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Wyświetlanie 1-2 z 2
Tytuł:
Determinants of foreign direct investment from EU-15 Countries in Poland
Autorzy:
Cieślik, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/1356753.pdf
Data publikacji:
2019-12-23
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
factor endowments
foreign direct investment
EU-15 member states
panel data analysis
Polska
Opis:
During the last two decades, Poland has become a large recipient of inward foreign direct investment (FDI). This article uses standard panel data techniques to study empirically the determinants of inward FDI in Poland during the period 1996–2015 made by multinational enterprises coming from the old European Union (EU)-15 member states. The estimated specification is derived from the knowledge-capital (KC) model and includes two types of capital: human and physical. The assembled empirical evidence points to the horizontal motive as the primary reason for undertaking FDI in Poland by multinational firms based in the old EU-15 member states. Moreover, the KC model does not seem to explain better the pattern of inward FDI in Poland compared to the standard ad hoc gravity model of international capital mobility.
Źródło:
Central European Economic Journal; 2019, 6, 53; 39 - 52
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market
Autorzy:
Ślepaczuk, Robert
Zenkova, Maryna
Powiązania:
https://bibliotekanauki.pl/articles/1356913.pdf
Data publikacji:
2019-08-07
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
Machine learning
support vector machines
investment algorithm
algorithmic trading
strategy
optimization
cross-validation
overfitting
cryptocurrency market
technical analysis
meta parameters
Opis:
This study investigates the profitability of an 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 the lowest quintile. 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. The portfolio is formed by ranking coins using the 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 of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.
Źródło:
Central European Economic Journal; 2018, 5, 52; 186 - 205
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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