Heterotic Risk Models

2016-01-23

We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of the factor covariance matrix size in the Russian-doll risk model construction. This appears to prove a powerful approach for constructing out-of-sample stable short-lookback risk models. Thus, for intraday mean-reversion alphas based on overnight returns, Sharpe ratio optimization using our heterotic risk models sizably improves the performance characteristics compared to weighted regressions based on principal components or industry classification. We also give source code for: a) building statistical risk models; and ii) Sharpe ratio optimization with homogeneous linear constraints and position bounds.

📄 PDF Abstract BibTeX arXiv:1508.04883

Code (0)

등록된 구현이 없습니다.

Tasks

Position

Multifactor Risk Models and Heterotic CAPM

2016-03-18

We give a complete algorithm and source code for constructing general multifactor risk models (for equities) via any combination of style factors, principal components (betas) and/or industry factors. For short horizons …

ETF Risk Models

2021-10-14 · Zura Kakushadze, Willie Yu

We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building …

Binary Classification

Hermitian Yang--Mills connections on general vector bundles: geometry and physical Yukawa couplings

2025-12-11 · Challenger Mishra, Justin Tan arxiv

We compute solutions to the Hermitian Yang-Mills equations on holomorphic vector bundles $V$ via an alternating optimisation procedure founded on geometric machine learning. The proposed method is fully general with resp…

Explore and Exploit with Heterotic Line Bundle Models

2020-03-10 · Magdalena Larfors, Robin Schneider

We use deep reinforcement learning to explore a class of heterotic $SU(5)$ GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds. We perform several experiments where A3C age…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Heterotic String Model Building with Monad Bundles and Reinforcement Learning

2021-08-16 · Andrei Constantin, Thomas R. Harvey, Andre Lukas

We use reinforcement learning as a means of constructing string compactifications with prescribed properties. Specifically, we study heterotic SO(10) GUT models on Calabi-Yau three-folds with monad bundles, in search of …

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)