Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
765 documents
This paper addresses how to construct a portfolio from underlying assets for use in statistical arbitrage, including pairs trading. The design seeks a portfolio with useful mean-reversion behavior while also controlling its variance, subject to an investment…
This paper extends a mean-reversion trading framework with stop-loss rules and leverage. It models security prices as an Ornstein–Uhlenbeck process and evaluates repeated trades through a self-financing portfolio, accounting for proportional transaction…
This study examines optimal investment in two cointegrated stocks for an investor with constant relative risk aversion utility. It extends earlier stochastic-control work by focusing on whether the optimization problem is well-posed and by supplying a…
This study evaluates a dynamic cryptocurrency pairs-trading strategy that selects assets and adapts its look-back window using cointegration tests and estimated mean-reversion speed. It applies Engle-Granger, Kapetanios-Snell-Shin, and Johansen tests in…
The paper formulates a portfolio design problem for statistical arbitrage, seeking combinations of underlying assets with strong mean-reverting behavior. Its objective balances a criterion measuring mean-reversion strength against portfolio variance, while…
The paper introduces Volatility & Model Adaption Trade-off (VMAT), a strategy for multivariate pair trading. It addresses a gap the authors identify in prior work: research often refines trading rules, while portfolio management across multiple time series…
This study formulates speculative trading as a sequential optimal stopping problem, choosing entry and exit times under general utility functions and price processes. It first relaxes the stopping problem by representing entry and exit events as jumps of Cox…
This research develops a pairs-trading approach for cointegrated cryptocurrency pairs using copulas to model dependence and identify relative mispricing. Pair selection combines linear and nonlinear cointegration tests with a correlation measure. The…
This study uses an agent-based model to explore how correlations between exchange rates can arise in foreign exchange markets. It focuses on interactions between market makers and an arbitrager exploiting triangular price relationships. The motivation is…
The document describes a pairs trading framework that incorporates environmental, social, and governance ratings when choosing stocks. It combines ESG-based pair selection with cointegration analysis to identify candidate relationships, then uses technical…
This paper develops a method for timing both entry into and liquidation of positions in mean-reverting price spreads. It frames the decisions as sequential optimal stopping problems and explicitly includes transaction costs. A refined signature-based optimal…
This study presents SimStock, a framework for identifying similar stocks from time-series behavior rather than relying only on conventional sector or regional labels. It combines self-supervised learning with temporal domain generalization to form…
The paper describes a method for constructing pairs trades among stocks in five sectors of India’s National Stock Exchange. It uses cointegration to identify stocks with a long-run association, then forms portfolios intended to trade short-term price…
This work compares financial mathematics and machine learning for dynamic portfolio optimization of two intertwined assets over a continuous-time, finite horizon. Its financial mathematics approach uses a cointelation model intended to combine short-term…
This paper describes a stock pairs-trading system that searches across an N-stock universe instead of monitoring only a preselected pair. It represents stocks as nodes in a directed graph, with edge weights based on instantaneous price differences and…
This paper treats pair trading as a combined decision problem rather than a pipeline that first selects an asset pair and then trades it. The authors argue that separating these stages can discard useful information: selection may ignore whether a pair can…
This document introduces a model-free, pathwise method for analyzing the risk and return of dynamic strategies such as pairs trading, mean-reversion, and statistical arbitrage. It decomposes a continuous trading signal into δ-excursions: movements away from…
This document presents a pairs trading model for two cointegrated assets that accounts for time-varying volatility using a Constant Elasticity of Variance specification. It frames portfolio selection as a stochastic control problem over a fixed horizon:…
This paper presents a framework for studying dependence between financial instruments beyond conventional correlation. It applies transfer entropy and convergent cross-mapping to identify causal relationships, then uses Fourier transform surrogates to…
The paper combines pairs trading with the Black-Litterman framework to construct mean-variance portfolios. Its motivation is that pairs trades may fail to return to equilibrium within the investment horizon, particularly during volatile or distressed…
The paper studies cointegration-based statistical arbitrage and compares ways to find candidate equity portfolios using multiple factors. It evaluates K-means clustering, graphical lasso across the equity pool, and a hybrid that combines the two. The…
This article presents Signature Trading, a portfolio optimization method that represents a strategy as a linear function of a path signature. Signatures encode the history of joint signal and asset movements, allowing the framework to include path dependence…
The study introduces an Optimal Trading Technique for multivariate pair trading on a cryptocurrency platform. It selects a group of fiat currencies linked to cryptocurrency and monitors them together for arbitrage opportunities. To reconcile competing…
This study investigates whether reinforcement learning can improve cryptocurrency pair trading, a statistical arbitrage approach that trades price differences between correlated assets. The authors build trading environments and train agents to choose when…