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 5

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(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(22/2021)370. Predicting football outcomes from Spanish league using machine learning models

Authors: Lewandowski Michał, Chlebus Marcin
High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with histori…

WP370. Predicting football outcomes from Spanish league using machine learning models

High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with historical forecasts extracted from bookmakers, which knowledge is commonly considered to be deep and high-quality. The aim of the paper was to design models with the highest possible predictive performances, get results close to bookmakers or even building better estimators. The work included detailed feature engineering based on previous achievements of this domain and own proposals. A built and selected set of variables was used with four machine learning methods, namely Random Forest, AdaBoost, XGBoost and CatBoost. The algorithms were compared based on: Area Under the Curve (AUC) and Ranked Probability Score (RPS). RPS was used as a benchmark in the comparison of estimated probabilities from trained models and forecasts from bookmakers' odds. For a deeper understanding and explanation of the demonstrated methods, which are considered as black-box approaches, Permutation Feature Importance (PFI) was used to evaluate the impacts of individual variables. Features extracted from bookmakers odds’ occurred the most important in terms of PFI. Furthermore, XGBoost achieved the best results on the validation set (RPS equals 0.1989), which obtained similar predictive power to bookmakers' odds (their RPS between 0.1977 and 0.1984). Results of the trained estimators were promising and this article showed that competition with bookmakers is possible using demonstrated techniques.


Lewandowski Michał, Chlebus Marcin
predicting football outcomes machine learning betting adaboost random forest xgboost catboost ranked probability score auc permutation feature importance

WP(5/2021)353. The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

Authors: Siwicki Dawid
The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained res…

WP353. The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw

The principal aim of this paper is to investigate the potential of machine learning algorithms in context of predicting housing prices. The most important issue in modelling spatial data is to consider spatial heterogeneity that can bias obtained results when is not taken into consideration. The purpose of this research is to compare prediction power of such methods: linear regression, artificial neural network, random forest, extreme gradient boosting and spatial error model. The evaluation was conducted using train, validation, test and k-Fold Cross-Validation methods. We also examined the ability of the above models to identify spatial dependencies, by calculating Moran's I for residuals obtained on in-sample and out-of-sample data.


Siwicki Dawid
spatial analysis machine learning housing market random forest gradient boosting

WP(15/2020)321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

Authors: Chlebus Marcin, Osika Zuzanna
The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to …

WP321. Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools

The research uses tree-based models to predict the success of telemarketing campaign of Portuguese bank. The Portuguese bank dataset was used in the past in different researches with different models to predict the success of campaign. We propose to use boosting algorithms, which have not been used before to predict the response for the campaign and to use Explainable AI (XAI) methods to evaluate model's performance. The paper tries to examine whether 1) complex boosting algorithms perform better and 2) XAI tools are better indicators of models' performance than commonly used discriminatory power's measures like AUC. Portuguese bank telemarketing dataset was used with five machine learning algorithms, namely Random Forest (RF), AdaBoost, GBM, XGBoost and CatBoost, which were then later compared based on their AUC and XAI tools analysis – Permutated Variable Importance and Partial Dependency Profile. Two best performing models based on their AUC were XGBoost and CatBoost, with XGBoost having slightly higher AUC. Then, these models were examined using PDP and VI, which resulted in discovery of XGBoost potenitial overfitting and choosing CatBoost over XGBoost. The results show that new boosting models perform better than older models and that XAI tools could be helpful with models' comparisons.


Chlebus Marcin, Osika Zuzanna
direct marketing telemarketing relationship marketing data mining machine learning random forest adaboost gbm catboost xgboost bank marketing XAI variable importance partial dependency profile
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