In the realm of financial markets, the manifestation of volatility clustering serves as a pivotal element, indicative of the inherent fluctuations characterizing financial instruments. This attribute acquires pronounced relevance within the sphere of cryptocurrencies, a sector renowned for its elevated risk profile. The present analysis, conducted through the Autoregressive Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) model, seeks to elucidate the enduring nature of volatility clustering and the occurrence of leverage effects within this domain. Over the course of a four-year time frame, it was observed that Bitcoin diverges from the anticipated Autoregressive Conditional Heteroskedasticity (ARCH) effects, in contrast to Ethereum and Cardano, which exhibit marked volatility clustering. Binance Coin, Ripple, and Dogecoin, whilst demonstrating moderate clustering, uniformly reflect the existence of leverage effects. An exception to this pattern was identified in Ripple, where it was discerned that positive market news exerts a disproportionate influence on log returns. The findings of this study illuminate the critical influence of both leverage effects and volatility clustering on the pricing dynamics of cryptocurrencies. It underscores the imperative for a nuanced comprehension of risk management in the context of cryptocurrency investments, given their susceptibility to abrupt price fluctuations. The distinct degrees to which these phenomena are manifested across diverse cryptocurrencies accentuate the necessity for a tailored risk management approach, resonant with the unique attributes of the asset in question. Such strategies, accounting for the potential amplification of losses through leverage, may encompass prudent position sizing, portfolio diversification, and the implementation of stress tests, thereby fortifying the investment against the dual perils of volatility clustering and leverage effects. The implications of this analysis serve to inform investors, providing a foundation upon which to construct risk management tactics that are responsive to the idiosyncrasies of the cryptocurrency market.
Fathin Faizah Said, Raja Solan Somasuntharam, Mohd Ridzwan Yaakub, Tamat Sarmidi
Abstract Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.
Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.
In this paper, we examine the dynamic relationship between Bitcoin prices and investor sentiment indicators. According to the correlation, the five time series variables of S&P cryptocurrency extensive digital market index, S&P 500 value index, bitcoin trading volume and the original Baidu index were reasonably selected to establish the comprehensive index of investor sentiment. The vector autoregressive VAR model is used to verify the relationship between the Bitcoin price (CLOSE) and the sentiment indicators. According to the Granger causality test and the pulse response function analysis, there is a bidirectional causal relationship between the two. The constructed indicators have important practical significance.
M. Vani Pujitha, Karan Kumar, D. Igna Sree, P. Yamini Devi
Particularly since Bitcoin's value and market capitalization have skyrocketed in recent years, the cryptocurrency market has attracted a lot of attention. For investors and traders, it is difficult to make wise selections due to the volatility and unpredictability of Bitcoin values. This project's goal is to forecast Bitcoin's price for the following 30 days. This prediction problem is particularly difficult due to the extreme price volatility of Bitcoin, the lack of conventional financial indications, and the complexity of the market. The price of Bitcoin for the following 30 days is predicted using a Long Short-Term Memory (LSTM) neural network in this research. The model is trained using a dataset of historical Bitcoin prices and their corresponding features such as trading volume and market capitalization. The LSTM model is trained to capture the temporal dependencies and patterns in the data, which allows it to make predictions. Previous studies have used various machine learning algorithms to predict Bitcoin prices, including ARIMA, SVM, and Random Forest. However, LSTM has shown superior performance in capturing the temporal dependencies in sequential data. The proposed LSTM model was evaluated using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and the results showed that the LSTM model outperforms the previous studies' models in terms of accuracy. The proposed model's predictions can assist investors and traders in making informed decisions about buying or selling Bitcoin in the future.
Ivan Ric P. Woogue, Sana Izumi, Angie M. Ceniza-Canillo
Cryptocurrencies are a type of digital currency that have been gathering attention as they are becoming more accepted as an investment medium and societies have become more receptive to cashless payments around the world. Previous studies have predominantly concentrated on the mainstream cryptocurrencies like Bitcoin and Ethereum. In contrast, this research focused on the correlation between meme coins, specifically Dogecoin, and tweets related to Dogecoin. The researchers have used a tweet dataset related to Dogecoin from Kaggle. VADER was used to get the sentiment scoring of the tweets. The input features used were tweet sentiments, tweet volume, and lag values at 1,3,8, and 12 hours. A total of 12 LSTM models using different combinations of the input features were developed utilizing the Bayesian Optimization algorithm. Half of the models were optimized for R-squared, and the other half were optimized for mean squared error (MSE). The models were evaluated using root mean squared error, mean directional accuracy, mean absolute error, R-squared, and percentage of duplicate values. The input features of the best-performing models had a combination of tweet sentiments, tweet volume, and all 4 lag values. The one optimized for R-squared is slightly better than the one optimized for MSE. However, the best-performing models still fell short since their R-squared was a negative value at −6.60364 and −6.21998 for those optimized for MSE and R-squared, respectively. This outcome may be attributed to the lack of available data since the tweet dataset used spans only approximately 4 months.
Over the past few years, there has been a notable surge in interest towards cryptocurrency, especially in the context of the crisis, where researchers have been diligently examining the influence of the coronavirus on cryptocurrency returns.Numerous studies have utilized econometric and machine learning techniques to forecast cryptocurrency prices, but most of them have focused solely on the financial domain. This paper introduces a novel approach called ANFPC (Adaptive Neuro Fuzzy Prediction Cryptocurrency), which combines insights from both the financial and health domains to predict the price fluctuations of various cryptocurrencies such as bitcoin, ethereum, cardano, xpr, and dogecoin. The approach relies on the ANFIS Model, a fusion of fuzzy logic and artificial neural network (ANN).The experimental findings demonstrate that ANFPC provides accurate predictions, as measured by metrics like Mae and Mse, outperforming traditional ANN and LSTM methods. This approach proves to be a valuable decision support tool for data analysts in the realm of cryptocurrency prediction.
Vincent Gurgul, Stefan Lessmann, Wolfgang Karl Härdle
We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public . • NLP data from social media improve the accuracy of cryptocurrency forecasting models. • As a target variable, local extrema are a valid alternative to daily price changes. • Deep learning language models substantially outperform dictionary-based methodologies. • Both pre-trained and fine-tuned language models effectively quantify market sentiment.
Mohamed El Badaoui, Brahim Raouyane, Samira El Moumen, Mostafa Bellafkih
This study assesses the effectiveness of machine learning (ML) models in predicting fluctuations in the direction of Bitcoin prices using conventional technical indicators. Given the volatile nature of Bitcoin markets, achieving precise predictions is of utmost importance. The study improves machine learning models including logistic regression (LR), decision trees (DT), random forests (RF), support vector machines (SVM), Xgboost, and artificial neural networks (ANN) by integrating established technical indicators. By employing Optuna for hyperparameter optimization and the 'Time Series Split' technique for cross-validation, the study optimizes the models for time series data. The results demonstrate that ML models, when integrated with technical indicators, significantly outperform predictions based solely on those indicators. Evaluation metrics include accuracy, recall, and the F1 score. Through the incorporation of signal indicators, ML models offer a robust approach to predicting changes in Bitcoin prices. The proposed ML approach achieves a test accuracy exceeding 65.15%, highlighting the potential synergy between traditional financial prediction tools and contemporary technology. This study underscores the potential effectiveness of combining ML models with technical indicators for forecasting Bitcoin price movements.
This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial application scenarios, including convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning. We also give an overview of cryptocurrencies by outlining the cryptocurrency history and discussing primary representative currencies. Based on the reviewed deep learning methods and cryptocurrencies, we conduct a literature review on deep learning methods in cryptocurrency research across various modeling tasks, including price prediction, portfolio construction, bubble analysis, abnormal trading, trading regulations and initial coin offering in cryptocurrency. Moreover, we discuss and evaluate the reviewed studies from perspectives of modeling approaches, empirical data, experiment results and specific innovations. Finally, we conclude this literature review by informing future research directions and foci for deep learning in cryptocurrency.
The rapid growth and unique advantages of cryptocurrencies have made them an attractive asset for investment portfolios. However, investing in cryptocurrencies comes with risks, especially market volatility. This paper explores the benefits of Bitcoin’s market dominance, including liquidity, stability, and practical utility. It also reviews multiple previous articles about using Artificial Intelligence (AI) for cryptocurrency trading to enhance the feasibility and profitability of cryptocurrency investments, focusing specifically on Bitcoin. It further discusses the potential advantages of using AI in developing predictive models for market trends and managing grid trading strategies to stabilize returns and lower risks. While research on the application of grid trading strategy with AI in the cryptocurrency market is limited, existing studies in other asset classes demonstrate the effectiveness of AI-based methods. Finally, this paper outlines novel potential future research that aims to integrate AI with the Grid Trading Strategy. This integration seeks to enhance the sustainability of cryptocurrency investments while simultaneously amplifying their profitability.
The nature of cryptocurrencies is decentralized and they have potential of large returns, due to this nature of crypto currencies, they have increased in approval in form of investment. Due to the erratic and volatile nature of the crypto currency market, it can be difficult to predict their pricing. As a result, reliable price forecasts are essential for investors to make wise investment choices. The proposed LSTM based approach will create machine learning models using open-source libraries like pandas, NumPy, and Scikit-learn. Cross-validation will be utilized to test the working of different models, and the one gives best output will be taken as the final model. The aim of this research is to create a machine learning algorithm that can forecast bitcoin values. Predicting the future price swings of crypto currencies like Bitcoin and Ethereum has become a crucial research subject as their use and popularity have increased. By using regression and deep learning algorithms, the work seeks to use different number of machine learning models to analyze historical bitcoin data and forecast future prices. The end result of this work will be a very effective and accurate model with R2 score of 0.96 for train data and 0.97 test data for forecasting crypto currency values, which traders, investors, and researchers may use to decide wisely on investing in crypto currencies.
Cryptocurrency markets' extreme volatility demands advanced predictive models. The proposed neural network approach utilizes extensive research and real-image exchange data, revealing the Digital Internet of Things' impact. Addressing consumer influence on prices is vital. Our frame working corporate “doesn't make sense. It should probably be “Our framework incorporates. At the core is an enhanced deep learning framework, integrating autoregressive integrated moving average (ARIMA) with convolutional neural networks (CNNs). This synergy captures intricate price patterns. We integrate sentiment analysis from various sources and block chain data for a holistic market view. Model robustness is bolstered with hyper parameter optimization and cross-validation. Real-time data integration ensures timely predictions. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics are used in performance evaluation. Empirical evidence high lights our model's superiority in predicting cryptocurrency market variations. Compared to traditional methods like ARIMA, it offers substantial improvements, benefiting traders, investors, and decision-makers. Future enhancements include ensemble models, hyper parameter tuning, advanced deep learning, realtime data integration, and model interpretability, empowering stakeholders with precise insights into evolving cryptocurrency markets.
Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more sophisticated MEV extraction. In this paper, we conduct the first systematic study on DeFi MEV activities in Flashbots bundle by developing ActLifter, a novel automated tool for accurately identifying DeFi actions in transactions of each bundle, and ActCluster, a new approach that leverages iterative clustering to facilitate us to discover known/unknown DeFi MEV activities. Extensive experimental results show that ActLifter can achieve nearly 100% precision and recall in DeFi action identification, significantly outperforming state-of-the-art techniques. Moreover, with the help of ActCluster, we obtain many new observations and discover 17 new kinds of DeFi MEV activities, which occur in 53.12% of bundles but have not been reported in existing studies.
In the digital age, the growth of blockchain technology, underpinning cryptocurrency, has been undeniable since its debut with Bitcoin in 2009. While the cryptocurrency market offers vast profit potential, its price volatility poses a significant challenge to investors and traders. Addressing this challenge, this study proposes a prediction method that fuses SARIMAX and LSTM into a Hybrid SARIMAX-LSTM model, considering trading volume as an exogenous factor. Through evaluation metrics like MSE, RMSE, MAPE, and MAE, it was found that this hybrid model provides more accurate forecasts than singular models, showcasing its potential to counteract cryptocurrency market volatility..
Sherin M. Omran, Wessam H. El-Behaidy, Aliaa A. A. Youssif
A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.
Ferdiansyah Ferdiansyah, Raja Zahilah, Sit Hajar, Deris Stiawan
LSTM is a promising tool for predicting the stock exchange. Still, when the LSTM Model faces an anomaly problem with a dataset of Bitcoin that has hit more change in value by fluctuation, it can be a problem for producing good evaluation results such as RMSE. This research is an improvement over the discoveries of previous research. We tried another perspective besides using five years of historical data prices to predict a six-day value. We found that the results of RMSE were not very good but exhibited good results on MAPE as a comparison evaluation method. We are using the last six days to predict the next day. Logically, this dataset has good dataset stability, but the dataset has quite a significant minute-by-minute change in day-by-day value. Furthermore, CNN-LSTM was selected in this research to give another perspective and improve the results. The results were quite good and greatly improved previous research.
Bitcoin has received a great deal of attention as a highly volatile asset with investors attempting to profit from its dramatic price fluctuations. We develop a hybrid deep learning model based on feature selection in different frequency domains to enrich the literature of Bitcoin price prediction. Indicators such as Technology, Economy, Green Finance and Media Attention are considered. We first decompose all the data into different frequencies through CEEMDAN approach, and then the data at the same frequency are integrated into a Random Forest model to reduce the subset of potential predictors by measuring the importance of different factors. Finally, the selected factors are put into the LSTM/GRU to make the prediction of different components of Bitcoin prices at the same frequency, and aggregate together to obtain the predicted Bitcoin prices. The empirical results show that our proposed model outperforms the benchmark models, which is verified by MCS test. The proposed hybrid method obtains much higher return on investment in simulated trading than other benchmark models. Our study inspired the investors to accurately predict Bitcoin price and dig possible relationships between different assets and its determinants in frequency domain.
Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito
Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.
In the last few years, the cryptocurrency market, especially Bitcoin, has attracted many people, including machine learning engineers. They have heavy competition in predicting the price or the rise and fall of the price in the future. To achieve this goal, they used various types of approaches like Linear Regression, SVM, and deep learning methods like Neural networks, Recurrent neural networks like RNN, LSTM, and GRU, Bidirectional neural networks, and a combination of methods. M.L and D.L engineers used various types of information to feed their models especially emotional analysis of people. Emotions that people express on social networks, especially Twitter. The purpose of this paper is to introduce a new approach to Bitcoin trend prediction using deep learning algorithms. By sentiment analysis of extracted data from Twitter and tracking the previous price. The data collected for this research is between January 2012 and December 2020. This article compares LSTM, Bi-LSTM, GRU, and Bi-GRU algorithms to predict the trend of Bitcoin price changes. The Bi-GRU algorithm better performance by registering a record of 72% accuracy in predicting the trend of Bitcoin price changes and improving 20% the speed of the learning process.
Purpose : This study focused on evaluating the efficiency of the global cryptocurrency market under the efficient market hypothesis.Methodology : The study explored the presence of calendar effects in the form of the day-of-the-week and the weekend effect in the top 25 global cryptocurrencies and a constructed index using autocorrelation corrected ARCH family models on the log-returns of prices. Non-normality is tackled through the use of a non-parametric bootstrapped approach in addition to parametric estimation. Additionally, rolling window regression is employed as a dynamic framework.Findings : The findings of this study showed the presence of calendar effects in terms of higher returns for certain days over others, thereby concluding that the global cryptocurrency market as a whole was not efficient.Practical Implications : As a result of its informational inadequacies, it was implied that cryptocurrency markets were not even efficient in the weak form. This had practical implications for investors, whose aggressive search and trading tactics appear warranted in the context of market anomalies, as well as other market players, like regulators attempting to comprehend the structure of the cryptocurrency market from an efficiency standpoint and researchers attempting to investigate the efficiency-related behavior and psychology of crypto markets.Originality : This study is exceptional in that it uses a sample of cryptocurrencies that objectively covers the larger global cryptocurrency markets over the most extended amount of time possible, making it unique both for the participants in the crypto markets and for its contribution to the literature on market efficiency. The study employed an evolutionary methodology, taking into account the time-varying behavior of financial assets. As far as we can tell, this is one of the first studies to use this methodology and model formulation.