Optimizing Optimization: The Next Generation of Optimization Applications and Theory (Quantitative Finance)

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Optimal solutions for optimization in practice 55


objectives of traditional investment funds. In Section 3.8, we illustrate a number
of bespoke optimization procedures to deal with the practical requests of clients.
The chapter concludes in Section 3.9 with a summary of current and future opti-
mization techniques.


3.2 Portfolio optimization


3.2.1 The need for optimization


Consistently adding value through selecting stocks, factors, and “ trends ” is an
obvious challenge for even accomplished asset managers. Many fail, not least
when trying to deliver performance net of management and trading costs; this
has led to the search for an alternative to “ alpha ” as a sales story.
Meanwhile , the “ cost savings ” story has been gaining momentum as “ pro-
ductized ” passive funds have gained significant share of the marketplace. This
generic “ asset product ” worldview is evidenced in the active world as well,
e.g., active funds ’ mandates often being defined in terms of the generic asset
product they are trying to beat. Most active funds mitigate the risk of excess
underperformance by implementing strict constraints on a generic tracking
error. This has led to a proliferation of the so-called active tilt funds.
Historically , active tilt funds are run using an optimizer to maximize alpha
while constraining tracking error, enforcing the “ tilt ” either by alpha determi-
nation or by systematically over-/underweighting some industry or factor, whilst
simultaneously constraining turnover or explicitly subtracting a cost term from
the objective function to control the cost of obtaining the strategic portfolio.


3.2.2 Applications of portfolio optimization


An optimizer is designed to create portfolios and trades that are optimal in
terms of risk, return, and transaction costs, subject to a wide range of real-
world trading and investment constraints. The most important applications of
the BITA Optimizer are in portfolio construction and rebalancing. Some exam-
ples are shown below.


3.2.3 Program trading


This form of optimization is only concerned with risk and transaction cost.
Given an initial position in stocks and index futures as the result of a program
trade, the problem is to trade off risk against costs to achieve an overall posi-
tion (which will include long and short positions in stocks and futures) that has
the desired total exposure (measured as total risk or value at risk).


3.2.4 Long–short portfolio construction


Many equity-based portfolio traders in hedge fund and trading firms, as well
as institutional fund managers in certain institutions, are allowed to take short

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