This project investigates the active realm of Bitcoin price forecasting through the glass of machine intelligence models, including Logistic Regression, Support Vector Machines (SVM), and XGBoost Classifier. Leveraging a different dataset including historical and actual-occasion Bitcoin price dossier, the study employs an orderly method for dossier collection, feature collection, model preparation, and judgment. The aim is to embellish the veracity of short-term and unending forecasts, making the challenges posed apiece explosive cryptocurrency retail. The project extends further hypothetical exploration, climactic in the incident of a convenient web connect. This connects employs HTML, CSS, and Flask API to provide authentic-opportunity forecasts, extending the gap betwixt leading predictive models and proficient uses.
L. Rahunathan, Suhana Nafais A, S Rivitha, K Saran · 5 authors
Cryptocurrencies have gained popularity since the launch of virtual currencies like Bitcoin, Ethereum, and many more. Because of their extreme volatility, the cryptocurrency markets present both opportunities and challenges for traders and investors. In this volatile market, accurate cryptocurrency price prediction is essential for making wise investment choices. In this research, created a ground-breaking deep learning-based technique for forecasting bitcoin values. LSTM, GRU, Random Forest Regressor (RFR), and Super Vector Regressor (SVR) are a few of the deep learning algorithms that are used. The proposed model considers a number of elements that may have an impact on the value of cryptocurrencies, including historical price information, trading volume, market mood, and technical signals. In this research cross model is suggested that combines LSTM and GRU for the accurate prediction. Based on the results of evaluation measures such RMSE, MAE, MSE, R-Square Error, and Variance Regression Score, the optimal model is selected. In comparison to previous deep learning models, the hybrid model of LSTM+GRU has performed well in price prediction, according to the results of evaluation metrics.
R. Tamilkodi, P. Kalyan Chakravarthy, Aisha Maryam, P P K Venkat · 6 authors
It is difficult to predict cryptocurrency values because of the market's extreme volatility. We suggest a prediction system that makes use of LSTM networks, a potent deep learning technique for identifying temporal patterns in time-series data, in order to solve this. In order to help investors make wise choices, this method is made to forecast the values of eight significant cryptocurrencies, such as BTC, ETH, and BNB. Performance indicators including MSE, MAPE, R2 Score, and RMSE are used to assess the model's efficacy. A comparison with alternative models, such as Gated Recurrent Unit (GRU), linear regression, and conventional time series analysis, is also carried out. thorough testing using a dataset with 8,000 timesteps that were captured at 4-hour intervals shows the LSTM model's better performance, attaining a MAPE of 80% between March 2021 and March 2023. This method offers insightful information that facilitates improved risk management and decision-making in the extremely unpredictable bitcoin market.
With the rapid development of the financial industry and artificial intelligence (AI) technology, the application of AI robots in finance has become a widely discussed topic in the academia. As an important part of the financial market, the secondary trading market of virtual currencies is characterized by high volatility, risk and decentralization, which poses significant challenges for traditional trading methods. AI technologies, especially machine learning and deep learning algorithms, provides a new path to optimize trading strategies and reduce investment risks thanks to their powerful data processing, pattern recognition and real-time analysis capabilities. This paper focuses on the characteristics of AI technology and the secondary trading market of virtual currencies, as well as the practical application of artificial intelligence technology in the financial industry, and explores the potential of applying AI robots to virtual currency trading. The research shows that AI robots can provide more accurate decision support for investors through massive data analysis, automatic trading execution and real-time risk assessment, improving market response speed and investment return rate. If the combination of AI robots and virtual currency trading is successful, it is expected to create more long-term and stable returns for investors, providing important theoretical and practical value for the development of financial investment.
Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai · 5 authors
Distributed ledger technology (DLT) and cryptocurrency have revolutionized the financial landscape and relevant applications, particularly in investment opportunities. Despite its growth, the marketâs volatility and technical complexities hinder widespread adoption. This study proposes a cryptocurrency trading system powered by advanced machine learning (ML) models to address these challenges. By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. The up-to-date price prediction information obtained by the machine learning model is incorporated by custom oracle contracts and is transmitted to portfolio smart contracts. The integration of smart contracts and on-chain oracles ensures transparency and security, allowing real-time verification of portfolio management. The deployed cryptocurrency trading system performs these actions automatically without human intervention, which greatly reduces barriers to entry for ordinary users and investors. The results demonstrate the feasibility of creating a cryptocurrency trading system, with the LSTM model achieving a return on investment (ROI) of 488.74% for portfolio management during the duration of 9 December 2022 to 23 May 2024. The ROI obtained by the LSTM model is higher than the performance of Bitcoin at 234.68% and that of other benchmarking models with RF and Bi-LSTM over the same timeframe. This approach offers significant cost savings, transparent portfolio management, and a trust-free platform for investors, paving the way for broader cryptocurrency adoption. Future work will focus on enhancing prediction accuracy and achieving greater decentralization.
Serdar Neslihanoglu, Arzu Altın Yavuz, Muhammad Irfan, Alina Cristina NuĆŁÄ
Over the last decade, investors are interested in model fitting and predicting the future potential value of cryptocurrencies. For this purpose, the multiple linear, Ridge, Lasso and Elastic net regressions allowing for variable selection and regularization are compared. This comparison has yet to be undertaken in the literature. The analysis is implemented using weekly data (from 2015 to 2019) regarding Bitcoin (BTC) and Ethereum (ETH), especially with relation to Google and Wikipedia trends and 17 common factors, including stock market indices, gold and oil prices, central bank interest rates, exchange rates and policy uncertainty. The empirical findings favor the Elastic net approach, which outperforms the others in terms of model fit and predictability. Within the Elastic net framework, while the Google trend for the term "Bitcoin" (positively) has the greatest impact on Bitcoin price, the Chinese Yuan (CNY) to US Dollar (USD) exchange rate (negatively) has the greatest impact on Ethereum price. Based on study findings, essential policy implications are put forward.
The cryptocurrency market, known for its high volatility and immense data availability, provides an excellent opportunity for predictive modeling. This paper explores the prediction of Ethereumâs price using four distinct models: Random Forest, Logistic Regression, Long Short-Term Memory Networks (LSTM), and CNN-LSTM hybrid models. The study evaluates the performance of these models based on metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Rsquared ( R2 ), and Accuracy (%). The findings highlight that Logistic Regression outperformed the other models with the lowest MSE (6741.12) and highest accuracy (98.66% ) [Table 1]. This research demonstrates the potential of combining traditional and advanced machine learning techniques to achieve robust price prediction in the cryptocurrency domain.
The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.
The modern concept of taxation has been accelerated by the emergence of internet-based economy and the use of cryptocurrencies. This shift raises various difficulties for tax authorities in terms of revenue estimates; it requires sophisticated methods for quantitative analysis of intricate economic trends. Previous works have mainly employed conventional ML methods which are weak in their ability to recognize dependencies in the features for data with high dimensionality, hence poor forecast precision. To counter these drawbacks, the use of CapsNets with CNNs, which forms a combined model contributing to better predictive capability is introduced. CapsNets ability to keep ownership of spatial hierarchies and sophisticated features of the input makes the method to accurately extract and analyze features from digital transaction data. Working in Python, the suggested model was compared to several traditional algorithms with which it demonstrated the highest accuracy rate at 99.1%. As concluded from the evaluation, the CapsNet-CNN model is not only resistant to the formulation of new structures, but also flexible to the fluidity of the digital economy environment. This work demonstates the promise of using modern DL methodologies for improving the accuracy of the tax revenues and provides insights for the policy makers who are keen on evoking sensitive and dynamic response strategies of the taxation authorities in a world where more and more of the economic operations are being carried out in the cyberspace. Future research will quantify the components in the model and expand the investigation to other industries and locations.
Abstract We present the first evidence of investorâtradingâbased disagreement's influence on crossâsectional cryptocurrency daily returns. We interpret abnormal trading volume as investor disagreement and find evidence in support of Miller's disagreement model: when shortâsale constraints are binding, high abnormal volume (high disagreement) assets experience lower future returns. Further supporting Miller, these same conditions associate with higher contemporaneous order imbalance, and ex post decreases in both buying and selling activities, with the former exceeding the latter in magnitude. By contrast, the effect of high disagreement disappears after a coin's margin trading is activated. We conclude that priceâoptimism models explain the disagreementâreturns relationship when opinion divergence is likely the dominant determinant of returns.
With the rapid growth of the cryptocurrency market, researchers increasingly study the price fluctuations and market behavior of digital assets. Gold, as a traditional safe-haven asset, often shows an inverse relationship with high-risk financial assets. Recently, scholars have focused on how gold market volatility affects cryptocurrencies, exploring potential co-movement or substitution effects. This study uses Python and econometric tools, including the Vector Autoregression (VAR) model, Granger causality test, impulse response functions, and forecast error variance decomposition, to analyze the impact of gold price changes on Bitcoin and Ethereum. Using weekly closing prices from 2018 to 2024, the results show that Bitcoinâs price is positively influenced by gold futures in the short to medium term, while gold shows a negative feedback response to Bitcoinâs returns with a two-period lag. Ethereum appears more independent and less affected by gold or Bitcoin. Strong interlinkages exist between Bitcoin and Ethereum, with Bitcoin playing a dominant role in influencing Ethereumâs price. This study has improved the understanding of the connections between cryptocurrencies and traditional assets., which also provides investors with insightful information on portfolio management.
This paper analyses the relationship between Bitcoin (BTC) values and the values of older cryp tocurrencies. Daily closing price data of Bitcoin (BTC), Litecoin (LTC), Namecoin (NMC), Peercoin (PPC), Ripple (XRP), Dogecoin (DOGE), Primecoin (XPM) and Nxt (NXT) for the period from De cember 16, 2013 to December 14, 2024 was used to perform Granger causality in two ways. The results show linear and nonlinear Granger causality in both directions between most cryptocu rrencies, BTC-LTC, BTC-XRP and BTC-DOGE relationships. On the other hand, BTC does not have a causal influence on NMC, PPC and XPM in the linear model. In contrast, in the nonlinear model, BTC Granger causes NMC. These insights are crucial for understanding the complex cryptocu rrency market price dynamics, aiding investors and analysts in making more informed decisions based on historical data and predictive relationships.
Abstract Bitcoin has emerged as a highly attractive and reliable investment asset for financial managers, businesses, and economic firms due to its unique features such as high security, decentralization, and potential for increased income. Consequently, Bitcoin price prediction has become a significant topic of interest among financial and economic analysts and researchers. Forecasting in such contexts often involves uncertain conditions and limited information. Grey systems theory, which specializes in analyzing problems with small samples and insufficient information, offers a promising approach. This study aims to predict the price of Bitcoin using an advanced model of grey systems theory: the fractional multivariable grey model (FGM(1, N )). The FGM(1, N ) model stands out by incorporating external factors into its predictions. Specifically, this research utilizes the FGM(1,3) model, considering the crude oil and gold prices to forecast Bitcoin price. The results demonstrate that the FGM(1,3) model provides more accurate predictions and better performance than the FGM(1,1) model, which does not include external factors like oil and gold prices. This study highlights the significant impact of crude oil and gold price trends on Bitcoin's market and underscores the effectiveness of the multivariable fractional grey model in financial forecasting.