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   Stock Market Quantitative Investment Strategies 


 
     
VARAN'S ELITE ARISTOCRAT PORTFOLIO
Varan's strategy was to construct an annually updated portfolio from the current list of Dividend Aristocrats (suredividend.com) in order to possibly enhance returns and reduce drawdowns and volatility. Varan adopted a momentum and variance/correlations based allocation approach that also reduces the number of stocks relative to the size of the original basket.
In particular, on the first trading day of every year:
1. Thirty stocks are selected whose total returns were the highest during the immediate prior year,
2. An algorithm "Most Diversified Portfolio" (MDP) that maximizes a measure of diversifaction of the portfolio (papers.ssrn.com/sol3/papers.cfm?abstract...) is used to allocate weights to the thirty stocks,
3. In general the MDP algorithm allocates no weight to a number of stocks in the basket, and thus size of the portfolio is further reduced from thirty,
4. For the convenience of implementation, investment is made in an equal weight portfolio of stocks that remain after the application of the MDP algorithm. Although this will strictly lead to a suboptimal portfolio, it may have the advantage that it does not require very precise estimation of the variance matrix of the stock returns. For the years 20032015 the method applied to the current list of dividend aristocrats (so there is an inherent selection bias in the results) yielded the following:
CAGR 14.3%
Sharpe Ratio 1.19
Sortino Ratio 2.15 (see definition of Sortino Ratio here)
Maximum Drawdown 26%
Recent one, three, five and ten year CAGRs are: 6%, 19%, 12.5%, 13%
It is also notable that these recent returns are consistently higher than the returns (whenever available) of SDY, VIG, VYM, NOBL, and VDIGX. The total return of this portfolio is higher than these ETFs since the dates of inception of the later (except that for VDIGX, the total return of the portfolio is higher for the period starting in 2003).
2018
2016
For 2016, the selections are shown below:



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