Blockchain Papers

Mohamed O. Ben Miloud, Eunjin Kim

No abstract is available for this record.

Sina Fakharchian

No abstract is available for this record.

Siyang Xie, Zhili Zhao, Longhao Li, Han Wu

While Bitcoin has been a hot topic in the investment world due to its rising value, gold remains a popular investment option. To create a value prediction model for the best investment strategy, we utilized LSTM and found that it had a higher fitting effect than other two models, grey prediction and time series. The accuracy rate is 88.7%, the loss rate is 0.135%. We test different batch sizes to ensure the accuracy of prediction and established an appropriate algorithm to calculate the best investment strategy for each day. To test the accuracy of the model, we use four methods, including testing the accuracy of the risk factor in the model, observing the growth of total asset value, calculating the error rate of the investment process, and performing robustness analysis under low investment costs. We also perform sensitivity analysis to determine the impact of transaction costs on the strategy and results. The results show that the fluctuation in Bitcoin transaction costs is more significant and can affect the frequency of trading activities, ultimately affecting the final profit.

Dalel Kanzari

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.

Kia Jahanbin, Mohammed Ali Zare Chahooki

OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.

Petr Hájek, Vladimír Olej

There has been great interest in developing hierarchical structures of fuzzy rule-based systems due to their flexibility allowing to model complex problems. To cope with the high degree of uncertainty arising from the characteristics of cryptocurrency markets, this paper proposes a hierarchical intuitionistic TSK (Takagi-Sugeno-Kang) fuzzy system equipped with a feature selection and feature ranking component. The proposed system uses intuitionistic fuzzy sets, allowing to effectively model investor uncertainty in the decision-making on cryptocurrency markets. The hierarchical structure is a parallel tree-like fuzzy system that is based on relevant features while considering feature dependencies. Computational efficiency is achieved by using fuzzy c-means clustering to produce rule antecedents. The proposed system is validated using multivariate bitcoin data for the period 2018 to 2022, showing that the proposed system can accurately predict bitcoin prices while retaining an interpretable hierarchical structure.

Adeyinka Adediran, Bola Babajide, Nataliia Osina

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.

Mohammad Javad Rajaei, Qusay H. Mahmoud

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.

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.

Giacomo di Tollo, Joseph Andria, Gianni Filograsso

Cryptocurrencies are nowadays seen as an investment opportunity, since they show some peculiar features, such as high volatility and diversification properties, that are triggering research interest into investigating their differences with traditional assets. In our paper, we address the problem of predictability of cryptocurrency and stock trends by using data from social online communities and platforms to assess their contribution in terms of predictive power. We extend recent developments in the field by exploiting a combination of stochastic neural networks (NNs), an extension of standard NNs, natural language processing (NLP) to extract sentiment from Twitter, and an external evolutionary algorithm for optimal parameter setting to predict the short-term trend direction. Our results point to good and robust accuracy over time and across different market regimes. Furthermore, we propose to exploit recent advances in sentiment analysis to reassess its role in financial forecasting; in this way, we contribute to the empirical literature by showing that predictions based on sentiment analysis are not found to be significantly different from predictions based on historical data. Nonetheless, compared to stock markets, we find that the accuracy of trend predictions with sentiment analysis is on average much higher for cryptocurrencies.

Lubna Noor AhmadChitkara, Pradeepta Kumar Sarangi, Merry Saxena, Ashok Kumar Sahoo · 5 authors

Trading in Digital currency is an opportunity for an alternate investment option and getting popularity day by day. Bitcoin is one of the most popular digital currencies based on technology implementation. Though it operates free from any central control, still many investors trade in bitcoin and also contribute to the economy. The objective of this research paper is to implement and analyse five different statistical and machine learning algorithms in next day bitcoin price prediction. The different algorithms implemented in this work are Random Forest, Support Vector Regression (SVR), Ridge regression, Lasso regression and Long Short Term Memory (LSTM) models. The data used in this work is the daily traded data for the period from November 2021 to February 2023. From the experiments, it is concluded that LSTM model and Lasso regression predict the same value for the next day bitcoin price with an accuracy of 97.88%followed by Random Forest and Support vector model with accuracy of 94.65% and 94.40% respectively. However, the highest accuracy is observed by the Ridge regression which is 98.02%.

Wildan Syahid Nugraha, Subiakto Soekarno

Cryptocurrencies are significant improvements in the digital age that have changed the way we think about money. The first cryptocurrency was Bitcoin, introduced in 2009 and was created by Nakamoto. Due to their potential ups and downs, many people now think that cryptocurrencies are appropriate for use as an investment instrument, especially millennials who are attracted to higher-risk investment alternatives. A number of different investing options such as cryptocurrencies, gold, and other conventional assets like equities and bonds have unique characteristics and advantages. It’s essential for investors to understand the similarities and differences between cryptocurrencies and other assets in order to create diversified portfolios. In this study, the optimum portfolio will be constructed using Bitcoin, Gold, LQ45 Index, and ABF IBI as the representative of Indonesia Bond Index. Mean-Variance Optimization will be used as an asset allocation method, and will be compared to the other methods such as Risk Parity, 60/40 Portfolio, and Equally Weighted to find a better risk-adjusted return. The Sharpe ratio analysis is used to evaluate the portfolio performance resulting from every method. The investment strategy will be simulated to know which strategy will result the best total return in the end of simulation period. According to risk, return, and the Sharpe ratio, Bitcoin could perform better than gold, LQ45, and ABF IBI. Furthermore, the Mean-Variance Optimization resulted the highest Sharpe ratio compared to the other methods. The optimal weight from the portfolio construction using Mean-Variance Optimization allocated 53% to ABFI index, 40% to Bitcoin, and 7% to gold, which resulted 48.2% portfolio return, 40.44% portfolio risk, and 1077.8% Sharpe ratio. From the investment strategy simulation, the quarterly rebalancing strategy was found to be the best strategy with the total return 223.36%.

Meduri V N S S R K Sai Somayajulu, Bonthu Kotaiah

Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods.

Qifei Yang, Yuhan Sun, Yuhao Wu

Due to many factors, Bitcoin has experienced huge price fluctuations since its emergence, and it has received extensive attention. Forecasting the price of bitcoin is of great significance for investors and for the country's future development. This paper collects the data of bitcoin price and indicator that may affect the price, and then use random forest algorithm for feature selection to remove all nonessential indicators. Then, CNN-Bi-LSTM-Attention model is built to train the data and predict the price of bitcoin. Finally, this model is compared with other models. It can be found that this model has higher prediction accuracy and better prediction effect than traditional models such as LSTM and CNN-LSTM.

Dr.Alamelu Mangai Jothidurai, Pratheek D Kanchan, Rahul Raj

Abstract: Cryptocurrency price prediction is a challenging task due to the high volatility and uncertainty of the market. Machine learning techniques can provide useful insights and forecasts for investors and traders. In this paper, we propose a novel approach for cryptocurrency price prediction using machine learning models and sentiment analysis. We collect historical price data of Bitcoin from yahoo business. We then apply various machine learning models, such as LSTM for the price prediction of the cryptocurrency using the past data. LSTM is a type of recurrent neural network that can manage long-term dependencies and sequential data. LSTM has three gates: forget gate, input gate, and output gate, which control the flow of information in and out of the memory cell. LSTM can be implemented in Python using the Keras and TensorFlow library. In this paper, we use LSTM as one of the machine learning models for cryptocurrency price prediction. We then use the average of the next 5 days of the predicted data to implement a buy-sell call strategy that aims to maximize the profit and minimize the risk. We evaluate our framework on a popular cryptocurrency Bitcoin.

Eddie Ngai, Salwani Abdullah, Salwani Abdullah, Mohd Zakree Ahmad Nazri · 6 authors

Over the past few years, Bitcoin has attracted the attention of numerous parties, ranging from academic researchers to institutional investors. Bitcoin is the first and most widely used cryptocurrency to date. Due to the significant volatility of the Bitcoin price and the fact that its trading method does not require a third party, it has gained great popularity since its inception in 2009 among a wide range of individuals. Given the previous difficulties in predicting the price of cryptocurrencies, this project will be developing and implementing a time series approach-based solution prediction model using machine learning algorithms which include Support Vector Machine Regression (SVR), K-Nearest Neighbor Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) to determine the trend of bitcoin price movement, and assessing the effectiveness of the machine learning models. The data that will be used is the close prices of Bitcoin from the year 2018 up to the year 2023. The performance of the machine learning models is evaluated by comparing the results of R-squared, mean absolute error (MAE), mean squared error (RMSE), and also through a visualization graph of the original close price and predicted close price of Bitcoin in a dashboard. Among the models compared, LSTM emerged as the most accurate, followed by SVR, while XGBoost and KNN exhibited comparatively lower performance.

Siniša Bogdan, Natali Brmalj, Elvis Mujačević

This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects.

Markus Frohmann, Manuel Karner, Said Khudoyan, Robert F. Wagner · 5 authors

Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.

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.

Shuyang Wang, Diego Klabjan

We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment of intraday cryptocurrency portfolio trading. We adopt a model selection method that evaluates on multiple validation periods, and propose a novel mixture distribution policy to effectively ensemble the selected models. We provide a distributional view of the out-of-sample performance on granular test periods to demonstrate the robustness of the strategies in evolving market conditions, and retrain the models periodically to address non-stationarity of financial data. Our proposed ensemble method improves the out-of-sample performance compared with the benchmarks of a deep reinforcement learning strategy and a passive investment strategy.

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.