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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Please send 2 files:

(1) the main text without the title of the article and the authors (DOC/DOCX file) and

(2) the title page including: the title of the paper, the authors and their affiliation (DOC/DOCX file).

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Number of Results 9

WP(14/2025)477. Can Artificial Intelligence Trade the Stock Market?

Authors: Jędrzej Maskiewicz, Sakowski Paweł
The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these…

WP477. Can Artificial Intelligence Trade the Stock Market?

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.


DOI: https://doi.org/10.33138/2957-0506.2025.14.477
Jędrzej Maskiewicz, Sakowski Paweł
Reinforcement Learning Deep Learning stock market algorithmic trading Double Deep Q-Network Proximal Policy Optimization

WP(10/2024)446. Predictive modeling of foreign exchange trading signals using machine learning techniques

Authors: Sugarbayar Enkhbayar, Ślepaczuk Robert
This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other s…

WP446. Predictive modeling of foreign exchange trading signals using machine learning techniques

This study aimed to apply the algorithmic trading strategy on major foreign exchange pairs and compare the performances of machine learning-based strategies and traditional trend-following strategies with benchmark strategies. It differs from other studies in that it considered a wide variety of cases including different foreign exchange pairs, return methods, data frequency, and individual and integrated trading strategies. Ridge regression, KNN, RF, XGBoost, GBDT, ANN, LSTM, and GRU models were used for the machine learning-based strategy, while the MA cross strategy was employed for the trend-following strategy. Backtests were performed on 6 major pairs in the period from January 1, 2000, to June 30, 2023, and daily, and intraday data were used. The Sharpe ratio was considered as a metric used to refer to economic significance, and the independent t-test was used to determine statistical significance. The general findings of the study suggested that the currency market has become more efficient. The rise in efficiency is probably caused by the fact that more algorithms are being used in this market, and information spreads much faster. Instead of finding one trading strategy that works well on all major foreign exchange pairs, our study showed it’s possible to find an effective algorithmic trading strategy that generates a more effective trading signal in each specific case.


DOI: https://doi.org/10.33138/2957-0506.2024.10.446
Sugarbayar Enkhbayar, Ślepaczuk Robert
machine learning algorithmic trading foreign exchange market rolling walk-forward optimization technical indicators

WP(27/2023)434. Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models

Authors: Teymurzade Sahil, Ślepaczuk Robert
This paper implements automated trading strategies with buy/sell signals based on Autoregressive Integrated Moving Average (ARIMA) and Vector autoregression (VAR) models. ARIMA and VAR models are compared based on several forecast error measures and …

WP434. Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models

This paper implements automated trading strategies with buy/sell signals based on Autoregressive Integrated Moving Average (ARIMA) and Vector autoregression (VAR) models. ARIMA and VAR models are compared based on several forecast error measures and investment performance statistics. The data used in this thesis are daily closing prices of Dow Jones Industrial Average, NASDAQ Composite and NYSE Composite indices. The trading period covers 20 years of data from 2000-11-30 to 2020-11-30. The sensitivity analysis is made by changing the initial parameters to test how robust the methods are to these changes. Results show that although ARIMA model performed remarkably well during the volatile periods, VAR based strategy had better investment performance and was less robust to the changes compared to the ARIMA based strategy. Additionally, we have found that error metrics might be insufficient to evaluate performance of forecasting models, as VAR with higher forecast errors outperformed ARIMA model in algorithmic trading strategies.


Teymurzade Sahil, Ślepaczuk Robert
ARIMA model VAR model time series analysis algorithmic trading strategies investment systems statistical models forecasting stock prices

WP(25/2022)401. Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index

Authors: Kryńska Katarzyna, Ślepaczuk Robert
This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four…

WP401. Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index

This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance. 


Kryńska Katarzyna, Ślepaczuk Robert
machine learning deep learning recurrent neural networks LSTM algorithmic trading ensemble investment strategy intra-day trading S&P 500 Index Bitcoin

WP(2/2022)378. The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods

Authors: Baiquan Ma, Ślepaczuk Robert
This research aims to compare the profitability of correlation-based pair trading strategy, cointegration-based pair trading strategy, and distance-based pair trading strategy on the Hong Kong stock market. We try to build an effective pair trading s…

WP378. The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods

This research aims to compare the profitability of correlation-based pair trading strategy, cointegration-based pair trading strategy, and distance-based pair trading strategy on the Hong Kong stock market. We try to build an effective pair trading strategy based on 50 stocks listed in the Hang Seng index joining them in market-neutral pairs. The dataset has a daily frequency and covers the period from 07/01/2013 to 07/01/2020. The result shows that all three methods are profitable in the Hong Kong stock market and can beat the market with regard to risk-adjusted return metrics. This result is quite sensitive to the varying number of pairs traded and rebalancing period and less sensitive to financial leverage degree. Moreover, the cointegration method is superior as compared to the correlation method and distance method.


Baiquan Ma, Ślepaczuk Robert
pairs trading strategy market neutral strategy algorithmic trading strategy distance method cointegration and correlation trading efficient market hypothesis investment portfolio algorithmic trading Hang Seng index

WP(27/2021)375. Robust optimisation in algorithmic investment strategies

Authors: Gómez Castellano Sergio, Ślepaczuk Robert
This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 19…

WP375. Robust optimisation in algorithmic investment strategies

This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 1990-01-01 to 2021-04-23. Each strategy is based on a different theory and aims to perform well in different market regimes. The objective is to have a set of uncorrelated investment strategies based on different logics such as trend-following, contrarian approach, statistical methods, and macro-economic news. Each strategy was individually generated following a personalized Walk-Forward optimisation, in which the model seeks to choose the most robust combination of parameters rather than the best one, in terms of risk-adjusted returns. The robustness of all strategies was tested by changing all parameters selected at the beginning of the optimisation. Additionally, the robustness of the portfolio of strategies is tested by applying it to another American index, Nasdaq Composite. Finally, the ensemble model was created based on the combination of the signals from all investment strategies for our two basis instruments. Results show that the portfolio obtains returns four (seven) times larger than the Buy & Hold strategy on S&P 500 (Nasdaq Composite) with a similar level of risk in the last 31 years.


Gómez Castellano Sergio, Ślepaczuk Robert
algorithmic trading strategies robust optimisation criteria overoptimisation walk-forward optimisation ensemble investment model

WP(39/2020)345. Applying Hurst Exponent in Pair Trading Strategies

Authors: Bui Quynh, Ślepaczuk Robert
This research aims to seek an alternative approach to pair selection for the purpose of pair trading strategy. We try to build an effective pair trading strategy based on 103 stocks listed in NASDAQ-100 index. The dataset has daily frequency and cove…

WP345. Applying Hurst Exponent in Pair Trading Strategies

This research aims to seek an alternative approach to pair selection for the purpose of pair trading strategy. We try to build an effective pair trading strategy based on 103 stocks listed in NASDAQ-100 index. The dataset has daily frequency and covers the period from 01/01/2000 to 31/12/2018. In this study, Generalized Hurst Exponent, Correlation and Cointegration are employed to detect the mean-reverting pattern in the time series of linear combination of each pair of stock. The result show that Hurst method cannot outperform the benchmark, which implies that market is efficient. This result is quite sensitive to varying number of pairs traded and rebalancing period and less sensitive to financial leverage degree. Moreover, Hurst method is better than cointegration method but is not superior as compared to correlation method.


Bui Quynh, Ślepaczuk Robert
generalized Hurst & Hurst exponent algorithmic trading strategies portfolio choice mean-reversion strategy pair trading correlation & cointegration trading efficient market hypothesis

WP(17/2019)302. Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach

Authors: Korzeń Kamil, Ślepaczuk Robert
The purpose of this research is to test the potential returns and robustness of an automated investment strategy. The strategy is based on momentum and macroeconomic approach, that consists of the technical core – momentum, and the additional m…

WP302. Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach

The purpose of this research is to test the potential returns and robustness of an automated investment strategy. The strategy is based on momentum and macroeconomic approach, that consists of the technical core – momentum, and the additional macro screening, which is used to determine whether investment signals generate relevant investment opportunities or just technical noise. In order to check whether the macroeconomic factor is the value added to the momentum strategy, the hybrid approach is tested and compared with the simple momentum and the macroeconomic strategy alone and then assessed on a risk-adjusted return basis. The main aim of this paper is to answer the question, whether an investor can gain surplus risk-adjusted returns from merging short-term momentum strategy with the long-term macroeconomic approach. Strategies are based on the data for the selected companies from the S&P500 index in the period ranging from 02/01/1990 to 31/12/2018.


Korzeń Kamil, Ślepaczuk Robert
investment strategy momentum macroeconomic indicators algorithmic trading risk adjusted returns

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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