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Given a lot of market-related features (~100 independent variables such as emerging market, developed market, s&p 500, tech sector returns, etc), I need to select a subset of them that are ideally independent and are the major drivers of the global stock market return during time t=t1 to t=t2.

Specifically, the model has to identify important/non-important variables when: 1) the number of independent variables (p) is large (~100) 2) the number of sample size (n) < the number of independent variables (p) and when n >= p

Are Lasso and PCA good ways to accomplish this? I guess Lasso is a simple, easy method. I think that the problem with PCA is that the interpretation of the result is not going to be easy...

Are there academic literature that deals with this problem (selecting a subset of large independent variables to predict the global stock market return)

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The literature on Lasso for asset pricing is quite recent and there are few references out there yet. The main ones are:

  1. Freyberger, Neuhierl, Weber - Dissecting Characteristics Nonparametrically - this uses Lasso.
  2. Huang and Shi (2016) - also lasso.
  3. Horowitz (2016) - gives a overview of model selection in high dimensional models

Also several papers on PCA:

  1. Giglio and Xiu (2016)

  2. Kelly, Pruitt, and Su (2017)

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  • $\begingroup$ thank you very much for your comment. These papers are definitely helpful.. but I was just wondering if there are papers that specifically deal with predicting the global equity market returns. $\endgroup$ – Jun Jang Jun 15 '18 at 12:34

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