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I'm not that familiar with MATLAB. However, in quadratic programming the main issue I've found is setting up the problem correctly and then the coding becomes much easier. As you noted this problem can be expressed as a quadratic cone problem and solved by quadprog but a good amount of more work needs to be done to get this in the correct form. You ...


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I think the problem is not that you optimize a wrong criterion, but the trading strategy itself. Compare this to testing a hypothesis: if you reject at p-value of 1% then the proportion of true discoveries among all discoveries is, say, 70% (high "expectancy"). If you reject at 10% then the true discovery proportion is 40% (lower "expectancy"), but you make ...


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Intuitively, I would be thinking about how SP500 and SP1500 are different. A few points that come to my mind: Different liquidity of constituents Number and frequency of rebalancing/constituents changes Different levels of diversification Different cost of matching the index Since, again intuitively (EDIT: and a little less so since I have added few ...


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You are asking two different questions: what would be the model result, and what would be the actual performance of an actual portfolio. The optimal model results with the S&P 1500 will be at least as good as the model results with the S&P 500. The S&P is a proper subset of the S&P 1500, so you can get the results of the S&P 500 model by ...


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Well, you are asking something very subjective. In addition it should be mentioned that S&P500 are the companies with higher capitalization of S&P1500. Therefore a huge weight of S&P1500 is set by S&P500. In fact, as it can be seen in 2008 both went down a 37%, in the other hand S&P500 has 80% of the total of the US equity Market. After ...


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On the same time period or different ones? It's difficult to say for a different time period. It's actually difficult to say for the same time period, because dynamics are non-stationary. Let's think about it like this: say you perform mean-variance on the S&P1500, with a short time period: this implies that your estimate of the covariance matrix is ...



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