The effectiveness of technical trading rules in cryptocurrency markets

Corbet, Shaen; Eraslan, Veysel; Lucey, Brian M.; Şensoy, Ahmet

doi:10.1016/j.frl.2019.04.027

Cited by 103 publications

(26 citation statements)

References 30 publications

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“…As reported by Cagli (2019), cryptocurrency literature began with an understanding of blockchain technology with studies such as Böhme et al (2015) and Sadeghi (2013). Later, the literature expanded in multiple directions including market efficiency (Gozgor et al , 2019; Hu et al , 2019; Sensoy, 2019; Al-Yahyaee et al , 2018; Vidal-Tomás and Ibañez, 2018; Bariviera, 2017; Jiang et al , 2018; Nadarajah and Chu, 2017; Urquhart, 2016); price behavior, volatility and their determinants (Aalborg et al , 2019; Akyildirim et al , 2019; Baumöhl, 2019; Bouri et al , 2019a; Charfeddine and Maouchi, 2019; Matkovskyy and Jalan, 2019; Phillip et al , 2019; Demir et al , 2018; Panagiotidis et al , 2018; Balcilar et al , 2017); portfolio implications, risk management, and hedging (Bouri et al , 2019b; Kurka, 2019; Wang et al , 2019; Wu et al , 2019; Corbet et al , 2018; Bouri et al , 2017; Dyhrberg, 2016) price discovery (Shen et al , 2019; Ciaian et al , 2015; Brandvold et al , 2015) investor behavior and sentiment (Aloosh and Ouzan, 2019; Baig et al , 2019; Ballis and Drakos, 2019; Kaiser and Stöckl, 2019; Ayvaz and Shiha, 2018) and finally applicability of technical trading (Corbet et al , 2019) and alternative trading strategies (Leung and Nguyen, 2019).…”

Section: Introductionmentioning

confidence: 99%

One size does not fit all: external driver of the cryptocurrency world

Erzurumlu¹,

Oygur²,

Kirik³

2020

SEF

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Purpose Considering the different motivation for the creation of each of these cryptocurrencies, the purpose of this paper is to examine whether there is a dominant external factor in the cryptocurrency world. Using a novel two-step time and frequency independent methodology, the authors examine a large scope of cryptocurrencies and external factors within the same period, and analytical framework. Design/methodology/approach The examined cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Monero and Dash. In total, 18 external factors from 5 factor families are selected based on the mining motivation of these cryptocurrencies. The study first examines discrete wavelet transform-based (WTB) correlations, reduce the dimension and focuson relevant pairs. Selected pairs are further examined by wavelet coherence to capture the intermittent nature of the relationships allowing the most needed “Flexibility of frequency and time domains”. Findings Each coin appears to operate as a unique character with the exception of Bitcoin and Litecoin. There is no prominent external driver. The cryptocurrency market is not a clear substitute for a specific factor or market. Two-step WTB filtered wavelet coherence analysis help us to analyze a large number of factor without the loss of focus. The co-movements within the cryptocurrencies spillover from Ethereum to altcoins and later to Bitcoin. Originality/value The study presents one of the first examples of two-step WTB filtered wavelet coherence analysis. The methodology suggests an approach for simultaneous examination of large number of variables. The scope of the study provides a rather holistic view of the co-movements of external factors and major cryptocurrencies.

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Section: Introductionmentioning

confidence: 99%

One size does not fit all: external driver of the cryptocurrency world

Erzurumlu¹,

Oygur²,

Kirik³

2020

SEF

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“…Furthermore, [48] find that BITCOIN has predictive power over other assets such as the SP500. Related to this, [36] show that BITCOIN spot volatility increased following the launch of BITCOIN futures contracts traded on the Chicago Board of Options Exchange (CBOE) and the Chicago Mercantile Exchange (CME). Similarly, [49] show a dynamic linkage of cryptocurrencies and BITCOIN.…”

Section: Dominance Effect and Cryptocurrency Predictabilitymentioning

confidence: 97%

“…However, [33] found the opposite when verifying that ARIMA improves forecasts compared with random forest and support vector machines. The forecasting literature also includes works evaluating other methods such as deep neural networks, long-term memory [34]; nonparametric regression methods [35]; variable-length moving average rule models [36], hybrid neuro-fuzzy controller [37]; adaptive multilevel time-series detection methodology [38]; nonparametric causality-in-quantiles test [39]; and technical analysis to predict BITCOIN [17].…”

Section: Cryptocurrency Predictabilitymentioning

confidence: 99%

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

Magner

Hardy

2022

Mathematics

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This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of Bitcoin, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and Bitcoin (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.

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“…Our focus is solely on blockchain adoption in banking and not cryptocurrencies. These literature focus on many cryptocurrency aspects such as price bubbles (Cheah and Fry 2015;Fry and Cheah 2016;Cheung et al 2015;Corbet et al 2018a;Gandal et al 2018;Bianchetti et al 2018;Chaim and Laurini 2019;Geuder et al 2019;Kallinterakis and Wang 2019;Sifat et al 2019;Xiong et al 2019;Shu and Zhu 2020), Bitcoin price determinants and characteristics (Akyildirim et al 2020;Ammous 2018;Beneki et al 2019;Bianchetti et al 2018;Bouoiyour et al 2016;Cagli 2019;Caporale et al 2018;De Sousa and Pinto 2019;Dwyer 2015;Corbet et al 2018b;Corbet et al 2019aCorbet et al , 2019bCorbet et al , 2019cFlori 2019;Corbet et al 2020aCorbet et al , 2020bHandika et al 2019;Hayes 2019;Fry 2018;Mensi et al 2019;Ma and Tanizaki 2019;Nadler and Guo 2020;Nguyen et al 2019aNguyen et al , 2019bPanagiotidis et al 2018;Phillips and Gorse 2018;Puljiz et al 2018;Pyo a...…”

Section: Buterinmentioning

confidence: 99%

Fantastic Beasts: Blockchain Based Banking

Daluwathumullagamage

Sims

2021

JRFM

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Blockchain is one of the primary digital technologies utilised in the finance industry with huge future potential. This study conducts a systematic literature review of a final sample of 407 prior literature from an initial set of 1979 records for the sample period of 2013–2020 with regard to blockchain adoption in banking. This review is further supplemented by a machine learning based textual analysis that identifies key themes, trends, divergences and gaps between academic and practitioner led industry literature. Moreover, the study highlights present, future use cases, adoption barriers and misconceptions of blockchains in banking, especially given COVID-19. Furthermore, this study identifies behavioural, social, economic, regulatory and managerial implications of blockchain based banking. In addition, our study identifies the cross-industry potential of blockchains via banking, thus, linking much disconnected prior literature. Finally, we develop a blockchain adoption framework and an adoption life cycle for banking. This study would be of interest to academics, bankers, regulators, investors, auditors and other stakeholders in financial markets.

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The effectiveness of technical trading rules in cryptocurrency markets

Cited by 103 publications

References 30 publications

One size does not fit all: external driver of the cryptocurrency world

One size does not fit all: external driver of the cryptocurrency world

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

Fantastic Beasts: Blockchain Based Banking

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