Journal of Empirical Finance最新文献

Stocks with high net gamma exposure systematically underperform stocks with low net gamma exposure. This effect is distinct from other well-known return predictors, and survives many robustness checks. We show that stocks with low net gamma exposure negatively predict future realized volatility, and argue that investors command a risk premium to hold low net gamma exposure stocks, which are riskier. Lastly, we show that the volatility predictability stems from a non-informational channel, and not from private information.

This study uses a comprehensive approach to optimize the portfolio allocation to equity sector Exchange Traded Funds. We combine data on the market prices of options written on the funds, the Heston stochastic volatility model, risk premium transformation, copulas, and optimization with stochastic dominance constraints. This comprehensive strategy provides significant performance out-of-sample gains relative to the passive and active alternative strategies, both before and after accounting for risk and transaction costs. Our findings point at market inefficiencies that can be exploited using sector funds, past public data, and blending multiple methods.

We show that thirteen well-known stock market anomalies have higher future abnormal returns when they exhibit a value orientation with respect to their historical levels. We find anomalies that exhibit a value orientation (cheap) outperform anomalies that exhibit a growth orientation (expensive) going forward by about 30 basis points (bps) per month. Furthermore, we find favorable anomalies based on combined value and momentum orientations outperform unfavorable anomalies by about 90 bps per month and exhibit more than double the Sharpe ratios. Alternatively, over 96 % of the dollar return for the 13 anomalies disappears when they have negative-momentum and expensive orientations.

In this study, we estimate the conditional correlation and coskewness between gold and stock returns using a bivariate regime-switching model. Motivated by Yang, Zhou, and Wang (2010), we define gold as a strong hedge if the average correlation is negative and the coskewness is positive in the sample. Gold is a strong safe haven if these hold under market turmoil. We empirically examine the property of gold in 24 countries for a sample period spanning over 40 years and find that gold acts as a strong hedge and safe haven in Brazil, India, Indonesia, Italy, Mexico, Russia, South Korea, and Turkey. The interplay between cultural characteristics and the state of financial markets collectively defines gold's role in various countries. We construct a conditional comoment-based dynamic trading strategy and add gold to the stock portfolio when it can serve as a hedge or safe haven. Its out-of-sample performance dominates the buy-and-hold and correlation-based strategies, especially when we consider the safe haven property of gold.

Inflation-swapped Treasury Inflation-Protected Securities (TIPS) are usually undervalued compared to cash flow-matched Treasury bonds. From 2005 to 2022, TIPS discounts are persistent, averaging approximately 3.18 % of the face value, with a peak of 16.10 %. We elucidate the factors associated with this persistent mispricing and the extent of this association. The results from feature selection techniques and the variable importance-in-projection method reveal that marking-to-market concerns and intermediation frictions are key to understanding the underpricing of TIPS relative to comparable nominal Treasury securities. We conclude that when strategic concerns overwhelm fundamental analysis, asset prices could deviate from fundamental values over a prolonged period.

We study how upstreamness and downstreamness affect stock returns in global value chains. Upstreamness and downstreamness, which are computed from world input–output tables, measure the average distance from final consumption and primary inputs. We find that downstreamness explains expected returns, whereas upstreamness does not. The downstreamness return premium reflects investors’ compensation for taking on supply-side risks that accumulate along global value chains, such as labor and competition risks. We show that investors perceive far downstream industries as riskier when their suppliers have high unionization rates or labor shares. In addition, far downstream industries operate in more competitive value chains and are characterized by elevated input and output price uncertainties, which makes them particularly risky.

We study the link between illiquidity and co-movement in illiquidity and the way asset managers trade off illiquidity and co-illiquidity in their portfolio allocation decision. By exploring two experiments – the 2005 SHO Regulation and 2016 Tick Size pilot program – we document the way fund managers manage co-illiquidity risk and the implication for the market degree of illiquidity and co-illiquidity.

We study the effect of VC-backing on innovation in newly public firms and find that it is negatively related to patents produced and citations received within the initial years following an IPO – our estimates indicate that VC-backed firms produce 13% fewer patents than nonVC-backed firms within the first year post-IPO. Our findings suggest that this adverse effect is a consequence of VCs timing their portfolio companies’ IPOs at the peak of innovation followed by a decline post-IPO. Additionally, VC-backing leads to higher growth in sales and productivity in newly public firms pointing to a shift in VC focus from creating into commercializing innovation post-IPO. We address endogeneity concerns with an instrumental variables approach.

Forecasting Realized Volatility (RV) is of paramount importance for both academics and practitioners. During recent decades, academic literature has made substantial progress both in terms of methods and predictors under consideration albeit with scarce reference to technical indicators. This paper examines the out-of-sample forecasting performance of technical indicators for S&P500 RV relative to macroeconomic predictors. Our main contribution is to demonstrate that these sets of predictors impact volatility at different frequencies and thus are complementary. Specifically, technical indicators perform especially strongly for forecasting the short frequency component which complements macroeconomic variables which perform strongly at longer frequencies. We demonstrate that amalgamation forecasts from these predictors that takes into account the frequency dimension leads to substantial improvements in forecast accuracy.

The paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and the risk premium of commodity futures contracts. The linear models use shrinkage methods based on either naive averaging or principal components. The nonlinear models use feedforward deep neural networks (DNN) either as stand-alone or in conjunction with a long short-term memory network (LSTM). Out of the four specifications considered, the LSTM-DNN architecture best captures the risk premium, which underscores the need to estimate models that are both nonlinear and recurrent. The superior performance of the LSTM-DNN portfolio persists after accounting for transaction costs or illiquidity and is unrelated to previously-documented commodity risk factors.