Decision Weights for Experimental Asset Prices Based on Visual Salience

We apply a machine-learning algorithm calibrated from general human vision to predict visual salience of parts of a stock price series. We hypothesize that visual salience of adjacent prices increases decision weights on returns computed from those prices. We analyze the inferred decision impact of these weights in three experimental studies that use either historical individual-stock returns or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. Their predictability goes beyond alternative theories over-weighting returns at the tails of the historical distribution, or with respect to salient difference from a reference return