Spurious Regressions in Financial Economics

By Wayne E. Ferson, Sergei Sarkissian, Timothy T. Simin. 2003

607 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/3ba874adffbf4162a9aafa49d4e533e0fb0fc777

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Guofu Zhou
2. Sydney C. Ludvigson
3. Stijn Van Nieuwerburgh
4. John Y. Campbell
5. Martin Lettau
6. Allan G. Timmermann
7. Serena Ng
8. David E. Rapach

In Finance

1. Guofu Zhou
2. Stijn Van Nieuwerburgh
3. Sydney C. Ludvigson
4. John Y. Campbell
5. Allan G. Timmermann
6. Martin Lettau
7. Jun Tu
8. Serena Ng

Citations in the corpus

• Reconciling the Return Predictability Evidence In-Sample Forecasts, Out-of-Sample Forecasts, and Parameter Instability (2006, background): “Nelson and Kim (1993), Stambaugh (1999), Ang and Bekaert (2006), Ferson, Sarkissian, and Simin (2003), and Valkanov (2003) conclude that the statistical evidence of forecastability is weaker once tests are adjusted for high persistence.”
• Investor sentiment aligned: : A powerful predictor of stock returns (2015, methodology): “Although the in-sample analysis provides more efficient parameter estimates and thus more precise return forecasts by utilizing all available data, Goyal and Welch (2008), among others, argue that out-of-sample tests seem to be a more relevant standard for assessing genuine return predictability in ”
• Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014, neutral): “Positive answers to these questions also allay datamining concerns, which are relevant for stock return predictability (e.g., Ferson et al. 2003).”
• East Asian Financial Contagion Under DCC-GARCH (2009, background): “Under some conditions, it has been shown that dummy variables could be persistent and, thus, their interpretation could be subject to statistical shortcomings (see Ferson et al., 2003).”
• Accounting Conservatism and Stock Price Crash Risk: Firm-level Evidence. (2012, neutral): “time-series lead-lag tests if our dependent and independent variables are somewhat sticky (e.g., Yule 1926; Hendry 1980; Ferson et al. 2003).”
• International Capital Markets and Foreign Exchange Risk (2004, methodology): “Hollifield and Uppal (1997) find that segmented commodity markets can lead to a deviation of UIP but fail to explain the negative relation between the expected change in exchange rates and interest rate differentials.”
• The empirical risk–return relation: A factor analysis approach ☆ (2006, background): “Small-sample inference is especially important when the right-hand side variables are highly persistent (e.g., Ferson, Sarkissian, and Simin, 2003) but, as Table 1 demonstrates, none of the factors from our preferred specifications are highly persistent.”
• Investor Sentiment and Economic Forces (2017, background): “Because investor sentiment indices are quite persistent, our predictive regressions are subject to the spurious regression critique of Ferson, Sarkissian, and Simin (2003).”
• Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2008, background): “These problems are exacerbated if researchers are data mining, considering large numbers of variables, and reporting only those results that are apparently statistically signicant [Foster, Smith, and Whaley (1997) and Ferson, Sarkissian, and Simin (2003)].”
• Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence (2011, neutral): “2Closely related issues pertaining to the use of persistent predictor variables have also been studied (see Stambaugh (1999), Ferson, Sarkissian, and Simin (2003), Baker, Taliaferro, and Wurgler (2006), Campbell and Yogo (2006), Ang and Bekaert (2007), and Goyal and Welch (2008), among others).”
• Institutional and individual sentiment: Smart money and noise trader risk? (2006, background): “Several authors (see Stambaugh, 1999, Valkanov, 2003, or Ferson et al., 2003) have documented this problem, which is mainly caused by highly persistent regressors that evolve as stochastic processes themselves.”
• Stock Price Booms and Expected Capital Gains (2017, unknown): “Finally, a highly serially correlated predictor variable (PD ratio), whose innovations are correlated with the variable that is to be predicted (future returns), gives rise to spurious regression and thus spurious correlation problems, see Ferson et al. (2003) and Campbell and Yogo (2006).”