Fahiz Baba‑Yara

The multifactor risk-return tradeoff is severely understudied relative to the market. In contrast to mixed evidence for the market, we find that the multifactor risk-return tradeoff is strongly positive when appropriately accounting for factor covariances. Our multifactor risk model shows that covariances contribute more to variation in factor returns than variances and performs at least as well in predicting multiple factors as benchmark models specifically designed to predict a single factor, both in- and out-of-sample. Consistent with a positive multifactor risk-return tradeoff, conditional multifactor alphas for a large set of anomalies are indistinguishable from unconditional alphas.

@unpublished{babayara2025multifactor,
  title={The Multifactor Risk-Return Tradeoff},
  author={Baba-Yara, Fahiz and Boons, Martijn and Frehen, Rik},
  note={Working Paper},
  year={2025}
}

We propose a novel measure of firm risk based on the news consumption patterns of employees inside the firm. We show that when employees read more negative news about the economy or their sector, the firm's future stock returns are significantly lower.

@unpublished{babayara2025risk,
  title={Risk from the Inside Out: Employee News Consumption},
  author={Baba-Yara, Fahiz and Grigoris, Fotis and Kantak, Preetesh},
  note={Working Paper},
  year={2025}
}

We decompose firm characteristics into persistent and transitory components. The pricing of characteristics is almost exclusively driven by their persistent components. A strategy that trades on the persistent component yields Sharpe ratios that are more than double those of standard characteristic-based strategies.

@article{babayara2024persistent,
  title={Persistent and Transitory Components of Firm Characteristics},
  author={Baba-Yara, Fahiz and Boons, Martijn and Tamoni, Andrea},
  journal={Journal of Financial Economics},
  volume={154},
  pages={103808},
  year={2024}
}

We use novel data covering 2 billion daily employee-article interactions across approximately 2 million firms to characterize firms' exposures to uncertainty in almost real-time. We find that, in the cross-section, firms that more intensely read about financial versus other uncertainty-related topics are those most exposed to changes in aggregate measures of economic uncertainty. Consistent with exposure to uncertainty being priced, public firms that spend more time reading these topics have a 2% higher cost of capital, translating into relatively low investment rates. Higher attention to financial uncertainty relates to 7% lower investment and 5% lower hiring on an annual basis.

@unpublished{babayara2023uncertain,
  title={Are Uncertain Firms Riskier?},
  author={Baba-Yara, Fahiz and Davis, Carter and Grigoris, Fotis and Kantak, Preetesh},
  note={Working Paper},
  year={2023}
}

We study the effect of the growth in investment capital on the average returns of popular commodity futures trading strategies over time. We find that approximately 80% of commodity futures strategies that generated statistically significant average returns before financialization (2004) are no longer profitable. The decline in strategy returns is primarily driven by an adverse change in the average returns of a few systematically priced factors in the cross-section of commodity futures. Commodity strategies with relatively higher exposure to the Dow Jones Commodity Index experience a significant reduction in average returns.

@unpublished{babayara2022commodity,
  title={Commodity Returns: Lost in Financialization},
  author={Baba-Yara, Fahiz and Bondatti, Massimiliano},
  note={Working Paper},
  year={2022}
}

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. A single common component captures about two-thirds of value return predictability.

@article{babayara2021value,
  title={Value Return Predictability across Asset Classes and Commonalities in Risk Premia},
  author={Baba-Yara, Fahiz and Boons, Martijn and Tamoni, Andrea},
  journal={Review of Finance},
  volume={25},
  number={2},
  pages={449--484},
  year={2021}
}

We investigate the extent to which modern academic machine learning models agree on which factors are priced in the cross-section of returns. We find considerable disagreement across models that achieve high Sharpe ratios in remarkably different ways. The majority of average returns attributed to factor exposure by any given model is generally deemed pure alpha by other models. We develop a theoretical model with complexity that suggests machine learning models using available data are unlikely to ever span others, although they achieve high Sharpe ratios and produce additional novel insights.

@unpublished{babayara2021limits,
  title={The Limits of Factor Model Spanning},
  author={Baba-Yara, Fahiz and Boyer, Brian H. and Davis, Carter},
  note={R&R at Review of Financial Studies},
  year={2021}
}

Previous research finds that machine learning methods predict short-term return variation in the cross-section of stocks, even when these methods do not impose strict economic restrictions. However, without such restrictions, the models' predictions fail to generalize in important ways, such as predicting time-series variation in returns to the market portfolio and long-short characteristic sorted portfolios. I show that this shortfall can be remedied by imposing restrictions that reflect findings in the financial economics literature in the architectural design of a neural network model and provide recommendations for using machine learning methods in asset pricing. Additionally, I study return predictability over multiple future horizons, shedding light on the dynamics of intermediate and long-run conditional expected returns.

@unpublished{babayara2020machine,
  title={Machine Learning and Return Predictability Across Firms, Time and Portfolios},
  author={Baba-Yara, Fahiz},
  note={Working Paper},
  year={2020}
}