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Wyświetlanie 1-2 z 2
Tytuł:
Is Bitcoin an emerging market? A market efficiency perspective
Autorzy:
Skwarek, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/22443141.pdf
Data publikacji:
2023-09-04
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
long-range dependence
bitcoin
market efficiency
emerging stock markets
Hurst exponent
Opis:
Despite recent studies focused on comparing the dynamics of market efficiency between Bitcoin and other traditional assets, there is a lack of knowledge about whether Bitcoin and emerging markets efficiency behave similarly. This paper aims to compare the market efficiency dynamics between Bitcoin and the emerging stock markets. In particular, this study indicates whether the dynamics of Bitcoin market efficiency mimic those of emerging stock markets. Thus, the paper’s contribution emerges from the combination of Bitcoin and emerging markets in the field of dynamics of market efficiency. The dynamics of market efficiency are measured using the Hurst exponent in the rolling window. The study uses daily data for the MSCI Emerging Markets Index and the Bitcoin market over the period 2011–2022. Our results show that there is at most a moderate correlation between the dynamics of Bitcoin and emerging stock markets’ efficiency over the entire study period. The strongest correlations occur mainly in periods of high economic policy uncertainty in the largest Bitcoin mining countries. Therefore, the association between Bitcoin market efficiency and emerging stock markets’ efficiency may strengthen with an increase in economic policy uncertainty. These findings may be useful for investors and portfolio managers in constructing better investment strategies.
Źródło:
Central European Economic Journal; 2023, 10, 57; 219-236
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification of Open-End Investment Funds Using Artificial Neural Networks. The Case of Polish Equity Funds
Autorzy:
Perez, Katarzyna
Szczyt, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/1964848.pdf
Data publikacji:
2021-11-07
Wydawca:
Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Tematy:
open-end investment fund classification
equity funds
artificial neural networks
emerging market
Opis:
In this study we utilise artificial neural networks to classify equity investment funds according to two fundamental risk measures—standard deviation and beta ratio—and to investigate the fund characteristics essential to this classification. Based on a sample of 4,645 monthly observations on 37 equity funds from the largest fund families registered in Poland from December 1995 to March 2018, we allocated funds to one of the classes generated using Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The results of the study confirm the legitimacy of using machine learning as a tool for classifying equity investment funds, though standard deviation turned out to be a better classifier than the beta ratio. In addition to the level of investment risk, the fund classification can be supported by the fund distribution channel, the fund name, age, and size, as well as the current economic situation. We find historical returns (apart from the last-month return) and the net cash flows of the fund to be insignificant for the fund classification.
Źródło:
Central European Economic Journal; 2021, 8, 55; 269-284
2543-6821
Pojawia się w:
Central European Economic Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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