Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.
The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions.Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability.To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting.Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data.In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle.To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability.Empirical validation has been conducted using real-time data from the Borsa stanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models.Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency.The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.
This study examines and compares the effectiveness of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) models in forecasting volatility across three distinct financial markets: cryptocurrencies, Indonesian stocks, and U.S. stocks. The research analyzes daily closing price data from April 2018 to September 2024, focusing on five major cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, and Ripple), five Indonesian blue-chip stocks (BBCA, BBRI, BYAN, BMRI, and TPIA), and five major U.S. stocks (Apple, Nvidia, Microsoft, Google, and Amazon). Using comparative analysis of ARCH(1), GARCH(1,1), and EGARCH(1,1,1) models, the study evaluates their predictive accuracy through multiple metrics including AIC, MAE, RMSE, and SMAPE. Results indicate that EGARCH(1,1,1) generally performs better for cryptocurrencies and U.S. stocks, while GARCH(1,1) shows superior performance for Indonesian stocks, suggesting that volatility patterns and optimal forecasting models vary across different market contexts.
Bitcoin is the most traded cryptocurrency by volume and market cap. A number of scholars have directed their research towards characterizing Bitcoin’s speculative behavior using a myriad of techniques such as technical analysis, price regression, and direction classification. For this work, research is conducted using the relatively nascent technique of on-chain data analysis. The goal of this research is to evaluate Bitcoin’s on-chain data in predicting future price direction. First, a classification process of on-chain data features that helps the reader understand their relevance is proposed. To address the curse of dimensionality, feature selection algorithms such as L1 regression, Boruta, and the dimensionality reduction algorithm Principal Component Analysis (PCA) are utilized. The research then explores advanced neural networks for next day price direction prediction, including the Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the Temporal Convolutional Network (TCN). Neural network models and trading strategies are then compared based on their return statistics. A comparative analysis of feature selection, learning model performance, and trading strategy performance is also conducted. Results from the research show that the Boruta feature selection algorithm combined with the CNN-LSTM model performs best compared to other combinations with a prediction accuracy of 82.03% over the testing period. In addition, the on-chain features within the category, realized value, and unrealized value classifications have higher predictive powers for next day price direction prediction. Finally, during trade simulations, the CNN-LSTM model with a Long-Short strategy had an annualized return of 1682.7% and a Sharpe Ratio of 6.47.
Financial institutions increasingly rely on sophisticated database architectures to gain competitive advantages in high-frequency trading and analytics environments. This article examines optimal database technologies for financial applications, comparing in-memory, columnar, time-series, and distributed ledger architectures across standardized financial workloads. Multiple case studies demonstrate how different architectures excel in specific contexts: in-memory processing delivers superior performance for order processing, columnar storage enables faster analytical queries for market analysis, while time-series databases efficiently handle pattern recognition for fraud detection. Performance bottlenecks, consistency trade-offs, regulatory compliance challenges, and security considerations are explored in depth. The results indicate that no single architecture provides optimal performance across all financial application requirements; instead, financial institutions must select technologies based on specific use cases, with heterogeneous architectures often delivering superior results. The article concludes by examining emerging technologies with potential to transform financial database landscapes, including persistent memory, hardware acceleration, specialized indexing structures, AI-integrated engines, and hybrid blockchain solutions.
The cryptocurrency market is characterized by its high volatility and complex temporal dependencies, posing significant challenges for accurate price prediction. This study introduces advanced hybrid Recurrent Neural Network (RNN) architectures—LSTM-GRU, GRU-BiLSTM, and LSTM-BiLSTM—to enhance the predictive accuracy of cryptocurrency price forecasting. By leveraging the strengths of each RNN variant, the hybrid models effectively capture intricate time-series patterns and nonlinear dependencies inherent in cryptocurrency data. The research follows a comprehensive methodology, including the collection of historical price data for Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC), rigorous data preprocessing, and the integration of hybrid architectures. Extensive experiments are conducted, and the models are evaluated using key performance metrics, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Results highlight the superior performance of hybrid RNNs, with LSTM-BiLSTM excelling in BTC price prediction, GRU-BiLSTM and LSTM-GRU demonstrating robust performance for ETH and LTC. This study not only establishes the efficacy of hybrid RNN architectures for time-series forecasting but also underscores their potential for real-world applications in trading strategies. The findings set a new standard for leveraging deep learning in cryptocurrency markets, paving the way for more accurate, reliable, and adaptive forecasting systems. Future work will focus on extending this approach to a broader range of cryptocurrencies and incorporating external market factors to further enhance predictive capabilities.
This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Senior Data Engineer - Lead, Citibank, USA, Pradeep Rao Vennamaneni
The financial services industry is transforming batch processing to real-time, AI-driven architectures. This article looks at how the frameworks Apache Kafka and Apache Spark are used as bases for building scalable and low-latency, fault-tolerant data pipelines, meeting the special requirements of the financial sector. These real-time applications include high-frequency trading, fraud detection, compliance monitoring, and customer engagement. They are made possible through these open-source platforms that publicly ingest, process, and make decisions. Integrating cloud-native infrastructure—using Kubernetes, service mesh, and container orchestration—ensures elasticity, security, and regulatory alignment. Large language models (LLMs) are now being entrenched into micro services for decision support, regulatory reporting automation, and the automation of client interactions. The article also contains detailed architectural guidance on how to integrate Kafka and Spark, tips for improving Kafka Spark performance, and best practices around observability and DevSecOps. Real-time stream processing combined with AI-driven analysis serves as a real-world use case for trade surveillance. The future impact of emerging trends such as edge-native computing, federated learning, and decentralized finance is also examined. Strategic recommendations to CTOs and architects for developing secure, AI-native, and future-proof financial systems are presented to close.
Cryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value in recent years, predicting its value and ensuring the reliability of these predictions, which have become crucial, have gained increasing importance. A method that combines Long Short-term Memory (LSTM) with conformal prediction is proposed in this paper. Initially, the high-dimensional features in the dataset are divided using the Spearman correlation coefficient method, and features below 0.75 and above 0.95 are excluded. Subsequently, an LSTM model is built, and data are fed into it and the data is used to train the model to generate predictions. Finally, the predicted values generated by the LSTM are fed into the conformal prediction model, and confidence intervals for these values are generated to verify their reliability. In the conformal prediction model, the quantile loss of the loss function is defined, and an Average Coverage Interval (ACI) predictor is designed to improve the accuracy of the results. The experiments are conducted using data from CoinGecko, which is a publicly available data. The results show that the LSTM-conformal prediction (LSTM-CP) combination improves reliability.
The initial driving force behind the development of the cryptocurrency market is the Bitcoin currency. The emergence and expansion of specialized exchanges were essential for trading this cryptocurrency. Consequently, experts in the field recognized the necessity of developing econometric models to forecast Bitcoin’s exchange rate. Proposals presented by econometricians at scientific conferences demonstrated that such models could help minimize risks and potential losses during the stages of investing in Bitcoin and selecting financial instruments, as well as forecast future returns. In this article, we attempt to present methods for constructing predictive econometric models to automate cryptocurrency trading and analyze Bitcoin’s price fluctuations using econometric modeling techniques. The economic development of the cryptocurrency market, the technological architecture of Bitcoin, and the principles of econometric modeling have been systematically examined, and the data were analyzed based on real statistical information. The article is structured in logical order, and the conclusions and recommendations are presented with scientific justification. The statistical methods, analytical charts, and forecasting models used in the study were selected according to the research topic, and the results are expressed clearly and in a scientific manner. The article’s plagiarism index is below 5%, which confirms its status as a fully original scientific work.
Bitcoin's high volatility poses significant challenges for short-term price prediction, making it a critical area of study for financial forecasting. Traditional models such as Long Short-Term Memory (LSTM) networks often encounter difficulties in handling long-range dependencies and non-stationary data, limiting their predictive accuracy under volatile conditions. This study introduces the Time-Series Transformer (TST) as a novel approach to predict Bitcoin's short-term prices. By leveraging self-attention mechanisms, TST effectively captures complex temporal patterns in historical Bitcoin data, including prices and trading volume. The data was segmented into fixed-length windows to facilitate model training and testing. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Scaled Error (MASE), and R-squared (R²) demonstrated TST’s superior performance over LSTM, particularly during periods of high market fluctuation. Furthermore, TST exhibited notable computational efficiency when working with large datasets, underscoring its scalability. These findings not only highlight TST’s potential for enhancing cryptocurrency price prediction but also pave the way for future research integrating external data sources and exploring further model enhancements for more robust financial forecasting.
In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.
Esam Mahdi, Carlos Martín-Barreiro, Xavier Cabezas
In this article, we introduce a novel deep learning hybrid model that integrates attention Transformer and Gated Recurrent Unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. By combining the Transformer's strength in capturing long-range patterns with the GRU's ability to model short-term and sequential trends, the hybrid model provides a well-rounded approach to time series forecasting. We apply the model to predict the daily closing prices of Bitcoin and Ethereum based on historical data that include past prices, trading volumes, and the Fear and Greed index. We evaluate the performance of our proposed model by comparing it with four other machine learning models: two are non-sequential feedforward models: Radial Basis Function Network (RBFN) and General Regression Neural Network (GRNN), and two are bidirectional sequential memory-based models: Bidirectional Long-Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU). The performance of the model is assessed using several metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), along with statistical validation through the nonparametric Friedman test followed by a post hoc Wilcoxon signed rank test. The results demonstrate that our hybrid model consistently achieves superior accuracy, highlighting its effectiveness for financial prediction tasks. These findings provide valuable insights for improving real-time decision making in cryptocurrency markets and support the growing use of hybrid deep learning models in financial analytics.
Recently, with the gradual development of machine learning technology, more and more people are trying to apply machine learning technology in various fields, and finance is one of the important fields. This work investigates the optimization of cryptocurrency portfolios by combining Long Short-Term Memory (LSTM) time series forecasting with traditional portfolio optimization methods. The focus of the paper is on using the historical price data from the past six years of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) to train LSTM models, which are then used to predict the prices of these cryptocurrencies for the period from January to June 2024. These predictions are subsequently incorporated into an extended Markowitz framework to optimize the portfolio on a monthly basis. The results indicate that the LSTM-enhanced portfolio optimization method yields higher returns and better risk management compared to traditional methods. This finding could prove that it is feasible and effective to apply machine learning methods, especially time series forecasting methods, to cryptocurrency portfolios.
Proshanta Kumar Bhowmik, Faiaz Rahat Chowdhury, Md Sumsuzzaman, Rejon Kumar Ray · 9 authors
Cryptocurrency markets in the USA, especially that of Bitcoin, are plagued by extreme volatility fueled by a dynamic intersection of macroeconomic forces, speculator behavior, and sentiment of investors. Conventional financial models cannot keep pace with the high-frequency price changes typical of digital assets, prompting the need for novel methodologies that can better account for unstructured data like social media sentiment, news reports, and discussion forum postings. The central aim of this study was to establish a strong model that integrates sentiment analysis and machine learning methods to forecast the price movements of Bitcoin. The dataset used included multi-source sentiment data and cryptocurrency market indicators, which allow for in-depth analysis of public emotion on cryptocurrency volatility. Sentiment was sourced from Twitter (tweet text with Bitcoin hashtags and keyword mentions), Reddit (r/Bitcoin and r/Crypto Currency subreddits), and financial headlines (Bloomberg, CoinDesk, Reuters), covering the timeframe of 2019–2024 to ensure the inclusion of various market cycles. Textual data was pre-cleaned to remove noise signals (bots, spam, non-English text) and annotated for sentiment polarity (positive, negative, neutral) using both VADER (Valence Aware Dictionary for sEntiment Reasoner) and fine-tuned BERT models for contextual relevance. In analyzing how sentiment affects the volatility of the Bitcoin market, we used various modeling methods such as Logistic Regression, Random Forest Classifier, and Support Vector Machines. Support Vector Machines stands slightly ahead in terms of accuracy, implying that it might be the strongest among the three for this particular task. Logistic Regression and Random Forest both show similar levels of accuracy, which means that both of them are also strong, though less optimal compared to the Random Forest model. The use of sentiment analysis in financial markets, especially in the cryptocurrency market, provides U.S.-based investors and traders with a valuable means of risk protection. Through the use of sentiment-aware forecasts, investors can make predictions of market trends and probable price movements based on public sentiment. Crypto-fintech platforms can leverage sentiment analysis to build real-time alert systems that update users on important market movements. Through social media and news channels, the platforms can issue alerts on impending price volatility or impending trends, allowing the user to react quickly to market forces. The capability to bring in real-time social media APIs for live predictions marks a critical leap for sentiment analysis in the cryptocurrency market. Through APIs like Twitter, Reddit, and other social media platforms, investors can get instant readings on public sentiment, which in turn will allow them to make real-time and better-informed trading decisions.
Dalia Elbanna, Ema Izati Zull Kepili, Nik Hadiyan Nik Azman
Ethereum's anonymity and uncontrolled cryptocurrency attraction have attracted investors.Ethereum's price dynamic inspired this study's prediction analyses.Previous study has focused on either technical analysis or on-chain analysis, leaving investors without the synergistic effects of integrating the two.This study addresses missed insights and lack of cross-comparisons by identifying variable relationships and dependencies and comparing a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost) in Ethereum price prediction.The dependent variable is Ethereum price and the independent variables are opening, high, low, closing, adjusted closing, volume traded, market capitalization, cumulative return, transactions, blocks, and gas utilized.Prices and market capitalization, traded volume, and volume are strongly correlated, and the LSTM model is the most promising due to its greater prediction accuracy and generality.The analysis reveals the bitcoin market's complexity, affecting investing and risk management.
Cryptocurrencies have attracted significant attention from investors, regulators and the media since their emergence. In a world where digital advancements are increasingly included in everyday relations, studying the behaviour of cryptocurrencies and their impact on financial markets becomes a necessity. This paper introduces a comparative analysis towards a hybrid model combining classical and modern methods for predicting cryptocurrency prices. This study deals with everyday recordings of 10 cryptocurrencies that represent different technological innovations and use cases. Studying these cryptocurrencies can help understand volatility, volumes and price movements. We aim to develop a time series statistical model and to study the effectiveness of deep learning (DL) models, specifically long short-term memory (LSTM) model and the autoregressive integrated moving average (ARIMA) model, for predicting cryptocurrency prices accurately and forecasting stationary data. Combining ARIMA and LSTM, we managed to obtain a high value of R² for Binance Coin (BNB) cryptocurrency (0.936) with an average R² for all evaluated cryptocurrencies of 0.6555.
Cryptocurrencies have become a significant asset class, attracting considerable attention from investors and researchers due to their potential for high returns despite inherent price volatility. Traditional forecasting methods often fail to accurately predict price movements as they do not account for the non-linear and non-stationary nature of cryptocurrency data. In response to these challenges, this study introduces the Helformer model, a novel deep learning approach that integrates Holt-Winters exponential smoothing with Transformer-based deep learning architecture. This integration allows for a robust decomposition of time series data into level, trend, and seasonality components, enhancing the model’s ability to capture complex patterns in cryptocurrency markets. To optimize the model’s performance, Bayesian hyperparameter tuning via Optuna, including a pruner callback, was utilized to efficiently find optimal model parameters while reducing training time by early termination of suboptimal training runs. Empirical results from testing the Helformer model against other advanced deep learning models across various cryptocurrencies demonstrate its superior predictive accuracy and robustness. The model not only achieves lower prediction errors but also shows remarkable generalization capabilities across different types of cryptocurrencies. Additionally, the practical applicability of the Helformer model is validated through a trading strategy that significantly outperforms traditional strategies, confirming its potential to provide actionable insights for traders and financial analysts. The findings of this study are particularly beneficial for investors, policymakers, and researchers, offering a reliable tool for navigating the complexities of cryptocurrency markets and making informed decisions.
Bitcoin is a decentralised digital currency that has been in existence for some time now. Its value has been volatile, with most of its prices fluctuating significantly, making it difficult to predict its prices when investing. This paper employed a Neural Basis Expansion Analysis Time Serie (N-BEATS) deep learning architecture to predict Bitcoin prices. The model was chosen because of its proven capabilities of modelling intricate patterns in time series data. An hourly Bitcoin price data of 729 days collected from Yahoo Finance extensively assesses the N-BEATS model's performance while comparing it with other machine learning models like the Linear Regression and the long-short-term-memory (LSTM) networks. Mean Absolute Error (MAE) and R-squared (R²) were utilised as performance evaluation metrics. N-BEATS surpassed the others by providing an R² score of 0.00240 and an MAE score of 0.9998. These findings are significant and shed light on how deep learning models can be used for financial forecasting. The result shows that the N-BEATS model is more accurate and reliable for predicting cryptocurrencies' prices, which may be very useful for investors in making decisions and managing risks.
This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.
Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy.
Md. Shahidul Islam, Monjira Bashir, Siddikur Rahman, Md Abdullah Al Montaser · 7 authors
The cryptocurrency market, with its record volatility and breakneck speed, is a revolutionary phenomenon that is reshaping the entire world's landscape. Unlike regular markets, cryptocurrencies undergo unprecedented volatility caused by a complex interaction of factors ranging from speculative trading to updates in regulations, technological innovations, and macroeconomic trends. The central objective of this research was to develop and evaluate machine learning-driven models of cryptocurrency price trend forecasting. The focus of this research project revolved around prominent cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other prominent altcoins, within the United States. The dataset employed in this analysis comprises vast historical price data, trading volumes, and key market indicators of major cryptocurrencies, i.e., Bitcoin (BTC), Ethereum (ETH), and other major altcoins. Historical price data is presented in terms of daily, hourly, and minute-level opening, closing, high, and low prices, providing detailed insights into temporal price behavior. Trading volumes, which reflect the intensity of trading action, are also provided to represent liquidity and investor participation behavior. The dataset also includes various market indicators, i.e., moving averages, relative strength index (RSI), Bollinger Bands, and other technical indicators, which play a pivotal role in establishing market patterns and momentum. Three models are chosen in this study: Logistic Regression, Random Forest Classifier, and XG Boost Classifier. For classification models, accuracy, precision, recall, and F1-score metrics are employed to evaluate the performance of the models in terms of predicting the directions of the markets (e.g., upward or downward directions). With the highest accuracy, Logistic Regression was the best-performing of the models tested, showing its relative superiority. The integration of AI forecasts into cryptocurrency trading has the potential to revolutionize the United States financial markets by providing traders and institutional investors with advanced tools to make decisions. The use of AI tools in cryptocurrency trading also has significant implications for United States regulation compliance. The integration of machine learning tools within cryptocurrency trading platforms is a significant step towards unleashing the true potential of AI in the financial markets. The field of AI-based cryptocurrency forecasting offers numerous areas of future research with the potential to break through present limitations and unlock new paths of market analysis. One of those areas is the use of deep learning models, i.e., Long Short-Term Memory (LSTM) networks, for time-series cryptocurrency forecasting.
Abstract Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A slight improvement in forecasting performance can lead to increased profitability; Therefore, obtaining a realistic forecast is very important for investors. Bitcoin, frequently mentioned in recent due to its volatility and chaotic behavior, has become an investment tool, especially during and after the COVID-19 pandemic. In this study, selected ML techniques were investigated for predicting cryptocurrency movements by using technical indicator-based data sets and measuring the applicability of the techniques to cryptocurrencies that do not have sufficient historical data. In order to measure the effect of data size, Bitcoin’s last 1 year and 7 years of data were used. Following the related literature, Google trends and the number of tweets were used as input features, in addition to the most commonly used twelve technical indicators. Random Forest, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost-XGB), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) network were optimized for best results. Accuracy, F1, and area under the ROC curve values were used to compare the model performance. For continuous data, ANN and SVM performed the best with the highest accuracy and outperformed the other ML models for complete and reduced sets. LSTM reached the best accuracy for trend data, but SVM, NB, and XGB models showed similar performance. The research shows that some indicators significantly affect prediction performance, and the data discretization process also improved the model’s accuracy. While the number of samples affects the results of many ML models, correctly optimized and fine-tuned models may also give excellent results even with less data.