Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: ]XdWu/M?EflU[VSnoa9IN'+\7FP]#[UHWC_x1+}WRHOmK]Uj/AWhkRD39

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 5

WP(7/2024)443. LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

Autorzy: Kashif Kamil, Ślepaczuk Robert
This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by…

WP443. LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies

This study focuses on building an algorithmic investment strategy employing a hybrid approach that combines LSTM and ARIMA models referred to as LSTM-ARIMA. This unique algorithm uses LSTM to produce final predictions but boost results of this RNN by adding the residuals obtained from ARIMA predictions among other inputs. The algorithm is tested across three equity indices (S&P 500, FTSE 100, and CAC 40) using daily frequency data spanning from January, 2000 to August, 2023. The architecture of testing is based on the walk-forward procedure which is applied for hyperparameter tunning phase that uses using Random Search and backtesting the algorithms. The selection of the optimal model is determined based on adequately selected performance metrics combining focused on risk-adjusted return measures. We considered two strategies for each algorithm: Long-Only and Long-Short in order to present situation of two various groups of investors with different investment policy restrictions. For each strategy and equity index, we compute the performance metrics and visualize the equity curve to identify the best strategy with the highest modified information ratio. The findings conclude that the LSTM-ARIMA algorithm outperforms all the other algorithms across all the equity indices what confirms strong potential behind hybrid ML-TS (machine learning - time series) models in searching for the optimal algorithmic investment strategies.


DOI: https://doi.org/10.33138/2957-0506.2024.7.443
Kashif Kamil, Ślepaczuk Robert
Deep Learning Recurrent Neural Networks Algorithmic Investment Strategy LSTM ARIMA Hybrid/Ensemble Models Walk-Forward Process

WP(3/2024)439. Supervised Autoencoder MLP for Financial Time Series Forecasting

Autorzy: Bieganowski Bartosz, Ślepaczuk Robert
This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation…

WP439. Supervised Autoencoder MLP for Financial Time Series Forecasting

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.


Bieganowski Bartosz, Ślepaczuk Robert
machine learning algorithmic investment strategy supervised autoencoders financial time series trading strategy risk-adjusted return

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

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

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

Autorzy: 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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