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.

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Number of Results 18

WP(24/2026)518. The impact of public transport accessibility on the Polish labour market – an econometric approach

Authors: Pakier Grzegorz, Zabarina Kateryna
This paper uses geographically weighted regression to examine the impact of transport accessibility on the registered unemployment rate. It utilises a transport accessibility index that was previously developed for municipalities in Poland. Analysis …

WP518. The impact of public transport accessibility on the Polish labour market – an econometric approach

This paper uses geographically weighted regression to examine the impact of transport accessibility on the registered unemployment rate. It utilises a transport accessibility index that was previously developed for municipalities in Poland. Analysis of three econometric models revealed that transport accessibility has a significant negative impact on the unemployment rate, which varies across the regions studied. The model using the average transport accessibility of municipalities within a given county showed a stronger relationship with the unemployment rate. This suggests that accessibility at the county level is more important than local accessibility. Furthermore, it was found that the type of municipality had no impact on the strength or direction of the examined relationship. The research confirms that public transport availability significantly influences unemployment levels in Poland, with considerable spatial variation in this relationship. The results emphasise the importance of considering the regional context of transport accessibility when devising anti-unemployment policies.


Pakier Grzegorz, Zabarina Kateryna
transport accessibility unemployment rate geographically weighted regression transport exclusion spatial mismatch

WP(18/2024)454. Explaining and forecasting abnormal returns and volume by investor sentiment indicators

Authors: Lis Szymon, Ślepaczuk Robert, Sakowski Paweł
This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to …

WP454. Explaining and forecasting abnormal returns and volume by investor sentiment indicators

This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to analyze sentiment indicators such as the VIX, AAII Investor Sentiment Survey, Consumer Confidence, and Baker-Wurgler Index. The findings reveal that investor sentiment significantly influences stock returns and trading volume, especially during uncertain times. Sentiment also affects financial metrics like SMB, HML, RMW, and CMA uniquely. This research provides new insights and practical implications for investors and analysts, emphasizing the importance of considering sentiment in investment strategies to better anticipate market movements and manage risks.


Lis Szymon, Ślepaczuk Robert, Sakowski Paweł
market sentiment factor models stock returns EMH Fama-MacBeth model quantile regression Baker-Wurgler index VIX index

WP(13/2023)420. From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement

Authors: Chlebus Marcin, Nowak Artur
Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble meta…

WP420. From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement

Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble metals: Gold, Silver, and Platinum in a form of multi-class classification. That task was performed using four algorithms: Logistic Regression, k-Nearest Neighbors, Random Forest and XGBoost. The study incorporated feature filtering methods such as Kendall-tau filtering and PCA, as well as five different data frequencies: 1, 5, 10, 15 and 20 trading days. From a total of 40 potential models for each metal, the best one was selected and evaluated using data from period 2018-2022. The result revealed that models utilizing only Technical Analysis indicators were able to predict price movements to a significant extent, leading to investment strategies that outperformed the market in two out of three cases.


Chlebus Marcin, Nowak Artur
precious metals algotrading machine learning multiclass classification logistic regression nearest neighbors random forest xgboost

WP(9/2023)416. Is international free-riding immanent to transboundary spatial conservation?

Authors: Valasiuk Sviataslau
Despite their recent global expansion in nominal terms, many transboundary nature protected areas tend to avoid hands-on cross-border co-operation. One common explanation which is widely seen a major obstacle towards the concerted transboundary conse…

WP416. Is international free-riding immanent to transboundary spatial conservation?

Despite their recent global expansion in nominal terms, many transboundary nature protected areas tend to avoid hands-on cross-border co-operation. One common explanation which is widely seen a major obstacle towards the concerted transboundary conservation is international free riding on the centralised decision-making level. I examine empirically whether international free-riding is embedded in citizens’ stated preferences for extended protection in the case of two transboundary nature protected areas Białowieża Forest and Fulufjället. I scrutinise a sub-set of merged survey samples from the four countries involved, including only the citizens assumingly incentivised to free-ride on unilateral foreign country’s conservation action. I apply attitudinal indicators to form effect-coded variables that measure free-riding, and control for use value, nationality, individual socioeconomic characteristics, and incentive compatibility of the survey design. The results indicate no widespread tendency of international free-riding; the conclusion is maintained with varying modelling approaches or sampling strategy employed.


Valasiuk Sviataslau
transboundary nature protected areas stated preferences public good free-riding binary logit negative binomial regression

WP(30/2022)406. Quantile regression analysis to predict GDP distribution using data from the US and UK

Authors: Tran Thi Huyen, Ślepaczuk Robert
This paper aims to find the best models to forecast one-quarter-ahead and one-year-ahead US and UK real GDP growth distributions by employing quantile regression with skewed-t distribution on different sets of relevant near-term predictors. The resea…

WP406. Quantile regression analysis to predict GDP distribution using data from the US and UK

This paper aims to find the best models to forecast one-quarter-ahead and one-year-ahead US and UK real GDP growth distributions by employing quantile regression with skewed-t distribution on different sets of relevant near-term predictors. The research data period starts in 1947Q1/1955Q1 for US/UK data and ends in 2021Q3/2020Q4 for one-quarter-ahead/one-year-ahead prediction. The out-of-sample period ranges from 1996Q3 to 2021Q3 for one-quarter-ahead prediction and to 2020Q4 for one-year-ahead forecasting. The author applies a two-step testing procedure, in which models with the lowest average error in out-of-sample period are selected to the next step where the cumulative distribution functions of probability integral transforms are computed for the out-of-sample period, to select the best models. The improvement in the final forecasts of the tested models results, among others, from the use of new macroeconomic data with a higher frequency and focusing on the specific properties of the tested models separately for the US and UK. The chosen best models indicate that there exist better models than the model proposed by Adrian et al. (2016) to predict US growth distributions and that near-term predictors can produce good UK growth forecasts. Additionally, some simplified models associated with significantly lower portion of model risk are detected to produce meaningful forecasts for both US and UK case. For the US data, there exist several models that can produce timely predicted results.


Tran Thi Huyen, Ślepaczuk Robert
GDP growth density forecast quantile regression US GDP UK GDP cumulative distribution function probability integral transform out-of-sample forecasting

WP(23/2021)371. Application of machine learning in quantitative investment strategies on global stock markets

Authors: Grudniewicz Jan, Ślepaczuk Robert
The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Ra…

WP371. Application of machine learning in quantitative investment strategies on global stock markets

The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500 and selected CEE indices in the period between 2002-01-01 to 2020-10-30. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis. The research results indicated that quantitative strategies generate better risk adjusted returns than passive strategies and that for the analysed indices predominantly Bayesian Generalized Linear Model and Naïve Bayes were the best performing models. More comprehensive rank approach based on the results for all analysed models and indices allowed to select Bayesian Generalized Linear Model as the model which on average generated the best results.


Grudniewicz Jan, Ślepaczuk Robert
quantitative investment strategies machine learning neural networks regression trees random forests support vector machine technical analysis equity stock indices developed and emerging markets information ratio

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

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

Authors: 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(30/2020)336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

Authors: Heba Mateusz, Chlebus Marcin
The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the…

WP336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the prediction of companies bankruptcy. Some authors of scientific papers claim that the process of evaluation of the companies situation requires comparison of its characteristics defined as financial ratio with situation of whole sector in order to obtain reliable conclusions. In this paper, a hypothesis that usage of the industry benchmarks (transformation of raw financial ratios values into sectoral deciles groups numbers) improves results of bankruptcy prediction logistic regression model is verified. Based on empirical results for Polish market, it turns out that although models estimated on different types of data have similar discriminatory power, logistic regression using raw financial ratios obtained a bit better results than its industry equivalents defined as sectoral deciles groups numbers. It is worth emphasizing that empirical part of paper uses information about 109K companies what is the rarity in bankruptcy prediction papers – researchers usually use small datasets that include less than several hundred records.


Heba Mateusz, Chlebus Marcin
bankruptcy prediction financial ratios industry financial ratios sectoral financial ratios logistic regression financial econometrics

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

Authors: 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
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