Using laboratory experiments, we provide evidence on three factors influencing trader performance: fluid intelligence, cognitive reflection, and theory of mind (ToM). Fluid intelligence provides traders with computational skills necessary to draw a statistical inference. Cognitive reflection helps traders avoid behavioral biases and thereby extract signals from market orders and update their prior beliefs accordingly. ToM describes the degree to which traders correctly assess the informational content of orders. We show that cognitive reflection and ToM are complementary because traders benefit from understanding signals’ quality only if they are capable of processing these signals.
Using simulations and experiments, we pinpoint two main drivers of trader performance: cognitive reflection and theory of mind. Both dimensions facilitate traders' learning about asset valuation. Cognitive reflection helps traders use market signals to update their beliefs whereas theory of mind offers traders crucial hints on the quality of those signals. We show these skills to be complementary because traders benefit from understanding the quality of market signals only if they are capable of processing them. Cognitive reflection relates to previous Behavioral Finance research as it is the best predictor of a trader's ability to avoid commonly-observed behavioral biases.
The multi-group asset flow model for asset price dynamics incorporates distinct motivations, e.g., trend and fundamentals (value) and assessments of value by different groups of investors. The stability and bifurcation properties are established for the curve of equilibria. We prove that if all trader groups focus on fundamentals, then all equilibria are stable. For systems in which there is one fundamental and one momentum (trend) group, we establish conditions for stability. In particular, an equilibrium that is stable becomes unstable as the time scale on which momentum investors focus diminishes. The computations examine the excursions, which we define as the maximum deviation in price of the trajectory from its initial price located near the curve of equilibria.
We present a methodology to study a data set of 119 260 daily closed-end fund prices using mixed-effects regressions with the objective of understanding price dynamics. There is strong statistical support that relative price change depends significantly on (i) the recent trend in a nonlinear manner, (ii) recent changes in valuation, (iii) recent changes in money supply (M2), (iv) longer-term trend, (v) recent volume changes and (vi) proximity to a recent high price. The dependence on the volatility is more subtle, as short-term volatility has a positive influence, while the longer term is negative. The cubic nonlinearity in the weighted price trend shows that a percentage daily gain of up to 2.78% tends to yield higher prices, but larger gains lead to lower prices. Thus, the nonlinearity of price trend establishes an empirical and quantitative basis for both underreaction and overreaction within one large data set, facilitating an understanding of these competing motivations in markets. Increasing money supply is found to have a significant positive effect on stock price, while proximity to recent high prices has a negative effect. The data set consists of daily prices during the period 26 October 1998 to 30 January 2008.Asset price dynamics, Momentum, Price trend, Money supply, Liquidity, Volume,
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