Suchergebnisse

Most theories in economics and finance predict what people will do, given what they know about the world around them. But what do people know about their environments? The study of information choice seeks to answer this question, explaining why economic players know what they know--and how the information they have affects collective outcomes. Instead of assuming what people do or don't know, information choice asks what people would choose to know. Then it predicts what, given that information, they would choose to do. In this textbook, Laura Veldkamp introduces graduate students in economics and finance to this important new research. The book illustrates how information choice is used to answer questions in monetary economics, portfolio choice theory, business cycle theory, international finance, asset pricing, and other areas. It shows how to build and test applied theory models with information frictions and it covers recent work on topics such as rational inattention, information markets, and strategic games with heterogeneous information

Emerging equity markets witness occasional surges in prices (frenzies) and cross-market price dispersion (herds), accompanied by abundant media coverage. An information market complementarity can explain these anomalies. Because information has high fixed costs, high volume makes it inexpensive. Low prices induce investors to buy information that others buy. Given two identical assets, investors learn about one; abundant information reduces its payoff risk and raises its price. Transitions between low-information/low-asset-price and high-information/high-asset-price equilibria resemble frenzies. Equity data and new panel data on news coverage support the model's predictions: Asset market movements generate news and news raises prices and price dispersion.

"Big data" financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others' information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others' demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient. (JEL C55, D83, G12, G14, O33)