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(15/2023)422. Ensembled LSTM with Walk Forward Optimization in Algorithmic Trading

Authors: Chojnacki Karol, Ślepaczuk Robert
This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were …

WP422. Ensembled LSTM with Walk Forward Optimization in Algorithmic Trading

This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compared to Buy and Hold benchmark and evaluated using Performance Metrics, that is Annualized Return Compounded, Maximum Drawdown, Maximum Loss Duration, and three types of Information Ratio. This research uses daily S&P 500 index data ranging from 2000 to 2023. Every strategy was optimized with novel walk forward approach consisting of numerous in sample and out of sample periods. MAC and best performing ML methods were subjected to sensitivity analysis. The results show that LSTM ensembled with XGBoost and MAC yields the most promising results in terms of risk-adjusted returns which suggest further research focused on ensembling of individual ML strategies. Finally, we show that classical methods of technical analysis (that is, MAC) are much less robust and indifferent to change in hyperparameters than machine learning based algorithms, especially LSTM.


Chojnacki Karol, Ślepaczuk Robert
Algorithmic Investment Strategies Machine Learning Recurrent Neural Networks Long Short-Term Memory XGBoost Walk Forward Optimization Trading algorithms Technical Analysis Indicators

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(27/2020)333. Predicting prices of S&P500 index using classical methods and recurrent neural networks

Authors: Kijewski Mateusz, Ślepaczuk Robert
This study implements algorithmic investment strategies with buy/sell signals based on classical methods and recurrent neural network model (LSTM). The research compares the performance of investment algorithms on time series of S&P500 index cove…

WP333. Predicting prices of S&P500 index using classical methods and recurrent neural networks

This study implements algorithmic investment strategies with buy/sell signals based on classical methods and recurrent neural network model (LSTM). The research compares the performance of investment algorithms on time series of S&P500 index covering 20 years of data from 2000 to 2020. This paper presents an approach for dynamic optimization of parameters during backtesting process by using rolling training-testing window. Every method was tested in terms of robustness to changes in parameters and evaluated by appropriate performance statistics e.g. Information Ratio, Maximum Drawdown, etc. Combination of signals from different methods was stable and outperformed benchmark of Buy & Hold strategy doubling its returns on the same level of risk. Detailed sensitivity analysis revealed that classical methods which used rolling training-testing window were significantly more robust to changes in parameters than LSTM model in which hyperparameters were selected heuristically.


Kijewski Mateusz, Ślepaczuk Robert
machine learning recurrent neural networks long short-term memory model time series analysis algorithmic investment strategies systematic transactional systems technical analysis ARIMA model

WP(22/2020)328. Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem

Authors: Chlebus Marcin, Dyczko Michał, Woźniak Michał
The main aim of this paper was to predict daily stock returns of Nvidia Corporation company quoted on Nasdaq Stock Market. The most important problems in this research are: statistical specificity of return ratios i.e. time series might occur to be a…

WP328. Nvidia’s stock returns prediction using machine learning techniques for time series forecasting problem

The main aim of this paper was to predict daily stock returns of Nvidia Corporation company quoted on Nasdaq Stock Market. The most important problems in this research are: statistical specificity of return ratios i.e. time series might occur to be a white noise and the fact of necessity of applying many atypical machine learning methods to handle time factor influence. The period of study covered 07/2012 - 12/2018. Models used in this paper were: SVR, KNN, XGBoost, LightGBM, LSTM, ARIMA, ARIMAX. Features which, were used in models comes from such classes like: technical analysis, fundamental analysis, Google Trends entries, markets related to Nvidia. It was empirically proved that there is a possibility to construct prediction model of Nvidia daily return ratios which can outperform simple naive model. The best performance was obtained by SVR based on stationary attributes. Generally, it was shown that models based on stationary variables perform better than models based on stationary and non-stationary variables. Ensemble approach designed especially for time series failed to make an improvement in forecast precision. It seems that usage of machine learning models for the problem of time series with various explanatory variable classes brings good results.


Chlebus Marcin, Dyczko Michał, Woźniak Michał
nvidia stock returns machine learning technical analysis fundamental analysis google trends stationarity ensembling

WP(2/2019)287. Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Authors: Zenkova Maryna, Ślepaczuk Robert
This study investigates the profitability of a algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns ar…

WP287. Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

This study investigates the profitability of a algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or lowest quantile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. Portfolio is formed by ranking coins using SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1 which quantifies the risk-weighted gain. The question on how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.


Zenkova Maryna, Ślepaczuk Robert
machine learning support vector machines investment algorithm algorithmic trading strategy optimization cross-validation overfitting cryptocurrency market technical analysis meta parameters
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