Blockchain Papers

Jui‐Cheng Hung, Hung‐Chun Liu, J. Jimmy Yang

No abstract is available for this record.

Gil Cohen

<abstract> <p>This research attempts to fit a polynomial auto regression (PAR) model to intraday price data of four major cryptocurrencies and convert the model into a real-time profitable automated trading system. A PAR model was constructed to fit cryptocurrencies' behavior and to attempt to predict their short-term trends and trade them profitably. We used machine learning (ML) procedures enabling our system to train using minutes' data for six months and perform actual trading and reporting for the next six months. Results have shown that our system has dramatically outperformed the naive buy and hold (B & H) strategy for all four examined cryptocurrencies. Results show that our system's best performances were achieved trading Ethereum and Bitcoin and worse trading Cardano. The highest net profit (NP) for Bitcoin trades was 15.58%, achieved by using 67 minutes bars to form the prediction model, compared to −44.8% for the B & H strategy. Trading Ethereum, the system generated 16.98% NP, compared to −33.6% for the B & H strategy, 61 minutes bars. Moreover, the highest NPs achieved trading Binance Coin (BNB) and Cardano were 9.33% and 4.26%, compared to 0.28% and −41.8% for the B & H strategy, respectively. Furthermore, the system better predicted Ethereum and Cardano uptrends than downtrends while it better predicted Bitcoin and BNB downtrends than uptrends.</p> </abstract>

Vaibhav Saha

Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market.

Lauren Al Hawi, Sally Sharqawi, Qasem Abu Al‐Haija, Abdallah Qusef

No abstract is available for this record.

Azamjon Muminov, Otabek Sattarov, Jinsoo Cho

As the adoption of cryptocurrencies, especially Bitcoin (BTC) continues to rise in today’s digital economy, understanding their unpredictable nature becomes increasingly critical. This research paper addresses this need by investigating the volatile nature of the cryptocurrency market, mainly focusing on Bitcoin trend prediction utilizing on-chain data and whale-alert tweets. By employing a Q-learning algorithm, a type of reinforcement learning, we analyze variables such as transaction volume, network activity, and significant Bitcoin transactions highlighted in whale-alert tweets. Our findings indicate that the algorithm effectively predicts Bitcoin trends when integrating on-chain and Twitter data. Consequently, this study offers valuable insights that could potentially guide investors in informed Bitcoin investment decisions, thereby playing a pivotal role in the realm of cryptocurrency risk management.

Zixiong Wang, Qiuying Chen, Sang-Joon Lee

The rapid expansion of the non-fungible token (NFT) market has attracted many investors. However, studies on the NFT price fluctuations have been relatively limited. To date, the machine learning approach has not been used to demonstrate a specific error in NFT sale price fluctuation prediction. The aim of this study was to develop a prediction model for NFT price fluctuations using the NFT trading information obtained from OpenSea, the world’s largest NFT marketplace. We used Python programs to collect data and summarized them as: NFT information, collection information, and related account information. AdaBoost and Random Forest (RF) algorithms were employed to predict the sale price and price fluctuation of NFTs using regression and classification models, respectively. We found that the NFT related account information, especially the number of favorites and activity status of creators, confer a good predictive power to both the models. AdaBoost in the regression model had more accurate predictions, the root mean square error (RMSE) in predicting NFT sale price was 0.047. In predicting NFT sale price fluctuations, RF performed better, which the area under the curve (AUC) reached 0.956. We suggest that investors should pay more attention to the information of NFT creators. We anticipate that these prediction models will reduce the number of investment failures for the investors.

Kittiwin Kumlungmak, Peerapon Vateekul

Recently, reinforcement learning has been applied to cryptocurrencies to make profitable trades. However, cryptocurrency trading is a very challenging task due to the volatility of the market, especially during bearish periods. In addressing this problem, the existing literature employs single-agent techniques such as deep Q-network (DQN), advantage actor-critic (A2C), and proximal policy optimization (PPO), or their ensembles. Moreover, in the context of cryptocurrencies, the mechanisms for restricting losses during a bearish market are insufficiently robust. Consequently, the performance of reinforcement learning methods for cryptocurrency trading in the existing literature is constrained. To overcome this limitation, in this paper, we propose a novel cryptocurrency trading method based on multi-agent proximal policy optimization (MAPPO) with a collaborative multi-agent scheme and a local-global reward function to optimize both the individual and collective performance of the agents. Both a multi-objective optimization technique and a multi-scale continuous loss (MSCL) reward are used to train agents using a progressive penalty to avoid consecutive losses of portfolio value. As a result, better cumulative returns are achieved than when baseline methods are used. In addition, the superiority of our method is emphasized by the result of the bearish test set, where only our method can make a profit. Specifically, our method obtains a 2.36% cumulative return, whereas the baseline methods result in negative cumulative returns. In comparison to FinRL-Ensemble, a reinforcement learning-based method, our method achieves a 46.05% greater cumulative return in the bullish test set.

Thomas Conlon, Shaen Corbet

No abstract is available for this record.

M. Thamban Nair, Mohamed I. Marie, Laila A. Abd-Elmegid

One of the most significant and extensively utilized cryptocurrencies is Bitcoin (BTC). It is used in many different financial and business activities. Forecasting cryptocurrency prices are crucial for investors and academics in this industry because of the frequent volatility in the price of this currency. However, because of the nonlinearity of the cryptocurrency market, it is challenging to evaluate the unique character of time-series data, which makes it impossible to provide accurate price forecasts. Predicting cryptocurrency prices has been the subject of several research studies utilizing machine learning (ML) and deep learning (DL) based methods. This research suggests five different DL approaches. To forecast the price of the bitcoin cryptocurrency, recurrent neural networks (RNN), long short-term memories (LSTM), gated recurrent units (GRU), bidirectional long short-term memories (Bi-LSTM), and 1D convolutional neural networks (CONV1D) were used. The experimental findings demonstrate that the LSTM outperformed RNN, GRU, Bi-LSTM, and CONV1D in terms of prediction accuracy using measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score (R2). With RMSE= 1978.68268, MAE=1537.14424, MSE= 3915185.15068, and R2= 0.94383, it may be considered the best method.

Anny Mardjo, Chidchanok Choksuchat

Despite their popularity in recent studies, most hybrid models that exploit the advantages of both classical time series and deep learning models were conducted in univariate forecasting context. For econometric domain which exogenous factors play a crucial role, more studies in multivariate forecasting is essential and should be encouraged. Thus, contributing to hybrid multivariate forecasting literature, a hybrid model named HyBiLSTM was proposed. The algorithm began with ARIMAX GARCHX model forecasting, followed by second forecasting of model residual using Grey Wolf Optimizer based hyperparameters Bidirectional LSTM model. With residuals instead of original multivariate features, LSTM can avoid to processes each feature independently and therefore, reducing convergence complexity and execution time. The final forecasting results was compounded from both models. Three quantitative measurements, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), were used to evaluate the established models using the historical daily runoff social and economic based data (01/07/2019-31/12/2022). The findings showed 1) the addition of exogenous factors improved the performance of ARIMA and GARCH models; 2) BiLSTM outperformed other LSTM variants when was integrated with ARIMAX GARCHX model; 3) Using SHAP, Bitcoin price was influenced by stock price, Twitter volume, gold price, and Twitter sentiment index; and 4) structural break had significant effect on forecasting. Other than expanding the literatures regarding hybrid models in multivariate context, this study provides practical contribution for investors by analyzing the factors that the investors can use as an early warning for Bitcoin price fluctuation.

Kia Jahanbin, Mohammad Ali Zare Chahooki

With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the text. This paper presents a hybrid model of transfer deep learning methods for the aspect-oriented sentiment analysis of influencers’ tweets to predict the trend of cryptocurrencies. In the first model, different aspects of tweets are extracted using the Concept Latent Dirichlet Allocation (Concept-LDA). Then, by using the pre-trained RoBERTa network and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) deep learning network and attention layer, sentiments of different aspects of tweets are determined. In the following, the price trend of seven cryptocurrencies, Bitcoin, Ethereum, Binance, Ripple, Dogecoin, Cardano, and Solana, is determined using the historical price and the polarity of tweets with BiGRU combined deep neural network and the attention layer. Also, we used the gridsearch method to select dropout hyper-parameters, learning rate, and the number of GRU units, and the Akaike Information Criterion (AIC) criterion confirmed the results of this proposed combination. The results show that the proposed model in the aspect-based sentiment analysis section has been able to achieve 5.94% accuracy and 9.9% improvement in the f1-score on the SemEval 2015 dataset and 2.61% improvement on the SemEval 2016 dataset in f1-score compared to the state-of-arts. Also, the results of predicting the price trend of cryptocurrencies show that the proposed model has correctly recognized the price trend in the next five days in 77% of cases according to the ROC-AUC criterion.

Fatemeh Feizian, Babak Amiri

Cryptocurrency as an alternative method of payment that acts both as a type of currency and as a virtual accounting system has always been of interest to investors. Since the public sentiment of a society about cryptocurrencies can affect the cryptocurrencies’ prices, a machine learning model based on sentiment analysis has been proposed to forecast the future prices of cryptocurrencies such as Bitcoin, Ethereum, EOS, Cardano, and Ripple using machine learning models that are suitable for time series data analysis to reduce the risk of investing in this market. It was shown that by applying weights to the sentiment scores of tweets according to the influence factor of the individuals, the accuracy of the prediction will increase and a significant difference between the accuracy scores was observed using the LSTM model according to the MAPE indicator (P=0.045). Also, a hybrid model is proposed based on the combination of features extracted from the texts by one of the dictionary-based text analysis models and the feature of weighted sentiment scores. It was shown that our proposed hybrid model outperformed the other models in predicting the prices of Ethereum, EOS, and Cardano according to the MSE indicator. Also, our proposed model based on weighted sentiment scores according to the influence factor of the Twitterers outperformed the other models in the prediction of the future prices of Bitcoin and Ripple, which indicates that the increase in the number of features will not always lead to an increase in the accuracy of our prediction models.

Jing Liu, Yuncheol Kang

No abstract is available for this record.

Michael Angelo, Ilhas Fadhiilrahman, Yudy Purnama

The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm.

S. Pourmohammad Azizi, Chien Yi Huang, Ti An Chen, Shu Chuan Chen · 5 authors

<abstract><p>In this article, an alternate method for estimating the volatility parameter of Bitcoin is provided. Specifically, the procedure takes into account historical data. This quality is one of the most critical factors determining the Bitcoin price. The reader will notice an emphasis on historical knowledge throughout the text, with particular attention paid to detail. Following the production of a historical data set for volatility utilizing market data, we will analyze the fundamental and computed values of Bitcoin derivatives (futures), followed by implementing an inverse problem modeling method to obtain a second-order differential equation model for volatility. Because of this, we can accomplish what we set out to do. As a direct result, we will be able to achieve our objective. Following this, the differential equation of the second order will be solved by an artificial neural network that considers the dataset. In conclusion, the results achieved through the utilization of the Python software are given and contrasted with a variety of other research approaches. In addition, this method is determined with alternative ways, and the outcomes of those comparisons are shown.</p></abstract>

Aleksandar Petrović, Luka Jovanović, Miodrag Živković, Nebojša Bačanin · 6 authors

The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM).The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies.The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem.The LSTM is further optimized by a swarm metaheuristic -arithmetic optimization algorithm (AOA).The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.

Thomas Conlon, Shaen Corbet, Yang Hou, Yang Hu · 5 authors

Despite frequent Blockchain splits stemming from Bitcoin, few studies have examined the determinants of Bitcoin fork returns. In this paper, we investigate the relationships between the returns of Bitcoin forks and a range of common risk factors, including Bitcoin, currency, network and equity-based factors. From a statistical perspective, we find consistent and significant associations between fork returns, their Bitcoin counterparts, and equity markets. Other common factors, such as the equity small-minus-big factor and changes in the Japanese Yen, are found to have occasional links with fork returns. From an economic perspective, Bitcoin returns are the predominant driver of fork returns, accounting for essentially all of the explained variation. These findings are confirmed using orthogonalised common factors and with an alternative methodology, quantile regression. This research broadens our understanding of Bitcoin forks, indicating that a change in blockchain protocol is insufficient to sever links with the Bitcoin parent.

Weihao Han, David Newton, Emmanouil Platanakis, Haoran Wu · 5 authors

Abstract We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.

Jaehyun Park, Yeong‐Seok Seo

Cryptocurrencies have recently attracted considerable attention, resulting in research mainly on deep learning-based price prediction models to maximize profit. Two research approaches have been adopted. Studies adopting the first approach directly predict the future cryptocurrency price. Long short-term memory (LSTM) and gated recurrent unit (GRU), which show high performance in time-series data, are mainly used for this approach. Further, studies adopting the second approach recommend actions to investors to maximize profits, such as “Sell”, “Buy”, and “Wait.” In this approach, classification models are used and results are derived based on probabilities. However, these action recommendation models do not consider the quality of the result. For example, it is risky to accept the result when the probability that the result of the action recommendation model for two classes is the correct answer is approximately 51%. To solve this problem, we recommend a method for adjusting the result of the action recommendation model based on Twitter sentiment analysis. The experimental results show that the proposed adjustment method improves the performance by approximately 3% compared to the conventional methods and are statistically validated.

Damian Zięba

No abstract is available for this record.

Hasib Shamshad, Fasee Ullah, Asad Ullah, Victor R. Kebande · 6 authors

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.

Dirk G. Baur, Jonathan R. Karlsen, Lee A. Smales, Allan Trench

No abstract is available for this record.

Hae Sun Jung, Seon Hong Lee, Haein Lee, Jang Hyun Kim

Predicting Bitcoin price trends is necessary because they represent the overall trend of the cryptocurrency market. As the history of the Bitcoin market is short and price volatility is high, studies have been conducted on the factors affecting changes in Bitcoin prices. Experiments have been conducted to predict Bitcoin prices using Twitter content. However, the amount of data was limited, and prices were predicted for only a short period (less than two years). In this study, data from Reddit and LexisNexis, covering a period of more than four years, were collected. These data were utilized to estimate and compare the performance of the six machine learning techniques by adding technical and sentiment indicators to the price data along with the volume of posts. An accuracy of 90.57% and an area under the receiver operating characteristic curve value (AUC) of 97.48% were obtained using the extreme gradient boosting (XGBoost). It was shown that the use of both sentiment index using valence aware dictionary and sentiment reasoner (VADER) and 11 technical indicators utilizing moving average, relative strength index (RSI), stochastic oscillators in predicting Bitcoin price trends can produce significant results. Thus, the input features used in the paper can be applied on Bitcoin price prediction. Furthermore, this approach allows investors to make better decisions regarding Bitcoin-related investments.

Abba Suganda Girsang, Stanley

Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social media. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocurrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.