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.

Please send your paper by e-mail: =kU.g06y]|[uG{Z9S+hEM}B\X&R]#[2[HtQyxj?mAh78R'55^9Vc}Rat? 

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 3

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

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

Authors: 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

Authors: 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
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