Blockchain Papers

Guiqiang Shi, John W. Goodell, Dehua Shen

No abstract is available for this record.

Vivek Mule

Abstract: With the help of specific factors, this effort seeks to improve the present analysis of bitcoin and forecast its price. After conducting a thorough investigation, it was determined which factors all contribute to daily fluctuations in the value of bitcoin. All of the data in this work is made up of various aspects from daily records from the previous few years. The first step in this endeavour is gathering all the data necessary to forecast the price of bitcoin. All of the data was compiled during the previous few years, and it was incorporated into this work. The Recurrent neural network (RNN) algorithm is employed in this work because it provides significantly improved accuracy than earlier techniques. In order for investors to invest in bitcoin easily and for beginners to this market or business, this study forecasts signs of change in the price of the cryptocurrency.

Yue Fa Zhou, Fei Huang

No abstract is available for this record.

Jingjing Zhang

Bitcoin has increased in popularity as a speculative asset. Since 2013, eventually becoming the most recognizable cryptocurrency. But it's worth noting that the price of Bitcoin has a very high degree of volatility and diversity, which means the ability to estimate prices accurately is crucial for making wise financial decisions. Although recent research has implemented machine learning to predict Bitcoin prices with greater precision, such as Long short-term memory (LSTM), few have focused on traditional machine learning methods. In this article, the author chose a data set including nearly eight years of daily bitcoin price data for closing price prediction. Four different machine learning algorithms were used simultaneously: the Linear Regression (LR), the Decision Tree (DT) and the Random Forest (RF). An artificial neural network, the Multilayer Perceptron (MLP) was also used in this study. The author altered parameter values using the cross-validation method before creating the models in order to get more precise predictions. Finally, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared are used as indicators to assess the outcomes from each model. The study's findings demonstrated that all three metrics of Linear Regression outperformed the performance of the other three models. Perhaps future research could focus more on traditional machine learning algorithms instead of going after complex models.

Salman Bahoo, Marco Cucculelli, Xhoana Goga, Jasmine Mondolo

Abstract Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance. In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. We find that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Furthermore, we show that the selected articles fall into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk and default evaluation, cryptocurrencies, derivatives, credit risk in banks, investor sentiment analysis and foreign exchange management, respectively. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.

Fatih Ecer, Tolga Murat, Hasan Dınçer, Serhat Yüksel

Abstract Crypto assets have become increasingly popular in recent years due to their many advantages, such as low transaction costs and investment opportunities. The performance of crypto exchanges is an essential factor in developing crypto assets. Therefore, it is necessary to take adequate measures regarding the reliability, speed, user-friendliness, regulation, and supervision of crypto exchanges. However, each measure to be taken creates extra costs for businesses. Studies are needed to determine the factors that most affect the performance of crypto exchanges. This study develops an integrated framework, i.e., fuzzy best–worst method with the Heronian function—the fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function (FBWM’H–FMARCOS’H), to evaluate cryptocurrency exchanges. In this framework, the fuzzy best–worst method (FBWM) is used to decide the criteria’s importance, fuzzy measurement of alternatives and ranking according to compromise solution (FMARCOS) is used to prioritize the alternatives, and the Heronian function is used to aggregate the results. Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness. Through case studies, some well-known cryptocurrency exchanges operating in Türkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process. The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology. “Perception of security,” “reputation,” and “commission rate” are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment. Further, the best score is achieved by Coinbase, followed by Binance. The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses. The findings may pave the way for investors to take appropriate actions without incurring high costs.

Nrusingha Tripathy, Sarbeswara Hota, Debahuti Mishra, Pranati Satapathy · 5 authors

Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies.

S. Gupta, Apoorva Hathi Katharaki, Yifan Xu, Bhaskar Krishnamachari · 5 authors

This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception.

Shubham Singh, Mayur Bhat

The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.

George Westergaard, Utku Erden, Omar Abdallah Mateo, Sullaiman Musah Lampo · 6 authors

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

Faraz Sasani, Mohammad Moghareh Dehkordi, Zahra Ebrahimi, Hakimeh Dustmohammadloo · 8 authors

Liquidity is the ease of converting an asset (physical/digital) into cash or another asset without loss and is shown by the relationship between the time scale and the price scale of an investment. This article examines the illiquidity of Bitcoin (BTC). Bitcoin hash rate information was collected at three different time intervals; parallel to these data, textual information related to these intervals was collected from Twitter for each day. Due to the regression nature of illiquidity prediction, approaches based on recurrent networks were suggested. Seven approaches: ANN, SVM, SANN, LSTM, Simple RNN, GRU, and IndRNN, were tested on these data. To evaluate these approaches, three evaluation methods were used: random split (paper), random split (run) and linear split (run). The research results indicate that the IndRNN approach provided better results.

Muhammad Nabil Rateb, Sameh Alansary, Marwa Khamis Elzouka, Mohamad Galal

Abstract Sentiment analysis is a powerful tool for extracting valuable insights from social media data. In this paper, more than one million tweets spanning three months (March, June, and December 2022) regarding three cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) during the Russian-Ukrainian War are considered. Two models, a convolutional neural network with long short-term memory (CNN-LSTM) and a support vector machine (SVM) with GloVe and TF-IDF features, are trained on a labeled dataset of more than fifty thousand tweets about Bitcoin labeled as (positive, negative, and neutral). A pretrained model (Pysentimento) for sentiment analysis is also employed to compare the performances of the three models. The models are tested on the labeled dataset and then evaluated on the unlabeled tweets, revealing that Pysentimento's level of accuracy outperforms the other two models. Google Trends, along with the opening and closing prices, and the volume of the three cryptocurrencies, in addition to the results of Pysentimento sentiment classification, are employed to apply the Pearson correlation coefficient and conduct price prediction analysis using the SARIMA model. It is found that Bitcoin may appeal to those seeking stability and a known record of accomplishment, while Binance Coin and Ethereum may attract investors looking for more diverse opportunities. Sentiment analysis using machine learning is found to provide invaluable information for cryptocurrency price forecasting and trading strategies, especially in the context of geopolitical events and market volatility.

M Rajaei, Qusay H. Mahmoud

In recent years, the global cryptocurrency market has attracted a diverse range of traders, from seasoned professionals to newcomers, resulting in a highly volatile environment. This volatility presents numerous opportunities for traders to capitalize on rapid price fluctuations. In this context, we introduce a Random Forest model designed to predict whether a coin will experience growth in the next trading candle, using several input features. We used Binance historical daily data from 1 Jan 2018 to 31 Dec 2021 to train our models and evaluated them using different time spans (varied between Jan 2022 to Oct 2023) as testing datasets. Moreover, we also used an over sampled training dataset to enhance the training process. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. To be more specific, regarding the testing datasets of 1 to 31 Oct 2023, 1 Jul 2023 to 30 Sep 2023, and LSK/USDT from 1 Jan 2022 to 31 OCT 2023, using a growth rate of 1%, we achieved 18%, 30%, and 68% profits, respectively. This study underscores the potential for leveraging well-designed machine learning models to achieve significant profits, even in bearish market conditions.

Otabek Sattarov, Jaeyoung Choi

Abstract The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

Dennis Koch, Vahidin Jeleskovic, Zahid Irshad Younas

This paper introduces a unique and valuable research design aimed at analyzing Bitcoin price volatility. To achieve this, a range of models from the Markov Switching-GARCH and Stochastic Autoregressive Volatility (SARV) model classes are considered and their out-of-sample forecasting performance is thoroughly examined. The paper provides insights into the rationale behind the recommendation for a two-stage estimation approach, emphasizing the separate estimation of coefficients in the mean and variance equations. The results presented in this paper indicate that Stochastic Volatility models, particularly SARV models, outperform MS-GARCH models in forecasting Bitcoin price volatility. Moreover, the study suggests that in certain situations, persistent simple GARCH models may even outperform Markov-Switching GARCH models in predicting the variance of Bitcoin log returns. These findings offer valuable guidance for risk management experts, highlighting the potential advantages of SARV models in managing and forecasting Bitcoin price volatility.

Ayu Zuo

Based on the dataset of Bitcoin Price dataset, this paper studied Bitcoin price prediction by using support vector machine model, random forest model, neural network model, XGBoost model and LightGBM model. The models were evaluated by MSE, RMSE, MAE, MAPE and R². First, we divided the Bitcoin Price dataset into a training set and a test set according to a ratio of 7:3, with 70 as the training set and 30 as the test set. We take the stock price change (return) as the target variable, the other variables as the input variables, and use the training set to train the model and the test set to test the model. After model comparison, we found that XGBoost's MSE, RMSE, MAE, MAPE and R² are all optimal, and its prediction effect is also the best. The performance of the other four models ranges from good to different, including LightGBM, random Forest, support vector machine and neural network. Among them, the MSE of the neural network is dozens of times that of the other four models, so it performs the worst. The XGBoost model performs well in dealing with high-dimensional sparse data and nonlinear relationships, while LightGBM and random Forest are suitable for dealing with large-scale data. Support vector machines are suitable for dealing with high-dimensional data and nonlinear relationships, while neural networks require more tuning and optimization to take advantage of their advantages. In summary, the research results of this paper can provide value for the prediction of Bitcoin price in the future, and also provide a certain reference for selecting a suitable machine learning model.

David Alaminos, M. Belén Salas, Manuel Á. Fernández-Gámez

In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alternative financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona, leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities.

Xiaolin Kong, Chaoqun Ma, Yi‐Shuai Ren, Konstantinos Baltas · 5 authors

No abstract is available for this record.

Indranil Ghosh, Rabin K. Jana, Dinesh K. Sharma

Purpose Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes. Design/methodology/approach Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts. Findings Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict. Originality/value The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Peyman Alipour, Sina Esmaeilpour Charandabi

In an attempt to assess the appropriateness of the best-practice lexicon-based approaches as opposed to novel learning-based models to extract the sentiment of textual content in the context of the cryptocurrency market, the current study provides further insights into the association between digital activity and price movement of cryptocurrencies. Using a sample of Bitcoin and Ethereum trade data, this study compares the performance of Harvard IV-4 and BERT models in conjunction with the well-known machine learning classifiers. It examines to what extent learning-based sentiment models can enhance the price movement prediction, compared to lexicon-based approaches, and whether the prediction is improved or impaired by introducing different features as input to the classifiers. Results indicate that the contribution of the selected learning-based model varies across the two cryptocurrencies, and predictions are better in the absence of trade volume as an input feature to the classifiers.

Kirill Mansurov, Alexander Semenov, D. Grigoriev, Andrei Radionov · 5 authors

No abstract is available for this record.

Gonzalo Lara de Leyva, Ashutosh Dhar Dwivedi, Jens Myrup Pedersen

No abstract is available for this record.

Reepu Reepu

No abstract is available for this record.

V. Ramalingam, P. Taruun, S.S. Onyx Nathanael Nirmal Raj

No abstract is available for this record.