Decision Weights for Experimental Asset Prices Based on Visual Salience
用机器学习算法从人类视觉中校准，预测股票价格图的视觉显著性，发现由此得出的决策权重能预测实验投资行为，且超越已有模型。
Abstract 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. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.