Estimating Equity Industry Exposures with Sector ETF Returns
Summary
The document proposes a way to build a basic industry factor model for US equities when paid industry classifications are unavailable. Instead of assigning each stock a binary membership in a known industry, select liquid sector or industry ETFs that together cover the market, then regress each stock's historical returns on the ETF returns. The regression coefficients serve as estimated industry exposures for the stock.
The ETF return series can also provide the factor covariance matrix, while the regressions' residuals can be used to estimate stock-specific residual volatility. Exponential weighting is suggested for these risk estimates. This is a practical proxy-based method rather than a verified substitute for a commercial classification system. Its usefulness depends on ETF coverage, the chosen historical window, and the stability and interpretation of regression coefficients; the document gives no test results or detailed selection procedure.
Key ideas
- Liquid sector and industry ETFs can serve as proxies for equity industry factors.
- Regress each stock's returns on ETF returns to estimate factor exposures.
- Estimate factor covariance from the ETF return series.
- Use regression residuals to estimate each stock's residual volatility.
- Exponential weighting may be applied to covariance and residual-volatility estimates.
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Full text
# Industry factors without GICS # Industry factors without GICS I'm working through the Quantitative Equity Portfolio Management book by Chincarini and Kim. I'd like to build a basic industry-based fundamental factor model. As this is a pet project for pedagogical purposes, I don't have the money to spend on Barra's GICS classifications. I also understand that other industry classifications (SIC and NAICS) are fairly useless for factor models. Is there a reasonable open-source or homemade alternative (using, say, k-means clustering or non-negative matrix factorization) to create my own industry factors for US equities? ## Answer by Swagato Acharjee (score 3) https://quant.stackexchange.com/a/31494 To build an industry factor model, you would need to calculate exposures to industries. If you had GICS (or similar) data available you could use a bottom up approach and calculate those exposures. 1 if the stock belongs to that industry, 0 if not. In the absence of such data you would need to infer those values. Here is one that I'd suggest (assuming you are looking at the US equity market) Sector/Industry ETFs (IShares, State Street) are good proxies for industries. Select a set of ETFs that are liquid and encompass the entire industry breadth of the US stock market. Conduct multiple regressions of stock returns on the selected ETF returns over a sufficiently long historical period. Use the obtained coefficients as industry exposures. The other parts of a risk model that you'd need are the covariance matrix and residual volatility of the instruments. For the covariance matrix use the etf returns over the same or different historical period as the multiple regression above and calculate their covariance. For the residual volatility - use the residuals of the multiple regression above and calculate their standard deviation. You may want to apply some exponential weighting in both the cases.
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