Risk-adjusted returns measure how many units of excess return are expected to be generated from however many units of risk. For most financial models the quantity being optimized is a measure of risk-adjusted return. Essentially the problem becomes, how can we adjust the model parameters in such a way that the output quantity is optimized. A simple optimization problem will consist of input variables (model parameters), and output quantities, and constraints on either the inputs, outputs, or both. Most machine learning models are optimization models. Fundamentally this is because they drive supply and demand for securities. Computational finance is about building computational models which can be used to predict, with some error margin, what the markets are likely to do given a number of inputs. That said markets do exhibit quantifiable factors such as the value, mean-reversion, firm-size, and momentum factors, which are believed to drive the returns in the market. Financial markets are complex adaptive systems which are almost always indistinguishable from random processes. Tucker Balch's online MOOC, Computational Investing. This article is a supplement to some of the topics presented in Dr. Warning: preg_replace(): The /e modifier is no longer supported, use preg_replace_callback instead in /home/customer/on line 47
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