A TensorFlow gradient descent framework optimizes portfolios over multiple objectives and constraints, matching exact solvers in simple cases and flexibly handling non-convex multi-objective problems.
Introduction to Risk Parity and Budgeting
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Although portfolio management didn't change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in the deepening of the relationship between risk and asset management. Risk parity then became a popular financial model of investment after the global financial crisis in 2008. Today, pension funds and institutional investors are using this approach in the development of smart indexing and the redefinition of long-term investment policies. Introduction to Risk Parity and Budgeting provides an up-to-date treatment of this alternative method to Markowitz optimization. It builds financial exposure to equities and commodities, considers credit risk in the management of bond portfolios, and designs long-term investment policy. This book contains the solutions of tutorial exercices which are included in Introduction to Risk Parity and Budgeting.
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Multi-objective Portfolio Optimization Via Gradient Descent
A TensorFlow gradient descent framework optimizes portfolios over multiple objectives and constraints, matching exact solvers in simple cases and flexibly handling non-convex multi-objective problems.