Adrian Fernandez-Perez

Long-run risks (LRR) models attribute asset prices to a latent, persistent component of expected consumption growth. We propose commodity futures carry as a LRR proxy and demonstrates that it satisfies the three conditions required of a LRR state variable: it is highly persistent, it positively predicts long-run consumption growth in-and out-of-sample, and its innovations command a positive, significant price of risk across 67 test portfolios. Studying the underlying economic mechanisms, we show that the role of commodity carry as a LRR variable arises from its ability to forecast future economic activity and to capture long-run inventory dynamics. Corroborating inventories as the economic channel, we find that carry constructed identically from financial futures, unaffected by inventories, fails to meet the LRR conditions.

This paper presents a comprehensive analysis of traditional versus selective hedging strategies in commodity futures markets. Traditional hedging aims solely to reduce spot price risk, whereas selective hedging also seeks to enhance returns by predicting movements in commodity futures prices. We construct selective hedges using a range of forecasting techniques, from simple historical averages to advanced machine learning models, and evaluate their performance based on the expected mean-variance utility of hedge portfolio returns. Out-of-sample results for 24 commodities do not favor selective hedging over traditional hedging as the former increases risk without delivering additional returns. These findings are robust across various hedge reformulations, expanding estimation windows, and rebalancing frequencies. This paper was accepted by Lukas Schmid, finance. Funding: This research was supported by grants from Audencia Business School and Auckland University of Technology, awarded during A. Fernandez-Perez’s prior tenure. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.04940 .

As water scarcity intensifies globally and regulatory pressures on corporate water usage increase, understanding how firms' water consumption affects their valuations becomes crucial for investors managing environmental risks and sustainable portfolios. We document a significant negative water usage premium in the U.S. equity markets. Firms with high water consumption systematically underperform their industry peers, generating lower risk-adjusted returns. Cross-industry analysis reveals this negative premium is stronger in high-water-consumption sectors like mining and manufacturing. We also document a positive relationship between water usage and operating costs, indicating that investors view water mainly as an operational expense rather than a climate risk factor. These findings enhance the understanding of water's role in sustainable investing and highlight its impact on firm performance across industries.

We investigate the impact of social media sentiment on the informational efficiency of financial markets. In particular, we examine the relationship between sentiment extracted from Twitter posts and two widely used efficiency measures: return autocorrelation and variance ratio. Our results show that more positive sentiment is associated with increased return autocorrelation and variance ratio the following day, suggesting a decline in informational efficiency. Furthermore, we demonstrate that the effect of social media sentiment on informational efficiency is driven by the herding behavior among traders, with more positive sentiment leading to more pronounced herding activity. These findings suggest that elevated social media sentiment contributes to a reduction in the quality of the information environment, ultimately leading to informationally inefficient equity prices.

This paper develops a Sustainable Style-Integrated portfolio framework for commodity futures that incorporates a carbon emissions tilt into an optimal multi-style long-short allocation. We show that modest emissions tilts generate small reductions in financial performance relative to conventional benchmarks, while substantially lowering portfolio carbon footprint and increasing resilience to climate risk shocks. We further document a hedging-cost channel through which decarbonization pressures transmit in commodity futures markets. Following the 2015 Paris Agreement, speculative capital reallocates away from higher-emission commodity futures toward lower-emission counterparts, raising hedging premium for brown commodity producers and reducing their inventory accumulation.

We examine the mechanism by which social media sentiment affects stock prices. Specifically, we assess the impact of Twitter feeds on stock returns at the intraday level. We find that an increase in buyer‐initiated trades has a significantly positive price impact. This impact, however, is stronger with an increase in the number of tweets and sentiment, and persists even after controlling for volatility, liquidity shock, and limit‐order activity. The impact of Twitter sentiment on prices causes a lingering mispricing effect that is not fully assimilated at the intraday level. Rather, this mispricing takes several days to correct.

We investigate the role of emotions in driving the stock returns of GameStop (GME) during the GME price bubble in January-February 2021. Our analysis encompasses the eight basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) referred to as the profile of mood states (POMS). Using the textual analysis of Reddit posts, we find that fear strongly predicts the intraday GME return. We also find that the predictive relation from emotion to returns varies in the time series: joy has the strongest relation just before the GME bubble peaks, fear at the bubble’s peak, and anger just after the bubble bursts. These findings shed light on the psychological underpinnings of trading surrounding the stock bubbles.