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: 2V$O!%M`|Q0#YkKu4j/*Hr-'b7?]#[*Fs7ip1Q^BpxM'Cdts!|QXlxk''

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 3

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

Autorzy: 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

WP(25/2018)284. Machine learning in algorithmic trading strategy optimization - implementation and efficiency

Autorzy: Ryś Przemysław, Ślepaczuk Robert
The main aim of this paper was to formulate and analyze the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes an…

WP284. Machine learning in algorithmic trading strategy optimization - implementation and efficiency

The main aim of this paper was to formulate and analyze the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of a strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The methods operated on the in sample data, containing 20 years of daily prices between 1998 and 2017. The problem was presented for three sets of two assets portfolios. In the first case, a strategy was trading on the SPX and DAX index futures, in the second on the AAPL and MSFT stocks and finally, in the third case on the HGF and CBF commodities futures. The major hypothesis verified in this thesis is that machine learning methods select strategies with evaluation criterion near to the highest one, but in significantly lower execution time than the Exhaustive Search.


DOI: http://doi.org.10.26405/WP/WNE/2018/284/025
Ryś Przemysław, Ślepaczuk Robert
machine learning algorithm trading investment automatization strategy optimization differential evolutionary method cross-validation overfitting

WP(39/2015)187. Cross-Sectional Returns With Volatility Regimes From Diverse Portfolio of Emerging and Developed Equity Indices

Autorzy: Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
This article aims to extend evaluation of classic multifactor model of Carhart (1997) for the case of global equity indices and to expand analysis performed in Sakowski et. al.(2015). Our intention is to test several modifications of these models to …

WP187. Cross-Sectional Returns With Volatility Regimes From Diverse Portfolio of Emerging and Developed Equity Indices

This article aims to extend evaluation of classic multifactor model of Carhart (1997) for the case of global equity indices and to expand analysis performed in Sakowski et. al.(2015). Our intention is to test several modifications of these models to take into account different dynamics of equity excess returns between emerging and developed equity indices. Proposed extensions include volatility regime switching mechanism (using dummy variables and the Markov approach) and the fifth risk factor based on realized volatility of index returns. Moreover, instead of using data for stocks of a particular market (which is a common approach in the literature), we check performance of these models for weekly data of 81 world investable equity indices in the period of 2000-2015. Such approach is proposed to estimate equity risk premium for a single country. Empirical evidence reveals important differences between results for classical models estimated on single stocks (either in international or US-only framework) and models evaluated for equity indices. Additionally, we observe substantial discrepancies between results for developed countries and emerging markets. Finally, using weekly data for the last 15 years we illustrate importance of model risk and data overfitting effects when drawing conclusions upon results of multifactor models.


Sakowski Paweł, Ślepaczuk Robert, Wywiał Mateusz
cross-sectional models asset pricing models equity risk premia equity indices new risk factors sensitivity analysis book to market momentum market price of risk emerging and developed equity indices data overfitting model risk
  • 1