Blockchain Papers

Nguyễn Đình Thuân, Nguyen Minh Nhut, Hoàng Đỗ Thanh Tùng, Vu Minh Sang

No abstract is available for this record.

Martin Pellon Consunji

Due to the rise in popularity of Bitcoin as both a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools to gain an advantage over the market. Although traditionally trading was done by professionals, nowadays a majority of market participants are market-data processing bots due to their inherent advantages in processing large amounts of data, lack of emotions of fear or greed, and predicting market prices through artificial intelligence. A large number of approaches have been brought forward to tackle this task, many of which rely on specially engineered deep learning methods with a focus on specific market conditions. The general limitation of these approaches, however, is the reliance on customized gradient-based methods which limit the scope of possible solutions and don't necessarily generalize well when solving similar problems. This paper proposes a method which uses neuroevolutionary techniques capable of automatically customizing offspring neural networks, generating entire populations of solutions and more thoroughly exploring and parallelizing potential solutions. Our approach uses evolutionary algorithms to evolve increasingly improved populations of neural networks which, based on sentimental and technical analysis data, efficiently predict future market price movements. The effectiveness of this approach is validated by testing the system on both live and historical trading scenarios, and its robustness is tested on other cryptocurrency and stock markets. Experimental results during a 30-day live-trading period show that this method outperformed the buy and hold strategy by over 260%, even while factoring in standard trading fees.

Rasa Bruzgė, Alfreda Šapkauskienė

Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzgė and Šapkauskienė [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.

Nuruddeen Usman, Kodili Nwanneka Nduka

This study uses a fractional integration method to evaluate the efficiency of cryptocurrencies before and after the period COVID-19 had been announced as being a pandemic. Evidence of long memory is confirmed across all subsamples. Additionally, we find a greater degree of persistence during the COVID-19 pandemic period than in the pre-pandemic period.

Shouyu Yao, Xiaoran Kong, Ahmet Şensoy, Erdinç Akyıldırım · 5 authors

We explore the impact of investor attention on idiosyncratic risk in the cryptocurrency markets. Taking the Google Trends Index as the measure of investor attention, we find that investor attention can significantly reduce cryptocurrencies’ idiosyncratic risks by increasing the liquidity. We further study possible cross-sectional variations of the effect of investor attention on idiosyncratic risk. Evidence shows that the investor attention effect is more pronounced for smaller-cap and younger cryptocurrencies. Moreover, a relatively stable external market environment and rising market state are conducive to the further play of the attention effect.

Andreas Hackethal, Tobin Hanspal, Dominique Marcel Lammer, Kevin Rink

Abstract Cryptocurrencies have received growing attention from individuals, the media, and regulators. However, little is known about the investors whom these financial instruments attract. Using administrative data, we describe the investment behavior of individuals who invest in cryptocurrencies with structured retail products. We find that cryptocurrency investors are active traders who are prone to investment biases and hold risky portfolios. Cryptocurrency investors are more likely to invest in stocks with high media sentiment and more likely to employ heuristics from technical analysis. In line with attention effects and anticipatory utility, we find that the average cryptocurrency investor substantially increases account logins and trading activity after his or her first cryptocurrency purchase. Furthermore, cryptocurrency investors tend to tilt their portfolios toward even more risky securities after cryptocurrency adoption. Our results document which investors are more likely to adopt new financial products and help inform regulators about investors’ vulnerability to cryptocurrency investments.

Kin-Hon Ho, Tse-Tin Chan, Haoyuan Pan, Chin Li

This paper investigates the effectiveness of candlestick patterns in cryptocurrency trading. Our data set includes historical daily opening, high, low, and closing prices of the top 23 cryptocurrencies by market capitalization. We examine 68 commonly used candlestick patterns using statistical analysis and find that the studied candlestick patterns are of little use in cryptocurrency trading. On the contrary, there are more patterns with relatively low accuracy. Investors should be cautious with their trading strategies and decisions when these patterns appear, as they may be a false trading signal that could cause losses rather than gains. To the best of our knowledge, this paper is one of the first research studies to investigate the effectiveness of candlestick patterns in cryptocurrency trading. Our findings could serve as a reference for investors when developing cryptocurrency trading strategies.

Hamid Jazayeriy, Mohammad Daryani

Cryptocurrencies can be traded 24 hours a day over the globe where crypto market never stop working. This market is known for being highly volatile and prices fluctuate rigorously in a minute. Even seasoned traders are unable to react quickly enough to this volatility. This is why automated trading bots get into the picture. On the other hand, crypto trading bots need more improvement to mimic expert traders' activities. In this paper, we propose a bot which is able to buy and sell using a dynamic price-action technique. Experimental results from back-testing show that the proposed bot effectively utilized the classical price-action technique for trading in cryptocurrency markets.

Saji Thazhungal Govindan Nair

Purpose Research on price extremes and overreactions as potential violations of market efficiency has a long tradition in investment literature. Arguably, very few studies to date have addressed this issue in cryptocurrencies trading. The purpose of this paper is to consider the extreme value modelling for forecasting COVID-19 effects on cryptocoin markets. Additionally, this paper examines the importance of technical trading indicators in predicting the extreme price behaviour of cryptocurrencies. Design/methodology/approach This paper decomposes the daily-time series returns of four cryptocurrency returns into potential maximum gains (PMGs) and potential maximum losses (PMLs) at first and then tests their lead–lag relations under an econometric framework. This paper also investigates the non-random properties of cryptocoins by computing the incremental explanatory power of PML–PMG modelling with technical trading indicators controlled. Besides, this paper executes an event study to identify significant changes caused by COVID-19-related events, which is capable of analysing the cryptocoin market overreactions. Findings The findings of this paper produce the evidence of both market overreactions and trend persistence in the potential gains and losses from coins trading. Extreme price behaviour explains volatility and price trends in crypto markets before and after the outbreak of a pandemic that substantiate the non-random walk behaviour of crypto returns. The presence of technical trading indicators as control variables in the extreme value regressions significantly improves the predictive power of models. COVID-19 crisis affects the market efficiency of cryptocurrencies that improves the usefulness of extreme value predictions with technical analysis. Research limitations/implications This paper strongly supports for the robustness of technical trading strategies in cryptocurrency markets. However, the “beast is moving quick” and uncertainty as to the new normalcy about the post-COVID-19 world puts constraint on making best predictions. Practical implications The paper contributes substantially to our understanding of the pricing efficiency of cryptocurrency markets after the COVID-19 outbreak. The findings of continuing return predictability and price volatility during COVID-19 show that profitable investment opportunities for cryptocoin traders are prevailing in pandemic times. Originality/value The paper is unique to understand extreme return reversals behaviour of cryptocurrency markets regarding events related to COVID-19 breakout.

Efe Çağlar Çağlı, Pınar Evrim Mandaci

This paper examines information transmission between Bitcoin derivatives and spot exchanges using 15-minutes interval data over May 2016 - September 2020. We employ a novel econometric framework with Fourier approximation, taking structural shifts in causal linkages, on the prices, returns, and volatilities of BitMEX, the derivatives market, and five other major spot exchanges, Coinbase, Bitstamp, Kraken, CEX.io, and Poloniex. Overall, the results provide robust evidence of information flow between the derivatives and spot exchanges, implying the markets react to new information simultaneously. The results are of importance for investors conducting portfolio allocation exercises and risk management strategies.

Guan‐Ying Huang, Yin‐Feng Gau, Zhenhua Wu

No abstract is available for this record.

Larisa Yarovaya, Damian Zięba

No abstract is available for this record.

Samuel Grone, Weitian Tong, Hayden Wimmer, Yao Xu

Compared with fiat currencies, cryptocurrencies are usually more vulnerable to speculation and thus lead to massive price fluctuations, which makes exchanging cryptocurrencies a potentially profitable but risky endeavor. We aim to contribute to the understanding of the arbitrage behavior involving multiple cryptocurrency exchange markets. Specifically, we applied a Bellman-Ford based algorithm to detect possible arbitrage opportunities. By investigating historical data from three cryptocurrency exchange markets, i.e., Gemini, Coinbase, and Kraken, we designed experiments to identify how often arbitrage was possible in the past as well as the factors that contribute to the existence of arbitrage. We believe this may bring insights into strategies to stabilize the cryptocurrency exchange markets.

Ghulame Rubbaniy, Kienpin Tee, Perihan Iren, Sonia Abdennadher

No abstract is available for this record.

Mark Schaub

The three largest cryptocurrencies by market value are examined for overreaction to positive and negative outlier return events. Bitcoin and Ethereum show significant reversals in value following outlier negative events suggesting overreaction. For positive events, significant cumulative gains (not reversals) followed outlier positive events for Ethereum and Tether showed a significant reversal in value after the positive events. Evidence is, therefore, mixed among the three main cryptocurrencies when it comes to how revaluations occur after outlier positive events.

Lei Yan, Nawazish Mirza, Muhammad Umar

No abstract is available for this record.

Zachary Ludwig, Patryk Perkowski

In this paper, I examine how social media affects cryptocurrencies and more traditional stocks. I use data on Twitter posts in combination with daily stock prices to estimate the causal effect of a tweet on stock and coin prices. To do this, I use a difference-indifference regression with index funds as my control group, which allows me to capture general market trends that coins and stocks would follow if not for intervention. I find that tweets have a significant impact on cryptocurrencies that last up to three days after the post. The increase in coin prices is driven by tweets from Tyler Winklevoss and tweets about Tezos and Ethereum specifically. Meanwhile, Twitter posts have no impact on more traditional stocks. These results suggest that social media can provide the public with valuable information in real time for fast moving and volatile crypto assets, while their effects on more stable and institutionalized traditional stocks are more muted.

M. Kabir Hassan, Fahmi Ali Hudaefi, Rezzy Eko Caraka

Purpose This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning. Design/methodology/approach An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive. Findings The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon. Research limitations/implications This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency. Practical implications The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies. Social implications Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis. Originality/value This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

Nathan E. Crone, Eoin Brophy, Tomás Ward

Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making money. This work brings an algorithmic trading approach to the Bitcoin market to exploit the variability in its price on a day-to-day basis through the classification of its direction. Building on previous work, in this paper, we utilise both features internal to the Bitcoin network and external features to inform the prediction of various machine learning models. As an empirical test of our models, we evaluate them using a real-world trading strategy on completely unseen data collected throughout the first quarter of 2021. Using only a binary predictor, at the end of our three-month trading period, our models showed an average profit of 86\%, matching the results of the more traditional buy-and-hold strategy. However, after incorporating a risk tolerance score into our trading strategy by utilising the model's prediction confidence scores, our models were 12.5\% more profitable than the simple buy-and-hold strategy. These results indicate the credible potential that machine learning models have in extracting profit from the Bitcoin market and act as a front-runner for further research into real-world Bitcoin trading.

Sonia Arsi, Soumaya Ben Khelifa, Yosra Ghabri, Héla Mzoughi

Cryptocurrencies are witnessing a growing interest from investors and the media. They are increasingly perceived as a new class of assets through added benefits, such as hedging capabilities and diversification. However, this does not preclude the fact that cryptocurrencies can be risky assets. Within such a context, diverse studies were carried out at various risk levels. Our chapter bridges this gap and tries to reconcile varying positions on risk across cryptocurrencies. Particularly, we provide a detailed overview on the main risks to be considered by crypto-traders, namely, technology, fraud, legal, market, liquidity, and COVID-19 pandemic risks. The main findings show that the occurrence of any technological failure tends to raise insecurity and distrust in the cryptocurrency technology. This fact can be even further spoiled through fraud schemes and fake trading volumes. Additionally, the legal framework of the cryptocurrencies is still inconclusive. In terms of market risk, these crypto-assets are riskier than fiat currencies and there is a significant risk contagion across large-cap cryptocurrencies. Then, a significant relationship exists between liquidity and efficiency in the cryptocurrency market, since price dynamics can influence the market liquidity. Finally, it sorts out that the COVID-19 pandemic heavily affected the cryptocurrency markets. This chapter underlines current challenges for investors, regulators, and policymakers.

Ki-Hong Choi, Sang Hoon Kang, Seong‐Min Yoon

Herding behaviour is an interesting phenomenon that has a serious impact on the market, leading to inefficient asset prices and high volatility in periods of market turmoil. We analysed the existence of herding behaviour in the cryptocurrency market using hourly price data of eight major cryptocurrencies and the cross-sectional standard absolute deviation (CSAD) approach. Our findings showed anti-herding behaviour at shorter time intervals and herding behaviour during longer periods. The trading decisions of cryptocurrency investors mimic the behaviour of other traders over time. We further found that herding behaviour is stronger over longer time intervals in a down market. When a market is declining, it suggests that fear increases and investors are forced to act quickly in response to market movements rather than using their information. Thus, investors need to fix the situation as quickly as possible to avoid making losses, hence they need to make rational choices based on knowledge rather than emotion or fear.

Dehua Shen, Andrew Urquhart, Pengfei Wang

Abstract This study examines intraday time series momentum in Bitcoin. Unlike stock markets, Bitcoin trades 24 h a day and therefore has not got a clear opening and closing period. Therefore, we use trading volume as a proxy for the market trading time and show that the first half‐hour positively predicts the last half‐hour return. We find that the first trading sessions with the highest volume or volatility are associated with the greatest predictability for intraday time series momentum. We also show that intraday momentum‐based trading yields substantial economic gains in terms of market timing and asset allocation, especially in periods of a market downturn in Bitcoin. Consistent with the finding in foreign exchange markets, our results also show that the Bitcoin intraday momentum is driven by liquidity provision rather than late‐informed trading.

Murat Tunç, Thomas van den Heuvel, Hasan Cavusoglu, Zhiqiang Zheng

Startups adopt non-fungible token (NFT) standard on Ethereum network and create marketplaces for collectible assets, trading cards and digital art. NFTs minted on blockchain must be paid a gas fee to miners at delivery. Due to increased traffic on Ethereum blockchain network, the constant upsurge in cost of minting bear hard on monetary security of token creators. As a potential cure for ever-rising minting cost, NFT platform managers adopt resale royalty which is a practice that transfers a fixed percentage of future sale amount to the creator of digital good. The adoption of resale royalty is seemingly beneficial for token creators as it provides a recurrent cash flow. However, it may have unintended consequences on the sale prices, which, in turn, affects the commission revenue for the platforms. In this paper, we develop several hypotheses for the impact of resale royalty on average sale prices on the primary and secondary markets. We leverage a panel dataset from a popular NFT marketplace and test our hypotheses using instrumental variables estimation. We find that the resale royalty leads to a significant decrease in the average primary sale price. We also find evidence that the average secondary sale price significantly increases with resale royalty. Our estimations suggest that token creators benefit from NFTs with resale royalty only after they are sold on the secondary market numerous times. Contrary to the conventional wisdom, re-sellers are better off when they make investments to NFTs with resale royalty even after adjusting for the royalty premium. We argue the managerial implications of the adoption of resale royalty for platform managers, token creators and re-sellers.

Ishaan Khetan, Palnaa Sheth, Sarthak Dalal, Sarthak Mistry · 6 authors

Throughout numerous cryptocurrency exchanges, markets exhibit recurrent opportunities for arbitrage. There are various types of arbitrage strategies that can be carried out. Taking consideration of the pricing data from three famous exchanges (Binance, Kucoin and Coinbase) for bitcoin, the data analysis on spot prices for each timeframe helps in determining the buy market and sell market. Apart from the analysis, the profits generated by performing arbitrage using one of the discussed strategies are quantified. The implemented algorithm can be modified to suit the trading strategy. Finally, the future scope, as well as the issues and potential risks involved in these strategies, are discussed.