We show fast price discovery at the market opening after macroeconomic announcements released overnight even when investors do not trade and learn from quotes while the stock market is closed. Leveraging a unique feature of unscheduled macroeconomic announcements arriving outside of regular trading hours of Chinese stock markets, our paper identifies the large impacts of learning from alternative sources other than prices on price discovery. We document that investors’ overnight learning from social media in the period between macro news arrival and market opening enhances the price discovery once market reopens for trades. Overnight learning among investors helps level the playing field across investors and mitigates reversals of overnight and intraday returns.

We develop a daily measure of Chinese monetary policy shocks that incorporates both quantity and interest rate-based policy changes. Our shock measure serves as a sufficient statistic tailored to the Chinese market, addressing the common issue in emerging markets of lacking a key proxy for monetary policy stance due to multi-dimensional objectives and complex toolkits. We validate its effectiveness in capturing unexpected monetary policy changes in China. Shedding light on monetary policy transmission, our shock series reveals that Chinese monetary policy significantly affects the equity and credit risk of non-financial firms and shifts real macroeconomic variables.

We employ the ChatGPT model to identify green products in the US product markets. Approximately 3.7% of US product announcements from 2002 to 2022 qualify as green products. Using a stacked Difference-in-Differences approach, we find that firms involved in severe environmental incidents launch 40% more green products within two years following the incidents. These incident-driven green products are notably novel, supported by high-quality green patents, and result in substantial environmental improvements for both producers and consumers. In contrast, green products introduced without the impetus of environmental incidents do not demonstrate meaningful environmental gains and often raise concerns of greenwashing.

While industry investors commonly view gold as a hedging asset, academic studies often find the opposite. We show that gold is a prominent hedging asset via three different approaches: a state-space model, predictive regressions, and principal component analyses. We find that, ceteris paribus, gold prices increase with expected stock market return μₜ and expected dividend growth rate gₜ. In bad times, μₜ rises while gₜ declines. It thus may seem that gold prices fall in bad times and that gold prices insignificantly or even negatively predict stock returns. However, after addressing the omitted-variable-bias introduced by gₜ, we find that gold prices significantly and positively predict stock returns.

We develop volatility risk premium (VRP) timing strategies that involve trading two assets: a volatility asset and

We examine the incentives motivating a mutual fund family to launch ESG funds, aiming to understand the supply-si

Durable capital is harder to finance not only due to its greater down payment, but also because of its larger

Industries with significant scale economies or learning-by-doing may come to be dominated by a single firm Econom

We study fare structure design and public transit use Leveraging a fare rise in the Beijing Subway that replaced a flat rate with one that varies by distance, time, and offers quantity discounts, we find inelastic demand, inflexible travel schedules, and

The domain of mutual fund management is characterized by an array of incentives that influence the decision-making of fund managers In this article, we provide a survey of the extensive literature on various types of incentives faced by mutual fund manag