MCFAM Seminar - Machine Forecast Disagreement and Equity Returns

Speaker: Turan G. Bali

Abstract: We propose a novel measure of divergence of opinion among investors about stock value based on the dispersion in machines’ expected return forecasts. Compared to financial analysts, machines have neither behavioral biases nor conflicts of interest, thus we argue that machine forecast disagreement provides an objective measure of investor disagreement. After introducing a new measure of firm-specific uncertainty proxied by the degree of disagreement of machines’ future return forecasts, we show that this newly proposed, objective measure of uncertainty (or investor disagreement) does have a significant impact on the cross-sectional pricing of individual stocks. We find a significantly negative cross-sectional relation between machine forecast disagreement (MFD) and future stock returns. A long-short portfolio of stocks sorted by MFD provides a six-factor Fama-French (2018) alpha of 0.72% (1.06%) per month for the value-weighted (equal-weighted) portfolio. The return predictability is driven by mispricing rather than compensation for risk. The disagreement premium is also stronger for stocks that are largely held by retail investors, that receive less investor attention and that are costlier to arbitrage.