Nonlinear price dynamics of S&P 100 stocks

TL;DR

This paper introduces a nonlinear modeling approach to analyze S&P 100 stock price dynamics, revealing how trader motivations and behaviors influence returns and trend reactions over a 14-year period.

Contribution

It develops a novel nonlinear methodology to quantify trader behavior and price dynamics, applicable to behavioral biases and technical analysis in financial markets.

Findings

Evidence of both under- and overreaction in trader behavior

A nonlinear relationship between return and trend magnitude

Quantification of trader motivation through shape parameters

Abstract

The methodology presented provides a quantitative way to characterize investor behavior and price dynamics within a particular asset class and time period. The methodology is applied to a data set consisting of over 250,000 data points of the S&P 100 stocks during 2004-2018. Using a two-way fixed-effects model, we uncover trader motivations including evidence of both under- and overreaction within a unified setting. A nonlinear relationship is found between return and trend suggesting a small, positive trend increases the return, while a larger one tends to decrease it. The shape parameters of the nonlinearity quantify trader motivation to buy into trends or wait for bargains. The methodology allows the testing of any behavioral finance bias or technical analysis concept.