Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.
Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.
This paper examines the efficiency and asymmetric multiracial features of NFTs (Mana, Tezos), and traditional assets (EGX30, Oil index) using Asymmetric Multiracial Cross-Correlations Analysis covering the period from January 2020 to May 2021. Considering the full sample with a significant variation among asset classes. (Oil-Tezos)and (Mana-Tezos) is the most efficient.Since their inception, the blockchain-based digital asset classes have received immense interest from investors and portfolio managers as an alternative investment platform. Along with other established traditional cryptocurrencies such as Bitcoin, Litecoin, Ripple, and Ethereum, new blockchain asset classes such as Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) have made a considerable contribution tothe asset market’s recent expansion (Aharon & Demir, 2021; Alam, Chowdhury, Abdullah, & Masih, 2023; Maouchi, Charfeddine, & el Montasser, 2021; Yousaf & Yarovaya, 2022).Fundamentally, NFTs and DeFi differ from traditional cryptocurrencies as they are not virtual currency. Where NFTs are non-transferable cryptographic digital assetscreated by Ethereum smart contracts and can be sold and traded, the interchangeability of NFTs when comparing the other cryptocurrencies is very low (Karim, Lucey, Naeem, & Uddin, 2022; Q. Wang, Li, Wang, & Chen, 2021; Y. Wang, 2022).The NFTs and DeFi are relatively contemporary and unexplored asset classes, but their market capitalization has grown substantially as risk minimizing assets, particularly during the COVID-19 period. In the NFT space, the KeywordsVolatility, NFTs, Traditional Financial Assets, and MGARCH
With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.
Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.
Mamun Ahmed, Sayma Alam Suha, Fahamida Hossain Mahi, Forhad Uddin Ahmed
Since entering the market in 2009, Bitcoin has had a price that is extremely erratic. Its price is influenced by factors such as adoption rates, regulatory changes, geopolitical occurrences, and macroeconomic developments. Experts believe that Bitcoin's price will rise in the long run due to limited supply and rising demand. Therefore, the aim of this study is to propose an ensemble feature selection and machine learning-based approach to predict bitcoin price. For this research purpose, the cryptocurrency-based dataset has been used, visualized, and preprocessed. Five different feature selection approaches (Pearson, RFE, Embedded Random Forest, Tree-based and Light GBM) are followed by ensemble methodology, with the maximum voting approach to extract the most significant features and generate a dataset with reduced attributes. Then the dataset with or without feature selection is used for bitcoin price prediction by applying ten different machine learning regressing models, which includes six traditional, four bagging and boosting ensemble techniques. The comparative result analysis through multiple performance parameters reveals that the decreased number of features improves the performance for each of the models and the ensemble models outperform other types of models. Therefore, Random Forest regression ensemble ML model can get the best prediction accuracy with 0.036018 RMSE, 0.029470 MAE and 0.934512 R2 employing the dataset with reduced features for estimating the value of bitcoin.
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.
R. C. Bose, Jeevan Nagarkar, Sushant Malik, Nisha Bharti
Predictability of the various financial instruments can lead to more trust and investment. The study examines the long-term and causal relationship between various Nifty indices and the Ethereum cryptocurrency. This study considers the data from April 2015 to December 2022 in two phases, pre-covid and post-covid. Johansen’s cointegration test was used to determine if the vectors in the data set are cointegrated, using the Max-Eigen and Trace tests for evaluation. The Granger causality test was also used to explore the short-term causal relationship between Ethereum and the five Nifty indices. The study found that post-pandemic daily returns of stock market indices have developed a significant cointegration with the cryptocurrency over time. The Granger causality test results showed bi-directional relationships of Nifty 50, Nifty 200 and Nifty Next 50 with Ethereum and a unidirectional relationship between Nifty Auto and Ethereum. The non-linear results reveal a one-way relationship pre-covid and a bi-directional relationship post-covid except for Nifty Banks. Johansen’s cointegration test, both in the pre-and post-covid-era, indicated that these indices had a substantial long-term cointegration with cryptocurrencies. This study also offers guidance to investors in making long-term investment decisions and to regulatory authorities. This implied that the investing decisions resulted in developing a causal relationship between the equity market and cryptocurrencies, which seemed very unlikely before 2020. This indicates that a new and young investor also considered cryptocurrencies a viable alternate investment option compared to traditional options such as fixed deposits, gold, and other fixed-income instruments.
The recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices.
This article presents a novel approach to cryptocurrency price forecasting, leveraging advanced machine-learning techniques.By comparing traditional autoregressive models with recurrent neural network approaches, the study aims to evaluate the forecasting accuracy of Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models across various cryptocurrencies, including Bitcoin, Ethereum, Dogecoin, Polygon, and Toncoin.The data for this empirical study was sourced from historical prices of these specific cryptocurrencies, as recorded on the CoinMarketCap platform, covering January 2022 to April 2024.The methodology employed involves rigorous statistical and neural network modelling where each model's parameters were meticulously optimized for the specific characteristics of each cryptocurrency's price data.Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used to assess the precision of each model.The main results indicate that LSTM and GRU models, leveraging deep learning techniques, generally outperformed the traditional ARIMA and SARIMA models regarding error metrics.This demonstrates a higher efficacy of neural networks in handling the non-linear complexities and volatile nature of cryptocurrency price movements.This study contributes to the ongoing discourse in financial technology by elucidating the practical implications of using advanced machine-learning techniques for economic forecasting.Importantly, it provides valuable insights that can directly inform and enhance the decision-making processes of investors and traders in digital assets.
Cryptocurrencies are digital assets that have attracted a lot of investment and attention. It is challenging and essential for investors and traders to predict their stock price movements. Making accurate predictions about cryptocurrency prices is crucial for avoiding losses and gaining profits. Our research proposes a novel method for predicting the stock closed prices of three popular cryptocurrencies: Bitcoin, Ethereum and Polkadot. The SVR (Support vector regression) machine learning method can provide robust and accurate predictions for nonlinear and nonstationary data. This paper compares SVR radial basis functions (RBFs) and hybrid kernels based on cryptocurrency data characteristics. SVR parameters such as regularization, gamma, and epsilon can also be tuned using grid search. Our approach is tested on real-world cryptocurrency stock prices collected from Yahoo Finance. Prediction performance is measured using regression metrics like MAPE (Mean absolute percentage error) and R2 score. In our work, a MAPE value of 0.07772 and an R2 score of 0.9999 have been obtained. The results of our experiments indicate that our approach is significantly more accurate and reliable than existing methods.
This work proposes a novel methodology to help in decision making in the cryptocurrency market. Two investment strategies have been designed for Ethereum (ETH), based on predictions of the price and trend of this cryptocurrency using real data. The two Ethereum cryptocurrency prediction systems rely solely on past values of other contextual stock indices, market indicators and online trends, and ignore any technical indicators of price evolution. Real data from cryptocurrency market has been collected and processed with different feature selection methods. Applying a regression approach with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks, prediction models for the ETH price for 1, 7 and 15 days are obtained and compared. Also, support vector machine (SVM) is applied to predict the ETH price trend by applying a classification approach. In both approaches, sentiment analysis has been included to check its effect on the prediction results. The reliability of these prediction models in the current market has been evaluated by designing two original knowledge-based investment strategies. They are tested over two different time periods with real cryptocurrency market data. The results show that it is possible to generate up to 5.16 profit factor with few operations using these models. Furthermore, adding sentiment analysis has shown to have little influence. In this way, we contribute to the advancement of our knowledge of this volatile and still young cryptocurrency market, and specifically of the evolution of Ethereum and the factors that can influence its behavior.
Cryptocurrency market has striking development and aims to build open, transparent and efficient financial market. More applications are tried to build on blockchain and using cryptocurrency in multiple scenarios, and DeFi ecosystem is set up. Many niche cryptocurrencies related to DeFi market may have wide utility and demand in the future. Therefore, more research is needed to focus these cryptocurrencies and try to establish sturdy price prediction. In this study, two cryptocurrencies, Binance Coin (BNB) and Huobi Tokens (HT), which are rooted in two crypto exchange platform are used for price prediction based on three machine learning models, i.e., Random Forest (RF), Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). Four prediction windows are selected (1, 3, 7 and 30 days span). The results for different prediction window are compared and discussed. 1 day prediction window outperforms all prediction windows with the average MSE 0.0117. Additionally, RF and XGBoost outperform LSTM with lower MSE and more stable performance, while RF and XGBoost have average MSE 0.0157 and 0.0158 separately. The research tries to predict cryptocurrencies that based on relatively niche market and discuss the performance in comprehensive ways, aiming at providing novel insights into cryptocurrency price prediction.
Bitcoin price volatility fascinates both researchers and investors, studying features that influence its movement. This paper expends on previous research and examines time series data of various exogenous and endogenous factors: Bitcoin, Ethereum, S&P 500, and VIX closing prices; exchange rates of the Euro and GPB to USD; and the number of Bitcoin-related tweets per day. A period of three years (from September 2019 to September 2022) is covered by the research dataset. A two-layer framework is introduced tasked with accurately forecasting Bitcoin price. In the first layer, to account for complexities in the analyzed data, variational mode decomposition (VMD) extracts trends from the time series. In the second layer, Long short-term memory and hybrid Bidirectional long short-term memory networks were used to forecast prices several steps ahead. This work also introduced an enhanced variant of the sine cosine algorithm to tune the control parameters of VMD and both neural networks for attaining the best possible performance. The main focus is on combining VMD with modified metaheuristics to improve cryptocurrency closing value forecast. Two sets of experiments were conducted, with and without VMD. The results have been contrasted with models tuned by seven other cutting-edge optimizers. Extensive experimental outcomes indicate that Bitcoin price can be forecasted with great accuracy using selected features and time series decomposition. Additionally, the best model was analyzed, and Shapley values indicated that features such as EUR/USD exchange rates, Ethereum closing prices, and GBP/USD exchange rates, have a significant impact on forecasts.
Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios Κ. Nasiopoulos
Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets.
This study investigates the application of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for detecting anomalies in Bitcoin trading data. With the growing significance of Bitcoin in the financial market, identifying irregular trading patterns is crucial for maintaining market integrity and preventing market manipulation. Utilizing a dataset from Kaggle, which includes features such as date, timestamp, open, high, low, close, volume, and number of trades, the data was aggregated from minute-by-minute to hourly intervals for more manageable analysis. The DBSCAN algorithm effectively identified a primary cluster comprising 29,612 data points and flagged 2 points as anomalies, achieving a precision of 1.0, recall of 0.0068, F1-score of 0.0135, and an AUC-ROC of 0.5034. The optimal parameters, determined through sensitivity analysis, were epsilon (ε) = 0.1 and min_samples = 3, yielding the highest silhouette score of 0.21499. These results underscore the algorithm's ability to accurately label anomalies while highlighting the challenge of comprehensive anomaly detection. The study contributes to the field of financial anomaly detection by demonstrating the effectiveness of DBSCAN in analyzing high-dimensional, noisy datasets. It also addresses gaps in the literature regarding the application of density-based clustering methods to Bitcoin trading data. Despite its contributions, the study acknowledges limitations, such as potential data aggregation impact and the need for further validation with different datasets. Future research directions include integrating additional features like social media sentiment and exploring hybrid approaches that combine supervised and unsupervised methods.
This study investigates the relationship between trading volume and Bitcoin price movements using Pearson and Spearman correlation methods. The aim is to determine if trading volume can reliably predict Bitcoin price changes. Using a comprehensive dataset of daily Bitcoin prices and trading volumes, various statistical techniques were employed. Pearson and Spearman correlation analyses revealed very weak and statistically insignificant relationships, with correlation coefficients of -0.023788 and 0.021093, respectively. Linear regression analysis further supported these findings, showing an insignificant regression coefficient for trading volume and a very low R-squared value of 0.000566. Volatility analysis, measured by the standard deviation of daily returns, demonstrated high price volatility, consistent with the cryptocurrency market's nature. This volatility is influenced by factors such as market sentiment, regulatory developments, and macroeconomic events. The study also utilized 30-day moving averages to smooth short-term fluctuations and highlight long-term trends in trading volume and closing prices, revealing underlying trends not visible in daily data. A 1-day lagged correlation analysis indicated a very weak relationship (0.008145) between trading volume on one day and price changes on the next, suggesting other factors drive price movements. Visualizations, including time series graphs, histograms, moving averages, and volatility graphs, further illustrated the lack of a clear pattern between trading volume and price changes. In conclusion, trading volume is not a significant predictor of Bitcoin price movements, highlighting the need for comprehensive analytical approaches considering multiple variables to understand and predict Bitcoin price dynamics better.
This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.
This study investigates the efficacy of ARIMA and LSTM models in predicting Bitcoin prices, emphasizing the importance of accurate price prediction for trading, risk management, and investment strategies in the volatile cryptocurrency market. The objectives are to analyze Bitcoin prices to identify underlying patterns and trends, compare the predictive performance of ARIMA and LSTM models, and provide insights into their practical applications for Bitcoin price prediction. A comprehensive dataset of Bitcoin prices from January 1, 2011, to December 31, 2023, sourced from CoinMarketCap, was used. Data preprocessing included handling missing values, removing duplicates, achieving stationarity through differencing, and normalizing data using MinMaxScaler. The ARIMA model's best-fitting parameters were identified using ACF and PACF plots, and it was trained with the statsmodels library. The LSTM model involved data preparation through windowing and train-test splitting, constructing a neural network with LSTM layers, and training using TensorFlow/Keras. Evaluation metrics included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with comparisons based on accuracy and computational efficiency. The ARIMA model demonstrated impressive performance with an MAE of 2.308392356829177e-215 and an RMSE of 0.0, indicating a near-perfect fit to the training data. The LSTM model achieved an MAE of 0.00021804577826689423 and an RMSE of 0.00021916977109865863, showing robust performance in handling nonlinear and long-term dependencies. The ARIMA model excelled in computational efficiency with a training time of 2.548070192337036 seconds and a prediction time of 0.0009970664978027344 seconds, while the LSTM model required 378.69622468948364 seconds for training and 0.6859967708587646 seconds for prediction. The results highlight ARIMA's effectiveness in capturing linear trends and its suitability for short-term trading strategies, while LSTM is better for long-term investment strategies due to its ability to model complex patterns. Despite potential overfitting in ARIMA and high computational demands for LSTM, the study suggests exploring hybrid models, incorporating additional data sources, and developing advanced techniques to enhance predictive accuracy in future research.
Over the past few years the interest in trading with a decentralized or virtual form of currency has significantly increased. This has led to the rise of the cryptocurrency market in the early 2000’s. Bitcoin, the pioneer in the field, has managed to dominate this volatile and cutthroat market till this day. Cryptocurrency is based on multiple technological frameworks and success stories of high returns in a short time frame have garnered the interest of young investors as well. Similar to the traditional stock market, multiple machine learning, Artificial Intelligence, Time Series Analysis models have come up to help investors, understand trends, patterns, and derive a deeper understanding of the asset as well as the market they are investing in. Our research aims to deal with this problem using time series methods such as Auto-Regressive (AR), Moving Average (MA) and Auto-Regressive Integrated Moving Average (ARIMA) models. In our analysis, we have implemented the models using both Excel method and Python. Our metric of evaluation is Mean Absolute Percentage Error (MAPE). In our work, data was taken from a website called ‘Yahoo finance’ [1] for Bitcoin cryptocurrency for a five-year time period i.e. from 1st June, 2017 to 31st May, 2024. Our Python methodology has resulted in a MAPE of 0.16 for AR model, 0.14 for MA model and 0.11 for ARIMA model. The same has been verified using Excel and the score has been validated.
Abstract We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state‐of‐the‐art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both in‐ and out‐of‐sample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series.
S. Saraswathi, J S Sridhala, A. Elavazhagan, Jasbir Singh Sabharwal · 5 authors
This research proposes an ensemble approach for Bitcoin price prediction, leveraging historical price data and sentiment analysis. The proposed ensemble approach combines the model with Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) to further improve the accuracy in prediction by considering dynamics in the market. The model also addresses the problem of generalization and overfitting, adaption to the changing, dynamic nature of the market. Historical price data and sentiment scores from the preprocessing of the text are combined to the ensemble framework. These data are then fed into GRU and BiLSTM models for training, as the data contain not only complex temporal patterns but also sentiment-driven trends. The ensemble strategy could be beneficial for the strengths of the models and for improving the performances of the predictors. Most importantly, features are engineered in terms of technical indicators, lagged variables, and external factors impacting the price of Bitcoin. Sentiment analysis with the news and on social media complements insight into market sentiment, which adds value to the prediction power of the model.
These days, there is a lot of demand for cryptocurrencies, and investors are essentially investing in them. The fact that there are already over 6,000 cryptocurrencies in use worldwide because of this, investors with regular incomes put money into promising cryptocurrencies that have low market values. Accurate pricing forecasting is necessary to build profitable trading strategies because of the unique characteristics and volatility of cryptocurrencies. For consistent forecasting accuracy in an unknown price range, a variation point detection technique is employed. Due to its bidirectional nature, a Bi-LSTM appropriate for recording long-term dependencies in data that is sequential. Accurate forecasting in the cryptocurrency space depends on identifying these connections, since values are subject to change over time due to a variety of causes. In this work, we employ four deep learning-based models that are LSTM, FB-Prophet, LSTM-GRU and Bidirectional-LSTM(Bi-LSTM) and these four models are compared with Silverkite. Silverkite is the main algorithm of the Python library Graykite by LinkedIn. Using historical bitcoin data from 2012 to 2021, we utilized to analyse the models’ mean absolute error (MAE) and root mean square error (RMSE). The Bi-LSTM model performs better than others, with a mean absolute error (MAE) of 0.633 and a root mean square error (RMSE) of 0.815. The conclusion has significant ramifications for bitcoin investors and industry experts.