Cryptocurrency trading has become a prominent financial domain, with a market capitalization exceeding one trillion USD. As the influence of social media on crypto markets grows, leveraging sentiment analysis (SA) becomes crucial for enhancing trading models. This work showcases the comparative study of advanced sentiment analysis tools, specifically Crypto-BERT, FinBERT, VADER, and SenticNet, and their effectiveness in confirming that Bitcoin is the best cryptocurrency. Also, the correlation between sentiment and prices using a pre-trained transformer DistilBERT is fine-tuned to understand how cryptocurrency prices fluctuate in the market. The study results in a correlation coefficient of 0.88.
Cryptocurrencies have revolutionized the financial landscape by providing decentralized and anonymous payment systems, making them an intriguing subject for investors and researchers. This article delves into applying machine learning techniques for predicting cryptocurrency prices, mainly focusing on Bitcoin, Ethereum, and Binance Coin. Employing a range of machine learning models, including XGBoost, Linear Regression, and Gaussian Processes, the study aims to evaluate their predictive performance comprehensively. The results are promising; our models outperform existing studies, achieving impressively low RMSE values of 0.0040 for Bitcoin, 0.028 for Ethereum, and 0.027 for Binance Coin. These findings contribute valuable insights into the volatility and dynamics of cryptocurrency prices and underscore the potential of machine learning in shaping financial decision-making. Future directions include integrating advanced deep learning models, additional data sources, and ensemble methods to enhance prediction accuracy and robustness.
The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpredictability and volatility of the cryptocurrency market present considerable challenges for investors aiming to forecast price movements and secure profitable investments. In response to this challenge, the current investigation was conducted to assess the efficacy of three Machine Learning (ML) algorithms, namely, Gradient Boosting (GB), Random Forest (RF), and Bagging, in predicting the daily closing prices of six major cryptocurrencies, namely, Binance, Bitcoin, Ethereum, Solana, USD, and XRP. The study utilised historical price data spanning from January 1, 2015 to January 26, 2024 for Bitcoin, from January 1, 2018 to January 26, 2024 for Ethereum and XRP, from January 1, 2021 to January 26, 2024 for Solana, and from January 1, 2019 to January 26, 2024 for USD. A novel approach was adopted wherein the lagging prices of the cryptocurrencies were employed as features for prediction, as opposed to the conventional method of using opening, high, and low prices, which are not predictive in nature. The data set was divided into a training set (80%) and a testing set (20%) for the evaluation of the algorithms. The performance of these ML algorithms was systematically compared using a suite of metrics, including R2, adjusted R2, Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings revealed that the GB algorithm exhibited superior performance in predicting the prices of Bitcoin and Solana, whereas the RF algorithm demonstrated greater efficacy for Ethereum, USD, and XRP. This comparative analysis underscores the relative advantages of RF over GB and Bagging algorithms in the context of cryptocurrency price prediction. The outcomes of this study not only contribute to the existing body of knowledge on the application of ML algorithms in financial markets but also provide actionable insights for investors navigating the volatile cryptocurrency market.
Mohammad Vahidpour, Amir Daneshvar, Mohsen Amini Khouzani, Mahdi HOMAYOUNFAR
Financial markets are characterised by their dynamic, non-linear, and fluctuating nature. Analysing financial time series in these contexts is a complex and challenging task. Candlestick patterns are recognised as among the most widely used financial tools and offer invaluable insights into market sentiment and psychology. However, manual analysis of these patterns presents significant challenges. Therefore, leveraging machine learning methods becomes a necessity for overcoming these challenges. In this study, a four-step framework was introduced in which the data preparation process is executed on the price data of the 20 cryptocurrencies. Forty-eight candlestick patterns were extracted alongside returns. Employing the long shortterm memory (LSTM) neural network, structured with multiple layers, each specialising in a specific cryptocurrency, enables individualised prediction of market returns. Evaluation of model accuracy and sensitivity is conducted via the confusion matrix, and two distinct trading strategies assess the capital portfolio. The research findings underscore the profitability of the proposed model across all scenarios. Candlestick patterns serve as powerful tools for understanding market sentiments and identifying shifts in market trends. However, their standalone efficacy is limited. Integrating them with other technical analysis tools facilitates more informed decision-making and fosters a deeper understanding of market dynamics.
Son yıllarda, para piyasalarında ve bankacılık sektöründe yaşanan krizlerin etkisiyle merkezi para otoritelerine olan güven sarsılmış ve bu nedenle merkezi olmayan bir sistem arayışına girilmiştir. Bu vesile ile, 1998 yılında ilk kripto para kavramı gündeme gelmiştir. Kripto piyasaları, kripto para birimlerinin alınıp satıldığı dijital veya sanal pazar yerlerini ifade etmektedir. Kripto para birimleri, güvenli finansal işlemler için kriptografi kullanan merkezi olmayan dijital varlıklar olarak karşımıza çıkmaktadır. 2009 yılına gelindiğinde ise ana kripto para birimi olan Bitcoin ile ilk işlem gerçekleştirilmiştir. Merkezi olmayan bu sanal paranın zaman içerisinde talebinde gözlemlenen artış ile birlikte piyasa değeri de hızla yükselmiştir. Çalışmanın amacı, piyasa değeri her geçen gün artan bu kripto para piyasalarının yapısını açıklamak ve ana kripto para birimi olan Bitcoin özelinde fiyat hareketlerinin seyrini incelemektir. 2015:02 - 2022:01 dönemleri arasında aylık veri setinin kullanıldığı analizde GPH yöntemi kullanılmıştır. Elde edilen sonuçlara göre, Bitcoin serisinin dirençli bir yapı sergilediği gözlenmektedir. Bitcoin fiyatlarını etkileyen herhangi bir iktisadi şokun belirli bir süre etkisini sürdüreceği ancak uzun dönemde etkisini kaybedeceği sonucuna varılmıştır.
The research presented in this paper is the first to introduce a thorough Descriptive-Predictive–Prescriptive (DPP) Framework for comprehending the interaction between social media and cryptocurrencies. Recognizing the underexplored domain of the social-media–cryptocurrency interaction, we delve into its many aspects, better understanding present dynamics, forecasting potential future trajectories, and prescribing best solutions for stakeholders. We evaluate social media speech and behavior connected to cryptocurrencies using big data analytics, translating raw data into meaningful insights using Natural Language Processing (NLP) techniques like sentiment analysis. When applied to an experimental dataset, the DPP nets superior results compared to the baseline approach, displaying an improvement of 3.44% of the Root Mean Square Error (RMSE) metric and 4.59% of the Mean Absolute Error (MAE) metric. The unique DPP framework enables a more in-depth assessment of social media’s influence on cryptocurrency trends, and lays the path for strategic decision-making in this nascent but rapidly developing field of study.
As a matter of fact, stock market prediction remains a challenging and crucial aspect of investment decision-making. Contemporarily, cryptocurrencies are one of the hottest underlying assets on account of its high volatility. In this case, based on high accuracy prediction, it is available to achieve large extra return from the crypto markets. With this in mind, this study investigates the usage of machine learning algorithms, including LSTM, GRU, as well as bi-LSTM, for predicting cryptocurrency prices, focusing on Bitcoin (BTC), Ethereum (ETH), as well as Litecoin (LTC). According to the analysis, the study reveals that the GRU model consistently outperforms other algorithms, MAPE and RMSE values across all three cryptocurrencies. These findings underscore the reliability and efficiency of the GRU model in cryptocurrency price prediction. Furthermore, the research compares the model's performance with previous studies, reaffirming its effectiveness and potential for practical application in investment strategies as well as decision-making.
With the growing concern of AI safety, there is a need to trust the computations done by machine learning (ML) models. Blockchain technology, known for recording data and running computations transparently and in a tamper-proof manner, can offer this trust. One significant challenge in deploying ML Classifiers on-chain is that while ML models are typically written in Python using an ML library such as Pytorch, smart contracts deployed on EVM-compatible blockchains are written in Solidity. We introduce Machine Learning to Smart Contract (ML2SC), a PyTorch to Solidity translator that can automatically translate multi-layer perceptron (MLP) models written in Pytorch to Solidity smart contract versions. ML2SC uses a fixed-point math library to approximate floating-point computation. After deploying the generated smart contract, we can train our models off-chain using PyTorch and then further transfer the acquired weights and biases to the smart contract using a function call. Finally, the model inference can also be done with a function call providing the input. We mathematically model the gas costs associated with deploying, updating model parameters, and running inference on these models on-chain, showing that the gas costs increase linearly in various parameters associated with an MLP. We present empirical results matching our modeling. We also evaluate the classification accuracy showing that the outputs obtained by our transparent on-chain implementation are identical to the original off-chain implementation with Pytorch.
Joy Dip Das, Ruppa K. Thulasiram, A. Thavaneswaran
This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, a novel hybridized architecture, AE-GRU, integrating the encoder-decoder principle with GRU is designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.
Crypto markets present significant challenges in financial time series forecasting with their high volatility and unpredictable nature. In this study, Optuna Based Optimized Transformer (OBOT) was proposed for time series forecasting for Bitcoin, the pioneer of cryptocurrency markets. To compare the proposed OBOT, Autoregressive Integrated Moving Average (ARIMA), Gradient Boosting Trees, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Temporal Convolutional Network (TCN) with optimized hyperparameters were used. In particular, after the success of Transformer models in natural language processing, studies have been conducted on their potential for time series problems. The models were evaluated using Optuna for hyperparameter optimization and their performance was compared with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The generalized performance of the models was tested by dividing the data set into different time steps (6–12) and training and test sets at different rates (0.5-0.5, 0.7-0.3, 0.8-0.2). The results show that the proposed OBOT approach stands out for Bitcoin with an RMSE value of 0.0079 and a MAE value of 0.0122. These findings reveal that the proposed OBOT approach have significant potential in crypto market forecasting and should be examined in more detail in future studies.
Kripto para birimleri, 2009 yılında ortaya çıkmalarından bu yana oldukça popüler hale gelmiştir. Özellikle Bitcoin'in 3 Ocak 2009'da piyasaya sürülmesinden sonra, diğer kripto para birimlerinin piyasaya çıkışı hız kazanmıştır. Bu popülerlik artışının ardından, kripto para birimlerinin tahmini önemli bir konu haline gelmiştir. Bu çalışmanın ana amacı, Bitcoin (BTC), Ethereum (ETH) ve Binance (BNB) kripto para getirilerini öngörmek için geleneksel zaman serisi yöntemlerinden olan ARIMA-GARCH ile birlikte LSTM (Long Short-Term Memory) derin öğrenme yaklaşımını kullanarak elde edilen tahmin performanslarını karşılaştırmaktır. Bu çerçevede, çalışma literatüre yeni bir katkı sunmayı amaçlamaktadır. Her bir kripto para birimi için farklı zaman aralıklarında günlük veriler kullanılmış ve bu veriler %90 eğitim ve %10 test verisi olarak bölünmüştür. Çalışmada, yöntemler RMSE ve MSE değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Genel olarak, BTC serisinde ARIMA-GARCH yöntemi eğitim verisinde daha iyi sonuçlar gösterirken, test verisi için LSTM yöntemi daha etkili olmuştur. BNB serisinde ise hem eğitim hem de test verisi için LSTM yöntemi daha üstün performans sergilemiştir. ETH serisinde ise her iki veri seti için ARIMA-GARCH yöntemi daha iyi sonuçlar ortaya koymaktadır. Bu çalışma, finansal veri tahmininde her iki yöntemin de önemli bir performans sergileyebildiğini vurgulamaktadır.
Gabriele Morello, Mojtaba Eshghie, Sofia Bobadilla, Martin Monperrus
The DISL dataset features a collection of $514,506$ unique Solidity files that have been deployed to Ethereum mainnet. It caters to the need for a large and diverse dataset of real-world smart contracts. DISL serves as a resource for developing machine learning systems and for benchmarking software engineering tools designed for smart contracts. By aggregating every verified smart contract from Etherscan up to January 15, 2024, DISL surpasses existing datasets in size and recency.
Bitcoin (BTC) represents an emerging asset class, offering investors an alternative avenue for diversification across various units of exchange. The recent global banking crisis of 9 March 2023 has provided an opportunity to reflect on how Bitcoin’s perception as a speculative asset may be evolving. This paper analyzes the volatility behavior of BTC in comparison to gold and the traditional financial market using GARCH models. Additionally, we have developed and incorporated a bank index within our volatility analysis framework, aiming to isolate the impact of financial crises while minimizing idiosyncratic risk. The aim of this work is to understand Bitcoin’s perception among investors and, more importantly, to determine whether BTC can be considered a new asset class. Our findings show that in terms of volatility and price, BTC and gold have responded in very similar ways. Counterintuitively, the financial market seems not to have experienced high volatility and significant price swings in response to the March 9th crisis. This suggests a consumer tendency to seek refuge in both Bitcoin and gold.
Cryptocurrency, a digital currency managed by decentralized networks, has gained immense popularity since the inception of Bitcoin. These digital assets, often characterized by extreme price volatility, have generated substantial interest from investors. Traditional financial models struggle to account for the unique dynamics and complexities of cryptocurrencies, prompting the adoption of deep learning techniques. This study investigates the use of Long Short-Term Memory (LSTM), Neural Networks, and Deep Learning (CNN) in predicting cryptocurrency prices. These deep learning models leverage various data sources, such as technical indicators and sentiment analysis, to gain a comprehensive understanding of cryptocurrency markets. The research evaluates the performance of these models using Root Mean Squared Error (RMSE) as the primary metric. The results demonstrate that the hybrid model, combining LSTM, Neural Networks, and Deep Learning, exhibits the highest predictive accuracy across multiple cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB). However, challenges persist, such as model adaptability to unforeseen market events and data noise. Future developments may involve incorporating external factors and interdisciplinary collaboration to create more holistic valuation models. Despite these challenges, the study underscores the potential of hybrid deep learning models in enhancing cryptocurrency valuation accuracy and their relevance in risk management strategies for investors and traders.
The valuation and prediction of cryptocurrency prices have become increasingly important in the financial market. Therefore, this study aims to focus on the selection and evaluation of machine learning models for cryptocurrency valuation. Thus, two types of machine learning models, gradient boosting trees (Xgboost and LightGBM) and neural networks, are compared to determine their effectiveness in generating features for cryptocurrency valuation. Additionally, correlation tests are conducted to identify the most suitable input variables for the models. The results demonstrate that the generated features have a significant impact on the accuracy of machine learning predictions for cryptocurrency prices. It highlights the potential of machine learning models in accurately predicting and evaluating the value of cryptocurrencies. Overall, the findings of this study contribute to the understanding of the role of machine learning in cryptocurrency valuation and provide valuable insights for investors and researchers. By leveraging machine learning techniques, investors can make informed decisions and develop effective investment strategies in the cryptocurrency market. This study contributes to cryptocurrency valuation research. Leveraging machine learning enables informed decisions and effective investment strategies. Furthermore, the findings inform the development of advanced machine learning models and algorithms for cryptocurrency valuation.
Yuxin Zhang, Rajiv Garg, Linda L. Golden, Patrick L. Brockett · 5 authors
Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.
In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.
Lalitha Ramachandran, S. Shanthana, A. Gokulakrishnan, Sunil Kumar · 6 authors
The financial industry is undergoing a revolution right now as a result of decentralised finance (DeFi), which offers innovative and decentralised solutions. In this context, investors are required to place a high value on their ability to foresee the performance of stocks. Decentralised finance is characterised by its decentralised structure, which makes it more challenging to make accurate predictions regarding stock prices. Due to the fact that they are unable to keep up with the dynamic nature of decentralised markets, traditional models produce estimates that are less than ideal. Within the present body of research, which primarily concentrates on centralised financial markets, there is a deficiency in the availability of specialist approaches for predicting DeFi stock prices. The purpose of this research is to investigate historical data, identify trends, and create predictions in order to forecast the stock values of the decentralised financial sector for the future. Deep Regression Learning is a subset of machine learning technology. By utilising deep neural networks to comprehend intricate data links, the model is able to improve its ability to make accurate predictions. When it comes to forecasting the stocks of decentralised financial institutions, the results demonstrate that the DREGL model that was recommended provides accurate results. The accuracy of the model is superior to that of traditional approaches, demonstrating that it has the potential to facilitate decision-making in the decentralised and unpredictable market for decentralised finance.
In the current study, the immediate correlation coefficient and root mean square error (RMSE) are combined to create a fusion model that can accurately predict cryptocurrency prices. Multivariate linear regression, MARS, artificial neural networks (ANN), random forests, support vector machines (SVM), bootstrap aggregation, decision trees, and extreme gradient boosting with XG Boost are just a few of the deep learning and machine learning models that we use. Utilizing long short-term memory (LSTM) is a crucial element. LSTM emerges as the most accurate model for predicting cryptocurrency prices during January 1, 2023, to March 31, 2023.
Gummadi Pavani, Konijeti Sri Vyshnavi, Methuku Samhitha, Tricha Anjali
Cryptocurrency has taken the financial world by storm, with its value and relevance growing daily. For financial players, predicting cryptocurrency prices accurately has become crucial. Considering the growing importance of cryptocurrencies in the financial industry, this study focuses on the urgent problem of predicting their prices. Previous research in this domain has predominantly employed conventional statistical and machine learning methodologies, artificial neural networks, deep learning, and reinforcement learning. But lot of these methods often prove insufficient in capturing the complex patterns and sentiment analysis that are pivotal in influencing the dynamics of cryptocurrency markets. This research presents a one such method that captures these complex patters that is LSTM with self-attention mechanisms with Long Short-Term Memory (LSTM) neural networks to improve prediction accuracy and compares it with traditional LSTM. Results showcase the self-attention LSTM's ability to identify long-range dependencies, providing a robust solution for navigating the complexities of cryptocurrency markets. Furthermore, the self-attention LSTM demonstrates a notable improvement in R-squared (R2) scores over the traditional LSTM, underscoring its efficacy in capturing nuanced market dynamics and explaining more variance in the cryptocurrency price data.
High Accessibility and Easy Investment makes Cryptocurrency an important income source for many people. Cryptocurrency is a kind of Digital/Virtual currency which is created using blockchain Technology and is protected by Cryptography. Cryptocurrencies enables users to Accept, Transfer and request the capital between the Users without the requirement of intermediaries such as banks. Now a day many Cryptocurrencies are available across the world such as Bitcoin, Litecoin, Monero, Dogecoin etc. This study is more determined over a very famous and demanding Cryptocurrency known as Bitcoin over the past years. Here, firstly we make an effort to predict the price of bitcoin by examining numerous numbers of parameters that affect the cost of bitcoin. Different kinds of Machine learning models will be used to estimate the price of Bitcoin. This study provides the accuracy and precision of each model that are used in this study and determine the suitable method to estimate the price more accurately.
In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.