Blockchain Papers

John W. Goodell, Sami Ben Jabeur, Foued Saâdaoui, Muhammad Ali Nasir

No abstract is available for this record.

Rui Zhong

The long short-term memory (LSTM) network and a cutting-edge method that combines wavelet decomposition and LSTM (W-LSTM) were applied to deep learning in this study's analysis of Bitcoin's price and movement. To be specific, it predicted next day’s both price and price movement (trend) with historical data. The input of the model is close price itself, basic trading information, and technical indicators calculated solely on basic trading information. Large number of numerical experiments come to the same conclusion that: for price prediction, only close price as input obtains the best performance for regression, and minor improvement achieved after 1-order wavelet decomposition; for price movement, no improvement after changing the number of input features or with the model W-LSTM has been spotted for the same network structure and hyper-parameters, and enlarging time step and batch size will improve accuracy and Matthews correlation coefficient despite of number of input and model used in this paper.

K. Dhivya, B Akoramurthy, B Surendiran, T. Sivakumar

Different sectors are being revolutionized by distributed ledger technology.According to the 2022 market valuation, Hyperledger is now the second-largestblockchain platform for smart contracts. The creation of numerous apps maybe sped up and simplified with smart contracts, but there are certain drawbacks as well. For instance, vulnerability contracts are created intentionallyto weaken candor, smart contracts are employed to conduct fraudulent activities, and there are many redundant contracts that squander the efficiency ofthe system for no real reason. To solve these problems, we provide in thisresearch Service Level Agreement(SLA) for Hyperledger smart contracts. Wecreated Hyperledger smart contracts and focused on how smart contracts andconsumers used data. By manually analyzing the transactions, we were ableto extract four behavioral characteristics that may be used to differentiatebetween various contract types. Then, a smart contract is built using theseto include 14 fundamental functionalities. We provide a data splitting algorithm for splitting the gathered smart contracts in order to create the experimental dataset. Then, we train and test our dataset using an LSTM network.The comprehensive experimental findings demonstrate that our method can discriminate between various contract types and may be used to identify maliciouscontracts and detect anomalies with acceptable precision, recall, and F1-score.

Mrityunjay Singh, Amit Kumar Jakhar, Aashima Juneja, Shivam Pandey

Cryptocurrency, a digital currency, acts as a medium of exchange through the Internet. The main agenda behind cryptocurrency being so popular these days is the desire for reliable, long-term value without the involvement of any central authority like banks. The power lies in the hands of the currency holders which resolve the problems of the traditional currencies by adopting a decentralized system. Predicting the future price of different cryptocurrencies is a prominent area of interest for individuals or investors. In this work, we use a dataset collected from the coinmarketcap website for the duration of September 2014 to March 2022. The outcome of this work is compared to the existing algorithms for time series data analysis namely the Auto Regressive Moving Average Model (ARIMA), FbProphet, and several ensemble models on the basis of their accuracy in predicting the future price. We also create different ensemble frameworks for the prediction of the cryptocurrency price. To form the ensemble models, we initially select the three best-performing regression models on the dataset, namely Extra Trees, Random Forest, and Decision Trees Regressors. Our findings indicate that the ARIMA model performs better than the ensemble model with the lowest RMSE MAE and MSE.

Erfan Varedi, Reza Boostani

In this study, a novel approach for feature selection has been presented in order to overcome the challenge of classifying positive and negative risk prediction in the cryptocurrency market, which contains high fluctuation. This approach is based on maximizing information gain with simultaneously minimizing the similarity of selected features to achieve a proper feature set for improving classification accuracy. The proposed method was compared with other feature selection techniques, such as sequential and bidirectional feature selection, univariate feature selection, and least absolute shrinkage and selection operator. To evaluate the feature selection techniques, several classifiers were employed: XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression, long short-term memory, and deep neural networks. The features were elicited from the time series of Bitcoin, Binance, and Ethereum cryptocurrencies. The results of applying the selected features to different classifiers indicated that XGBoost and random forest provided better results on the time series datasets. Furthermore, the proposed feature selection method achieved the best results on two (out of three) cryptocurrencies. The accuracy in the best state varied between 55% to 68% for different time series. It is worth mentioning that preprocessed features were used in this research, meaning that raw data (candle data) were used to derive efficient features that can explain the problem and help the classifiers in predicting the labels.

Xiangyang Xu, X. Xu

With the rapid development of data science, quantitative trading models have become prevalent in financial markets. We calculate a series of indices based on the price data of gold and bitcoin from 2016 to 2021. On the basis of ARIMA model in time series algorithm, we build a prediction model that forecasts that very day's gold and bitcoin price relying solely on the past stream of daily prices to date. After completing the construction of the prediction model, we establish the quantitative trading model. We use AHP method to get buying scores of gold and bitcoin, which are the criteria for buying and selling. We then draw up some numbers and compare them with buying scores to decide whether to buy or sell and the number of shares bought and sold each day. After this, we use dynamic programming to find the theoretical maximum profit. Comparing this with the result of our quantitative trading model, we conclude that our model has significant superiority. Generally, the trading model established in this paper has good sensitivity to adapt to market changes and has strong risk resistance.

Artem Koldovskyi

This paper presents an advanced econometric model specifically designed to analyze the intricate relationship between blockchain technology and various economic variables. The model serves as a robust framework for comprehending the impact of blockchain on investment patterns, adoption rates, and market trends. By quantifying these relationships, the model enables predictions regarding future trends in the blockchain industry and facilitates the identification of factors influencing growth or hindering adoption. With its wide-ranging applicability, the model offers profound insights for policymakers, investors, entrepreneurs, and researchers, shedding light on the economic implications of this rapidly evolving technology.The findings of this study reveal a multitude of significant insights regarding the economic implications of blockchain technology. The econometric model demonstrates a strong positive relationship between blockchain investment and adoption rates, indicating that increased investment leads to higher adoption levels. Moreover, the model identifies specific market trends and factors that influence the growth and adoption of blockchain technology. By highlighting these factors, stakeholders can make informed decisions and strategize accordingly.The econometric model forblockchain technology offers numerous applications and implications for various stakeholders. Policymakers can leverage the model's insights to develop regulatory frameworks that foster blockchain innovation while mitigating risks. Investors can utilize the model to make data-driven investment decisions and identify lucrative opportunities within the blockchain industry. Entrepreneurs can gain valuable insights into the factors driving adoption and tailor their business strategies accordingly. Additionally, researchers can expand their understanding of the relationship between technology and economic variables, contributing to the development of new theories and frameworks.

Fang Wang, Marko Gacesa

This study extends the examination of the Efficient-Market Hypothesis in Bitcoin market during a five year fluctuation period, from September 1 2017 to September 1 2022, by analyzing 28,739,514 qualified tweets containing the targeted topic "Bitcoin". Unlike previous studies, we extracted fundamental keywords as an informative proxy for carrying out the study of the EMH in the Bitcoin market rather than focusing on sentiment analysis, information volume, or price data. We tested market efficiency in hourly, 4-hourly, and daily time periods to understand the speed and accuracy of market reactions towards the information within different thresholds. A sequence of machine learning methods and textual analyses were used, including measurements of distances of semantic vector spaces of information, keywords extraction and encoding model, and Light Gradient Boosting Machine (LGBM) classifiers. Our results suggest that 78.06% (83.08%), 84.63% (87.77%), and 94.03% (94.60%) of hourly, 4-hourly, and daily bullish (bearish) market movements can be attributed to public information within organic tweets.

Amogh Shukla, Tapan Kumar Das, Sanjiban Sekhar Roy

TRON is a decentralized digital platform that provides a reliable way to transact in cryptocurrencies within a decentralized ecosystem. Thanks to its success, TRON’s native token, TRX, has been widely adopted by a large audience. To facilitate easy management of digital assets with TRON Wallet, users can securely store and manage their digital assets with ease. Our goal is first to develop a methodology to predict the future price using regression and then move on to build an effective classifier to predict whether a profit or loss is made the next day and then make a prediction of the transaction success rate. Our framework is capable of predicting whether there will be a profit in the future based on price prediction and forecasting results using regressors such as XGBoost, LightGBM, and CatBoost with R2 values of 0.9820, 0.9825 and 0.9858, respectively. In this work, an ensemble-based stacking classifier with the Whale optimization approach has been proposed which achieves the highest accuracy of 89.05 percent to predict if there will be a profit or loss the next day and an accuracy of 98.88 percent of TRX transaction success rate prediction which is higher than accuracies obtained by standard machine learning models. An effective framework will be useful for better decision-making and management of risks in a cryptocurrency.

Orhan Koc

Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.

Hilmi Tunahan AKKUŞ, Mesut Doğan

The aim of this study is to analyse the dynamic connectedness relationship (DCR) between cryptocurrency, NFT, and DeFi assets. In the study, two cryptocurrencies consisting of Bitcoin and Ethereum, two NFTs consisting of Tezos and The Sandbox, and two DeFi assets consisting of Chainlik and Uniswap were analysed. The results showed that Ethereum cryptocurrency and Chainlink DeFi assets spilled volatility to other crypto assets. The other variables were assets that received volatility, and the volatility spillover relationship between NFT assets is less than other crypto assets.

Gil Cohen

No abstract is available for this record.

Arun Kumar Marandi, Gordhan Jethava

An approach to machine learning (ML) is a technique for forecasting that is widely employed in the global manufacturing, advertising, finance, travel, and transportation sectors today. The global financial market of today has had a significant impact on several facets of digital pricing. Large firms employ a variety of digital pricing strategies to produce the highest profit margins possible while conducting international commerce. However, successful machine learning applications also provide important benefits in the global markets for cryptocurrencies. It has been found that companies may trade money over virtual channels more easily and at a lower cost with the usage of ML methods than they previously could. Additionally, all trading techniques in the cryptocurrency and digital pricing markets may be quickly improved by machine learning to increase profits and build adaptable business expertise for future experimentation.By conducting surveys and asking questions about the subject, researchers will explore the specific research issue using an efficient quantitative method. However, using a probability sampling technique, researchers have developed three key study questions and gathered the opinions of about 80 participants. Utilizing a variety of cutting-edge ML technologies to improve the effectiveness of digital pricing and cryptocurrencies has gotten simpler in the current stock marketplaces. In order to expand the area of future research, the research article provides some significant insight into the effects of ML methods and how they affect digital pricing and bitcoin trading.

Anil Kumar Bhuyan, Darshana A. Naik, Sachin Sharma, Anita Gehlot · 6 authors

We examine the bitcoin currency's predictability over prediction timeframes of 1 to 15 minutes. As a result, we evaluate a variety of machine learning methods and discover that, although all models perform better than a random predictor, Bidirectional recurrent neural networks and extreme Gradient Boosting Classifier (XGB Classifier) are particularly effective for the investigated prediction tasks. We employ a wide range of features, including technological, asset-based, interest-based, and block chain technology characteristics. Our findings indicate that technical characteristics continue to be the most important for the majority of techniques, followed by a few blockchain- and interest-based features. Furthermore, we see that reliability rises with increasing prediction horizons. Although a quantile-based long-short trading strategy may provide market return of up to 34% before transaction costs, the very brief holding durations result in negative yields after transaction expenses are incurred.

Svend Pasak, Riyanto Jayadi

The increasing popularity of cryptocurrencies as a means of financial inclusion for investment and trade has become a major concern for individuals seeking to benefit from the cryptocurrency market. This study aims to provide insights for cryptocurrency investors, financial sector professionals, and academics by utilizing machine learning techniques such as ARIMA and LSTM to compare the accuracy of modeling performance on datasets predicting the prices of five cryptocurrencies, namely Bitcoin, Ethereum, Binance Coin, Tether, and Cardano. Data was obtained by downloading from the Yahoo Finance website using Jupyter notebook. The LSTM method outperformed the ARIMA method, achieving a lower MAPE value of less than 10 percent and effectively capturing price movements, providing valuable information for decision-making.

Luka Jovanović, Ivana Strumberger, Nebojša Bačanin, Miodrag Živković · 6 authors

Machine learning as a subset of artificial intelligence presents a promising set of algorithms for tackling increasingly complex challenges. A notable ability of this subgroup of algorithms to tackle tasks without explicit programming coupled with the expanding availability of computational resources and information transparency has made it possible to utilize algorithms to forecast prices. In recent years, cryptocurrency has increased in popularity and has seen wider adoption as a payment method. Cryptocurrency trading and mining have become a potentially very lucrative venture. However, due to the instability of cryptocurrency prices, casting accurate predictions can be quite challenging. A novel way of approaching this challenge is by tackling it through time-series forecasting. A particularly promising method for tackling this type of problem is through the utilization of long-short-term memory artificial neural networks to attain accurate prediction results. However, the forecasting accuracy of machine learning models is highly dependent on adequate hyperparameter settings. Thus, this work presents an improved variation of the arithmetic optimization algorithm, tasked with selecting the best values of a long-short term neural network casting price predictions. The presented approach has been evaluated on publicly available real-world Ethereum trading price data. The attained results of a comparative analysis against several popular metaheuristics indicate that the presented method achieved excellent results, and outperformed aforementioned algorithms in one and four-step ahead predictions.

Preeti Pandey, Geeta Sharma

Cryptocurrencies are digital currency form that utilizes an online distributed ledger technology called a blockchain. Cryptocurrency exhibits excellent features such as immutability, security, and decentralization. However, the cryptocurrency price varies in the market due to several reasons. Recently, cryptocurrency price prediction has been a concern for several researchers. The price prediction of cryptocurrency is a global research subject matter. Several deep learning and machine learning algorithms were utilized in current research to predict cryptocurrency prices. This present study intends to resolve this issue by reviewing research includes prediction of the cryptocurrency closing price in the market at a particular time. Several existing studies revealed that it is optional to predict the precise value of the future cryptocurrency price to acquire gains in the financial sector. This predicament can be handled with a knowledge of deep learning algorithms and analysis of distinguishing features of different algorithms.

Nisha Rathee, Ankita Singh, Tanisha Sharda, Nimisha Goel · 6 authors

No abstract is available for this record.

Wipawee Nayam, Yachai Limpiyakorn

The concept of digital cash has the potential to completely change how people think about money. Digital currency has emerged as a possible alternative for exchanging currency and traditional payment systems, in addition to a popular investment option due to its potential for high returns. One of the three main varieties of digital currency is cryptocurrency that is secured by blockchain technology. Bitcoin, Ethereum, and many other cryptocurrencies exist in crypto markets. Investing in cryptocurrencies still carries risks and uncertainties due to the price volatility. It is thus important to approach such investments with caution and thoroughly research the market and its risks before making investment decisions. This paper presents an application of AI technology for learning the price movement of Ethereum (ETH) which is second only to Bitcoin in market capitalization. Based on the Technical factor, the XGBoost model is constructed for classification of return on Ethereum close price. The technical indicators such as moving averages and relative strength index, together with the Bitcoin price trend are chosen to determine influence on Ethereum price further used for computing the short-term return separate into 3 classes: downtrend, sideway, and uptrend. The model performance is measured by multiclass ROC-AUC, achieving the micro-average ROC-AUC of 0.66 saying the model is reasonably good at predicting the overall trend of ETH price.

Athanasia Dimitriadou, Andros Gregoriou

In this paper we predict Bitcoin movements by utilizing a machine-learning framework. We compile a dataset of 24 potential explanatory variables that are often employed in the finance literature. Using daily data from 2nd of December 2014 to July 8th 2019, we build forecasting models that utilize past Bitcoin values, other cryptocurrencies, exchange rates and other macroeconomic variables. Our empirical results suggest that the traditional logistic regression model outperforms the linear support vector machine and the random forest algorithm, reaching an accuracy of 66%. Moreover, based on the results, we provide evidence that points to the rejection of weak form efficiency in the Bitcoin market.

Radhakrishna Dodmane, K. R. Raghunandan, Krishnaraj Rao N S, Bhavya Kallapu · 7 authors

The advancements in communication speeds have enabled the centralized financial market to be faster and more complex than ever. The speed of the order execution has become exponentially faster when compared to the early days of electronic markets. Though the transaction speed has increased, the underlying architecture or models behind the markets have remained the same. These models come with their own disadvantages. The disadvantages are usually faced by non-institutional or small traders. The bigger players, such as financial institutions, have an advantage over smaller players because of factors such as information asymmetry and access to better infrastructure, which give them an advantage in terms of the speed of execution. This makes the centralized stock market an uneven playing field. This paper discusses the limitations of centralized financial markets, particularly the disadvantage faced by non-institutional or small traders due to information asymmetry and better infrastructure access by financial institutions. The authors propose the usage of blockchain technology and the data highway protocol to create a decentralized stock exchange that can potentially eliminate these disadvantages. The data highway protocol is used to generate new blocks with a flexible finality condition that allows for the consensus mechanism to configure security thresholds more freely. The proposed framework is compared with existing frameworks to confirm its effectiveness and identify areas that require improvement. The evaluation of the proposed approach showed that the improved highway protocol boosted the transaction rate compared to the other two mechanisms (PoS and PoW). Specifically, the transaction rate of the proposed model was found to be 2.2 times higher than that of PoS and 12 times higher than that of the PoW consensus model.

M. Karthik, Vethath Suryakumar. R, S Syedjaffar., Dharneesh. M.E

Abstract: Our project aim is to predict the future price of the bitcoin using machine learning algorithms. In the modern world cryptocurrency is become more trendy and reaches the youngsters to invest in the stock market and to generate some profitable trades. To invest their money in cryptocurrency we are just helping the investors like peoples and also involving the organization to invest in the bitcoin and to make good profitable trades. Initially it utilizes the historical data to predict the future price of the bitcoin. It involves considering factors such as market sentiment, news and events ,technical analysis, and global economic trends. Different machine learning algorithms are applied on the a data and the accuracy is compared to see which algorithm performed better. It includes the performance metrics like precision, recall scores are also taken into consideration for evaluating the model In cryptocurrency market it contains 'n' number of coins .Among those coins we can take any coin to predict the future price which will able to help the investors who are all investing their money in cryptocurrency market.

Nuray Güneri Tosunoğlu, Hilal Abacı, Gizem Ateş, Neslihan Saygılı Akkaya

Abstract Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and $${R}_{OOS}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>OOS</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.

C. Allen Butler, Martin Crane

Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R2 of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R2 of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons.