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Thursday, March 16 • 4:00pm - 5:30pm
Poster: Convex Optimization for High-Dimensional Portfolio Construction

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Investors in financial markets face a series of complex trade-offs in
constructing an optimal investment portfolio. For example, an investor
might seek to simultaneously maximize his expected return and minimize
his risk, while also diversifying across asset classes, minimizing his
transaction costs, and satisfying a series of legal and fiduciary
requirements. Portfolio construction techniques, dating from the
Nobel-prize winning work of Markowitz in the 1950s, attempt to address
these often-contradictory constraints in a principled way, but the
resulting optimization problems are computationally intractable for
large-scale (many asset) portfolios. Beyond that, classical methods
are known to be statistically unstable and highly sensitive to
estimated input, making them difficult to apply successfully to noisy
financial data.

By recasting portfolio construction as a constrained (penalized)
regression problem, we pose a new class of techniques for portfolio
optimization based on high-dimensional statistical theory. Exploiting
this connection to existing theory allows us to show that our
techniques have superior properties across a wide range of scenarios.
Furthermore, we show that our techniques are computationally many
orders of magnitude faster than what currently appears in the
literature. Finally, we show, empirically and in simulation, that they
produce portfolios which are competitive with, and in many cases,
superior to those produced by existing methods.


Thursday March 16, 2017 4:00pm - 5:30pm
Exhibit Hall BRC