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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(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 5

WP(27/2024)463. Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

Authors: Filip Stefaniuk, Ślepaczuk Robert
The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Me…

WP463. Informer in Algorithmic Investment Strategies on High Frequency Bitcoin Data

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.


DOI: https://doi.org/10.33138/2957-0506.2024.27.463
Filip Stefaniuk, Ślepaczuk Robert
Machine Learning Financial Series Forecasting Automated Trading Strategy Informer Transformer Bitcoin High Frequency Trading Statistics GMADL

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(42/2020)348. Does Bitcoin Improve Investment Portfolio Efficiency?

Authors: Sakowski Paweł, Turovtseva Daria
The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return ma…

WP348. Does Bitcoin Improve Investment Portfolio Efficiency?

The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.


Sakowski Paweł, Turovtseva Daria
Portfolio optimization portfolio theory cryptocurrency Bitcoin Markowitz model asset allocation portfolio diversification investment opportunities

WP(17/2018)276. Are demand shocks in Bitcoin contagious?

Authors: Zięba Damian, Śledziewska Katarzyna
The main aim of this paper is to examine interdependencies between prices of cryptocurrencies, with the special focus on Bitcoin. The analysis is conducted in two stages and results are compared between two consequent sub-periods. In order to analyze…

WP276. Are demand shocks in Bitcoin contagious?

The main aim of this paper is to examine interdependencies between prices of cryptocurrencies, with the special focus on Bitcoin. The analysis is conducted in two stages and results are compared between two consequent sub-periods. In order to analyze topological properties of cryptocurrency market, the Minimum-Spanning Tree technique is used. Results indicate that Bitcoin plays one of the most important roles in the cryptocurrency market, while other cryptocurrencies form clusters and such forming has a sensible economic interpretation. In the second stage, main cryptocurrencies from each of formed clusters are analyzed using the Vector Autoregression methodology. The results from VAR (1) indicate that demand shocks in Bitcoin price are not contagious to other cryptocurrencies, while some interdependencies within the formed clusters may be observed. Overall, results indicate that conclusions drawn from the analysis of Bitcoin shall not be generalized to the entire cryptocurrency market.


DOI: https://doi.org.10.26405/WP/WNE/2018/276/017
Zięba Damian, Śledziewska Katarzyna
cryptocurrency market Bitcoin Minimum-Spanning Tree Vector Autoregression

WP(9/2018)268. Momentum and contrarian effects on the cryptocurrency market

Authors: Krzysztof Kość, Sakowski Paweł, Ślepaczuk Robert
We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap a…

WP268. Momentum and contrarian effects on the cryptocurrency market

We report the results of investigation of the momentum and contrarian effects on cryptocurrency markets. The investigated investment strategies involve 100 (amongst over 1200 present as of date Nov 2017) cryptocurrencies with the largest market cap and average 14-day daily volume exceeding a given threshold value. Investment portfolios are constructed using different assumptions regarding the portfolio reallocation period, width of the ranking window, the number of cryptocurrencies in the portfolio, and the percent transaction costs. The performance is benchmarked against: (1) equally weighted and (2) market-cap weighted investments in all of the ranked assets, as well as against the buy and hold strategies based on (3) S&P500 index, and (4) BTCUSD price. Our results show a clear and significant dominance of the short-term contrarian effect over both momentum effect and the benchmark portfolios. The information ratio coefficient for the contrarian strategies often exceeds two-digit values depending on the assumed reallocation period and the width of the ranking window. Additionally, we observe a very significant diversification potential for all cryptocurrency portfolios with relation to the S&P500 index.


DOI: http://doi.org.10.26405/WP/WNE/2018/268/009
Krzysztof Kość, Sakowski Paweł, Ślepaczuk Robert
cryptocurrencies bitcoin blockchain momentum effect contrarian effect investment strategy efficiency of financial markets new asset class asset allocation
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