Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: nYINUv&TesAShC91mWL/|r]QR~i]#[fI86?ekEGd}F\Z1zS`B%#XBG[oV

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 6

WP(33/2026)527. Does the messenger matter? Scenario credibility and information source in valuing health risk reductions from air pollution

Autorzy: Boros Anna, Bartczak Anna
Perceived realism of the policy scenario presented in stated preference (SP) valuation depends on trust in the scientific soundness of the projected policy outcomes (epistemic credibility) and in policymakers' capability to deliver them (implemen…

WP527. Does the messenger matter? Scenario credibility and information source in valuing health risk reductions from air pollution

Perceived realism of the policy scenario presented in stated preference (SP) valuation depends on trust in the scientific soundness of the projected policy outcomes (epistemic credibility) and in policymakers' capability to deliver them (implementation credibility). Using a discrete choice experiment (DCE) with 1,100 Polish urban residents, we examine how policy scenario credibility shapes preferences for air pollution mitigation reducing risks from chronic obstructive pulmonary disease (COPD). We contrast domain experts and artificial intelligence (AI) with no-source control as information sources on policy outcome projections. We find that epistemic and implementation credibility are strongly correlated; thus, we treat credibility as a single variable affecting preferences. Using a hybrid mixed logit (HMXL) model, we demonstrate that higher perceived credibility increases programme support, lowers cost sensitivity, and thus elevates willingness to pay (WTP) for the programme and morbidity risk reduction. We find no evidence that the information source influences preferences or scenario credibility.


DOI: https://doi.org/10.33138/2957-0506.2026.33.527
Boros Anna, Bartczak Anna
air pollution artificial intelligence chronic obstructive pulmonary disease credibility discrete choice experiment information source

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

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

Autorzy: 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(4/2021)352. Concept of peer-to-peer lending and application of machine learning in credit scoring

Autorzy: Klimowicz Aleksy, Spirzewski Krzysztof
Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and interna…

WP352. Concept of peer-to-peer lending and application of machine learning in credit scoring

Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point.
We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists of applying a machine learning algorithm in building a credit scorecard, namely logistic regression.


Klimowicz Aleksy, Spirzewski Krzysztof
artificial intelligence peer-to-peer lending credit risk assessment credit scorecards logistic regression machine learning

WP(34/2020)340. What factors determine unequal suburbanisation? New evidence from Warsaw, Poland

Autorzy: Bogusz Honorata, Winnicki Szymon, Wójcik Piotr
This article investigates the causes of spatially uneven migration from Warsaw to its suburban boroughs. The method is based on the gravity model of migration extended by additional measures of possible pulling factors. We report a novel approach to …

WP340. What factors determine unequal suburbanisation? New evidence from Warsaw, Poland

This article investigates the causes of spatially uneven migration from Warsaw to its suburban boroughs. The method is based on the gravity model of migration extended by additional measures of possible pulling factors. We report a novel approach to modelling suburbanisation: several linear and non-linear predictive models are estimated and explainable AI methods are used to interpret the shape of relationships between the dependent variable and the most important regressors. It is confirmed that migrants choose boroughs of better amenities and of smaller distance to Warsaw city center.


Bogusz Honorata, Winnicki Szymon, Wójcik Piotr
suburbanisation gravity model of migration machine learning models explainable artificial intelligence

WP(18/2020)324. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling

Autorzy: Kłosok Marta, Chlebus Marcin
recent years many scientific journals have widely explored the topic of machine learning interpretability. It is important as application of Artificial Intelligence is growing rapidly and its excellent performance is of huge potential for many. There…

WP324. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling

recent years many scientific journals have widely explored the topic of machine learning interpretability. It is important as application of Artificial Intelligence is growing rapidly and its excellent performance is of huge potential for many. There is also need for overcoming the barriers faced by analysts implementing intelligent systems. The biggest one relates to the problem of explaining why the model made a certain prediction. This work brings the topic of methods for understanding a black-box from both the global and local perspective. Numerous agnostic methods aimed at interpreting black-box model behavior and predictions generated by these complex structures are analyzed. Among them are: Permutation Feature Importance, Partial Dependence Plot, Individual Conditional Expectation Curve, Accumulated Local Effects, techniques approximating predictions of the black-box for single observations with surrogate models (interpretable white-boxes) and Shapley values framework. Our prospect leads toward the question to what extent presented tools enhance model transparency. All of the frameworks are examined in practice with a credit default data use case. The overview presented prove that each of the method has some limitations, but overall almost all summarized techniques produce reliable explanations and contribute to higher transparency accountability of decision systems.


Kłosok Marta, Chlebus Marcin
machine learning explainable Artificial Intelligence visualization techniques model interpretation variable importance
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