Technical trading rules in the cryptocurrency market

Grobys, Klaus; Ahmed, Shaker; Sapkota, Niranjan

doi:10.1016/j.frl.2019.101396

Cited by 65 publications

(40 citation statements)

References 27 publications

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“…We notice that the 26-week rolling window predictive model improves the forecast accuracy for all five cryptocurrencies and that these results are statistically significant at the 1% level according to the MSPE-adjusted statistics (Rapach et al, 2010). This suggests that the lagged return information contains future return information and is consistent with the finding of Grobys et al (2020). Interestingly, when we extend the rolling window size, the predictive power decreases.…”

Section: Results Of Statistical Measuressupporting

confidence: 70%

“…Negative bubbles in cryptocurrency markets are also observed in certain periods (Fry and Cheah, 2016). These market features imply that considering past price information is beneficial for investors, as reported by Grosby, Ahmed, and Sapkota (2020), and 4 Shen, Urquhart, and Wang (2019) and Philippas, Philippas, Tziogkidis, and Rjiba (2020) find that media attention is a driving force a change in cryptocurrency prices. In contrast, some studies present that macro fundamentals such as money supply are important in the long-run (Kristoufek, 2015;Bouoiyour, Selmi, Tiwari, and Olayeni, 2016;Li and Wang, 2017).…”

Section: Introductionmentioning

confidence: 77%

“…This is because our evaluation approach needs to reflect a change in market states. Furthermore, Grobys et al (2020) report that the effectiveness of moving average technical indicators.…”

Section: Results Of Statistical Measuresmentioning

confidence: 99%

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Economic Evaluation of Cryptocurrency Investment

Sakemoto

2020

SSRN Journal

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This study proposes a method to enhance cryptocurrency portfolios constructed by forecast models. This study forecasts returns on four liquid cryptocurrencies (Bitcoin, Litecoin, Ripple, and Dash) and determines the weights on the cryptocurrencies based upon a dynamic allocation framework. We assess the performances of the portfolios using the performance fee measure.Our results present that the proposed portfolios outperform the benchmark portfolio with the conventional level of the risk aversion parameter. The economic gain for an investor is equivalent to 12% per week. The economic gain is sensitive to a change in the risk aversion parameter, which contrasts with the studies of exchange rates which is due to the high volatility on the cryptocurrencies. Our predictors are related to the price momentum effects and they outperform widely used network factors.

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Section: Results Of Statistical Measuressupporting

confidence: 70%

Section: Introductionmentioning

confidence: 77%

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Economic Evaluation of Cryptocurrency Investment

Sakemoto

2020

SSRN Journal

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“…Even simple technical trading rules such as moving average or trading range breakout are frequently found to outperform buy-andhold benchmarks, producing excess returns of the order of 8.76% p.a. along with lower volatility than buy-and-hold [41], [42], [43]. Machine learning techniques such as gradient boosting trees or long-short term memory networks, often augmented with the use of social media data in particular, were likewise found to produce significant excess returns [44], [45], [46], [47].…”

Section: Literature Reviewmentioning

confidence: 99%

Pairs Trading in Cryptocurrency Markets

Fil

Krištoufek

2020

IEEE Access

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Pairs trading is a strategy based on exploiting mean reversion in prices of securities. Even though these strategies have been shown to perform well for equities, their performance is unknown for the field of cryptocurrencies, usually perceived as inefficient and predictable. We apply the distance and cointegration methods to a basket of 26 liquid cryptocurrencies traded on the Binance exchange, specifically at 5-minute, 1-hour and daily frequencies. In our backtests, the strategies underperform classical benchmarks. However, the results are quite sensitive to parameter settings and external factors such as transaction costs or execution windows. Higher-frequency trading delivers significantly better performance, and while the most common daily distance method returns-0.07% monthly, this increases to 11.61% monthly for 5-minute frequency. Additionally, we find evidence of simple mean-reverting behavior in intraday prices that is missing in daily data, and which provides further support for the inefficiency of cryptocurrency markets. INDEX TERMS cryptocurrencies, intraday trading, pairs trading

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“…Bitcoin seems to be the most significant systemic risk receiver, and Ethereum the largest systemic risk emitter. Like Bouri did, Grobys also developed a study in (Grobys et al 2019) based on the daily data of the price of cryptocurrencies. This time, their processing involved determining the moving average trading strategies employ.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cryptocurrency Spectrum Short Analysis

Şcheau

Crăciunescu

Brici

et al. 2020

JRFM

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Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies.

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Technical trading rules in the cryptocurrency market

Cited by 65 publications

References 27 publications

Economic Evaluation of Cryptocurrency Investment

Economic Evaluation of Cryptocurrency Investment

Pairs Trading in Cryptocurrency Markets

A Cryptocurrency Spectrum Short Analysis

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