Asset Prices and the Trading Process
Sunday, Jan. 5, 2020 8:00 AM - 10:00 AM (PDT)
- Chair: Haoxiang Zhu, Massachusetts Institute of Technology
What Moves Stock Prices? The Role of News, Noise, and Information
AbstractWe develop a return variance decomposition model to separate the role of different types of information and noise in stock price movements. We disentangle four components: market-wide information, private firm-specific information revealed through trading, firm-specific information revealed through public sources, and noise. Overall, 31% of the return variance is from noise, 37% from public firm-specific information, 24% from private firm-specific information, and 8% from market-wide information. Since the mid-1990s, there has been a dramatic decline in noise and an increase in firm-specific information, consistent with increasing market efficiency.
Identifying Price Informativeness
AbstractWe show that outcomes (parameter estimates and R-squareds) of regressions of prices on fundamentals allow us to recover exact measures of the ability of asset prices to aggregate dispersed information. We formally show how to recover absolute and relative price informativeness in dynamic environments with rich heterogeneity across investors (regarding signals, private trading needs, or preferences), minimal distributional assumptions, multiple risky assets, and allowing for stationary and non-stationary asset payoffs. We implement our methodology empirically, finding stock-specific measures of price informativeness for U.S. stocks. We find a right-skewed distribution of price informativeness, measured in the form of the Kalman gain used by an external observer that conditions its posterior belief on the asset price. The recovered mean and median are 0.05 and 0.02 respectively. We find that price informativeness is higher for stocks with higher market capitalization and higher trading volume.
- G1 - General Financial Markets