The best portfolios are, no doubt, ones that earn the highest returns with the least volatility. After ascertaining that you possess several informative signals for the same group of securities, you may be wondering how to capitalize on these signals as a whole. If the signals are high-quality enough to predict specific returns, rather than just being the simple "buy/sell" type, then you can take one of two possible approaches:
1) If you are deciding how to allocate capital to different strategies, run 'backtests' to determine the historical average return and the standard deviation of each strategy, as well as the historical correlation between such strategies, and then use mean-variance portfolio optimization to find the Sharpe-optimal 'weighting' of each strategy in a multistrategy fund. While this is great for finished strategies that utilize completely different structures (for instance, using typically unrelated strategies like stat arb and global macro), it unfortunately does not solve the heart of the problem: if there are no strategies, but only signals, how do we construct a multisignal portfolio at the securities level?
2) use historical price data to generate signals, and use all signals from a given day in the past to do multivariate linear regression and determine the sensitivity in the output to the sensitivity in input. Alternately, we can train an artificial neural network to do something similar, and pray it doesn't overfit the data. Lastly, we can set up an machine learning system that uses fuzzy logic to 'reason' its way through investment decisions and portfolio construction, analyzing historical patterns of how certain signals contradict the others when something bad is about to happen. In such an event, the system can work out a more informed expectation of the returns, which can then be fed into a portfolio optimizer.
