Brane World
This article attempts to discuss some key aspects of the Brane World scenario in a popular fashion, without using any formulas.
physics.pop-ph↗
arXiv subjects
Zura Kakushadze
Publications and source records attributed to Zura Kakushadze.
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.
q-fin.PM↗
Factor Models for Alpha Streams
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor model alpha covariance matrix. We discuss various risk factors for alphas such as: style risk factors; cluster risk factors based on alpha taxonomy; principal components; and also using the underlying tradables (stocks) as alpha risk factors, for which computing the factor loadings and factor covariance matrices does not involve any correlations with alphas, and their number is much larger than that of the relevant principal components. We draw insight from stock factor models, but also point out substantial differences.
q-fin.PM↗
Can Turnover Go to Zero?
Internal crossing of trades between multiple alpha streams results in portfolio turnover reduction. Turnover reduction can be modeled using the correlation structure of the alpha streams. As more and more alphas are added, generally turnover reduces. In this note we use a factor model approach to address the question of whether the turnover goes to zero or a finite limit as the number of alphas N goes to infinity. We argue that the limiting turnover value is determined by the number of alpha clusters F, not the number of alphas N. This limiting value behaves according to the "power law" ~ F^(-3/2). So, to achieve zero limiting turnover, the number of alpha clusters must go to infinity along with the number of alphas. We further argue on general grounds that, if the number of underlying tradable instruments is finite, then the turnover cannot go to zero, which implies that the number of alpha clusters also appears to be finite.
q-fin.PM↗