The emergence of cryptocurrencies has dramatically impacted the financial sector, drawing significant attention and sparking widespread debates across platforms like Facebook and Reddit. These discussions offer crucial insights into public sentiment, which uniquely influences cryptocurrency valuations, setting them apart from traditional financial products. Due to this sensitivity to public opinion, both the academic and financial sectors are closely monitoring these dynamics. This paper utilizes Natural Language Processing (NLP) technologies, specifically the DeBERTa model, to perform an in-depth sentiment analysis of cryptocurrency-related discussions on Reddit. Our methodology classifies sentiments into five levels—very positive, positive, neutral, negative, and very negative—allowing for a precise assessment of public emotions and acceptance of cryptocurrencies. The findings provide vital data for market analysis and underscore the significant influence of public sentiment on the cryptocurrency markets.
Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.
Moiz Qureshi, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman · 5 authors
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments.
The most well-known encrypted money, Bitcoin, has a lot of promise in the future. Investors and traders always try to find a technique to forecast cryptocurrency prices to lower their risks and boost profits. However, predicting the price of cryptocurrencies is a difficult undertaking because of their unpredictability, volatility, and mobility. Different prediction architectures have been developed by researchers using machine learning (ML), deep learning (DL), and statistical methods. In this work, predictions are made utilizing the AutoRegressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBOOST), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) models. The historical bitcoin market data is chosen, it spans the months of January 2012 through September 2020. The LSTM model performs well when compared to other models, producing a minimal Mean Absolute Error (MAE) of 5.836 and a minimal Root Mean Squared Error (RMSE) of 7.472. Increased return on investment can be achieved by investors by making well-informed decisions on what to buy, hold, or sell. That’s particularly the case with predictions about the price of Bitcoin.
In recent years, Bitcoin has gained significant attention as a leading cryptocurrency, with its price volatility drawing the interest of both investors and researchers. Predicting the future price of Bitcoin is a challenging task due to its inherent unpredictability and fluctuating market dynamics. Accurate forecasting of Bitcoin's price can provide valuable insights for traders and investors to make informed decisions. This study aims to evaluate and compare the prediction accuracy of Bitcoin prices using two distinct machine learning techniques: the Novel Apriori Algorithm and Linear Regression. This study investigates the efficiency of predicting Bitcoin prices using two machine learning techniques: the Novel Apriori Algorithm and Linear Regression. The primary objective is to forecast the Bitcoin price using these algorithms and assess their prediction accuracy. A pretest power analysis was conducted with an 80 % power level and a sample size of 20, with two distinct groups. The software implementation of both algorithms yielded an accuracy of 83.85% for the Novel Apriori Algorithm and 82.70% for the Linear Regression Algorithm. Statistical analysis, using an independent sample$t$-test, indicated a negligible difference in accuracy between the two methods ($p>0.05$), with a mean difference of$\mathbf{0. 7 6 0}$. Despite this, the Novel Apriori Algorithm demonstrated a marginally higher accuracy compared to the Linear Regression Algorithm, thus suggesting its slightly better performance in forecasting Bitcoin prices.
V. SimhadriAppanna, M. Manohara, B.V. Sai Thrinath, D. Leela Rani · 6 authors
The proliferation of mobile devices and personal computing has revolutionized stock and crypto currency trading. While many struggle with navigating trading intricacies, adept practitioners find lucrative opportunities for wealth accumulation. Automated price prediction systems, particularly the Long Short-term Memory (LSTM) model, offer passive trading approaches, eliminating exhaustive decision-making processes. Acquiring and organizing data, followed by rigorous calculations and analysis, culminates in accurate price forecasts. Though not infallible, these models discern trends and project crypto currency trajectories. Notably, Bitcoin serves as a prime example. These systems offer invaluable insights, aiding investors in strategic decision-making amid the dynamic crypto currency landscape.
Investment in cryptocurrencies has garnered substantial attention in the recent past as the prices for these digital currencies started recording all-time highs. While there are numerous contenders in the cryptocurrency market, bitcoin has emerged to be the most popular and sought after digital currency. Despite its popularity, the theoretical understanding of the value of this cryptocurrency is still limited. Hence this study aims to find out the significant predictors of the bitcoin price and build a machine-learning based model to evaluate and predict the complex phenomenon of bitcoin price. Here we contribute to the extant literature by searching for the potential contributors of bitcoin prices ranging from fundamental, macroeconomic, financial, speculative, and technical sources to the most marked event of 2020 i.e., Covid19 pandemic. For this purpose, we have used state-of-the-art machine learning, deep learning, and statistical time-series models (univariate and multivariate) to forecast bitcoin price. The study revealed that deep learning models performed almost at par with Random Forest model for both pre- and whilst-Covid19 era. Traditional time-series models, namely VAR and VECM gave the most consistent performance within acceptable margins for both pre- and whilst-Covid era. We have also found that macroeconomic factors play an important role in determining bitcoin price formulation process during both periods, while mining difficulty and market sentiment factors gain more importance during pre-Covid period. In addition, number of covid cases is also found to be a significant factor for the prediction of bitcoin price during whilst-Covid period.
The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors.
A main challenge of cryptocurrency trading is selecting technical indicators which fits the dynamic nature of the cryptocurrency market. This research proposes a framework that integrates a genetic algorithm with a neural network to effectively explore the efficacy of traditional technical indicators in cryptocurrency. It optimizes both the selection of technical indicators and neural network parameters through tailored genetic operations such as mutation and crossover, allowing for enhanced exploration of the solution space. Through rigorous testing on historical cryptocurrency market data in two distinct periods, the proposed model demonstrates superior predictive accuracy and improved trading performance compared to traditional methods, generating a 19.33% profit in the first period and 7.13% in the second period, outperforming the buy-and-hold benchmark. The results highlight the robustness of the model, which consistently delivered positive returns across varying market conditions, including both bullish and bearish phases.
The growing interest in cryptocurrencies such as Bitcoin highlights the need for effective predictive models in this volatile market. This study developed and trained a model based on the Long Short-Term Memory (LSTM) Recurrent Neural Network architecture to forecast Bitcoin values with a low error percentage. The results confirmed the effectiveness of the LSTM model in predicting Bitcoin prices, demonstrating its ability to handle the high volatility characteristics of this market. Hypotheses regarding the efficiency of shorter versus longer lookback periods and the influence of data volume on model performance were tested. The experiments showed that increasing the volume of data used in training significantly increases the accuracy of predictions, evidenced by the lower error rates (MAPE and RMSE) obtained with larger data volumes. However, a saturation point was observed, after which further increases in data volume did not result in significant improvements. Regarding the lookback period, the results indicated that 30-day periods presented with the best performance, with lower forecast errors. Very short or very long lookback periods tend to increase the error, which highlights the importance of proper window selection for this type of model. Statistical analysis confirmed the significant influence of data volume and lookback period on model performance, although the interaction between these factors did not show statistical significance.
This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryptocurrency market, traditional forecasting models often fall short. By leveraging RNNs' capability to capture long-term patterns in time-series data, this research aims to improve accuracy in price prediction and develop effective trading strategies. The project follows a structured approach involving data collection, preprocessing, and model refinement, followed by rigorous backtesting for profitability and risk assessment. This work contributes to both the academic and practical fields by providing a robust predictive model and optimized trading strategies that address the challenges of cryptocurrency trading.
Bitcoin öncülüğünde hayatımıza giren kripto paralar, niş bir dijital varlıktan ana akım bir finansal enstrümana dönüşmüş durumda. Bu hızlı gelişim, kripto para piyasasının oldukça dalgalı yapısını anlamak ve yönlendirmek için kapsamlı analiz yöntemlerine olan ihtiyacı da artırdı. Makine öğrenimi ve derin öğrenmenin gelişimi, kripto para analizinde daha karmaşık öngörü modelleri sunarak büyük miktardaki veriden öğrenme yeteneği sağlamaktadır. Bu gelişmiş teknikler, karmaşık piyasa modellerini analiz edebilir, geçmiş verilerden çıkarımlar yapabilir ve kısa vadeli fiyat tahminleri yapmada yardımcı olabilmektedir. Bu çalışmanın amacı derin öğrenme yöntemleri kullanarak ileriye dönük bir fiyat tahminlemesi yapmaktır. Bu amaç doğrultusunda kripto paralar içerisinde lokomotif görevi gören Bitcoin fiyatını tahmin etmektir. Bu amaç doğrultusunda kripto para yatırımcılarının tercih ettiği diğer kripto paralar arasından seçilen Litecoin, Cardano, Tron, Solana, Ripple, Floki, Etherum ait 30.01.2023- 30.08.2024 tarihleri arasındaki fiyatlarından oluşan toplam 580 adet veri kullanılmıştır. Bitcoin fiyat tahmin için derin öğreneme yöntemlerinden LSTM, GRU, RNN ve CNN modelleri seçilmiştir. Eğitim aşamasında 580 veriden oluşan veri setinin %75’i kullanılmıştır. Modellerin tahmin doğruluk oranlarına bakıldığında LSTM %75, GRU %82’i, RNN %83 ve CNN %62’lik bir başarı göstermiştir.
Evgenii Onishchuk, Maksim Dubovitskii, Eduard Horch
This empirical study presents the Decentralized Exchanges Comparison Service (DECS), a novel tool developed by 1inch Analytics to assess exchange efficiency in decentralized finance. The DECS utilizes swap transaction monitoring and simulation techniques to provide unbiased comparisons of swap rates across various DEXes and aggregators. Analysis of almost 1.2 million transactions across multiple blockchain networks demonstrates that both 1inch Classic and 1inch Fusion consistently outperform competitors. These findings not only validate 1inch's superior rates but also provide valuable insights for continuous protocol optimization and underscore the critical role of data-driven decision-making in advancing DeFi infrastructure.
Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.
Paolo Giudici, Alessandro Piergallini, Maria Cristina Recchioni, Emanuela Raffinetti
We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To this end, we extend the Shapley–Lorenz method, normalised by construction, to Artificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal to a time series of Bitcoin prices, which acts as the response variable, along with time series of classical financial prices, which act as explanatory variables. Three main findings emerge from the analysis. First, recurrent neural networks lead to a better performance, in terms of accuracy and robustness, with respect to classic neural networks. Second, the best performing models indicate that Bitcoin prices are affected mostly by their lagged values, and that their explainability, in terms of classical financial assets, is limited. Third, although limited, the contribution of classical assets to Bitcoin price prediction is well captured by recurrent neural networks.
In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. Our approach employs distributional learning to directly estimate the cumulative distribution function of future time series conditional distributions using the continuous ranked probability score. By incorporating a temporal structure within the latent space and utilizing versatile quantile models, such as the generalized lambda distribution, we enable distributional forecasting by generating synthetic time series data for future time points. To assess the effectiveness of our method, we conduct experiments using a multivariate dataset of real cryptocurrency prices, demonstrating its superiority in forecasting high-volatility scenarios.
Bitcoin’s volatile nature has made its price prediction a sought-after mathematical model in the FinTech industry. Existing studies, however, need to look into the critical aspect of time-lagged sentiment in Bitcoin price forecasting. This omission is significant because time-lagged sentiment captures delayed market reactions that are not immediately apparent in price movements. Moreover, the correlation between time-lagged sentiment and technical indicators and the limitations of individual machine learning and deep learning models necessitates a comprehensive approach for accurate and reliable Bitcoin price predictions. This paper introduces the multimodal fusion Bitcoin (MFB), an innovative generalized multimodal fusion approach that effectively integrates BiLSTM and BiGRU layers for complex feature extraction. The model employs the BorutaShap algorithm for feature selection and utilizes attention mechanisms and spatial dropout for optimization and generalization. MFB’s training and validation use news and tweet data combined with Bitcoin technical indicators to explore the impact of time-lagged sentiment on price movements, leading to more accurate and timely market predictions. The MFB performs superior Bitcoin prediction performance, achieving 97.63% accuracy and an MAE of 0.0065. Experiments highlight MFB’s capability to outperform existing models, offering significant insights for investors in making informed decisions. MFB’s innovative methodology, particularly in next-hour Bitcoin price forecasting, marks an advancement in financial forecasting. By capturing the nuanced dynamics of market sentiment and its delayed effects, MFB is a pioneering multimodal fusion approach in the FinTech domain, revolutionizing Bitcoin price prediction.
This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts. • We forecast Bitcoin volatility using intraday data with machine learning models. • High-frequency Bitcoin data benefits Bitcoin volatility predictions. • We convert time series to images to improve Bitcoin volatility prediction. • Our approach outperforms HAR and GARCH, especially in short-term forecasts. • Image transformation can capture non-linear features such as clustering effect.
As a crucial component of the digital economy, the market price fluctuations of Non-Fungible Tokens (NFTs) are influenced by various factors, making accurate prediction extremely important. This paper leverages a Graph Neural Network (GNN) model to analyze features such as user interaction frequency, user influence, and the popularity of discussion topics within social networks, aiming to predict the volatility of NFT market prices. Experimental results demonstrate that the GNN model achieves a prediction accuracy of 92%, significantly outperforming traditional time series models and linear regression models in key metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R². The study finds that high-influence users and trending discussion topics in social networks are the primary drivers of price volatility. This research not only validates the effectiveness of the GNN model in processing complex social network data but also provides new theoretical insights and practical references for understanding and predicting market behaviors in the digital asset space. The findings offer a solid foundation for the design and optimization of price prediction models in the future digital economy.
While many studies show that more advanced LLMs excel in tasks such as mathematics and coding, we observe that in cryptocurrency trading, stronger LLMs sometimes underperform compared to weaker ones. To investigate this counterintuitive phenomenon, we examine how LLMs reason when making trading decisions. Our findings reveal that (1) stronger LLMs show a preference for factual information over subjectivity; (2) separating the reasoning process into factual and subjective components leads to higher profits. Building on these insights, we propose a multi-agent framework, FS-ReasoningAgent, which enables LLMs to recognize and learn from both factual and subjective reasoning. Extensive experiments demonstrate that this fine-grained reasoning approach enhances LLM trading performance in cryptocurrency markets, yielding profit improvements of 7\% in BTC, 2\% in ETH, and 10\% in SOL. Additionally, an ablation study reveals that relying on subjective news generates higher returns in bull markets, while focusing on factual information yields better results in bear markets. Code is available at https://github.com/Persdre/FS-ReasoningAgent.
The cryptocurrency is the encrypted, digital and peer-to-peer currency invented using blockchain technology in 2009. It is implemented as medium of exchange between computers of the network without interference from any centralised authority. The Bitcoin is most widely used and valuable cryptocurrency across the world. In India, also many people prefer the Bitcoin for their investment. People want to be more aware of the possibilities and opportunities that cryptocurrencies can present, to maintain the confidence and trust rate of utilising cryptocurrencies. The goal of this paper is to predict the future value of Bitcoin cryptocurrency in Indian Rupees (INR), with machine learning using Python. The dataset of approximately past 768 days from current date is trained to predict the INR value of Bitcoin for next 10 days.
Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as \(R^{2}\) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.