A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

Panagiotidis, Theodore; Stengos, Thanasis; Vravosinos, Orestis

doi:10.3390/jrfm13020033

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…In order to identify the most important variables that determine the returns of the most traded cryptocurrencies, we apply principal component-guided sparse regression (PC- LASSO, Tay et al 2018) following Panagiotidis et al (2020). This is a new method for large data to shrink predictions toward the most important principle components of the feature matrix by combining LASSO sparsity penalty with a quadratic penalty.…”

Section: Pc-lassomentioning

confidence: 99%

“…We use a principal component-guided sparse regression (PC-LASSO) model of Tay et al (2018) for the identification of the most important variable. In the literature for cryptocurrencies, LASSO based models are employed by Panagiotidis et al (2018), observing that search intensity, gold returns and policy uncertainty are the most important variables for Bitcoin returns; and by Panagiotidis et al (2020), observing that policy uncertainty and stock market volatility are among the most important variables out of forty-one variables for the identification of Bitcoin returns using several subperiods of the overall period of 2010-2018. In this study, we use PC-LASSO to identify the most important variables regarding the returns of top five cryptocurrencies for the pre and post break point.…”

Section: Introductionmentioning

confidence: 99%

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Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

Deniz

Stengos

2020

JRFM

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This paper examines the behaviour of Bitcoin returns and those of several other cryptocurrencies in the pre and post period of the introduction of the Bitcoin futures market. We use the principal component-guided sparse regression (PC-LASSO) model to analyze several sample sizes for the pre and post periods. Besides the neighbourhood of the break time, the current period is also investigated as returns start to recover after some time. Search intensity is observed to be the most important variable for Bitcoin for all periods, whereas for the other cryptocurrencies there are other variables that seem more important in the pre period, while search intensity still stands out in the post period. Furthermore, GARCH analyses suggest that search intensity increases the volatility of Bitcoin returns more in the post period than it does in the pre period. Our empirical findings suggest that the top five cryptocurrencies are substitutes before the launch of Bitcoin futures. However, this effect is lost, and moreover, there are spillover effects on altcoins during both the post and the recovery period. We find a spillover effect of the introduction of bitcoin futures on altcoins and this effect seems to persist during the recovery period.

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Section: Pc-lassomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

Deniz

Stengos

2020

JRFM

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“…In [34,35] a continuous time model for Bitcoin price dynamics is studied to detect bubbles; regarding the existence of a bubble, in [36] it is proved it holds from early 2013 to mid-2014, but, not in late 2017 as supposed. Evidence of bubbly Bitcoin behaviour, mainly in the 2017-2018 period, is shown in [37], where it is also proved that economic policy uncertainty and stock market volatility play the most important role in Bitcoin values.…”

Section: Introductionmentioning

confidence: 94%

Bitcoin Analysis and Forecasting through Fuzzy Transform

Guerra

Sorini

Stefanini

2020

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Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

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“…According to Kristoufek (2014), the formation of Bitcoin prices cannot be explained by standard economic theories, such as the discounted cash flow model, purchasing power parity, or uncovered interest rate parity, as there are several features of foreign currency supply and demand. Therefore, the most extensive research was carried out on possible internal and external factors affecting the price of cryptocurrencies, particularly the most famous Bitcoin cryptocurrency (Panagiotidis et al 2019;Poyser 2017;Kavvadias 2017;Letra 2016;Hayes 2015;Kristoufek 2014;. Panagiotidis et al (2019) concluded that uncertainty in economic policy and stock market volatility are among the most important variables between 41 examined potential drivers of Bitcoin returns.…”

Section: Overview Of the Previous Researchmentioning

confidence: 99%

“…Therefore, the most extensive research was carried out on possible internal and external factors affecting the price of cryptocurrencies, particularly the most famous Bitcoin cryptocurrency (Panagiotidis et al 2019;Poyser 2017;Kavvadias 2017;Letra 2016;Hayes 2015;Kristoufek 2014;. Panagiotidis et al (2019) concluded that uncertainty in economic policy and stock market volatility are among the most important variables between 41 examined potential drivers of Bitcoin returns. Internal and external factors affecting the Bitcoin price were defined by Poyser (2017) in his research.…”

Section: Overview Of the Previous Researchmentioning

confidence: 99%

The Impact of Unsystematic Factors on Bitcoin Value

Merkaš

Roška

2021

JRFM

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The results of empirical analyses confirm that analysed unsystematic factors, the Stock-to-Flow index (S2F), and information on the Bitcoin (BTC) are directly correlated with BTC values. These results are expected and in line with the economic theory; however, this research paper aimed to investigate the impact of unsystematic factors on the value of decentralised virtual cryptocurrency BTC. Its aim was also to analyse the reasons for significant oscillations of market values in relation to the S2F and S2FX model and thus confirm the reliability of these models in the estimation of BTC value. The research further confirms the strong influence of non-technical information directly linked with the BTC. The limitations of this paper are the lack of possibilities for examining the impact of non-technical information affecting the Bitcoin price deviation regarding the S2F model. In addition to all mentioned limitations, the research results indicate the relevance of the S2F and S2FX models and show a strong impact of (half) the information on the value of cryptocurrencies.

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A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns

Cited by 15 publications

References 18 publications

Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

Cryptocurrency Returns before and after the Introduction of Bitcoin Futures

Bitcoin Analysis and Forecasting through Fuzzy Transform

The Impact of Unsystematic Factors on Bitcoin Value

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