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• Justin Birru
  The Ohio State University

Quantitative research analysts (Quants) produce in-depth quantitative and econometric modeling of market anomalies to assist sell-side analysts and institutional clients with stock selection strategies. Quant-backed analysts exhibit more efficient forecasting behavior on anomaly predictors — stock recommendations and target prices on anomaly-longs (anomaly-shorts) are more (less) favorable. Investment value of such analysts’ research is higher and their research reports are more likely to discuss quantitative modeling and market anomalies. Quant research facilitates “smart money” trades of institutional clients on anomaly stocks — Quants are associated with an increased (decreased) likelihood of purchasing underpriced (overpriced) stocks unconditionally and in response to fund inflows. Thematic reports authored by Quants generate abnormal reactions for corresponding stocks, suggesting that market participants recognize quantitative research. Finally, we provide evidence that, all else equal, cross-sectional return predictability of anomaly long-short strategies is attenuated for stocks with increased Quant coverage. Overall, the evidence is consistent with Quants adding value to financial markets, and consequently, increasing market efficiency with respect to anomaly predictors.