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62 dokumenti
Vine copulas extend copula-based dependence modeling beyond pairs by decomposing a high-dimensional joint density into marginal densities and conditional bivariate copulas. The article explains how conditional probabilities support this decomposition and…
The article surveys hedge ratio estimation methods, separating single-period approaches that assume independent, identically distributed returns from multi-period approaches that model changes over time. The static methods covered are ordinary least squares…
The article draws on employee accounts and public descriptions of several quantitative investment firms to discuss how research teams are organized. Common themes include scientific inquiry, freedom to test ideas, cross-disciplinary discussion, shared…
The article explains why financial time series are often made stationary for statistical inference and supervised machine learning, then presents fractional differentiation as a way to reduce nonstationarity while retaining more of a price series’ memory…
The article describes Hierarchical Equal Risk Contribution (HERC), a portfolio allocation method that combines hierarchical clustering with cluster-aware capital allocation and risk balancing. It first groups assets from their return correlations, selects a…
The article explains CorrGAN, a generative adversarial network designed to create synthetic financial correlation matrices. The motivation is that historical market data can be costly, restricted, biased toward the events that occurred, and sparse in extreme…
This article presents a pairs trading method that selects stocks using correlations between their returns. In a formation period, it calculates monthly returns, finds each stock’s most correlated peers, and forms an equal-weighted peer portfolio. Regression…
The release notes describe additions to a financial machine learning library, including time bars and information driven bars, structural break tests, market microstructure measures, entropy estimators, volatility estimators, clustering, dependence metrics,…
This tutorial presents preprocessing and labeling methods for supervised trading models. Fractional differentiation is used to make price features more stationary while retaining more of their historical dependence than ordinary differencing may preserve.…
The article explains a stochastic control framework for convergence trades between cointegrated assets. Earlier approaches constrain positions to be delta-neutral and fix the relative stock weights; the generalized approach allows individual asset weights to…
The article reviews a proposed arbitrage portfolio that combines equity mean reversion with momentum across stock market indices. Its study separates data into an in-sample period from November 2005 to October 2007 and an out-of-sample period from November…
This lecture series surveys advanced pairs and statistical arbitrage methods. Topics include distance-based pair selection and dependence measures, cointegration with mean first-passage time for choosing trading boundaries, PCA strategies, machine learning…
The document introduces Hierarchical Risk Parity (HRP) as a portfolio allocation method intended to reduce sensitivity to noisy return estimates and covariance-matrix inversion in traditional mean-variance optimization. It explains HRP in three stages:…
The document presents an analytical approach to choosing entry and exit thresholds for mean-reversion trading. It models a tradable process with an Ornstein–Uhlenbeck dynamic and uses first-passage-time calculations to derive the expected duration and…
The document explains a mean-reversion strategy that uses a C-vine copula to model dependence among a cohort of stocks. It converts returns into empirical quantiles, fits candidate vine structures and bivariate copulas, then uses conditional probabilities to…
This March 2019 research update summarizes a project report on applying financial machine learning methods to trend-following and mean-reverting strategies. The report combines event-based sampling, the triple-barrier labeling method, and meta-labeling, and…
This paper describes the motivation and design of a Python research package intended to make methods from financial machine learning easier to implement and study. It frames Lopez de Prado’s work as a research process built around data preparation, sampling,…
This article explains how stock selection should be matched to the trading strategy that uses copula-based signals. Copulas transform asset returns into conditional probability or cumulative mispricing series, but do not specify a trading rule on their own.…
This introduction describes how copulas can model the dependence between two assets separately from the distribution of each asset. Marginal returns may each appear normally distributed without their joint behavior being normal; a Gaussian model can also…
This tutorial explains Hierarchical Equal Risk Contribution (HERC), a portfolio allocation method that combines hierarchical clustering with risk-based weighting. It motivates the approach by describing how conventional mean-variance optimization can be…
This technical article explains how to sample from and fit bivariate copulas, which model dependence between two variables separately from their marginal distributions. Sampling from a fitted copula can help compare simulated quantile pairs with historical…
This article presents an unsupervised learning framework for narrowing the search for equity pairs that may exhibit mean reversion. It first applies principal component analysis to asset returns to represent shared risk exposures, then uses density-based…
This article describes a basic distance approach to pairs trading. During a formation period, asset price series are normalized so their scales are comparable, then candidate pairs are selected using squared Euclidean distance. The spread’s historical…
This broad introduction defines pairs trading as taking opposing positions in co-moving assets when their relative prices depart from an equilibrium, with the expectation that the relationship will persist and prices will converge. It distinguishes pairs…