1,426 research outputs found
Combining Alpha Streams with Costs
We discuss investment allocation to multiple alpha streams traded on the same
execution platform with internal crossing of trades and point out differences
with allocating investment when alpha streams are traded on separate execution
platforms with no crossing. First, in the latter case allocation weights are
non-negative, while in the former case they can be negative. Second, the
effects of both linear and nonlinear (impact) costs are different in these two
cases due to turnover reduction when the trades are crossed. Third, the
turnover reduction depends on the universe of traded alpha streams, so if some
alpha streams have zero allocations, turnover reduction needs to be recomputed,
hence an iterative procedure. We discuss an algorithm for finding allocation
weights with crossing and linear costs. We also discuss a simple approximation
when nonlinear costs are added, making the allocation problem tractable while
still capturing nonlinear portfolio capacity bound effects. We also define
"regression with costs" as a limit of optimization with costs, useful in
often-occurring cases with singular alpha covariance matrix.Comment: 21 pages; minor misprints corrected; to appear in The Journal of Ris
Notes on Alpha Stream Optimization
In these notes we discuss investment allocation to multiple alpha streams
traded on the same execution platform, including when trades are crossed
internally resulting in turnover reduction. We discuss approaches to alpha
weight optimization where one maximizes P&L subject to bounds on volatility (or
Sharpe ratio). The presence of negative alpha weights, which are allowed when
alpha streams are traded on the same execution platform, complicates the
optimization problem. By using factor model approach to alpha covariance
matrix, the original optimization problem can be viewed as a 1-dimensional root
searching problem plus an optimization problem that requires a finite number of
iterations. We discuss this approach without costs and with linear costs, and
also with nonlinear costs in a certain approximation, which makes the
allocation problem tractable without forgoing nonlinear portfolio capacity
bound effects.Comment: 42 pages; clarifying remarks added, minor misprints corrected; to
appear in The Journal of Investment Strategie
Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections
This paper provides a review of some connecting literature in Decision Sciences, Economics,
Finance, Business, Computing, and Big Data. We then discuss some research that is related to the
six cognate disciplines. Academics could develop theoretical models and subsequent econometric
and statistical models to estimate the parameters in the associated models. Moreover, they could
then conduct simulations to examine whether the estimators or statistics in the new theories on
estimation and hypothesis have small size and high power. Thereafter, academics and practitioners
could then apply their theories to analyze interesting problems and issues in the six disciplines and
other cognate areas
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