Hedge Fund Returns and Total Factor Productivity

Abstract

This study explores whether hedge funds’ investment behavior can predict variations in productivity levels using a structural vector autoregressive model (SVAR) and a vector error correction model (VECM). As <i>informed</i> traders in the stock market with superior skills, hedge funds may quickly capture news shocks regarding future production growth in advance. With quarterly series of <i>TFP</i> provided by John Fernald (2014) and hedge fund index (<i>HFI</i>) returns obtained from the Credit Suisse/Tremont database, I find a contemporaneous correlation coefficient of 0.9791 between two endogenous variables over the sample period from 1Q:1994 to 2Q:2023, indicating a high degree of similarity in their movements. A Granger Causality Test rejects the hypothesis, “ ΔLn(<i>HFI</i>) does not Granger Cause Δ<i>TFP</i>”, suggesting that the information inferred from the hedge fund index are valuable in predicting future economic productivity. Finally, the forecast error variance decompositions using the VECM model indicate that over 65% of the variation in Δ<i>TFP</i> even after 20 quarters can be attributed to a shock to the Δ<i>Ln</i>(<i>HFI</i>) .