Technological Innovations and Stock Prices

Question 1

Brown, Fazzari and Petersen 2009

Answer 1

• Gist: CF and external equity matter for YOUNG, innovative (R&D) firms.
• U.S. firms finance R&D from volatile sources: cash flow and stock issues.
• estimate dynamic R&D models for high-tech firms and find significant effects of cash flow and external equity for young, but not mature, firms.
• The financial coefficients for young firms are large enough that finance supply shifts can explain most of the dramatic 1990s R&D boom
• → which implies a significant connection between finance, innovation, and growth.

Question 2

Hsu 2009

Answer 2

• use aggregate patent data and research and development (R&D) data to measure technological innovations in the U.S.
• find that patent shocks and R&D shocks have positive and distinct predictive power for U.S. market returns and premiums.
• Similar patterns are also found in international data including other G7 countries, China, and India.

Question 3

Lie and Xue 2009

Answer 3

• Title: “A Bayesian’s Buble”
• Based on macroeconomic data, we identify a Bayesian investor’s belief evolution when facing a possible structural break in the economy.
• show that such belief evolution plays a significant role in explaining both the stock market boom and crash during 1998 to 2001.
• conclude that a rational investor’s uncertainty about the future of the U.S. economy provides an alternative explanation for the late 1990s stock market “bubble.”

Question 4

Pastor and Veronesi 2009

Answer 4

• general equilibrium model in which stock prices of innovative firms exhibit “bubbles” during technological revolutions.
• In the model, the average productivity of a new technology is uncertain and subject to learning.
• During technological revolutions, the nature of this uncertainty changes from idiosyncratic to systematic.
• The resulting bubbles in stock prices are observable ex post but unpredictable ex ante
• they are most pronounced for technologies characterized by high uncertainty and fast adoption.
• find empirical support for the model’s predictions in 1830-1861 and 1992-2005 when the railroad and Internet technologies spread in the United States.