AI@CBS

This paper studies product ranking mechanisms of a monopolistic online platform in the presence of social learning. The products’ quality is initially unknown, but consumers can sequentially learn it as online reviews accumulate. A salient aspect of our problem is that consumers, who want to purchase a product from a list of items displayed by the platform, incur a search cost while scrolling down the list. In this setting, the social learning dynamics, and hence the demand, is affected by the interplay of two unique features: substitution and ranking effects.

We exploit regional variations in exposure to heat stress to study if physical climate risk is priced in municipal and corporate bonds as well as in equity markets. We find that local exposure to damages related to heat stress equaling 1% of GDP is associated with municipal bond yield spreads that are higher by around 15 basis points per annum (bps), the effect being larger for longer-term, revenue-only and lower-rated bonds, and arising mainly from the expected increase in energy expenditures and decrease in labor productivity.

As financial technology improves and data becomes more abundant, do market prices reflect this growing information and allocate capital more efficiently? While a number of recent studies have documented rises in aggregate price efficiency, we show that there are large cross-sectional differences. The previously-documented increases are driven by a rise in the informativeness of large, growth stocks. The informational efficiency of smaller assets' prices or prices of assets with less growth potential are either flat or declining.

Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been devoted to methods for protecting individual privacy during analyses of large datasets. However in many settings, global properties of the dataset may also be sensitive (e.g., mortality rate in a hospital rather than presence of a particular patient in the dataset).

In a monetary union, the risk-free rate cannot respond to country-level fiscal shocks, leaving only default spreads and convenience yields to respond. Empirically, we find that convenience yields play an important role as fiscal shock absorbers in the Eurozone. Consistent with downward-sloping demand for safety, Eurozone countries earn larger convenience yields after they release positive fiscal news.

Higher U.S. government debt/output ratios do not forecast higher future surpluses or lower real returns on Treasurys. Neither future cash flows nor discount rates account for the variation in the current debt/output ratio. The market valuation of Treasurys is surprisingly insensitive to the macro fundamentals. Instead, the future debt/output ratio accounts for most of the variation. Systematic surplus forecast errors may help to account for these findings. Since the start of the GFC, surplus projections have anticipated a large fiscal correction that failed to materialize.

This research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations.

We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy.