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: eNQEhnI#G@6u1.oy3ObT%Sl&X_0]#[]=D-R]-v!-vh!IghsXXL05QyaPw 

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 27

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(22/2025)485. A survey of statistical arbitrage pair trading with machine learning, deep learning, and reinforcement learning methods

Authors: Sun Yufei
Pair trading remains a cornerstone strategy in quantitative finance, having consistently attracted scholarly attention from both economists and computer scientists. Over recent decades, research has expanded beyond traditional linear frameworks&mdash…

WP485. A survey of statistical arbitrage pair trading with machine learning, deep learning, and reinforcement learning methods

Pair trading remains a cornerstone strategy in quantitative finance, having consistently attracted scholarly attention from both economists and computer scientists. Over recent decades, research has expanded beyond traditional linear frameworks—such as regression- and cointegration-based models—to embrace advanced methodologies, including machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL). These techniques have demonstrated superior capacity to capture nonlinear dependencies and complex dynamics in financial data, thereby enhancing predictive performance and strategy design.
Building on these academic developments, practitioners are increasingly deploying DL models to forecast asset price movements and volatility in equity and foreign exchange markets, leveraging the advantages of artificial intelligence (AI) for trading. In parallel, DRL has gained prominence in algorithmic trading, where agents can autonomously learn optimal trading policies by interacting with market environments, enabling systems that move beyond price prediction to dynamic signal generation and portfolio allocation.
This paper provides a comprehensive survey of ML-, DL-, RL-, and DRL-based approaches to pair trading within quantitative finance. By systematically reviewing existing studies and highlighting their methodological contributions, it offers researchers a structured foundation for replication and further development. In addition, the paper outlines promising avenues for future research that extend the application of AI-driven methods in statistical arbitrage and market microstructure analysis.


DOI: https://doi.org/10.33138/2957-0506.2025.22.485
Sun Yufei
Pair Trading Machine Learning Deep Learning Reinforcement Learning Deep Reinforcement Learning Artificial Intelligence Quantitative Trading

WP(21/2025)484. Performance of Pairs Trading Strategies Based on Principal Component Analysis Methods

Authors: Sun Yufei
This thesis examines market-neutral, mean-reversion-based statistical arbitrage strategies in the Chinese equity market, using two factor decomposition methods: Principal Component Analysis (PCA) and sector-based Exchange-Traded Funds (ETFs). Residua…

WP484. Performance of Pairs Trading Strategies Based on Principal Component Analysis Methods

This thesis examines market-neutral, mean-reversion-based statistical arbitrage strategies in the Chinese equity market, using two factor decomposition methods: Principal Component Analysis (PCA) and sector-based Exchange-Traded Funds (ETFs). Residual returns are modeled as mean-reverting Ornstein–Uhlenbeck (OU) processes, generating contrarian signals. A 60-day rolling window ensures out-of-sample estimation. Realistic frictions are included via a 10-basis-point round-trip cost. Backtests from 2005 to 2024 compare four configurations: synthetic ETFs, fixed PCA, dynamic PCA, and trading-time volume adjustments. Both PCA- and ETF-based strategies deliver robust Sharpe ratios near 0.90–0.95. PCA portfolios perform better under high cross-sectional volatility, while ETF-based models remain stable during structural shifts. Incorporating trading volume enhances returns, especially for ETF models. Sensitivity analysis highlights the importance of threshold tuning and rolling-window lengths. These findings stress the critical role of factor construction and signal design in market-neutral strategies, suggesting further improvement via adaptive PCA and volume-weighted signals.


DOI: https://doi.org/10.33138/2957-0506.2025.21.484
Sun Yufei
Quantitative Trading Pair Trading Principal Component Analysis 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(14/2025)477. Can Artificial Intelligence Trade the Stock Market?

Authors: Jędrzej Maskiewicz, Sakowski Paweł
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these…

WP477. Can Artificial Intelligence Trade the Stock Market?

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.


DOI: https://doi.org/10.33138/2957-0506.2025.14.477
Jędrzej Maskiewicz, Sakowski Paweł
Reinforcement Learning Deep Learning stock market algorithmic trading Double Deep Q-Network Proximal Policy Optimization

WP(27/2024)463. Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

Authors: Filip Stefaniuk, Ślepaczuk Robert
The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Me…

WP463. Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.


DOI: https://doi.org/10.33138/2957-0506.2024.27.463
Filip Stefaniuk, Ślepaczuk Robert
Machine Learning Financial Series Forecasting Automated Trading Strategy Informer Transformer Bitcoin High Frequency Trading Statistics GMADL

WP(16/2024)452. Enhancing literature review with NLP methods Algorithmic investment strategies case

Authors: Łaniewski Stanisław, Ślepaczuk Robert
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading, based on filtering 136 million research papers to 14,342 articles ranging from 1956 to Q1 2020. We compare previously used practice…

WP452. Enhancing literature review with NLP methods Algorithmic investment strategies case

This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading, based on filtering 136 million research papers to 14,342 articles ranging from 1956 to Q1 2020. We compare previously used practices such as keyword-based algorithms and embedding techniques with state-of-the-art dimension reduction and clustering for topic modeling method (BERTopic) to compare the popularity and evolution of different approaches and themes. We show new possibilities created by the last iteration of Large Language Models (LLM) like ChatGPT. The analysis reveals that the number of research articles on algorithmic trading is increasing faster than the overall number of papers. The stocks and main indices comprise more than half of all assets considered, but the growing trend in some classes is much stronger (e.g. cryptocurrencies). Machine learning models have become the most popular methods nowadays, but they are often flawed compared to seemingly simpler techniques. The study demonstrates the usefulness of Natural Language Processing in asking intricate questions about analyzed articles, like comparing the efficiency of different models. We demonstrate the efficiency of LLMs in refining datasets. Our research shows that by breaking tasks into smaller ones and adding reasoning steps, we can effectively address complex questions supported by case analyses.


Łaniewski Stanisław, Ślepaczuk Robert
trading quantitative finance neural networks literature review knowledge representation natural language processing (NLP) topic modeling model comparison artificial intelligence

WP(10/2024)446. Predictive modeling of foreign exchange trading signals using machine learning techniques

Authors: Sugarbayar Enkhbayar, Ślepaczuk Robert
This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other s…

WP446. Predictive modeling of foreign exchange trading signals using machine learning techniques

This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other studies in that it considered a wide variety of cases including different foreign exchange pairs, return methods, data frequency, and individual and integrated trading strategies. Ridge regression, KNN, RF, XGBoost, GBDT, ANN, LSTM, and GRU models were used for the machine learning-based strategy, while the MA cross strategy was employed for the trend-following strategy. Backtests were performed on 6 major pairs in the period from January 1, 2000, to June 30, 2023, and daily, and intraday data were used. The Sharpe ratio was considered as a metric used to refer to economic significance, and the independent t-test was used to determine statistical significance. The general findings of the study suggested that the currency market has become more efficient. The rise in efficiency is probably caused by the fact that more algorithms are being used in this market, and information spreads much faster. Instead of finding one trading strategy that works well on all major foreign exchange pairs, our study showed it’s possible to find an effective algorithmic trading strategy that generates a more effective trading signal in each specific case.


DOI: https://doi.org/10.33138/2957-0506.2024.10.446
Sugarbayar Enkhbayar, Ślepaczuk Robert
machine learning algorithmic trading foreign exchange market rolling walk-forward optimization technical indicators

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
  • 1
  • 2
  • 3