Conference Agenda

Tips to navigate the program:

  • Overview of all papers on a specific day: click on the day (e.g. Date: Thursday, 24/Aug/2019). To download papers, you will need to access the session by clicking on its title first.
  • Program index: click on the Authors tab below.
  • Location name: to display all sessions taking place in that room
  • Search box: to search for authors, papers and sessions.

Please notes that changes in the program might occur.

If your name is not displayed in the program, please register in our conference system.

If your paper information is not up to date, please send us an email at efa2019@novasbe.pt.

Registration to the conference closes on August 1st, 2019: www.conftool.com/efa2019

 
Session Overview
Session
BF-2: Behavioral Factors in Valuation: Theory
Time:
Thursday, 22/Aug/2019:
15:30 - 17:00

Session Chair: Liyan Yang, University of Toronto
Session Chair: Fernando Anjos, Nova School of Business and Economics
Location: D -112

Show help for 'Increase or decrease the abstract text size'
Presentations

Choosing to Disagree

Naveen Gondhi1, Snehal Banerjee2, Jesse Davis3

1INSEAD, France; 2UCSD; 3UNC Chapel Hill

Discussant: Francesco Sangiorgi (Frankfurt School of Finance & Management)

The rational expectations paradigm restricts the subjective beliefs of investors to align with the objective distribution. We relax this constraint and analyze how investors optimally choose their subjective beliefs about the information contained in their private signals and in prices. We show that investors systematically choose to deviate from rational expectations. In any symmetric equilibrium, investors optimally exhibit overconfidence in their private information but under-react to the information in prices. However, when aggregate risk aversion is sufficiently low, such symmetric equilibria do not exist.Instead, there exists an asymmetric equilibrium in which investors endogenously separate into (i) fundamental investors, who also ignore the information in prices, and (ii) "technical" traders, who overweight the information in prices when forming beliefs. Relative to the corresponding rational expectations equilibrium, these equilibria feature higher (i) return predictability, (ii) price informativeness, (iii) trading volume and (iv) return volatility. Finally, we show that such deviations by informed investors improve the welfare of liquidity traders under the objective distribution.

efa2019-BF-2-1750-Choosing to Disagree.pdf


Trading Complex Risks

Felix Fattinger

University of Melbourne, Department of Finance, Australia

Discussant: Adrian Buss (INSEAD)

This paper studies how complexity impacts markets’ ability to aggregate information and distribute risks. I amend fundamental asset pricing theory (Debreu, 1959; Arrow, 1964) to reflect agents’ imperfect knowledge (Knight, 1921) about complex dividend distributions and test its clear-cut predictions in the laboratory. Market equilibria corroborate complexity aversion-implied trading behavior. Complexity reduces the price elasticity of trading strategies associated with lower consumption risk. However, complex risks are overpriced. Absent of aggregate risk, the reduction in price sensitivity overcomes the variation in subjective beliefs, allowing for efficient risk sharing. While complexity induces noise in individual trading decisions, market outcomes remain theory-consistent. This striking feature reconciles with a random choice model, where bounds on rationality are reinforced by complexity. Markets’ effectiveness in aggregating beliefs about complex risks depends on the trade-off between reduced price sensitivity and reinforced bounded rationality. My findings show how individual heterogeneity creates aggregate stability, contrasting representative singular behavior.

efa2019-BF-2-1581-Trading Complex Risks.pdf


Stock Return Extrapolation, Option Prices, and Variance Risk Premium

Adem Atmaz

Purdue University, United States of America

Discussant: Cameron Peng (London School of Economics)

This paper presents a model of stock return extrapolation in the presence of stochastic volatility. In the model, consistent with survey evidence, following positive stock returns, in addition to expecting future stock returns to be higher, the investor also expects future returns to be less volatile. The model is highly tractable, leading to closed-form solutions for the stock price, its dynamics, variance risk premium as well as providing analytic option pricing formulas that nest the Heston stochastic volatility formulation. The key novel predictions of the model are as follows. The magnitude of the stock price variance elasticity and the negative correlation between the stock return and its variance are both increasing in the extrapolation degree. A more bearish investor sentiment, due to extrapolating bad recent stock performance, leads to a more negative variance risk premium and a higher option prices with the price increases being more pronounced for out-of-the-money options as in data. The variance risk premium predicts future stock market returns negatively even after controlling for the realized variance, consistent with empirical evidence.

efa2019-BF-2-311-Stock Return Extrapolation, Option Prices, and Variance Risk Premium.pdf


 
Contact and Legal Notice · Contact Address:
Conference: EFA 2019
Conference Software - ConfTool Pro 2.6.128+TC
© 2001 - 2019 by Dr. H. Weinreich, Hamburg, Germany