Blockchain Papers

Mohamed O. Ben Miloud, Eunjin Kim

No abstract is available for this record.

Isik Akin, Muhammad Zubair Khan, Affan Hameed, Kaouthar Chebbi · 5 authors

No abstract is available for this record.

Dalel Kanzari

No abstract is available for this record.

Junming Zhao, Tianding Zhang

No abstract is available for this record.

Hyeonoh Kim, Eojin Yi, Jooyoung Jeon, Taeyoung Park · 5 authors

No abstract is available for this record.

Fozia Zeeshan, Narayan Nepal, Mohammad Norouzifard

In the context of cryptocurrency forecasting, this paper provides a comprehensive analysis of various prediction methods, including financial methods, statistical methods, machine learning, and deep learning. It investigates the causes of the effectiveness of the most well-known and accurate techniques. In addition, the study compares the results of its RMSE, MAE, and MAPE to those of other studies that have used the same dataset with same time period. This study investigates the effect of different evaluation matrices on the accuracy of models and compares the performance of two distinct cryptocurrencies on various deep-learning models. By analyzing the relationship between evaluation metrics and the accuracy of price predictions, the study aims to facilitate the development of more precise models for predicting the prices of cryptocurrencies. This study adds to the literature on cryptocurrency forecasting by evaluating several approaches to see which methods provide the most reliable results. Researchers and practitioners can make informed decisions regarding the development and application of cryptocurrencies if they comprehend the factors that contribute to the accuracy of cryptocurrency prediction models. In addition, the study emphasizes how bi-LSTM and LSTM can be used to forecast various cryptocurrencies and how price fluctuations can be measured and predicted with an accuracy level greater than 80%. Overall, this study contributes to the advancement of knowledge and the development of cryptocurrency price prediction methods, thereby augmenting decision-making processes in cryptocurrency markets.

Tanya Araújo, Paulo S. F. Barbosa

Abstract The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.

Sera Şanlı, Mehmet Balcılar, Mehmet Özmen

No abstract is available for this record.

Abdulrezzak Zekiye, Fadi Amroush, Semih Utku, Öznur Özkasap

Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.

K Rama Rao, M Lakshmi Prasad, G. Ravi Kumar, R Natchadalingam · 6 authors

Cryptocurrency is now widely accepted as a payment and exchange method, permeating nearly every aspect of the financial sector. Similar to the non-stationary and very erratic price movements of traditional stocks, cryptocurrency price swings are highly unpredictable. The rising popularity of cryptocurrencies has prompted an increase in the number of studies conducted to predict their future prices. The importance of cryptocurrency forecasting has grown significantly with the advent of deep learning. portfolio optimisation and decision making cannot be achieved without the creation of a smart forecasting model. The primary contribution of this study is the utilize of traditional deep learning (DL) models in combination with the three most popular ensemble learning algorithms (ensemble-averaging, bagging, and stacking) to predict the hourly values of major cryptocurrencies. Traditional DL strategies consisting of combinations of long short-term memory (LSTM), Bi-directional (BiLSTM), and convolutional layers were utilized to assess the suggested ensemble methods. The ensemble techniques were tested on their capacity to forecast the price of a cryptocurrency an hour basis (regression) and to determine whether the price will rise or fall relative to the present (classification). Our in-depth experimental research shows that combining ensemble learning with deep learning can produce robust, stable, and trustworthy forecasting strategies.

Avinash Malik

Abstract Bitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.

Sasan Barak, Navid Parvini

Abstract Despite the growing literature in cryptocurrency forecasting and their price drivers, the relationship between their price and other financial time series is an ongoing matter of debate. This study proposes a three‐step methodology to cover these arguments. First, we conduct an ad hoc analysis using transfer entropy (TE) to study the causal relationship between Bitcoin (BTC) returns and a vast array of financial time series. Then, we utilize variables with a significant amount of information flow toward BTC returns to forecast multi‐step‐ahead BTC returns. Finally, we use explainable artificial intelligence post hoc analysis methods to discover the contribution of each input feature to the overall forecasting. The results indicate a significant change in the information flow pattern in the first days of the COVID‐19 pandemic outbreak. Additionally, our proposed TE‐based feature‐selection method outperforms both benchmarks, a nonfeature‐selection model, and backward stepwise regression.

Saswat Patra, Neha Gupta

Cryptocurrencies have gained much attention in recent times with investors, speculators, and regulators showing a keen interest in the cryptocurrency markets. However, not much attention has been paid to quantifying their risk measures. This paper estimates the risk in the cryptocurrency markets using Value-at-Risk and Expected Shortfall. We use Johnsons Su distribution to model the innovations in the returns and present a comparative analysis of different fat-tailed and skewed distributions used in modeling the returns. The estimation takes into account endogenously determined structural breaks in the data. We employ several backtesting methodologies to test the efficacy of the forecasts. Empirical results show that the Johnson’s Su distribution gives exceptional results, and outperforms other fat-tailed distributions and the normal distribution, especially at the 1% (for long positions) and 99% levels (for short positions). Furthermore, our results are robust to different subsamples and the methodology employed (recursive or rolling window). Our results have clear policy implications for various market participants, regulators, and the government.

Anqi Liu, Hossein Jahanshahloo, Jing Chen, Arman Eshraghi

Despite the growing literature on Bitcoin and other cryptocurrencies, we know relatively little about who are involved in trading, transacting and using these assets and how they behave. Examining millions of Bitcoin transaction records, we show that less than 1% of Bitcoin users contribute to more than 95% of the market volumes. These ‘whales’ are often associated with strategic trading/transaction volumes, market reactions and timing patterns. Using K-means clustering on a comprehensive transaction dataset, we establish a typology of traders by learning their trading exchange patterns, strategies and impact risk and market microstructure. Our approach ‘learns’ and identifies five distinct groups or types of Bitcoin users, which are somewhat, though not entirely, comparable to popular categorisations used in conventional market such as fundamental, technical, retail and institutional traders as well as market makers. Four of these groups present distinguishable trading patterns with a strong impact on liquidity provision and trading signals.

Haoyang Yu, Yutong Sun, Yulin Liu, Luyao Zhang

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

Nrusingha Tripathy, Sarbeswara Hota, Debahuti Mishra

The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.

Mingzhe Wei, Ioannis Kyriakou, Georgios Sermpinis, Charalampos Stasinakis

Abstract This study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of 12 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7846 technical rules, five log moving average‐based ratios and 59 fundamental factors are used to test predictability and profitability through the Lucky Factors methodology and Superior Predictive Ability test. We observe predictability for a small set of technical and fundamental rules, while only the short‐term log moving average‐based ratio and Hashrate Index demonstrate genuine in‐sample and out‐of‐sample profitability. Our findings question the value of both technical and fundamental analysis on cryptocurrencies.

Morteza Khosravi, Majid Mirzaee Ghazani

No abstract is available for this record.

Thabani Ndlovu, Delson Chikobvu

In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.

Bedirhan Sakinoğlu, H. Altay Güvenir

In recent years, the attention drawn by cryptocurrencies has increased as their popularity grows rapidly. This situation attracts investors, entrepreneurs, regulators, and the general public. However, these coins may die and become dead coins. A coin is declared dead if no activity is recorded for more than one year. Numerous coins die without completing their one-year timeframe and this issue causes investors to lose a significant amount of money. In this study, we develop a deep neural network architecture based on long short-term memory (LSTM) to predict the death risk of a coin in a specified timeframe. In order to do this, time-series data consisting of the closing price and volume values of 4733 dead coins are utilized. The goal of our model is to inform investors about the death risk of the coin and improve their overall portfolio performance.

Ting-Jen Chang, Tian‐Shyug Lee, Chih-Te Yang, Chi-Jie Lu

No abstract is available for this record.

Yang Zhao, Maojun Zhang, Ziting Pei, Jiangxia Nan

No abstract is available for this record.

Asheetu Bhatia Sarin

With the unprecedented growth of technological development and digitalization across the globe, cryptocurrency has emerged to attract investors. It all started in the year 2013 when there were large fluctuations in the financial market. The novelty of this emerging asset class has led researchers to devise anomalous trade patterns and behavioral fallacies in the crypto market. This chapter will help researchers, academicians, and investors in understanding the importance of cognitive and emotional biases in the cryptocurrency market concerning investment decision-making. Moreover, the reader will be able to gain an understanding of the existing market and the challenges of cryptocurrency and financial technologies.

Eduardo José Costa Lopes, Reinaldo A. C. Bianchi

Cryptocurrency has become a popular asset in global financial markets, meaning that individual investors and asset management companies worldwide are considering this new investment class. The main contribution of this research is to address an intra-day forecasting problem with hourly granularity by comparing deep network architectures, including ones with attention mechanisms for the Ethereum intrinsic cryptocurrency (ETH). Since variations on the deep learning model parameter values may also introduce variability in the results produced by the models, different statistical validations were considered part of the comparison process. Finally, this work shows that the Temporal Convolutional Network model (TCN) outperformed other architectures considered for a short-term forecast period in terms of processing time. The TCN deep learning model is also amongst the most accurate models, using an auto-regressive integrated moving average model (ARIMA) as a baseline.