Cryptocurrency Price Research Articles

A survey on cryptocurrency price prediction

A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

Analysis of LSTM and Derivative Models for Bitcoin Prediction Research

Bitcoin, the first cryptocurrency, has attracted much attention in the digital currency market, and its price volatility is affected by a variety of factors, posing a challenge to investors. For the sake of analytical feasibility, many studies have adopted simplified modeling assumptions. This may overlook certain key nonlinear features of the market, such as the complexity of investor behavior and the volatility of market sentiment. This study explores the application of Long Short-Term Memory (LSTM) networks and their derivative models in predicting Bitcoin prices. Recognizing the complex nature of Bitcoin’s market dynamics, the research delves into the effectiveness of LSTM in capturing the nonlinear patterns of cryptocurrency prices. Furthermore, it extends the analysis to derivative models like MSM-LSTM and Empirical Mode Decomposition (EMD) LSTM, evaluating their ability to enhance prediction accuracy by addressing the limitations of the standard LSTM. This study provides an in-depth analysis of the effectiveness of LSTM and its derived models in the practical application of Bitcoin price prediction, with a special focus on their ability to capture non-linear patterns in the market. In Bitcoin price prediction, LSTM models have limitations but also great potential in capturing nonlinear patterns in the market. Extended models based on their own can perform well in Bitcoin price prediction. This study contributes to the field by suggesting strategies to improve the accuracy of the model and by providing ideas for developing trading strategies based on the results of the analysis.

Cryptocurrency Price Tracker: A Comprehensive Examination of Market Trends, Real-Time Updates, and User-Centric Features

The cryptocurrency market, characterized by its volatility and rapid fluctuations, necessitates robust tools for tracking and analyzing prices in real-time. This project proposes the development of a cryptocurrency price tracker, leveraging the MERN (MongoDB, Express.js, React.js, Node.js) stack for efficient data management and user interface design. The platform aims to provide users with timely and accurate information on cryptocurrency prices, market trends, and historical data. Key features include real-time price updates, customizable watchlists, advanced charting tools, and secure authentication mechanisms. The project focuses on enhancing user experience, scalability, security, and performance to meet the evolving demands of cryptocurrency enthusiasts and traders. By integrating cutting-edge technologies and adhering to best practices in software development, the cryptocurrency price tracker aims to become a valuable resource for both novice and experienced users navigating the complexities of the cryptocurrency market. Keywords: Cryptocurrency, Price Tracker, MERN Stack, Real-time Data, User Experience, Performance.

Eksplorasi Fundamental Cryptocurrency dalam Volatilitas Harga

This research aims to comprehensively explore the fundamental factors that influence cryptocurrency price volatility, and apply them to adoption, regulation and investment strategies. The fundamental factors explained include supply, demand, mining activity, transaction fees, block size, blockchain technology, and context mechanisms. This research uses a qualitative and quantitative approach with literature study and secondary data analysis. The research results show that cryptocurrency price volatility is significantly influenced by the interaction of supply and demand, mining activity as reflected in the hash rate, as well as transaction costs and block size in the blockchain network. Additionally, blockchain technology and the context mechanisms used also contribute to price volatility. High price volatility hinders the use of cryptocurrencies as a stable medium of exchange and investment instrument.

Forecasting and Analysis of Cryptocurrency Trends

The development of financial technology has given rise to a new class of asset known as cryptocurrency, which has presented a significant potential for study. Forecasting cryptocurrency prices is challenging because of their dynamic and volatile nature. There are hundreds of different cryptocurrencies in circulation today. With prospects in peer-to-peer payments, remittance, e-commerce and retail, and media & entertainment, the cryptocurrency business appears to have a bright future. From 2019 to2024, the worldwide cryptocurrency industry is anticipated to expand at a 32% CAGR. The distributed ledger technology's immutability and transparency, the rise in remittances from poor nations, shifting monetary laws, and a sharp rise in venture capital investments are the main factors propelling this market's growth. We created a short-term prediction (between five and thirty days) for the cryptocurrencies using the ARIMA model. When it came to predicting cryptocurrency time series, we discovered that the suggested method outperformed the ARIMA-ARFIMA models in both periods of gradual rising (falling) and transition dynamics (change of trend). Index Terms: digital currency,cryptocurrency, and ARIMA model.

Volatility modeling of cryptocurrency and identifying common GARCH model

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student’s t, Generalized Error, Skew Normal, Skew Student’s t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations.

Insights into Bitcoin and energy nexus. A Bitcoin price prediction in bull and bear markets using a complex meta model and SQL analytical functions

Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process.

A Research: Investigation of Financial Applications with Blockchain Technology

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.

Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction

Cryptocurrency Price Prediction for Next 30 Days with Machine Learning

The goal of this project is to create a model from machine learning that will predict cryptocurrency prices toward the following 30 days. The main goal is to build a prediction model that is able to predict future swings in prices for selected cryptocurrencies by using historical market data that encompasses price patterns, trading volumes, and appropriate indicators. The key objective of the model is to empower traders and investors functioning in the fluctuating and unpredictable the digital currency market to make well-informed decisions. Throughout every moment of the next 30 days, the model aims to give traders an advantage of strategy in maintaining the cryptocurrency ecosystem through provision of practical information. Key Words: Cryptocurrency, Prediction, LTSM, Normalization, Features Extraction, Pediction Date

Evaluations of the machine learning schemes for cryptocurrency prediction

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.

Cryptocurrency assets valuation based on statistics model and machine learning

As a matter of fact, cryptocurrency is an exchange media, which uses cryptographic functions to trade, which achieves decentralisation, transparency, and invariance using blockchain. With the rapid development since the first white book proposed in 2008. The market value of cryptocurrency has been exponentially expanded rapidly in recent years. On this basis, programmatic trading strategy are paid more attention by people in trading cryptocurrency contemporarily. With this in mind, this study provides three models, that is, Exponential Smoothing Model (ESM), Autoregressive Integrated Moving Average (ARIMA) model and Long short-term memory (LSTM) model to forecast the future price of cryptocurrency and use sum of square error to analyse the results of these prediction. According to the analysis, LSTM is the best model to forecast the price of cryptocurrency in this article. Overall, machine learning might be more useful to forecast in this fields than traditional statistical models. Investors and researchers in the future could continue to forecast the price of cryptocurrency combined with LSTM model.

Cryptocurrency price prediction based on Xgboost, LightGBM and BNN

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.

Cryptocurrency assets valuation prediction based on LSTM, neural network, and deep learning hybrid model

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.

Sentiment analysis model for cryptocurrency tweets using different deep learning techniques

Abstract Bitcoin (BTC) is one of the most important cryptocurrencies widely used in various financial and commercial transactions due to the fluctuations in the price of this currency. Recent research in large data analytics and natural language processing has resulted in the development of automated techniques for assessing the sentiment in online communities, which has emerged as a crucial platform for users to express their thoughts and comments. Twitter, one of the most well-known social media platforms, provides many tweets about the BTC cryptocurrency. With this knowledge, we can apply deep learning (DL) to use these data to predict BTC price variations. The researchers are interested in studying and analyzing the reasons contributing to the BTC price’s erratic movement by analyzing Twitter sentiment. The main problem in this article is that no standard model with high accuracy can be relied upon in analyzing textual emotions, as it represents one of the factors affecting the rise and fall in the price of cryptocurrencies. This article aims to classify the sentiments of an expression into positive, negative, or neutral emotions. The methods that have been used are word embedding FastText model in addition to different DL methods that deal with time series, one-dimensional convolutional neural networks (CONV1D), long-short-term memory networks (LSTMs), recurrent neural networks, gated recurrent units, and a Bi-LSTM + CONV1D The main results revealed that the LSTM method, based on the DL technique, achieved the best results. The performance accuracy of the methods was 95.01, 95.95, 80.59, 95.82, and 95.67%, respectively. Thus, we conclude that the LSTM method achieved better results than other methods in analyzing the textual sentiment of BTC.

Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

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.

On the resilience of cryptocurrencies: A quantile-frequency analysis of bitcoin and ethereum reactions in times of inflation and financial instability

This study examines the resilience of Bitcoin and Ethereum to inflation and financial instability amidst major economic and political disruptions, including the U.S.-China trade war, the COVID-19 pandemic, the Ukrainian conflict, the collapse of Silicon Valley Bank, and the ensuing energy crisis. We employ wavelet coherence analysis and the quantile coherence method to unravel the complex relationship between these cryptocurrency prices, the 5-year breakeven inflation rate, and the U.S. Financial Stress Index (FSI), utilizing daily data from April 16, 2018, to April 14, 2023. Our results reveal that during turbulent times, investors adjust their Bitcoin holdings in response to inflation expectations, driving up Bitcoin prices in both the short and long term. However, Bitcoin’s effectiveness as an inflation hedge diminishes in bearish market conditions, and it proves unreliable as a hedge against financial instability. The relationship between Ethereum and inflation expectations is found to be more context-dependent. Also, while Ethereum’s vulnerability to financial instability is less persistent in the long run, it increases in bearish markets.

Bullish Sentiment on Price Upward Trend: A Netnographic Study of Cryptocurrency Communities

Cryptocurrency as a digital decentralized currency has attracted many investors and obtained a lot of support from communities. Previous studies have concluded that there were indeed connections between community sentiment and cryptocurrency price movement. However, most of the research was conducted using sophisticated methods that are difficult to utilize by cryptocurrency investors. This research objective was to find practical ways to identify bullish sentiment during price upward trend especially during the Covid-19 omicron variant outbreak that started in the last quarter of 2021. Netnography method was used as a qualitative approach for this research to get insight from cryptocurrency communities. LunarCrush web application as a more user-friendly tool, was being used to analyze bullish sentiment and price data. During December 2021 until January 2022, 303 price upward trend data from 264 coins had been collected and analyzed. The result of this research revealed 5 categories of bullish sentiment messages from top influencers which are community booster, official information, project updates, achievement, and trading plan. However, it can be concluded that price movements were not always related to bullish sentiment. Thus, bullish sentiment should not be used as the sole factor to identify price upward trends. Furthermore, investors should join the cryptocurrency community to understand the characteristics of bullish sentiment and not just rely on data from monitoring tools. Interestingly, there were no Covid-19 related topics on bullish sentiment collected. Hence, it did not necessarily need to publish good news related to Covid-19 handling to create bullish sentiment among the investors.

Blockchain-based deep learning in IoT, healthcare and cryptocurrency price prediction: a comprehensive review

PurposeDeep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.Design/methodology/approachThe integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.FindingsBy responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.Originality/valueThere is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.