Choosing Fixed or Random Effects for Stock Return Panels
Summary
The document introduces fixed effects and random effects as alternative ways to model differences across companies in panel data. Its example concerns testing whether Google search volume predicts stock returns, trading volume, or volatility for companies in Norway’s OBX index, alongside control variables. It recommends basing the choice on the assumptions behind each model and using statistical tests to inform the decision.
Fixed effects capture company-specific differences through separate intercepts. Random effects treat those differences as random, and can account for the sampling process and estimate effects of variables that do not change over time; the answer also notes that the approaches use different estimation procedures. The exchange gives no analysis of the proposed dataset or test results, so it does not establish which model is appropriate for this study. Its guidance is conceptual, and the choice depends on whether the models’ assumptions fit the data.
Key ideas
- Fixed effects represent individual differences with separate intercepts.
- Random effects model individual differences as random.
- Random effects can estimate the influence of time-invariant predictors.
- Model selection depends on assumptions and statistical evidence; neither approach is universally preferable.
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# Panel data - Use fixed effect or random effect in predicting stock returns # Panel data - Use fixed effect or random effect in predicting stock returns I'm currently writing a master thesis where I look at the predictive power of Google search volume in predicting the movement of the Norwegian Stock Market (Oslo Børs). I'm using Google search volume index (SVI) as a predictor (with other control variables) regressed against three measures: the stock return, the trading volume and the volatility. My sample consist 28 companies from the OBX index. The regression will be based on both cross-sectional and time-series data, thus it is necessary to arrange the data as panel data in order to analyse both types of data simultaneously. Panel data analysis is normally conducted with either fixed effect or random effect. And we run the Hausman test to see which of the two should be applied. So to my question, regardless what the Hausmen test tells, in my case which of those two effects should be applied? Or which one would make more sense to be applied? ## Answer by user22485 (score 1) https://quant.stackexchange.com/a/38696 I am actually unsure of your question. Both the fixed effects and random effect models will depend on the assumptions that you make. To keep it simple, in the Fixed effects model: all the individual differences are captured by differences in the intercept parameter. In the Random effects model: all individual differences are captured by the intercept parameters but the individual differences are treated as random rather than fixed. If random effects are present, it is preferred for several reasons, including, 1) The random effects estimator takes into account the random sampling process by which the data were obtained 2)The random effects estimator permits us to estimate the effects of variables that are individually time-invariant 3)The random effects estimator is a GLS estimation procedure, and the fixed effects estimator is a LS estimator. So to answer your question, test for random effects and then see which model is preferred, but there is no clear you should use this model rather than that one.
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