broad set of machine learning (ML) methods to examine cross-sectional variation in corporate bond returns in China. Using macroeconomic indicators together with bond- and issuer-specific characteristics, we find that ML techniques outperform traditional linear models in both statistical and economic terms. These models are particularly effective at capturing distinctive features of the Chinese market, including the dominance of state-owned enterprises, implicit government guarantees, and rapid market evolution. We compare long-short and long-only portfolio strategies to account for practical constraints on short selling. The results indicate that ML methods are effective in markets where institutional features and information asymmetries play a central role in asset pricing.

This paper examines the effects of an educational program on Sustainable Finance Literacy (SFL) and its influence on sustainable investment decisions. Through a randomized controlled trial and an incentivized choice experiment, we found that our SFL program significantly improves literacy. The program also increased the probability of investing in a highly sustainable fund by 6 percentage points on the extensive margin and decreased allocations between 3.2% and 2.7% for the less sustainable funds on the intensive margin. Among participants who already held pro-sustainability attitudes, the treatment additionally led to more investments in the highly sustainable fund on the intensive margin. Higher SFL further led to more critical sustainability assessments of mid-tier funds and reduced tendencies to chase past high returns.

Does the recent proliferation of technology in lending process have an impact on business loan market competition? Using a theoretical model that assumes heterogeneity in lenders’ screening abilities and borrowers’ investment horizons, we show that FinTech (Traditional) lenders primarily supply unsecured (asset-backed) loans to borrowers with short-term (long-term) projects. The model builds on the interplay between screening ability and collateral requirements to characterize the competition between two ex-ante symmetric lenders. Lenders use screening technology and collateral requirements to mitigate competition and restrict the supply of credit through an endogenous segmentation of the loan market. As information technology improves, the effect on credit supply and equilibrium interest rates becomes more nuanced and depends on the market segment. The results offer a supply-side explanation for the growth of unsecured lending.