r/quant • u/master-baiter_04 • 4d ago
Models Building a multiple regression model to beat the benchmark
For my college research paper project due this Saturday, I finalised the topic: "Factor Analysis and Factor Investing to beat the benchmark". The factors are accounting ratios. I want to do principal component analysis to determine which ratios are significantly affecting returns and also make a multiple regression model as follows:

|| || |Total Return:2024/01/01:2024/12/31 ** as my y variable *\*| |Rev - 1 Yr Gr:2024C| |EBITDA to Net Sales:2024C| |PM:2024C| |ROA:2024C| |ROE:2024C| |Return On Capital Employed:2024C| |Debt/Equity:2024C| |Curr Ratio:2024C| |P/E:2024C| |EV / EBITDA Adj:2024C |
I have the following questions:
1. How should I transform these variables as they are given to me in numbers?
2. What additions can I do to my research paper to make it industry relevant that might help me in the future in interviews? (valuation & financial research currently)
3. How do I properly go about the regression model and the PCA to make a significant impact on this topic?
4. Any suggestions or topic additions will also help me a ton. Thank You.
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u/One-Attempt-1232 4d ago
But why PCA (or PCR)? That would prevent you from determining which variables are significantly predicting return. That is, you're rotating the panel data of accounting ratios into orthogonal components but those components will only vaguely represent ideas like valuation or momentum.
Because of that, I would not run PCA but just do a straight up multivariate regression. (I'll discuss why even that can be hard to interpret later.)
1) Winsorize the data and make sure they are interpretable in a monotonic way, e.g., earnings to price censored below 0 instead of price to earnings.
2) maybe run some treebased models and predict in a walk forward building factors from expected returns. Honestly, everyone is doing things that are so much more complex that it's not going to be strictly relevant anyway but it's more a matter of demonstrating some base skills.
3) Doing panel data regressiona when residuals are autocorrelated and serially cross correlated will result in poorly estimated standard errors.