Sparks on forecasting models-优快云博客

博客讨论了投资组合下行标准差计算的难点，指出使用保证金买入时需引入VAR控制风险；探讨了一致投票与多数投票在选择中的效果；决定采用会计与非会计变量作为内在因素；提及多数交易系统未解决交易成本问题，自家系统有持续学习等优势，强调模型背后理念比算法更重要。

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Just finished reading the set of papers titled Developments in Forecast Combination and Portfolio Choice, since it's due tomorrow. (I'm a bit lazy currently><) Some of points I came to:

The difficulty of calculating downside standard deviation of a portfolio: Hmm...I've never realized such issue, maybe because I use absolute deviation directly... gonna check the model file later.
If buying on margin, downside SD is not enough to control the risk, VAR here should be brought in as an constraint to possible margin calls.
Unanimous vote vs. majority vote: why the former leads to a better result in selection? Should I use?
The idea of using accounting variables and nonaccounting varialbes are inspiring. I decide to use them as part of the inner personalities instead of as the front-end screening module as designed before.
Most trading system described still haven't address the issue of transaction costs. Moreover, our in-develop system has advantages on continous learning, portfolio-oriented (instead of single stock), and providing clear trading instructions.
The philosphy behind a model is much more critical than the algorithm used to implement it.