Yeajun Kang, Wonwoong Kim, Hyunji Kim, Minwoo Lee · 6 authors
A smart contract is a digital contract on a blockchain. Through smart contracts, transactions between parties are possible without a third party on the blockchain network. However, there are malicious contracts, such as greedy contracts, which can cause enormous damage to users and blockchain networks. Therefore, countermeasures against this problem are required. In this work, we propose a greedy contract detection system based on deep learning. The detection model is trained through the frequency of opcodes in the smart contract. Additionally, we implement Gredeeptector, a lightweight model for deployment on the IoT. We identify important instructions for detection through explainable artificial intelligence (XAI). After that, we train the Greedeeptector through only important instructions. Therefore, Greedeeptector is a computationally and memory-efficient detection model for the IoT. Through our approach, we achieve a high detection accuracy of 92.3%. In addition, the file size of the lightweight model is reduced by 41.5% compared to the base model and there is little loss of accuracy.
Fatma Ben Hamadou, Taicir Mezghani, Ramzi Zouari, Mouna Boujelbène Abbes
Purpose This study aims to assess the predictive performance of various factors on Bitcoin returns, used for the development of a robust forecasting support decision model using machine learning techniques, before and during the COVID-19 pandemic. More specifically, the authors investigate the impact of the investor's sentiment on forecasting the Bitcoin returns. Design/methodology/approach This method uses feature selection techniques to assess the predictive performance of the different factors on the Bitcoin returns. Subsequently, the authors developed a forecasting model for the Bitcoin returns by evaluating the accuracy of three machine learning models, namely the one-dimensional convolutional neural network (1D-CNN), the bidirectional deep learning long short-term memory (BLSTM) neural networks and the support vector machine model. Findings The findings shed light on the importance of the investor's sentiment in enhancing the accuracy of the return forecasts. Furthermore, the investor's sentiment, the economic policy uncertainty (EPU), gold and the financial stress index (FSI) are the top best determinants before the COVID-19 outbreak. However, there was a significant decrease in the importance of financial uncertainty (FSI and EPU) during the COVID-19 pandemic, proving that investors attach much more importance to the sentimental side than to the traditional uncertainty factors. Regarding the forecasting model accuracy, the authors found that the 1D-CNN model showed the lowest prediction error before and during the COVID-19 and outperformed the other models. Therefore, it represents the best-performing algorithm among its tested counterparts, while the BLSTM is the least accurate model. Practical implications Moreover, this study contributes to a better understanding relevant for investors and policymakers to better forecast the returns based on a forecasting model, which can be used as a decision-making support tool. Therefore, the obtained results can drive the investors to uncover potential determinants, which forecast the Bitcoin returns. It actually gives more weight to the sentiment rather than financial uncertainties factors during the pandemic crisis. Originality/value To the authors’ knowledge, this is the first study to have attempted to construct a novel crypto sentiment measure and use it to develop a Bitcoin forecasting model. In fact, the development of a robust forecasting model, using machine learning techniques, offers a practical value as a decision-making support tool for investment strategies and policy formulation.
Kafila, R. Appavu Raj, P Pavithra, Soyabkhan Mehbubkhan Baloch · 6 authors
Blockchain technology dependent on cryptocurrency have lately excited the curiosity of capitalists. They focused on forecasting the financial item's risk and return ratios. As a result, financial items require an autonomous algorithm to anticipate the return percentage of cryptocurrency. Deep learning (DL) algorithms that were lately developed lay the path for the return percentage forecasting procedure. This paper proposes a blockchain financial product utilizing DL for a smart return rate prediction (RRP-DLBFP) method. The suggested RRP-DLBFP method entails creating a long short-term memory (LSTM) framework for return percentage forecasting. Furthermore, the Adam optimization is used to effectively change the LSTM algorithm's hyperparameters, resulting in improved forecasting accuracy. The Ethereum rate of return has been selected as the aim of guaranteeing the RRP-DLBFP method's superior performance, and its outcomes are studied in various metrics. In terms of several assessment variables, the model's results demonstrated the superiority of the RRP-DLBFP method over the present latest methods. The suggested RRP-DLBFP exhibits MSE values of 0.0435 & 0.0655, accordingly, contrasted with a mean of 0.6139 & 0.723 for comparing techniques in both training and evaluation.
This paper presents an approach for predicting the price of Bitcoin using machine learning techniques. We used historical data of Bitcoin prices and extracted relevant features such as trading volume, social media sentiment, and market capitalization to train and test the models. Numerous machine learning algorithms, such as random forest, gradient boosting, and neural networks, were tested by us and evaluated their performance using metrics such as mean squared error and accuracy. Our results show that machine learning models can effectively predict the price of Bitcoin with a reasonable degree of accuracy. We also discuss the limitations of our approach and suggest future research directions to improve the performance of Bitcoin price prediction models. Our findings suggest that machine learning can be a useful tool for investors and traders in making informed decisions about Bitcoin investments.
This article aims to investigate the presence of herding behavior in artificial intelligence (AI)–themed cryptocurrencies following the launch of ChatGPT. The authors analyze daily data from major AI-themed cryptocurrencies between November 2022 and February 2023. This study finds evidence of irrationality among investors in this market segment who tend to imitate others’ decisions regardless of their own beliefs during down events. The authors connect this finding to the herding theory in financial economics and highlight the implications for investors and policymakers. This article contributes to the literature on the impact of AI on financial markets and suggests the need for further research in this area. Finally, this study provides important policy implications, as it could help investors better understand the risks associated with this emerging asset class.
Leonardo H.S. Fernandes, JOSÉ W. L. SILVA, Aurelio F. Bariviera, Kleber E S Sobrinho · 5 authors
This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a non-linear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behaviour for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behaviour than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the non-linear cross-correlations excluding the pair Bitcoin vs Dogecoin (í µí»¼ í µí±¥í µí±¦ (0) = −1.14%). At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the non-linear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.
Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and understanding its determinants are crucial. This paper applies machine learning (ML) techniques to forecast cryptocurrency volatility using internal determinants (e.g., lagged volatility, previous trading information) and external determinants (e.g., technology, financial, and policy uncertainty factors). Both Random Forest and Long Short-Term Memory (LSTM) networks significantly outperform traditional volatility models such as GARCH. Furthermore, we explore two optimization models—Genetic Algorithm and Artificial Bee Colony—to tune the hyper-parameters of LSTM. Our results indicate that the application of these optimization models substantially improves forecasting performance. Moreover, using SHapley Additive exPlanations, an interpretation method, we find that internal determinants play the most important roles in volatility forecasts. Finally, our results show that models trained with determinants from multiple cryptocurrencies outperform those trained with determinants from a single cryptocurrency, suggesting that considering a broader range of determinants can capture the complex dynamics in the cryptocurrency market.
For more than a decade, as the number and value of cryptocurrencies exploded, more and more investors flocked to the cryptocurrency market with the expectation of positive returns. The price of cryptocurrencies, on the other hand, is extremely volatile. As a result, there is a great need to develop an accurate price prediction model to assist investors in making decisions and profit. This paper focuses on developing an LSTM-based prediction model for Bitcoin, Ethereum, EOS, and Solana cryptocurrency price prediction and calculating their RMSE and MAPE. Furthermore, four models are compared using this calculated MAPE. Based on the comparison results, the impact of cryptocurrency volatility, liquidity, and technology level on the accuracy of the LSTM prediction model is also examined. The paper concludes that the LSTM model can predict the price of Bitcoin more accurately because Bitcoin has the least volatility, the most liquidity and uses the oldest but most secure consensus mechanism.
This article reveals a specific category of solutions for the 1+1 variable order (VO) nonlinear fractional Fokker-Planck equations. These solutions are formulated using VO q-Gaussian functions, granting them significant versatility in their application to various real-world systems, such as financial economy areas spanning from conventional stock markets to cryptocurrencies. The VO q-Gaussian functions provide a more robust expression for the distribution function of price returns in real-world systems. Additionally, we analyzed the temporal evolution of the anomalous characteristic exponents derived from our study, which are associated with the long-term (power-law) memory in time series data and autocorrelation patterns.
This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.
Purpose- Forecasting techniques and models are extremely important for people and organizations that are in the right decision making and investment stage. Forecast accuracy enables successful decisions and allows investors to maximize their profits. The development of finance and related technologies in the world and innovative financial instruments have made it interesting for investors. The most popular of these developments is undoubtedly Bitcoin, a product of blockchain technology. The purpose of this study is to predict the future values of Bitcoin. Methodology- In this study, future predictions are made using an LSTM model based on Bitcoin's historical data and indicators of key market forecasters. In this study, 3 different data sets were created by selecting 1 indicator from 4 different indicator types. The 10 Bitcoin data coming after the last value is estimated. Findings- In this study, 3 different data sets were created by selecting an indicator from 4 different indicator groups. These datasets were first trained with the iterative neural network LSTM model and then tested with real values. At the same time, the next 10 bitcoin price values were also predicted in a 15-minute period. Error rates at the end of the model were compared with each other. The 1st dataset, with the most used indicators in the datasets, produced the lowest error rate. Conclusion- The dataset 1, which consists of the most used indicators of the datasets, gave the lowest error rate. According to this result, the rate of reaching realistic values increases as the use of indicators increases. Keywords: LSTM, bitcoin, cryptocurrency, neural network, prediction JEL Codes: C53, C45, G10
Cryptocurrencies are highly anonymous, poorly regulated in many countries, and can issue tokens at nearzero cost using existing platforms. As a result, there is no shortage of fraudulent cryptocurrencies that raise large sums of money through hype, then disappear and do little actual project development. The prevalence of fraudulent cryptocurrencies not only harms investors but can also prevent sound companies from raising funds. To remedy this situation, it would be useful to develop a method to determine whether a particular cryptocurrency is fraudulent or not. The information in cryptocurrency whitepapers could be useful in detecting fraudulent cryptocurrency, but there are no clear criteria to evaluate the reliability and feasibility of their content. Besides, most studies analyzing whitepapers focus on the success or failure of ICO ”fundraising” and fail to adequately consider the ongoing development and operation of the project. On the other hand, a few studies have attempted to detect fraudulent cryptocurrencies from whitepapers, but their results suggest the possibility of identifying fraud with high accuracy. The objective of this paper is to build a model to detect fraudulent cryptocurrencies from whitepapers using natural language processing and machine learning techniques, and to verify whether the model has sufficient predictive accuracy in detecting fraud, after solving the problems of previous studies. We collected 250 cryptocurrency whitepapers consisting of 150 frauds and 100 controls, extracted features, and applied multiple machine learning methods to classify frauds and controls. Then analyzed the feature differences between the fraud and control groups, and examined the tendency of fraudulent cryptocurrency whitepapers. We observed 0.841 F1 Score for the best prediction model, which outperforms previous studies. Furthermore, the performance of K-Means, which is unsupervised learning, was not significantly lower than that of other machine learning methods, and a certain level of accuracy was confirmed. Therefore, there is a possibility that K-Means can be used in cases where fraud criteria cannot be clearly defined. We also found that fraudulent cryptocurrency whitepapers used relatively more business and finance-related words. On the other hand, whitepapers in the control group tended to use more blockchain-related technical terms.
In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data. In this article, we delve into the challenging problem of forecasting cryptocurrency prices using mathematical extrapolation techniques. We highlight the scarcity of research in this domain, underlining the necessity for in-depth investigation. The article outlines the unresolved issues related to extrapolation-based cryptocurrency price prediction, such as market volatility and non-linearity. It primarily aims to showcase the potential of extrapolation for predicting bitcoin prices. The analysis involves a year-long bitcoin price trend, with the application of linear and polynomial extrapolation methods. While some correlation exists, notable discrepancies, especially during abrupt price changes, are evident. The conclusion emphasizes the limitations of extrapolation and advises a diversified approach to cryptocurrency investment decisions, considering various factors beyond mathematical data.
Unlike traditional currencies that rely on centralized such as banks or governments, cryptocurrencies have become popular due to its decentralized transactions. Decentralization takes advantage of no requirement for intermediaries, thus reducing transaction fees and processing times. However, investing in cryptocurrencies incurs risks and uncertainties due to price volatility and rapid changes. The fact that prediction of asset prices is complex due to the influence of multiple factors on price movements. This paper studied the technical factor to analyse the short-term returns of Ethereum (ETH) in the periods of 1-10 days. The historical data containing ETH closing price are collected from CoinGecko. The twenty-two indicators are chosen from Momentum, Volatility, and Sentiment factors as candidates to provide valuable insights in market trends. By calculating various indicators based on past closing prices, this study utilizes XGBoost, a powerful boosted decision trees ensemble, to discover patterns in previous trading. The model performance is evaluated using the multi-class AUC-ROC metric, which measures the accuracy of predicting three types of ETH returns: Downtrend, Sideway, and Uptrend. The results show that the models achieve accuracy scores ranging from 0.65 to 0.67. Moreover, the study emphasizes the importance of considering momentum indicators when making investment decisions in Ethereum. Keywords—cryptocurrency investment, technical factor, Ethereum, XGBoost, machine learning
Financial markets are complex, evolving dynamic systems. Due to their irregularity, financial time series forecasting is regarded as a rather challenging task. In recent years, artificial neural network applications in finance for such tasks as pattern recognition, classification, and time series forecasting have dramatically increased. The objective of this paper is to present this versatile framework and attempt to use it to predict the stock return series of four public-listed companies on the New York Stock Exchange. Our findings coincide with those of Burton Malkiel in his book, A Random Walk Down Wall Street; no conclusive evidence is found that our proposed models can predict the stock return series better than that of a random walk.
The aim of this work is to utilize the kernel regression (KR) approach to predict the closed-price for cryptocurrencies. This study makes use of three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The min-max normalization method was used to scale feature values to a common range, often between 0 and 1. Furthermore, support vector regression (SVR) and long-short term memory (LSTM) were used to compare the prediction model-based on KR. The result of the KR models utilizing RMSE and MAPE demonstrated that the predictive model-based on KR gave more satisfying results.
Time series data from bitcoin has nonlinear data fluctuations so that a model is needed that can accommodate data with these conditions. The method that can be used for nonlinear time series data cases such as bitcoin is the LSTAR-GARCH model. LSTAR-GARCH is a combination of the LSTAR model and the GARCH model. Bitcoin investment also contains an element of risk. To find out the value of risk, the <em>Expected Tail Loss </em>risk measurement tool can be used. <em>Expected Tail Loss </em>(ETL). The data used in this study are historical daily bitcoin price data for the period April 1, 2022 to April 1, 2023. The modeling results obtained based on the MAPE value show that the LSTAR-GARCH model is the best model with the smallest MAPE value of 30% compared to the AR, LSTAR, or AR-GARCH models. The expected Taill loss value of bitcoin is -0.06784.
Abstract In recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long short‐term memory, convolutional neural network and attention mechanism. Using an ensemble of three pre‐trained language models, we extracted sentiment of cryptocurrency‐related tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14‐ and 21‐days' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices.
B. Murali Krishna, I. Sapthami, Venkata Ramana Banka, Chittibabu Ravela
People are now investing more on crypto currency and Bit coin. Hence, predicting the price of a bit coin can save people from financial loss. Moreover, the price of bit coin also fluctuates based on the stock market prices. In order to predict the price of the crypto currency, this research study has used LSTM (Long Short Term Memory) to generate assessments based on bitcoin stock queries. Based on stock demand, the proposed LSTM model integrates with the Yahoo Finance to predict the price of the Bitcoin.