Behavioral Finance (Includes: Behavioral Economics)
Information Economics
In order to study the relationship between volume and investor disagreement, we develop a dynamic model in which differences in the interpretation of public information lead to trade. We obtain a closed-form, linear equilibrium in which risk-averse agents take into account future trading opportunities when solving for their optimal portfolio allocations. In this setup, we study which restrictions on the disagreement process yield empirically observed patterns in volume and returns. We show that when investors have infrequent but major disagreements, there is positive autocorrelation in volume and positive correlation between volume and absolute price changes.
Motivated by the insight of Keynes (1936) on the importance of higher order beliefs in financial markets, we examine the role of such beliefs in generating drift in asset prices. We show that in a dynamic setting, a higher order difference of opinions is necessary for heterogeneous beliefs to generate price drift. Such drift does not arise in standard difference of opinion models, since investors’ beliefs are assumed to be common knowledge. Our results stand in contrast to Allen, Morris and Shin (2006) and others, as we argue that in rational expectation equilibria, heterogeneous beliefs do not lead to price drift.
I develop a dynamic framework that nests rational expectations (RE) and difference of opinions (DO) models to study how investors use prices to update their beliefs. I show that when investors condition on prices (RE case), investor disagreement is related positively to return volatility and expected returns, but negatively to return autocorrelation. When investors do not use prices (DO case), these relationships are reversed. I test these predictions on the cross-section and time-series of stocks, using analyst forecast dispersion as a proxy for disagreement. The results suggest that while investors use prices on average, the extent to which they do so varies significantly. Furthermore, I find that firms in which investors condition on prices more are smaller in size, have lower market to book ratios and lower analyst coverage, and exhibit lower return volatility and volume.
Most models that analyze the relationship between information quality and cost of capital do so in a single firm setting, and predict that the relationship is negative. Given the lack of consistent empirical support for this prediction, we reconsider this relationship in a setup where covariance risk, and not variance risk, determines the cost of capital. In a standard asset pricing setup with multiple firms and multiple signals, we show that observed beta and information quality are negatively related for positive beta stocks, but positively related for negative beta stocks. The model also predicts that while the relation between cost of capital and the interaction of observed beta and information quality is negative, the relation between cost of capital and information quality itself is positive. We test the model using proxies of information quality based on mean analyst forecast errors and accruals quality and find evidence consistent with our predictions.
We estimate that from August 1996 to October 2006, the liquidity premium for newly issued 10-year Treasuries relative to more seasoned issues was $227 per $100,000, or $167M per year. Traditional models of liquidity suggest that long investors pay this premium for liquid securities that they can easily sell in the future. We are the first paper to document that short-sellers paid about 50% of this liquidity premium. Our results provide empirical support for more recent theoretical work which suggests that short-sellers also pay a premium for liquid securities that they can easily purchase or borrow.
This course counts toward the following majors: Analytical Finance, Finance
This course combines the materials of FINC-430 and FINC-441 into an intensive one-quarter course available to One-Year students and first-year students interested in accelerating their studies of finance. Students choosing this option should expect the presentations, readings and other homework to be at least double those of the regular courses. By combining these two courses into one quarter, students are able to take more advanced finance electives during their first year and have the opportunity to include an extra finance elective in their course schedules. Please note that this course carries the weight of one course only.
Prerequisites: Knowledge of (a) probability and statistics through linear regression and (b) financial accounting. Requirement (a) may be satisfied with prior or concurrent registration in DECS 434, sufficient previous course work in statistics or attending Finance I statistics tutorials (available fall quarter only). Requirement (b) may be satisfied with prior or concurrent registration in ACCT 430 or sufficient previous course work in financial accounting. MECN 430 is recommended.
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