A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Unified pair trading approach using hierarchical reinforcement learning.
Algorithm combines ESG ratings with pairs trading for sustainable investing.
RL enhances cryptocurrency trading profits.
Solves pair trading problem using consumption-investment theory.
Pairs trading strategy improved using Ornstein-Uhlenbeck process.
Optimizes cryptocurrency trading pairs for efficiency and decentralization.
Paper finds optimal selling rule for pairs trading with stock constraints.
Novel pairs trading strategy for cointegrated cryptocurrencies using copulas.
CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.
Enhances pairs trading with neural networks and Kalman Filters.
VMAT strategy improves multivariate pair trading performance.
Enhanced pairs trading with Black-Litterman model outperforms market indexes.
Develops optimal trading strategy for illiquid currency pairs.
This paper uses cointegration to identify profitable pair-trading strategies for Indian stocks.
A pairs trading model with time-varying volatility using stochastic control.
Pairs trading is a market-neutral strategy that exploits historical correlation between stocks to achieve statistical arbitrage. Existing pairs-trading algorithms in the literature require rather restrictive assumptions on the underlying stochastic stock-price processes and the so-called spread function. In contrast to…
The paper introduces a new pairs trading model using nonlinear and non-Gaussian state-space models.
Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and numb…
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
A new method uses preference relations to reconcile contradictory trading signals from multiple securities.
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
MTRGL learns temporal correlations from multi-modal data for improved pair trading.
A novel graphical matching approach improves pairs trading by reducing portfolio variance and risk-adjusted returns.
The goal of this study is to explain and examine the statistical underpinnings of the Bollinger Band methodology. We start off by elucidating the rolling regression time series model and deriving its explicit relationship to Bollinger Bands. Next we illustrate the use of Bollinger Bands in pairs trading and prove the e…
We carry out a large-scale empirical data analysis to examine the efficiency of the so-called pairs trading. On the basis of relevant three thresholds, namely, starting, profit-taking, and stop-loss for the `first-passage process' of the spread (gap) between two highly-correlated stocks, we construct an effective strat…
Pairs trading strategy fails to outperform market benchmarks, but performs well during bear markets.
This paper is concerned with a pairs trading rule. The idea is to monitor two historically correlated securities. When divergence is underway, i.e., one stock moves up while the other moves down, a pairs trade is entered which consists of a pair to short the outperforming stock and to long the underperforming one. Such…
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from…
This paper studies liquidity providers in decentralized exchanges.
We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum likelihood method to construct the optimal static pairs trading portfolio that best fits the Ornstein-Uhlenbeck process, and rigorously estimate the parameters.…
Study analyzes impact of concentrated liquidity on trading fees and provider returns.
A Monte Carlo method for pairs trading on mean-reverting spreads with Lévy processes.
A large class of trading strategies focus on opportunities offered by the yield curve. In particular, a set of yield curve trading strategies are based on the view that the yield curve mean-reverts. Based on these strategies' positive performance, a multiple pairs trading strategy on major currency pairs was implemente…
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
Agent learns to trade currency pairs with improved risk management.
This paper studies the problem of determining the optimal cut-off for pairs trading rules. We consider two correlated assets whose spread is modelled by a mean-reverting process with stochastic volatility, and the optimal pair trading rule is formulated as an optimal switching problem between three regimes: flat positi…
This paper derives a robust on-line equity trading algorithm that achieves the greatest possible percentage of the final wealth of the best pairs rebalancing rule in hindsight. A pairs rebalancing rule chooses some pair of stocks in the market and then perpetually executes rebalancing trades so as to maintain a target …
This work's purpose is to understand the dynamics of limit order books in order-driven markets. We try to illustrate a dynamical trading mechanism attached to the microstructure of limit order markets. We capture the iterative nature of trading processes, which is critical in the dynamics of bid-ask pairs and the switc…
Study shows post-COVID commodity futures returns and volatility changed for different products.
Optimizes trading strategy for cointegrated assets with bounded risk.
This paper considers ideal gas-like models of trading markets, where each agent is identified as a gas molecule that interacts with others trading in elastic or money-conservative collisions. Traditionally, these models introduce different rules of random selection and exchange between pair agents. Unlike these traditi…
A pair trade is a portfolio consisting of a long position in one asset and a short position in another, and it is a widely applied investment strategy in the financial industry. Recently, Ekström, Lindberg and Tysk studied the problem of optimally closing a pair trading strategy when the difference of the two assets is…
Enhances cryptocurrency pair trading with DRL, outperforming classical methods.
A simple trading model based on pair pattern strategy space with holding periods is proposed. Power-law behaviors are observed for the return variance , the price impact and the predictability for both models with linear and square root impact functions. The sum of the traders' wealth displays a positive v…
We propose a mathematical procedure for finding informed traders in ultra-high frequency trading. We wrote it as Vector ARMA and found condition of its stationarity. For the price exposure complied with ARMA(1,2) we proved that underlying asset price difference can be derived as ARMA(1,1) process. For validation of the…
Deep neural networks identify robust arbitrage strategies in financial markets.
This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and ge…
Deep learning predicts currency volatility accurately.