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)


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)

  • $\begingroup$ 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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