An overreaction implementation of the coherent market hypothesis and option pricing

Inspired by the theory of social imitation (Weidlich 1970) and its adaptation to financial markets by the Coherent Market Hypothesis (Vaga 1990), we present a behavioral model of stock prices that supports the overreaction hypothesis. Using our dynamic stock price model, we develop a two factor general equilibrium model for pricing derivative securities. The two factors of our model are the stock price and a market polarization variable which determines the level of overreaction. We consider three kinds of market scenarios: Risk-neutral investors, representative Bernoulli investors and myopic Bernoulli investors. In case of the latter two, risk premia provide that herding as well as contrarian investor behaviour may be rationally explained and justified in equilibrium. Applying Monte Carlo methods, we examine the pricing of European call options. We show that option prices depend significantly on the level of overreaction, regardless of prevailing risk preferences: Downward overreaction leads to high option prices and upward overreaction results in low option prices. --behavioral finance,coherent market hypothesis,market polarization,option pricing,overreaction,chaotic market,repelling market

Fundamentalists Clashing over the Book: A Study of Order-Driven Stock Markets

Agent-based models of market dynamics must strike a compromise between the structural assumptions that represent the trading mechanism and the behavioral assumptions that describe the rules by which traders take their decisions. We present a structurally detailed model of an order- driven stock market and show that a minimal set of behavioral assumptions suffices to generate a leptokurtic distribution of short- term log-returns. This result backs up the conjecture that the emergence of some statistical properties of financial time series is due to the microstructure of stock markets.price dynamics, statistical properties of returns, behavioral and structural assumptions, agent-based simulations

Agent-based financial markets and New Keynesian macroeconomics: A synthesis

We combine a simple agent-based model of financial markets with a standard New Keynesian macroeconomic model via two straightforward channels. The result is a macroeconomic model that allows for the endogenous development of stock price bubbles. Even with such a simplistic comprehensive model, we can show that the behavioral foundations of the stock market exert important influence on the macroeconomy, e.g. they change the impulse-response functions of macroeconomic variables significantly. We also analyze financial market transaction taxes as well as asset price bubble deflating monetary policy, and find that both can be used to reduce volatility and distortion of the macroeconomic aggregates. --agent-based financial markets,New Keynesian macroeconomics,stock market,transaction tax,Taylor rule

Non-Parametric Causality Detection: An Application to Social Media and Financial Data

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about the statistical relationships among the variables of the study and can effectively control a large number of factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201

Optimal Market Timing

We use a fully-specified neoclassical model augmented with costly external equity as a laboratory to study the relations between stock returns and equity financing decisions. Simulations show that the model can simultaneously and in many cases quantitatively reproduce: procyclical equity issuance; the negative relation between aggregate equity share and future stock market returns; long-term underperformance following equity issuance and the positive relation of its magnitude with the volume of issuance; the mean-reverting behavior in the operating performance of issuing firms; and the positive long-term stock price drift of firms distributing cash and its positive relation with book-to-market. We conclude that systematic mispricing seems unnecessary to generate the return-related evidence often interpreted as behavioral underreaction to market timing.

Modeling Empirical Stock Market Behavior Using a Hybrid Agent-Based Dynamical Systems Model

We describe the development and calibration of a hybrid agent-based dynamical systems model of the stock market that is capable of reproducing empirical market behavior. The model consists of two types of trader agents, fundamentalists and noise traders, as well as an opinion dynamic for the latter (optimistic vs. pessimistic). The trader agents switch types stochastically over time based on simple behavioral rules. A system of ordinary differential equations is used to model the stock price as a function of the states of the trader agents. We show that the model can reproduce key stylized facts (e.g., volatility clustering and fat tails) while providing a behavioral interpretation of how the stock market itself can cause periods of high volatility and large price movements, even when the economic value of the stock grows at a constant rate

Disposition Matters: Volume, Volatility and Price Impact of a Behavioral Bias

In this paper, we estimate the behavioral component of the Grinblatt and Han (2002) model and derive several testable implications about the expected relationship between the preponderance of disposition-prone investors in a market and volume, volatility and stock returns. To do this, we use a large sample of individual accounts over a six-year period in the 1990's in order to identify investors who are subject to the disposition effect. We then use their trading behavior to construct behavioral factors. We show that when the fraction of irrational' investor purchases in a stock increases, the unexplained portion of the market price of the stock decreases. We further show that statistical exposure to a disposition factor explains cross-sectional differences in daily returns, controlling for a host of other factors and characteristics. The evidence is consistent with the hypothesis that trade between disposition-prone investors and their counter-parties impacts relative prices.