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Since Harry Markowitz’s seminal work in 1952, which initiated modern portfolio theory, portfolio allocation strategies have been intensely discussed in the literature. With the development of online optimization techniques, dynamic learning algorithms have proven to be an effective approach to build portfolios. The purpose of this paper was to implement a new version of the Exponential Gradient algorithm in which important information about the risk of stocks are considered in the algorithm’s projection step. The portfolios built were compared with the Dow Jones Industrial Average Index (DJIA) and Best Constant Rebalanced Portfolio (BCRP). We used DJIA data from January 2000 to December 2017 with daily observations. The EG beta algorithm outperformed the DJIA in all tests performed, and it was very close to BCRP in periods of market upturn and was able to outperform it in downturns.

Online Portfolio Optimization with Exponential Gradient and Time Varying CAPM