Introduction This article presents a method for short-term cryptocurrency price forecasting utilizing news headlines. Methods The study analyzes the impact of news on asset prices within one hour of publication, employing machine learning-based classification with BERT and GPT models, as well as GloVe vector representations. Results The proposed cascade classifier model enhances prediction accuracy by initially assessing the strength of a news item and subsequently forecasting the direction of price movement. Experimental results demonstrate the effectiveness of the developed classification model. Discussion The model achieves an accuracy of 79% in predicting price movements, confirming the potential of leveraging news headlines to improve short-term forecasts in cryptocurrency markets.
Adnan Sagheer, Ali Raza, Muhammad Rizwan Rashid Rana, Faiza Kiran
The highly stochastic, nonlinear, and volatile nature of Bitcoin prices poses significant challenges for accurate forecasting using traditional statistical models. To address this, we propose a hybrid deep learning architecture that combines the strengths of Convolutional Neural Networks (CNNs) for spatial feature extraction with Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), for capturing long-term temporal dependencies. This integrated framework effectively models both spatial and temporal patterns from historical Bitcoin price data. The model was trained and evaluated using real-world Bitcoin datasets.Experimental results demonstrate that the proposed CNN+LSTM model outperforms traditional machine learning and standalone deep learning approaches. Specifically, it achieves a Root Mean Square Error (RMSE) of 245.76, a Mean Absolute Error (MAE) of 11.45, a Mean Absolute Percentage Error (MAPE) of 15.68%, an R² score of 0.92, and a Mean Bias Error (MBE) of 6.94. These results highlight the effectiveness and reliability of the proposed hybrid model in enhancing the accuracy and stability of financial time series forecasting, providing valuable insights for traders, investors, and financial analysts.
Xinran Huang, Linzhi Tan, Haozhe Su, Jeremy EngâTuck Cheah
ABSTRACT One of the critical risks associated with cryptocurrency assets is the soâcalled downside risk, or tail risk. Conditional ValueâatâRisk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio. In this paper, we propose a new approach to portfolio construction based on a deep learning CVaR utility function. This approach is designed to address the issue of tail risk. We evaluate the performance of this approach in comparison to other portfolio construction techniques, including the naĂŻve, minimum variance and meanâvariance portfolios. Our findings indicate that the proposed approach outperforms traditional optimisation models.
Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting âcryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast âthe closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume âpatterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity âproxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer âunderstanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine âlearning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the âliquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed âtheir performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These âresults underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating âthese liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers âseeking to create smarter and more risk-aware strategies in the U.S. digital assets marketâ.
Cross-border payment systems face growing complexity and urgency, driven by rapid globalization, fluctuating exchange rates, and the expansion of decentralized finance (DeFi). Simultaneously, the looming advent of quantum computing threatens to undermine traditional cryptographic methods, pressing the need for future-proof solutions. In this paper, we propose a Quantum-Safe Fuzzy Transformer framework that unifies fuzzy logic with Transformer-based sequence modeling, enhanced by post-quantum cryptographic primitives. Our approach tackles two critical challenges: handling data uncertainty and market volatility-common in cross-border transactions-through fuzzy membership functions seamlessly embedded in the self-attention mechanism, and ensuring robust security against quantum-era threats via lattice-based signatures and key exchanges. Empirical evaluations on a simulated DeFi payment network demonstrate that the proposed model maintains high transaction throughput and low latency, even under stress-test conditions reflecting extreme exchange rate fluctuations. Furthermore, the quantumsafe cryptographic layer defends settlement integrity, highlighting the practicality of post-quantum methods for realworld payment pipelines. By fusing explainable fuzzy transformations with a resilient cryptographic infrastructure, this work paves the way for an AI-driven, trust-minimized ecosystem capable of withstanding the next wave of financial and computational revolutions.
This paper proposes Temporal Convolutional Networks (TCNs) for cryptocurrency forecasting at minute-resolution. TCNs show better accuracy to XGBoost, LightGBM, and LSTM. However, TCNs are less robust than the tree models regarding volatility. While TCNs require much more computations at inference than LightGBM, they run faster than LSTMs. The performance of TCNs is best configured using a TCN with dilated convolutions to capture the temporal patterns, and with residual connections for the stability.
Investment advisory services are now commonly offered by consulting firms with financial experts, typically for a monthly fee. Financial markets require specialized knowledge, but advancements in artificial intelligence have revolutionized this field. Deep learning algorithms, especially Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are widely used to predict asset price trends in nonlinear time-series data. However, they demand large datasets and are prone to overfitting. Recently, combining deep learning with reinforcement learning has shown promise, though it requires intensive research and computational resources. This study introduces the BTC-PDPR (Bitcoin Price Direction Prediction Robot) model, which predicts Bitcoin's daily price direction using the Random Forest Regressor. As an ensemble-based machine learning model, it works effectively with smaller datasets and identifies key technical indicators influencing price trends. The model achieved a 99.20% accuracy rate on data from March 2018 to the present. It runs efficiently in Google Colab (v5e1 configuration), producing results in just 22 seconds. This paper outlines the methodology, reviews relevant studies from 2017 to 2024, highlights gaps in the literature, and emphasizes the studyâs contributions to the field.
Cryptocurrencies like Bitcoin can be considered commodities under the Commodity Exchange Act (CEA) and the Commodity Futures Trading Commission (CFTC) has jurisdiction over cryptocurrencies considered to be commodities, particularly in the context of futures trading. This paper presents a method for long and short term trend prediction of certain cryptocurrencies which is predicated on an application of the Fractal Market Hypothesis. This is an area of market theory where the self-affine properties of a fractal stochastic field are used to model a financial time series. After an introduction to the underlying theory and mathematical modelling, a fundamental analysis of Bitcoin and Ethereum to U.S. Dollar exchange markets is conducted. This analysis is based on a consideration that a changes in polarity of the 'Beta-to-Volatility' and the 'Lyapunov-to-Volatility' ratios to indicate an impending change to the Bitcoin/Ethereum price trend signal. This is used to recommend a long, a short or a hold trading position for which algorithms are provided (coded in Matlab) and 'back-tested'. An optimisation of these algorithms is conducted, leading to a strategy for implementing an ideal range of the key parameters for 'driving' the algorithms developed. This is based on maximising the accuracy and profitability to assure a high level of confidence. The application of the trading strategy developed through this approach is demonstrated to provide useful information to aid cryptocurrency investments and quantify the likelihood that the market will become bull or bear dominant. Under stable conditions, Machine Learning (using the 'TuringBot') is shown to provide useful estimates of future price values and/or fluctuations over small event horizons in time. This minimises any \lq trading delay' caused by filtering the data and increases returns by providing optimal trade positions within a \lq micro-trend' that is too fast for detection otherwise. In certain cases, this increase can reach ~10%. The results presented confirm that Bitcoin and Ethereum exchanges are self-affine (fractal) stochastic fields with L\'evy distributions, displaying a Hurst Exponent of ~ 0.32, a Fractal Dimension of ~ 1.68 and Levy Index of ~1.22. They also confirm that the Fractal Market Hypothesis and its indices provide a suitable market model, that generates returns on investments that outperform all Buy and Hold strategies based on more standard market indices.
Ricardo Arcifa, Yuhang Ye, Yuansong Qiao, Brian Lee
Automated market makers (AMMs) have revolutionized decentralized finance (DeFi), enabling trustless asset exchange through algorithmic liquidity pools. One notable AMM is Uniswap, which improved capital efficiency by introducing concentrated liquidity, allowing Liquidity Providers (LPs) to allocate capital within specific price ranges. However, this flexibility requires active management to maximize fee generation while effectively addressing impermanent loss (IL) and gascosts. We formulate concentrated liquidity management (CLM) as a stochastic control problem and propose deep reinforcement learning (RL) strategies, specifically Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), to optimize liquidity positioning. Unlike heuristics, RL agents adapt dynamically to market conditions. We adopt a two-stage synthetic-to-historical evaluation strategy, involving RL model training on synthetic data and evaluation on historical data. Results show that training on synthetic data and testing historic data allows RL-based strategies to perform well across regimes, capturing more fees while keeping net performance ahead of a passive buy-and-hold strategy. These findings highlight the robustness and practical viability of such strategies for CLM.
<ns3:p>The increasing integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) in financial markets has revolutionized the predictive modeling of asset prices. In cryptocurrency markets, where price movements are largely driven by investor sentiment, sentiment analysis has emerged as a valuable tool for understanding market behavior. This study investigates the correlation between sentiment polarity extracted from FinBERT and FinancialBERTâtwo pre-trained NLP models optimized for financial text analysisâand the price fluctuations of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). The research explores the role of sentiment indicators as leading signals for price trends by examining their correlation across different time lags (immediate, 12-hour, and 24-hour periods).The study utilizes a hybrid sentiment model which uses FinBert and FinancialBert using time lagged correlation models, aggregating sentiment scores from multiple financial news sources retrieved via the MediaStack API, while historical cryptocurrency prices were obtained from the CoinGecko API. A dataset of 1,300 news articles over 90 days was analyzed, revealing that Ethereum exhibited the strongest sentiment-price correlation (0.3819, increasing to 0.3900 after 24 hours), followed by Bitcoin (0.2899 to 0.2919) and XRP (0.1005 to 0.1205). These findings suggest that market sentiment has a delayed impact on price movements, with Ethereum being the most responsive to sentiment fluctuations. This research highlights the potential of AI-driven sentiment analysis as a supplement to traditional financial indicators, offering new opportunities for algorithmic trading and risk assessment in decentralized finance (DeFi) markets. Future research should explore real-time applications, multilingual sentiment tracking, and hybrid predictive models to enhance the accuracy of sentiment-based cryptocurrency forecasting.</ns3:p>
I Putu Bramasta Priadinata, I Gede Iwan Sudipa, Ni Putu Suci Meinarni, I Made Leo Radhitya ¡ 5 authors
Cryptocurrency is a highly volatile digital asset that requires accurate predictive methods. This study compares the performance of three deep learning architectures LSTM, GRU, and Bi-LSTM in forecasting the prices of Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) using univariate historical data. Evaluation was conducted through regression metrics (RMSE and MAPE) and classification of price movement into five categories, ranging from very bearish to very bullish, assessed using a confusion matrix. The results show that GRU performed best for BTC (RMSE 974.72, MAPE 1.18%), while Bi-LSTM outperformed others for ETH and BNB (RMSE 43.19 and 6.83; MAPE 1.16% and 1.08%) and achieved the highest classification accuracy (55% and 52%). However, overall classification accuracy remains low, reflecting the complexity of cryptocurrency price patterns. The study is limited by its univariate approach without incorporating external variables. Its contribution lies in combining regression and classification evaluation, and it recommends exploring multivariate and ensemble models in future research.
Frederic Haase, Tom Celig, Oliver Rath, Detlef Schoder
Abstract The emergence of cryptocurrencies and decentralized finance (DeFi) applications brings unique challenges, including high volatility, limited fundamental valuation methods, and significant informational reliance on social media. Consequently, traditional trading algorithms and decision support systems (DSS) often fall short in effectively capturing these dynamics, underscoring the need for tailored solutions. Recent research on sentiment analysis in cryptocurrency trading has provided mixed evidence regarding its predictive power, highlighting limitations in generalizability and reliability due to the inherent noise of social media content. Addressing these limitations, this study explores crowd-based trading signals, explicit buy and sell recommendations shared by users on social media platforms including X (formerly Twitter), Reddit, Stocktwits, and Telegram. We apply an event study methodology to analyze over 28,000 trading signals extracted using natural language processing (NLP) techniques based on large language models (LLMs). Our findings demonstrate that these explicit crowd-based signals significantly predict short-term cryptocurrency price movements, particularly for assets with lower market capitalization and recent negative returns. An out-of-sample trading strategy using these signals achieves superior risk-adjusted returns, outperforming both a standard cryptocurrency index (CCI30) and the S&P 500. Additionally, we uncover the role of automated accounts (signal bots) actively disseminating trading recommendations. This research advances literature by introducing a precise alternative to sentiment analysis, contributing to the understanding of social media as a distributed financial information environment, and raising theoretical considerations about algorithmic agency and trust. Practical implications span investors, social media platforms, and regulators.
Highly accurate predictions of cryptocurrency prices are of paramount importance to investors and researchers, as they can guide investment strategies and market analysis. However, due to the nonlinear and volatile nature of the cryptocurrency market, it is challenging to assess the distinct characteristics of time-series data, which results in difficulties in generating appropriate and reliable price forecasts. Numerous studies have been conducted on cryptocurrency price prediction using different deep learning-based algorithms, as these techniques have shown promise in capturing the complex patterns and trends in this market. This study proposes three types of recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and Bi-Directional LSTM, for exchange rate predictions of the three major cryptocurrencies in the world, as measured by their market capitalization: Bitcoin, Ethereum, and Litecoin. The experimental results on the three major cryptocurrencies using both Root Mean Squared Error and Mean Absolute Percentage Error demonstrate that the Bi-LSTM model performed better in prediction than LSTM and GRU, and can be considered the most effective algorithm for this task. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The study suggests that the proposed prediction models are accurate and reliable in forecasting cryptocurrency prices and can be beneficial for investors, traders, and researchers in the cryptocurrency market.
Khaled Shaalan, Hana Yousuf, Ali Q Saeed, Abdalla Elnekiti ¡ 7 authors
Sentiment analysis in financial texts plays a vital role in understanding market trends, especially in decentralized finance (DeFi) markets where information flow is rapid and largely unstructured. The emergence of large language models has significantly enhanced the ability to extract meaningful insights from unstructured data sources. However, existing sentiment analysis models often underperform on decentralized market texts due to their reliance on traditional financial datasets and their inability to capture domain-specific language, slang, and context. These models typically struggle with the informal, volatile, and jargon-rich nature of DeFi communications found in forums, social media, and blockchain-related discussions. To address these limitations, this paper propose Decentralized Finance Sentiment Extraction using a RoBERTa-based Neural Transformer (DeFiSENT). This framework incorporates a domain-adapted preprocessing pipeline tailored to DeFi language. It fine-tunes the RoBERTa model on curated, labeled datasets from decentralized platforms such as Reddit, Twitter, and DAO channels. Additionally, DeFiSENT integrates context-aware tokenization and class imbalance strategies to enhance performance in detecting sentiment across imbalanced and noisy text data. The proposed method can be deployed in applications such as real-time crypto sentiment dashboards, DeFi asset risk monitoring, and automated trading signal generation, offering a robust tool for both researchers and investors. Experimental evaluations demonstrate that DeFiSENT significantly outperforms baseline models in accuracy, F1-score, and generalization across unseen DeFi datasets. It effectively captures nuanced sentiments and outliers in dynamic financial discourse, providing timely and interpretable sentiment insights within decentralized finance ecosystems.
Bitcoin is a promising investment asset for the future, offering a viable option for long-term investors. This study seeks to evaluate and compare the effectiveness and performance of several models, including Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Gradient Boosting Regression Process (GBRP), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), in predicting Bitcoin prices. The dataset spans Bitcoin price data from 2012 to 2024. The findings reveal that each model demonstrates a positive trend in forecasting Bitcoin price movements. Therefore, Bitcoin is a valuable asset for those willing to invest; however, it may not be suitable for novice investors due to its high volatility. This study advances the development of predictive models leveraging machine learning and statistical methodologies, offering critical insights into Bitcoinâs price behavior for informed investment decision-making.
Michael Smith, Valerie Kilders, Todd Kuethe, Nicole Olynk Widmar
We examine the relationship between market performance of leading cryptocurrencies (Bitcoin and Ethereum), meme-stocks (AMC, GameStop), and subjects of corporate boycotts (Bud Light) using weekly market price and volume data along with social media data of weekly mentions (which total 337 million in this dataset) and net sentiment. Using vector autoregression (VAR) time series analysis along with Granger causality testing and structural breaks, we successfully predict trade volume of these various assets using social media data and price data. We also find that closing price data and trade volume are reliable predictors of net sentiment about crypto in online and social media. However, we struggle to predict the closing price for the group of assets studied. We also employ impulse response functions, finding evidence of a dynamic relationship occurring between online and social media net sentiment and online media volume with closing price and trade volume. These functions show that investor sentiment operates with a short memory lasting around 3 weeks, additionally these functions show that price generates a shock on trade volume but that crypto and meme-stock markets experience this differently. Our findings reinforce the notion that meme-stock traders and herd investors do not trade on market fundamentals but are instead sensitive to herding (or sentiment) movements. Our findings also suggest that compared to these meme-stock investors, crypto markets have more traditional motivations of loss aversion.
For any meaningful instructional delivery to take place, the teacher must clearly understand who the learners are: their strengths, weaknesses, environment, the goal of instruction, the pace to mention but a few.This process is better referred to as instructional analysis.This paper posits that instructional analysis, the foundational phase of instructional design, serves as the indispensable basis for achieving high-quality and impactful instructional delivery.It explores the multifaceted components of instructional analysis, including learner analysis, context analysis, content/task analysis, and performance analysis, demonstrating how insights derived from these processes directly inform strategic decisions regarding instructional strategies, media selection, and assessment design.Drawing upon established instructional design models and contemporary research, this paper highlights the benefits of thorough instructional analysis in optimizing learning outcomes, enhancing engagement, and ensuring the relevance and efficiency of educational interventions.It also addresses practical challenges in conducting instructional analysis, offering considerations for educators and designers in diverse learning environments, particularly within the evolving nature of education in the 21st century.
The financial industry faces the challenge of balancing data utilization and privacy protection. Federated learning (FL) offers a promising solution by enabling secure collaborative training. This paper focuses on an analysis of the key technologies for several financial applications that can benefit from FL. Specifically, we examine precision marketing based on multimodal FL (MMFL), anti-money laundering strategies leveraging federated graph learning (FGL), and credit card risk assessment utilizing vertical federated learning (VFL). Furthermore, we identify the key challenges in large-scale applications of FL in the financial industry. Additionally, we propose forward-thinking applications of FL in the finance sector, including the use of federated large language models (LLMs) for intelligent customer service (ICS) and decentralized FL integrated with blockchain for financial audit. Finally, we conduct a case study by using a consumer complaints dataset to verify the feasibility and effectiveness of federated LLMs in ICS.
This study investigates the effectiveness of a hybrid forecasting model that combines Generalized Autoregressive Conditional Heteroskedasticity (GARCH) with Long Short-Term Memory (LSTM) neural networks, specifically applied to the cryptocurrency market, focusing on Bitcoin and Ethereum.The inherent volatility of cryptocurrencies presents substantial challenges for accurate price prediction, necessitating advanced methodologies that can adapt to fluctuating market conditions.We first utilize GARCH models to analyze and capture the time-varying volatility in the returns of Bitcoin and Ethereum, enabling a comprehensive understanding of the underlying market dynamics.Following this, we implement LSTM networks to exploit their capability to model complex, non-linear relationships in sequential data, enhancing the predictive power of the model.The performance of the GARCH-LSTM framework is rigorously evaluated using historical price data for Bitcoin and Ethereum, employing key metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess forecasting accuracy.The results demonstrate that the hybrid approach significantly outperforms traditional forecasting methods, providing more reliable predictions and insights into market trends.This study contributes to the growing body of literature on cryptocurrency forecasting by illustrating the potential of combining econometric techniques with advanced machine learning methods, offering valuable implications for traders and investors in the cryptocurrency ecosystem.However, the experimental results revealed that the LSTM model outperformed the other eight methods in terms of forecasting performance measures, the RMSPE validation is 0.112561, and the RMSE validation is 0.011456.
Anshul Agrawal, Sanjeev Kadam, Vibhanshu Jha, Ved Prakash
Sentiment analysis and forecasting play a crucial role in understanding market emotions and their trends. When there is a high volatility or variability in the market, it becomes quite difficult for investors and market participants to predict market trends. In this study, VADER analysis is used for sentiment analysis, and the long short-term memory network (LSTM) model is applied for forecasting to predict Bitcoin prices during volatile periods, including the COVID-19 pandemic and the RussiaâUkraine war. In this research, we analyze the daily closing prices of Bitcoin from 2020 to 2023, extracted from Twitter news. The data are divided into two sub-periods: the first from 2020 to 2022, covering the COVID-19 period and the second from 2022 to 2023, covering the RussiaâUkraine war. This study aims to determine patterns and fluctuations in Bitcoin price sentiment over the observed time period and explain how sentiment dynamics are linked to Bitcoin price movements under erratic market conditions. The growing influence of social media on financial markets underlines the significance of this analysis. This study delivers valuable insights for investors, analysts, market participants and policymakers to manage their portfolios and mitigate risk during volatile periods.
This systematic meta-review analyzes over 75 papers (2020-2025) applying deep learning (DL) techniques to cryptocurrency trading, adhering to PRISMA guidelines. It evaluates various DL architectures, including LSTM, GRU, CNN, and Transformers, and finds that DL methods outperform traditional approaches in managing the high volatility and non-linear patterns of crypto markets. Key findings highlight the promise of hybrid and ensemble models, the benefits of integrating blockchain data, sentiment analysis, and macroeconomic factors for improved predictions, and the potential of deep reinforcement learning for developing autonomous trading strategies with risk-adjusted returns. However, challenges such as model interpretability, nonstationary data, and real-world deployment persist. The review emphasizes emerging directions like explainable AI (XAI) for transparent decision-making and high-frequency trading applications, providing a critical synthesis of methodologies, empirical results, and research gaps to inform both academic research and practical trading system development.