Machine Learning for Asset Managers Quotes

“the instability caused by covariance structure can be measured in terms of the magnitude between the two extreme eigenvalues. Accordingly, the condition number of a covariance or correlation (or normal, thus diagonalizable) matrix is defined as the absolute value of the ratio between its maximal and minimal (by moduli) eigenvalues.”
― Marcos Lopez de Prado, Machine Learning for Asset Managers

“Given the finite and nondeterministic nature of these observations, the estimate of the covariance matrix includes some amount of noise. Empirical covariance matrices derived from estimated factors are also numerically ill-conditioned, because those factors are also estimated from flawed data. Unless we treat this noise, it will impact the calculations we perform with the covariance matrix, sometimes to the point of rendering the analysis useless.”
― Marcos Lopez de Prado, Machine Learning for Asset Managers

“Rather than imposing a functional form, particularly when that form is unknown ex ante, they would allow algorithms to figure out variable dependencies from the data. And rather than making strong assumptions on the data, the algorithms would conduct experiments that evaluate the mathematical properties of out-of-sample predictions. This relaxation in terms of functional form and data assumptions, combined with the use of powerful computers, opened the door to analyzing complex data sets, including highly nonlinear, hierarchical, and noncontinuous interaction effects.”
― Marcos Lopez de Prado, Machine Learning for Asset Managers