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: z?-ZyDOm[85PBba0VX#E3Hp{eQx]#[r#zFc/3^9&uC2yYy8a{9@*UqnBe

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 7

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(2/2023)409. Prudent populists? The short-term macroeconomic impact of populist policies in Poland

Autorzy: Brzeziński Michał, Sałach-Dróżdż Katarzyna
Recent research shows that populist governments in general depress countries’ economic growth and fail to improve income distribution. We study the short-term macroeconomic consequences of populist policies implemented in Poland since 2015. Usi…

WP409. Prudent populists? The short-term macroeconomic impact of populist policies in Poland

Recent research shows that populist governments in general depress countries’ economic growth and fail to improve income distribution. We study the short-term macroeconomic consequences of populist policies implemented in Poland since 2015. Using the augmented synthetic control method, we find that the populists boosted the GDP per capita by almost 8% between 2016 and 2019. We do not find any effect of populism on inflation and estimate only small labor market impacts. The populists improved tax revenue collection and reduced public debt. A generous child benefit program and other redistributive policies introduced by the populists significantly reduced overall poverty and almost eradicated absolute child poverty. Our results imply that anti-establishment populists may be macroeconomically prudent, at least in the short run.


Brzeziński Michał, Sałach-Dróżdż Katarzyna
populism economic growth public finance income distribution Poland

WP(14/2022)390. Investor Sentiment in Asset Pricing Models: A Review

Autorzy: Lis Szymon
Despite the number of works on investor sentiment in asset pricing models the results did not allow to obtain a coherent knowledge about this sentiment. Most of the researchers used different measures and various models to study the impact of sentime…

WP390. Investor Sentiment in Asset Pricing Models: A Review

Despite the number of works on investor sentiment in asset pricing models the results did not allow to obtain a coherent knowledge about this sentiment. Most of the researchers used different measures and various models to study the impact of sentiment on stocks returns. However, the empirical relationship between investor sentiment and stock market behavior remains unclear. This study focuses on reviewing the methodologies and empirical findings of 71 papers published between 2000 and 2021 that apply different investor sentiment measures for modeling returns. The research confirmed two out of the three research hypotheses that the investor sentiment proxies and higher complexity of the model with the investor sentiment indicator improve the coefficient of determination. The second one was rejected, however, this may be due to too small a sample. For the hypothesis that models with more complex sentiment have better predictive power than those with simpler proxies, the number of studies was insufficient to refer strongly to the hypothesis.


Lis Szymon
Investor sentiment Asset pricing Multifactor models Behavioral finance Risk factors stock market behavior

WP(11/2021)359. Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts

Autorzy: Lis Szymon, Chlebus Marcin
No model dominates existing VaR forecasting comparisons. This problem may be solved by combine forecasts. This study investigates the daily volatility forecasting for commodities (gold, silver, oil, gas, copper) from 2000-2020 and identifies the sour…

WP359. Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts

No model dominates existing VaR forecasting comparisons. This problem may be solved by combine forecasts. This study investigates the daily volatility forecasting for commodities (gold, silver, oil, gas, copper) from 2000-2020 and identifies the source of performance improvements between individual GARCH models and combining forecasts methods (mean, the lowest, the highest, CQOM, quantile regression with the elastic net or LASSO regularization, random forests, gradient boosting, neural network) through the MCS. Results indicate that individual models achieve more accurate VaR forecasts for the confidence level of 0.975, but combined forecasts are more precise for 0.99. In most cases simple combining methods (mean or the lowest VaR) are the best. Such evidence demonstrates that combining forecasts is important to get better results from the existing models. The study shows that combining the forecasts allows for more accurate VaR forecasting, although it's difficult to find accurate, complex methods.


Lis Szymon, Chlebus Marcin
Combining forecasts Econometric models Finance Financial markets GARCH models Neural networks Regression Time series Risk Value-at-Risk Machine learning Model Confidence Set

WP(14/2020)320. Why wealth inequality differs between post-socialist countries?

Autorzy: Brzeziński Michał, Sałach Katarzyna
We provide the first attempt to understand how differences in households' socio-demographic and economic characteristics account for disparities in wealth inequality between five post-socialist countries of Central and Eastern Europe. We use 2013…

WP320. Why wealth inequality differs between post-socialist countries?

We provide the first attempt to understand how differences in households' socio-demographic and economic characteristics account for disparities in wealth inequality between five post-socialist countries of Central and Eastern Europe. We use 2013/2014 data from the second wave of the Household Finance and Consumption Survey (HFCS) and the reweighted Oaxaca-Blinder-like decompositions based on recentered influence function (RIF) regressions. Our results show that the differences in homeownership rates account for up to 42% of the difference in wealth inequality measured with the Gini index and for as much as 63-109% in case of the P50/P25 percentile ratio. Differences in homeownership rates are related to alternative designs of housing tax policies but could be also driven by other factors. We correct for the problem of the 'missing rich' in household surveys by calibrating the HFCS survey weights to top wealth shares adjusted using wealth data from national rich lists. Empirically, the correction procedure strengthens the importance of homeownership rates in accounting for cross-country wealth inequality differences, which suggests that our results are not sensitive to the significant underestimation of top wealth observations in the HFCS.


Brzeziński Michał, Sałach Katarzyna
wealth inequality decomposition recentered influence function (RIF) regressions survey weight calibration Household Finance and Consumption Survey (HFCS) post-socialist transition Central and Eastern Europe (CEE) housing homeownership missing rich

WP(9/2019)294. Wealth inequality in Central and Eastern Europe: evidence from joined household survey and rich lists’ data

Autorzy: Brzeziński Michał, Sałach Katarzyna, Wroński Marcin
We study how the problem of the 'missing rich', the underrepresentation of the wealthiest in household surveys, affects wealth inequality estimates for the post-socialist countries of Central and Eastern Europe (CEE). The survey data from the…

WP294. Wealth inequality in Central and Eastern Europe: evidence from joined household survey and rich lists’ data

We study how the problem of the 'missing rich', the underrepresentation of the wealthiest in household surveys, affects wealth inequality estimates for the post-socialist countries of Central and Eastern Europe (CEE). The survey data from the second wave of the Household Finance and Consumption Survey (HFCS) are joined with the data from the national rich lists for Esto-nia, Hungary, Latvia, Poland and Slovakia. Pareto distribution is fitted to the joined survey and rich lists' data to impute the missing observations for the largest wealth values. We provide the first estimates of the top-corrected wealth inequality for the CEE region in 2013/2014. Despite a short period of wealth accumulation during the post-1989 market economy period, our adjust-ment procedure reveals that wealth inequality in the Baltic countries is comparable to that of Germany (one of the most wealth unequal countries in Europe), while in Poland and Hungary it has reached levels observed in France or Spain. We discuss possible explanations of these find-ings with reference to the speed and range of privatization processes, extent of income inequali-ty, and the role of inheritances and wealth taxes in the region.


Brzeziński Michał, Sałach Katarzyna, Wroński Marcin
wealth inequality missing rich Pareto distribution rich lists Forbes Household Finance and Consumption Survey transition countries Central and Eastern Europe

WP(7/2016)198. Determinants of Polish Enterprises' Propensity to Lease

Autorzy: Białek-Jaworska Anna, Nehrebecka Natalia
The paper identifies determinants of financing the purchase of tangible assets by Polish enter-prises with use of financial and operating lease. Analysis of financial lease was based on sta-tistical annual reports for the years 1995−2011 (about…

WP198. Determinants of Polish Enterprises' Propensity to Lease

The paper identifies determinants of financing the purchase of tangible assets by Polish enter-prises with use of financial and operating lease. Analysis of financial lease was based on sta-tistical annual reports for the years 1995−2011 (about 50 thousands companies per year), while research of operating lease used half-year reports for the years 2007−2011, both with use of System GMM. The literature of the subject continuously pursues to explain the rela-tions between choosing a lease and other sources of capital, mainly bank loans. Therefore, the main focus of the study is on the dependence between the capital lease and the long-term bank loan, as well as on the relations between the operating lease, the short-term loan and the long-term debt-based financing with no financial lease element (net). The analyses indicate that medium-sized and large companies are more inclined to finance their assets with capital leas-es than small business and that large companies use more capital leases than small businesses do. It was found that a higher degree of the long-term loan financing results in a lower degree of capital leasing, which indicates the long-term bank loan and capital lease substitutability, similarly as reported by Marston & Harris (1988), Beattie, Goodacre & Thomson (2000), Singh (2011), as well as Lin et al. (2013). The findings for factors affecting the propensity to finance assets with operating leases proves the substitutability of an operating lease and a short-term bank loan, similarly as reported by Beattie, Goodacre & Thomson (2000), Filareto-Deghaye & Singh (2011), as well as Singh (2011).


Białek-Jaworska Anna, Nehrebecka Natalia
lease bank credit financial lease operating lease substitutability corporate finance dynamic panel data system GMM
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