This paper presents an advanced framework for analyzing cryptocurrency market microstructure through the integration of deep learning techniques and social media sentiment analysis. The proposed approach combines BERT-based sentiment analysis with market microstructure indicators to capture complex market dynamics. The framework processes multi-source data streams, including social media content and order book information, to generate comprehensive market insights. Experimental evaluation conducted on cryptocurrency market data from January 2022 to December 2023 demonstrates superior performance compared to traditional approaches. The model achieves 91.2% prediction accuracy and maintains a Sharpe ratio of 2.34 in trading simulations. The attention mechanism effectively identifies relevant market signals with 92.3% precision, while the temporal feature extraction module captures multi-scale market patterns. The applications have been successful with the capability of the ability to below 100 milliseconds, fit for high applications. The studies made for fields by creating the processing system for market microstructure focuses for commercial and investigators. The framework's performance stability across different market conditions validates its practical applicability in cryptocurrency trading and market analysis.
The stock market is a vital component of the financial sector. Due to the inherent uncertainty and volatility of the stock market, stock price prediction has always been both intriguing and challenging. To improve the accuracy of stock predictions, we construct a model that integrates investor sentiment with Long Short-Term Memory (LSTM) networks. By extracting sentiment data from the “Financial Post” and quantifying it with the Vader sentiment lexicon, we add a sentiment index to improve stock price forecasting. We combine sentiment factors with traditional trading indicators, making predictions more accurate. Furthermore, we deploy our system on the blockchain to enhance data security, reduce the risk of malicious attacks, and improve system robustness. This integration of sentiment analysis and blockchain offers a novel approach to stock market predictions, providing secure and reliable decision support for investors and financial institutions. We deploy our system and demonstrate that our system is both efficient and practical. For 312 bytes of stock data, we achieve a latency of 434.42 ms with one node and 565.69 ms with five nodes. For 1700 bytes of sentiment data, we achieve a latency of 1405.25 ms with one node and 1750.25 ms with five nodes.
The integration of Artificial Intelligence (AI) in finance has significantly transformed various aspects of the industry, from algorithmic trading and risk management to regulatory compliance and decentralized finance (DeFi). AI-driven models enhance market prediction accuracy, automate trading strategies, and improve fraud detection, thereby increasing efficiency and reducing financial risks. Moreover, AI-powered robo-advisors and credit scoring systems contribute to financial inclusion by offering personalized and data-driven services. Despite these advancements, challenges such as AI explainability, data privacy concerns, algorithmic bias, and regulatory constraints remain critical research areas. Additionally, emerging trends, including quantum computing, AI-enhanced DeFi, and privacy-preserving machine learning, are expected to further shape the future of AI applications in finance. This paper provides a comprehensive review of AI-driven innovations in financial markets, banking services, and regulatory compliance while discussing ongoing challenges and future research directions.
Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.
Ibrahim Garba Kabo, Georgina N. Obunadike, Nuruddeen A. Samaila
Bitcoin, the leading cryptocurrency, has gained significant attention due to its high volatility and potential economic impact. Traditional financial forecasting models struggle to accurately predict Bitcoin prices due to its sensitivity to various factors, including market sentiment and macroeconomic conditions. Existing models primarily rely on historical price data, often neglecting external influences such as public sentiment and economic indicators like Gross Domestic Product (GDP). To address these limitations, this study explores a hybrid approach that integrates Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models with sentiment analysis and GDP data to enhance Bitcoin price prediction accuracy. The study evaluates the predictive capabilities of these models under different scenarios. When trained on Bitcoin price data combined with sentiment analysis and GDP data, the ARIMA model achieved a Mean Absolute Error (MAE) of 2081.66, Root Mean Square Error (RMSE) of 2518.35, and an R-squared value of 0.9143. In comparison, when trained on Bitcoin data alone, it exhibited lower accuracy. The LSTM model demonstrated superior performance, achieving an MAE of 1253.24, RMSE of 1717.65, and an R-squared value of 0.9602 when incorporating sentiment and GDP data, significantly outperforming its standalone counterpart. The results highlight the effectiveness of integrating sentiment analysis and GDP data in cryptocurrency price prediction, demonstrating that hybrid models provide greater forecasting accuracy than traditional approaches. This study offers a robust framework for financial time series forecasting, aiding investors, analysts, and policymakers in making more informed decisions in the cryptocurrency market.
Cryptocurrencies have received a lot of attention from central banks, investors, and governments worldwide. The insufficiency of any method of political guideline and their market is far from "effective", so they want novel regulation methods shortly. From an econometric perspective, the technique underlying the growth of the cryptocurrencies' volatility was observed to demonstrate similarities and differences with other economic time series, e.g., foreign exchange yields. Accurate prediction of cryptocurrency price fluctuations is significant for effectual portfolio management and improves economic models by identifying potential risks and attacks. With the growing use of AI in various fields, its application in financial markets, especially cryptocurrencies and stocks, is an emerging research area. This study presents an Empirical Evaluation of Fuzzy Bidirectional Long Short-Term Memory with a Soft Computing-based Decision-Making Model for Predicting Volatility of Cryptocurrencies (FBLSTMSC-DMPVC) technique. The primary focus of the FBLSTMSC-DMPVC technique is to present a robust and intelligent framework for an advanced decision-making model to predict cryptocurrency volatility. Initially, the presented FBLSTMSC-DMPVC method performs the data preprocessing process using Z-score normalization to ensure all features are standardized and scaled. Furthermore, the fuzzy bidirectional long short-term memory (FBLSTM) method predicts cryptocurrency volatility. To enhance the hyperparameters of the FBLSTM technique, the improved carnivorous plant algorithm (ICPA) is employed. A wide range of simulation is accomplished to ensure the impact of the FBLSTMSC-DMPVC technique. The FBLSTMSC-DMPVC technique portrayed a superior MAPE value of 0.7939 for BTC, 0.8633 for ETH, 0.6187 for LTC, and 0.6667 for XRP, demonstrating its performance across various cryptocurrencies.
Albi Isufaj, Caio De Castro Martins, Marc Cavazza, Helmut Prendinger
This paper explores the applicability of Convergent Cross Mapping (CCM) and its extension, Time Delay Convergent Cross Mapping (TDCCM), to assess the causal relationships between Bitcoin, the S&P 500 index, and gold. Unlike conventional causality analysis methods, such as Granger causality or transfer entropy, CCM accounts for non-separable, weakly connected dynamic systems, and TDCCM explicitly incorporates time lags during cross-mapping, enabling the detection of complex causal relationships in systems with shared nonlinear behavior. This makes it particularly suitable for financial time series that often exhibit chaotic and nonlinear dynamics, particularly during periods of market instability. We integrate TDCCM with simplex projection and sequential locally weighted global linear map (S-map) algorithms, applying a sliding window approach to identify short time intervals characterized by high levels of nonlinearity and chaoticity. Using this approach, we uncovered a strong causal relationship between Bitcoin and the S&P 500 index during the onset of the COVID-19 pandemic. Our analysis reveals a bidirectional causal relationship between Bitcoin and the S&P 500 index, highlighting their interconnectedness during periods of heightened economic uncertainty. Furthermore, we find a unidirectional causal influence of Bitcoin on gold, reflecting Bitcoin’s evolving role as a macroeconomic indicator and its growing relevance as an alternative store of value. These findings provide insight into the dynamics between cryptocurrencies and traditional financial markets, particularly during periods of global economic disruption. • We use Time Delay Convergent Cross Mapping (TD-CCM) to identify and quantify lagged causal interactions between financial time series (Bitcoin, Gold, and the S&P 500 index), which is a more recent and less explored method for Time Series causality. • We report a comprehensive and replicable methodology to apply TD-CCM to non-linear TS, based on combining the S-Map and Prediction Decay algorithm with a sliding window technique, while validating with surrogate analysis. • We show evidence of strong causal influence from Bitcoin to Gold and bidirectional causality between Bitcoin and the S&P 500 Index during significant economic events like the COVID-19 pandemic.
Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is translating this information into reliable consumer opinion as it contains jargon, abbreviations, and reference links with previous content. Several ensemble models have been introduced to mine the enormous noisy range on social platforms. Still, these need more predictability and are the less-generalized models for social sentiment analysis. Hence, an optimized stacked-Long Short-Term Memory (LSTM)-based sentiment analysis model is proposed for cryptocurrency price prediction. The model can find the relationships of latent contextual semantic and co-occurrence statistical features between phrases in a sentence. Additionally, the proposed model comprises multiple LSTM layers, and each layer is optimized with Particle Swarm Optimization (PSO) technique to learn based on the best hyperparameters. The model's efficiency is measured in terms of confusion matrix, weighted f1-Score, weighted Precision, weighted Recall, training accuracy, and testing accuracy. Moreover, comparative results reveal that an optimized stacked LSTM outperformed. The objective of the proposed model is to introduce a benchmark sentiment analysis model for predicting cryptocurrency prices, which will be helpful for other societal sentiment predictions. A pretty significant thing for this presented model is that it can process multilingual and cross-platform social media data. This could be achieved by combining LSTMs with multilingual embeddings, fine-tuning, and effective preprocessing for providing accurate and robust sentiment analysis across diverse languages, platforms, and communication styles.
The cryptocurrency market is currently one of the most interesting areas for investment, attracting both experienced and casual investors. Although it can offer high returns, it also poses significant risks due to its high volatility. In this context, artificial intelligence, particularly through deep learning and machine learning algorithms, has played a key role in developing applications that provide investment advice, with the aim of maximizing returns and reducing investment risks. This study proposes a system for forecasting the closing prices of ten of the leading cryptocurrencies currently available in the market, presented in a web application capable of making predictions ranging from one to four hours. To achieve this, different models using various machine learning and deep learning algorithms were analyzed and tested, including Recurrent Neural Networks, time series analysis algorithms such as ARIMA, and even some more conventional regression algorithms. For algorithm comparison, minute step Bitcoin price data over a 30-day period was used to forecast prices 60 minutes ahead. Through extensive experimentation, the GRU neural network demonstrated superior predictive accuracy, achieving MAPE = 0.09\%, MSE = 5954.89, RMSE = 77.17, and MAE = 60.20. A web application was also developed, which integrates the best-performing model to provide real-time price predictions for multiple cryptocurrencies.
Cryptocurrencies do not have proper economic fundamentals. Consequently, economic variables cannot predict crypto prices. According to economic theory, cryptocurrencies are unbacked assets that are inherently unforecastable. However, a growing strand of literature suggests global crypto markets to be informationally inefficient. It implies the possibility of return predictability based on past information. Forecasting the allegedly unforecastable becomes feasible. Keeping it sophisticatedly simple, past infomation can be captured by autoregressive integrated moving average (ARIMA) processes of principal components. However, Principal Component Analysis (PCA) for crypto price series is due to their non-Gaussian property not applicable and requires the assumption of a stochastic trend model. Making use of the Central Limit Theorem, Independent Component Analysis (ICA) overcomes this deficiency. We show that ICA combined with ARIMA modeling more than triples the predictability of global crypto price dynamics. • Crypto markets are found to be inefficient in the sense of majority games. • ICA based ARIMA more than triples predictability of crypto price dynamics. • ICA based ARIMA is most reliable for directional out-of-sample predictions.
ABSTRACT This study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVID‐19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.
Cryptocurrency is the most innovative financial and technological breakthrough of this generation. Investment in cryptocurrency grew from USD 11.18 billion in December 2016 to USD 2.147 trillion in April 2024; however, the rationality of investor exuberance is uncertain. This paper explores stakeholders' perceptions of cryptocurrency using a machine-learning approach based on artificial intelligence (AI). In particular, we employ a lexicon-based emotion-detection sentiment analysis to investigate stakeholder perceptions, using 2.3 million open-source data points. We divide the findings into positive, neutral, and negative stakeholder perception pillars based on factors such as trustworthiness in cryptocurrency, motives, cryptocurrency awareness and knowledge, ownership, socioeconomic characteristics of users, and usage. Our analysis reveals that 51 percent of the stakeholders have a positive perception of cryptocurrency, whereas 40 percent have a neutral perception and 9 percent a negative perception. After identifying the perceptions, we investigate the relationship between cryptocurrency prices and stakeholder perceptions using the autoregressive distributed lag (ARDL) framework with time-series data from August 2017 to July 2023. The long- and short-term results confirm that positive and negative perceptions have statistically significant effects on cryptocurrency prices. Individual investors comprise the largest share of those with a positive perception, as 54 percent have a positive view of cryptocurrency. Institutional investors, however, have the largest share of those with a neutral perception because of the lack of a well-established regulatory framework for cryptocurrency. However, 39 percent of institutional investors hold a positive perception is growing, a sign of a growing trend, as they are among the major investor groups with an interest in investing in crypto. Other stakeholders, such as the government, academia, and other miscellaneous groups, have a negative perception. Our results demonstrate that cryptocurrency has affected social change, social inclusion, and sustainability. Moreover, our findings offer social insights about crypto stakeholders’ perceptions about the design of strategies to promote cryptocurrency and the establishment of a sustainable crypto ecosystem. • The study investigates the stakeholders' perception towards cryptocurrency. • AI-based sentiment analysis identifies that 51% of stakeholders have a positive perception towards cryptocurrency. • The Autoregressive Distributed Lag model integrated with the UECM model was used to investigate the cointegration between cryptocurrency and stakeholder perception. • A positive perception of cryptocurrency has a significant positive effect on its price.
The emergence of Bitcoin as a pioneering cryptocurrency has transformed financial markets, garnering widespread interest from academicians, policymakers, and investors. The market's inherent volatility and the rapid integration of public information into price movements continue to present a formidable challenge in accurately forecasting Bitcoin prices despite its potential. The limitations of conventional financial models, which frequently need to consider the distinctive attributes of cryptocurrencies, further exacerbate this challenge. Despite the proliferation of ML in various fields, existing models have not fully harnessed these techniques, performing only marginally better than random guesses due to the unique challenges posed by the high volatility and complex dynamics of cryptocurrency markets. This study introduces a systematic review of ML methods specifically tailored for Bitcoin price prediction, with a focus on evaluating the robustness, accuracy, and appropriateness of advanced ML techniques like Long Short-Term Memory (LSTM) networks. The novelty lies in its comprehensive assessment of these methods in the context of data-driven marketing, aiming to enhance both academic understanding and practical applications in financial technology. The previous studies haven't Machine Learning (ML) has become a formidable instrument that has the potential to improve the accuracy of forecasting; however, there still needs to be more comprehension regarding the most effective ML models in this field. The study's importance is derived from its systematic examination of various machine learning (ML) techniques employed to predict the price of Bitcoin, with a particular emphasis on their integration into data-driven marketing strategies. The results will substantially contribute to both academic research and practical applications, providing valuable insights that can be used to develop more dependable forecasting tools, thereby benefiting investors, marketers, and policymakers.
Yang Zhou, Chi Xie, Gang‐Jin Wang, Jue Gong · 5 authors
Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.
N. Hafidi, Zakaria Khoudi, Mourad Nachaoui, Soufiane Lyaqini
Accurate cryptocurrency price forecasting is crucial for investors and researchers in the dynamic and unpredictable cryptocurrency market. Existing models face challenges in incorporating various cryptocurrencies and determining the most effective hyperparameters, leading to reduced forecast accuracy. This study introduces an innovative approach that automates hyperparameter selection, improving accuracy by uncovering complex interconnections among cryptocurrencies. Our methodology leverages deep learning techniques, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, in conjunction with the Genetic Algorithm (GA) to optimize hyperparameters. We propose and compare two architectures, LAO and LOEE, utilizing these methods to enhance forecast accuracy and address the challenges of the cryptocurrency market. This cutting-edge approach not only improves forecasting capabilities but also provides valuable insights for managing cryptocurrency investments and conducting research. By automating hyperparameter selection and considering interconnections between cryptocurrencies, our approach offers a practical solution for accurate cryptocurrency price prediction in a dynamic market environment, benefiting both investors and academics.
Our research investigates the predictive performance and robustness of machine learning classification models and technical indicators for algorithmic trading in the volatile cryptocurrency market. The main aim is to identify reliable approaches for informed decision-making and profitable strategy development. With the increasing global adoption of cryptocurrency, robust trading models are essential for navigating its unique challenges and seizing investment opportunities. This study contributes to the field by offering a novel comparison of models, including logistic regression, random forest, and gradient boosting, under different data configurations and resampling techniques to address class imbalance. Historical data from cryptocurrency exchanges and data aggregators is collected, preprocessed, and used to train and evaluate these models. The impact of class imbalance, resampling techniques, and hyperparameter tuning on model performance is investigated. By analyzing historical cryptocurrency data, the methodology emphasizes hyperparameter tuning and backtesting, ensuring realistic model assessment. Results highlight the importance of addressing class imbalance and identify consistently outperforming models such as random forest, XGBoost, and gradient boosting. Our findings demonstrate that these models outperform others, indicating promising avenues for future research, particularly in sentiment analysis, reinforcement learning, and deep learning. This study provides valuable guidance for navigating the complex landscape of algorithmic trading in cryptocurrencies. By leveraging the findings and recommendations presented, practitioners can develop more robust and profitable trading strategies tailored to the unique characteristics of this emerging market.
Non-fungible tokens (NFTs) have gained mainstream attention in the fintech community, but there is little research on their statistical properties. This study investigates the long-memory characteristics of NFT returns and volatility, focusing on their potential for predicting price movements. As NFTs do not conform to traditional models, understanding their unique features is crucial for comprehending complex market dynamics. This study aims to reveal the impact of macroeconomic factors on NFT prices, understand their correlation and develop predictive models using autoregression and artificial intelligence (AI) technology. This research utilized datasets from the Centers for Disease Control and Prevention (CDC), U.S. Bureau of Labor Statistics, Bureau of Economic Analysis, Christie’s, Dune, and Google Trends. Correlation and p value tests revealed strong relationships between NFT prices and variables such as weekly volume, pandemics, inflation and security. The Baseline Model using autoregression with NFT volume, security and technology factors outperformed all other models demonstrating the speculative volatility of NFTs. The Transformer Model using transformers, an architecture used by ChatGPT, Gemini and Stable Diffusion, showed high accuracy with less feature selection and preprocessing efforts. This study provides a novelty using a systematic approach for researchers to perform financial forecasting and contributes to the scarce literature on NFTs. This research offers valuable insights to investors and private agents regarding the right economic conditions for NFT investments by reducing portfolio risks and making informed decisions. To the authors’ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.
This study explores the integration of technical indicators, specifically Exponential Moving Averages (EMA) and Volume Weighted Average Price (VWAP), into machine learning models for cryptocurrency price forecasting. Our findings reveal that including these indicators can complicate the modeling process without necessarily improving performance. Support Vector Regression (SVR) and Random Forest Regressor (RFR) models outperform deep learning approaches such as Long Short-Term Memory (LSTM) networks, demonstrating higher predictive accuracy with simpler feature sets. These findings emphasize the challenges of high-dimensional data and the critical role of rigorous feature selection and preprocessing in financial forecasting. Practical implications and trade-offs between model complexity and prediction accuracy are discussed, providing valuable insights for researchers and practitioners in financial analytics.
<p>Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.</p>
Muhamad Abdul Fatah, Martanto Martanto, Arif Rinaldi Dikananda, Ahmad Rifa’i
Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.
Moch Panji Agung Saputra, Riza Andrian Ibrahim, Renda Sandi Saputra
The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions.
Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti
The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection.
Bo Gao, Yuan Wang, Qingsong Wei, Yong Liu · 6 authors
Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure and fair transactions. However, poorly integrated oracles remain susceptible to manipulation, enabling attackers to exploit smart contract logic for unfair asset valuation and financial gain. While many such vulnerabilities are only detected after deployment, smart contracts are typically immutable once deployed, making post-hoc fixes costly or infeasible. This highlights the critical need for detecting oracle manipulation risks before deployment. In this paper, we propose$AiRacleX$, a novel LLM-driven framework that enables pre-deployment detection of price oracle manipulation vulnerabilities by leveraging the complementary strengths of multiple large language models (LLMs). Our approach begins with domain-specific knowledge extraction, where an LLM model synthesizes precise insights about price oracle vulnerabilities, eliminating the need for profound expertise from developers or auditors. This knowledge forms the foundation for a second LLM model to generate structured, context-aware Chain-of-Thought prompts, which guide a third LLM model in accurately identifying manipulation patterns in smart contracts. We evaluate$AiRacleX$on 60 known vulnerabilities from 44 real-world DeFi exploits and Code4rena projects spanning 2021-2023. The results show that$AiRacleX$achieves a 2.58 times improvement in recall over the state-of-the-art GPTScan, with comparable precision. Our framework also demonstrates strong extensibility and efficiency, and supports deployment with open-source LLMs to enhance security and reduce operational cost.
Anak Agung Surya Pradhana, Kadek Suarjuna Batubulan
Forecasting Ethereum price changes presents challenges due to the cryptocurrency market’s volatility and rapid fluctuations. This study applies Long Short-Term Memory (LSTM) networks to predict Ethereum price trends using hourly historical data. The LSTM model captures temporal dependencies effectively, achieving moderate accuracy with a Root Mean Squared Error (RMSE) of 11.42. It performs well in stable market conditions, with predicted prices closely aligning with actual values, validating its potential for identifying long-term trends. However, the model struggles during high-volatility periods, failing to predict abrupt price spikes and market crashes accurately. Overfitting is also observed, indicated by disparities between training and test errors, limiting the model’s generalizability to unseen data. To address these issues, this research suggests incorporating features such as trading volumes, market sentiment, macroeconomic indicators, and blockchain metrics to enhance predictive accuracy. Additionally, employing advanced architectures like attention mechanisms, hybrid models, and real-time learning frameworks is recommended to improve adaptability and robustness in dynamic market environments. These enhancements aim to create a more comprehensive and reliable predictive tool. This study contributes to the advancement of predictive analytics in cryptocurrency markets, offering valuable insights for traders, investors, and policymakers navigating the complexities of digital finance.