Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

Please send your paper by e-mail: gl*R_GxjH&Tc%8X=43O61LC\sSW]#[_\w=I2`[$t6VuSP&t@E.=.~R|DD 

Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

Please read the detailed editing requirements before submitting your text. 

Number of Results 4

WP(23/2025)486. Does Pair Trading Still Work During Extreme Events? A Comprehensive Empirical Evidence from Chinese Stock Market

Authors: Sun Yufei
This study evaluates the performance of pairs trading strategies in the Chinese stock market across extreme market environments, including the Financial Crisis, Bull and Bear phases, and the COVID-19 period. Using a comprehensive stock dataset and in…

WP486. Does Pair Trading Still Work During Extreme Events? A Comprehensive Empirical Evidence from Chinese Stock Market

This study evaluates the performance of pairs trading strategies in the Chinese stock market across extreme market environments, including the Financial Crisis, Bull and Bear phases, and the COVID-19 period. Using a comprehensive stock dataset and incorporating transaction costs, we find that most portfolios deliver near-zero excess returns after costs. However, in volatile conditions—especially during the Financial Crisis—top-performing portfolios achieved monthly returns up to 156 basis points. The strategy underperforms in stable or bullish markets with fewer mean-reversion opportunities. COVID-19 introduced challenges that further reduced profitability. Results highlight the critical role of transaction costs and the importance of advanced pair selection methods, such as combining the Sum of Squared Deviations (SSD), Hurst exponent, and the Number of Zero Crossings (NZC), which consistently outperform traditional approaches. While generally unprofitable, pairs trading can succeed under specific market regimes, offering insights into risk management and strategy adaptation.


DOI: https://doi.org/10.33138/2957-0506.2025.23.486
Sun Yufei
Pairs Trading Statistical Arbitrage Sum of Squared Deviations Hurst Exponent Chinese Stock Market

WP(20/2025)483. Performance of Pairs Trading Strategies Based on Renko and Kagi Charts

Authors: Sun Yufei
This paper investigates the profitability and robustness of pairs trading strategies based on non-parametric technical chart constructions—Renko and Kagi—across the U.S. and Chinese equity markets. Within a market-neutral, mean-reversion …

WP483. Performance of Pairs Trading Strategies Based on Renko and Kagi Charts

This paper investigates the profitability and robustness of pairs trading strategies based on non-parametric technical chart constructions—Renko and Kagi—across the U.S. and Chinese equity markets. Within a market-neutral, mean-reversion framework, the study examines strategy performance under varying market regimes, including the Global Financial Crisis (GFC) and the COVID-19 period. Using historical data from indices such as the S&P 500 and the CSI series, pairs are selected via statistical patterns in Renko and Kagi charts. Robustness checks consider varying trading horizons, the number of pairs, and transaction costs. Results show that both chart-based strategies generate significant excess returns and exhibit strong Sharpe ratios before costs. While trading frictions reduce profitability, Renko-based strategies remain resilient, especially during crises. The findings highlight that adaptive and non-parametric charting methods can effectively capture transient mispricings and provide viable alternatives for statistical arbitrage in turbulent markets.


DOI: https://doi.org/10.33138/2957-0506.2025.20.483
Sun Yufei
Pairs trading; Quantitative strategies; Statistical arbitrage; Kagi Chart; Renko Chart; H-Strategy

WP(19/2025)482. A survey of statistical arbitrage pairs trading strategies with non-machine learning methods, 2016-2023

Authors: Sun Yufei
This review examines the growing literature on pairs trading frameworks, which involve relative value arbitrage strategies between two or more securities. Existing research is categorized into five main categories: distance methods use nonparametric …

WP482. A survey of statistical arbitrage pairs trading strategies with non-machine learning methods, 2016-2023

This review examines the growing literature on pairs trading frameworks, which involve relative value arbitrage strategies between two or more securities. Existing research is categorized into five main categories: distance methods use nonparametric distance measures to identify pairs trading opportunities; cointegration methods rely on formal cointegration tests to reveal stationary time series of spreads; time series methods focus on finding optimal trading rules for mean-reverting spreads; stochastic control methods aim to determine the optimal portfolio holdings in pairs trading relative to other available securities; and the "Other Methods" category encompasses other relevant pairs trading frameworks, albeit with a more limited supporting literature. Through a comprehensive review of over 100 papers published between 2016 and 2023, the survey identifies the key strengths and weaknesses of each approach, providing insights relevant for future research and practical implementation.


DOI: https://doi.org/10.33138/2957-0506.2025.19.482
Sun Yufei
statistical arbitrage pairs trading distance method cointegration method time series method stochastic control method mean-reversion

WP(9/2024)445. Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

Authors: Korniejczuk Adam, Ślepaczuk Robert
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of …

WP445. Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market

The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase the immunity to transaction costs over existing approaches.  The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to the changes of some key parameters


DOI: https://doi.org/10.33138/2957-0506.2024.9.445
Korniejczuk Adam, Ślepaczuk Robert
graph clustering algorithms statistical arbitrage algorithmic investment strategies pair trading strategy Kelly criterion machine learning risk adjusted returns
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