We explore in a series of incentivized experiments how stock market developments affect emotional arousal (proxied by pupil dilation, electrodermal activity, and heart rate variation), and how this emotional arousal affects investment behavior. Experiencing stock market downswings increases emotional arousal, while upswings do not trigger such an effect. The subsequent interplay between emotional arousal and investment behavior is by no means one-dimensional. The heightened level of emotional arousal after downswings reduces financial risk taking and thus the money put at stake, while the exposure to financial risks itself increases subsequent emotional arousal.

We apply a machine-learning algorithm, calibrated using general human vision, to predict the visual salience of prices of stock price charts. We hypothesize that the visual salience of adjacent prices increases the decision weights on returns computed from those prices. We analyze the inferred impact of these weights in two experimental studies that use either historical price charts or simpler artificial sequences. We find that decision weights derived from visual salience are associated with experimental investments. The predictability is not subsumed by statistical features and goes beyond established models.