A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight. By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad-hoc investment strategy that adapts to changes in the market. In this paper we present an efficient Bayesian portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated by Blum and kalai. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period.
At the end of the article, I posted a link to an example portfolio that I liked by Tim Dettmers. Afterward, I had a few people ask me to compile a larger list of great data science portfolios and projects. While not a portfolio, but rather a project, I think this is a great format to try and exemplify. Melissa Runfeldt did a great job defining and motivating her problem, discussing how she gathered data and explaining her methods with images of results. All in a way that would be easy for a non-technical person to follow (at least at a high level).