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: \g'q3&WcpzvY}fj{RM=8kCDFZEQ]#[TWu]wr?TRk\Lq}bj4V00t!!8c2=

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 3

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(5/2020)311. Preferences for demand side management—a review of choice experiment studies

Autorzy: Gołębiowska Bernadeta
This review of choice experiment (CE) studies deals with the valuation of electricity supply attributes in the residential sector. We consider the willingness to pay and the willingness to accept changes in the electricity supply. The results could b…

WP311. Preferences for demand side management—a review of choice experiment studies

This review of choice experiment (CE) studies deals with the valuation of electricity supply attributes in the residential sector. We consider the willingness to pay and the willingness to accept changes in the electricity supply. The results could be used to determine consumers' preferences for demand-side management (DSM) programs and could serve as a reference for formulating policies. DSM is an option for constructing a low-carbon electricity system, improving energy efficiency, and achieving the sustainable development of an economy. The results from CEs justify investment in new solutions. The research shows that consumers are open to DSM, but they prefer simple programs to complex ones. Decision-makers could introduce DSM programs that enable power outages and provide compensation for households. The societal advantages of DSM are not obvious to consumers, so the implementation of DSM requires communication and more research on peoples' preferences.


Gołębiowska Bernadeta
choice experiments demand-side management energy households review

WP(7/2011)47. Known Knowns and Known Unknowns of Immigrant Self-employment. Selected issues

Autorzy: Nestorowicz Joanna
The paper presents a review of selected definitional issues, theoretical concepts and most recent empirical evidence related to the phenomenon of immigrant self-employment. Based on the appraisal of gathered material it also points to possible areas …

WP47. Known Knowns and Known Unknowns of Immigrant Self-employment. Selected issues

The paper presents a review of selected definitional issues, theoretical concepts and most recent empirical evidence related to the phenomenon of immigrant self-employment. Based on the appraisal of gathered material it also points to possible areas of development of future research in the field.


Nestorowicz Joanna
self-employment ethnic entrepreneurship middleman minority ethnic enclave literature review state of the art
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