2 citations · 2 across the 17 of their papers we have counts for
8 papers · 1 filter
Robustness or Crowding: Experimental Design for Trading Strategy Capacity
Alejandro Rodriguez Dominguez, Miquel Noguer i Alonso
How much capital a trading strategy can absorb before its edge disappears is a causal question about how much is deployed, but it is answered with observational proxies that rest o…
Path Portfolio Optimization: Defect, Lift, and the Price of Path Complexity
Miquel Noguer i Alonso
This paper builds Path Portfolio Optimization: portfolio theory on a path-first framework in which the signature is the universal coordinate of the price path, and asks whether it…
The Mathematics of Heuristic Portfolio Optimization (HPO)
Miquel Noguer i Alonso
Practitioners allocate capital with forecast-light rules such as equal weight, inverse volatility, risk parity, HRP, and return-adjusted HRP (RA-HRP). This paper develops \emph{Heu…
Anticipatory Portfolio Optimization
Miquel Noguer i Alonso
A portfolio is \emph{anticipatory} when its optimizer acts on a richer model than the myopic, price-taking estimator used to calibrate it. Enrichment may be informational, via enla…
Distributional Portfolio Optimization (DPO): A Unified Framework for Distributions over Weights, Returns, and Parameters
Miquel Noguer i Alonso
Classical portfolio optimization treats expected returns, covariances, and allocations as deterministic. Modern practice replaces at least one by a distribution: a posterior over p…
A Unified Theory of Ownership Concentration, Overlap, and Dependence
Miquel Noguer i Alonso, Iro Tasitsiomi
Ownership concentration is not a scalar. For a normalized investor-stock matrix , it has three irreducible layers: concentration across investors, concentration across stocks, a…
Financial Information Theory
Miquel Noguer i Alonso
This paper introduces a comprehensive framework for Financial Information Theory by applying information-theoretic concepts such as entropy, Kullback-Leibler divergence, mutual inf…
Deep Reinforcement Learning for Asset Allocation in US Equities
Miquel Noguer i Alonso, Sonam Srivastava
Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and…