Zhuohuan Hu, F. Richard Yu, Zizhou Zhang, Haoran Zheng · 6 authors
This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences. Keywords - CNN;GANs; Cryptocurrency; Prediction.
Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
We examine whether disagreement in social media discussions related to financial markets affects subsequent volatility and abnormal trading volume. We also compare how traditional and digital asset markets differ by comparing stocks and Bitcoin. We show that social media disagreement is positively associated with future market volatility and abnormal trading volume in the stock market. The effect of disagreement is more pronounced at the individual stock level than at the index level. A higher level of social media disagreement also increases the probability of extremely negative stock market returns. In contrast, disagreement in Bitcoin-related social media weakly affects subsequent volatility but does not affect trading volume or extremely negative returns. Our findings also reveal that market activity impacts the disagreement in the stock market and Bitcoin communities differently.
Guiding through the complicated and unstable setting of The rapid shifts in cryptocurrency markets urge a need for creative predictive techniques that surpass standard financial analysis instruments. This paper offers an innovative new hybrid forecasting model by combining the strong analytical properties of LSTM, Bi-LSTM, and XGBoost. This model takes advantage of LSTM networks to interpret temporal sequences. in historical pricing data, the Bidirectional LSTM layer improves. analysis that includes future data insights. XGBoost complements this frame-work through the systematic reduction of errors of prediction. by application of progressive ensemble learning techniques. The synergy of these techniques furnish a substantial method that is fit for capturing the the inconsistent behavior of cryptocurrency values and enhancing forecasting. accuracy. Comparing this hybrid strategy to empirical tests have shown that. An examination across historical data shows that traditional models exhibit major enhancements in precision of predictions, illustrating its practical benefit. for financial analysts and investors. The successful application of combining these sophisticated technologies denotes a serious progress in predictive methods for the intense and constantly changing sector of digital currencies.
Dünyada en çok rağbet gören kripto para birimi olması nedeniyle Bitcoin (BTC), yatırımcılar ve araştırmacılar için son yıllarda dikkat çekici hale gelmiştir. Merkezi bir para birimi olmaması ve spekülasyonlara açık olması BTC fiyatında yüksek oynaklığa sebep olmaktadır. BTC fiyatının oynaklığının dikkate alınarak tahminlenmesi özellikle yatırımcılar için büyük önem taşımaktadır. Son yıllarda Makine Öğrenmesi (ML) yöntemlerinin gelişmesiyle birlikte birçok finansal alanda olduğu gibi kripto paraların fiyat tahminlemesinde sıklıkla ML yöntemlerine başvurulmaktadır. ML yöntemleri geleneksel ekonometrik yöntemlerin aksine veri setinde meydana gelen dalgalanmaları herhangi bir varsayıma ihtiyaç duymadan dikkate almakta ve çoğu zaman daha iyi sonuçlar vermektedirler. Bu çalışmada, 01.01.2018 ile 21.12.2023 tarihleri arasında BTC fiyatı geleneksel ekonometrik yöntem olan ARIMA ile ML yöntemi olan LSTM kullanılarak tahminlenmeye çalışılmıştır. Yöntemler karşılaştırılırken performans kriterleri olarak RMSE, MAE ve MAPE kriterleri kullanılmıştır. Çalışmanın sonuçlarına göre LSTM yöntemi en düşük RMSE ve MAPE değerlerine sahip olmuştur.
This research explores the application of machine learning techniques to predict Bitcoin price dynamics. Bitcoin, as a decentralized digital currency, has garnered significant attention due to its high volatility and potential for rapid value fluctuations. This study aims to develop a machine learning model that analyzes historical Bitcoin price data to identify patterns and predict future price trends. While precise short-term predictions may be challenging, the model can provide valuable insights into overall price trends and inform investment strategies. This research contributes to a better understanding of Bitcoin price dynamics and has the potential to assist investors in making more informed decisions.
Cryptocurrencies fluctuate in markets with high price volatility, which becomes a great challenge for investors. To aid investors in making informed decisions, systems predicting cryptocurrency market movements have been developed, commonly framed as feature-driven regression problems that focus solely on historical patterns favored by domain experts. However, these methods overlook three critical factors that significantly influence the cryptocurrency market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations, which can affect investors’ collaborative behaviors, 2) overall market sentiment, heavily influenced by news, which impacts investors’ strategies, and 3) technical indicators, which offer insights into overbought or oversold conditions, momentum, and market trends are often ignored despite their relevance in shaping short-term price movements. In this paper, we propose a dual prediction mechanism that enables the model to forecast the next day’s closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Furthermore, we introduce a novel refinement mechanism that enhances the prediction through market sentiment-based rescaling and fusion. In experiments, the proposed model achieves state-of-the-art performance (SOTA), consistently outperforming ten comparison methods in most cases. Our code and data can be found at https://github.com/aamitssharma07/SAL-Cryptopulse
In cryptocurrency market, smart contracts are used to transfer billions of dollars, and risk analysis of these contracts is crucial for ensuring their security in this rapidly evolving digital landscape. This study uses eight key risk criteria of smart contracts from the literature. The importance of these criteria was determined through a survey of experts by applying neutrosophic PIPRECIA method to weigh experts' opinion significance based on their knowledge and experiences. This leads to improve the credibility of the risk analysis process. The risk of nine specific smart contracts was then evaluated by applying a hybrid of the DEMATEL and PIPRECIA multi-criteria methods to weigh the evaluation criteria and calculate the security score for each smart contract. The security scores assigned to each smart contract provide valuable insights for traders and investors in the cryptocurrency market, highlighting the contract's robustness and reliability in the context of smart contract risks.
Tatiana Silveira Camacho, Guilherme Jonas da Silva
As a highly speculative asset, bitcoin’s (BTC) demand is particularly based on the perceptions of agents about the financial asset. Especially institutions that began leaving a bigger footprint in the market, causing changes in transaction flow and price cycles. To assess how halving dynamics changed the BTC market, Wavelet methodology was carried out with daily data (from January 2011 to December 2021), on price and transaction count. Decomposition in scale and frequency indicate that flows were altered by the arrival of new investors, and stronger correlations between prices and transactions were found at lower frequencies (longer time horizon).
Cryptocurrency trading has become sophisticated by the day as market volatility and information are continuously enhanced. The reliance of traditional trading systems on historical data and human prediction result in inefficiency and risk for both investors and stockbrokers. The paper provides Imagine using Artificial Intelligence for trading cryptocurrency, which can provide better results than human trading. The machine learning-based approach uses real-time information, whether it be a buy or sell action, while considering real-time information such as social media for sentiment analysis, and adapting trading to achieve better results. The model will use advanced machine learning models including supervised, unsupervised learning, exploratory models, and reinforcement learning to provide tangible results such as improved accuracy in predicting price and sentiment analysis. The results show that the model has significantly improved its efficiency and performance, yielding a Root Mean Squared Error of 0.032 and a Sharpe Ratio of 2.1. These means that it has demonstrated the capability to yield superior risk-adjusted returns and higher reliability, making it a perfect alternative for investors.
Igba Emmanuel, Moral Kuve Ihimoyan, Babatunde Awotiwon, Akinkunmi Rasheed Apampa
This paper explores the integration of advanced machine learning models, including BERT, GPT, and the Prophet algorithm, with finance investment strategies to enhance predictive modeling and trend analysis in blockchain technology. The rapid evolution of blockchain has transformed financial ecosystems, offering decentralized platforms for secure and transparent transactions. However, predicting market trends and investment opportunities within this domain remains a complex challenge due to high volatility and the multifaceted nature of financial data. By leveraging the natural language processing capabilities of BERT and GPT for sentiment analysis and market behavior prediction, combined with the time-series forecasting strength of the Prophet algorithm, this study aims to provide a robust framework for analyzing blockchain-driven financial markets. Furthermore, the integration of finance investment strategies ensures practical applicability by aligning machine learning insights with real-world investment decision-making processes. The proposed approach demonstrates potential for optimizing portfolio management, enhancing risk mitigation, and improving strategic investment in blockchain ecosystems. This work bridges the gap between cutting-edge machine learning technologies and financial innovation, offering valuable insights for researchers and practitioners in both domains.
Md Zahidul Islam, Md. Shahidul Islam, Md Abdullah Al Montaser, Md Rasel · 7 authors
The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.
Sibtain Syed, Syed Muhammad Umar Talha, Arshad Iqbal, Naveed Ahmad · 5 authors
Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.
The global crypto market has garnered attention from investors due to its high return potential, yet the extreme volatility and associated risks pose significant concerns. This study aims to analyze the risks and returns of cryptocurrency investments using a quantitative approach on global crypto market data. The methods employed include volatility analysis using the GARCH model, as well as risk and return evaluation through CAPM. The results indicate that cryptocurrencies exhibit significantly higher volatility compared to traditional assets, with substantial return potential but accompanied by high risk. These findings imply the need for stricter diversification strategies and risk management for crypto investors to optimize returns and minimize losses.
This study examines the bitcoin price in USD in the world by developing a suitable time series model to identify its future trends. This data set consists of monthly bitcoin prices from August 2010 to July 2024. It was found that the original series is not stationary and not seasonality. The stationary was achieved by the first difference. Of the parsimonious models identified based on the Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF) of the stationary series, an auto-regressive integrated moving average (ARIMA) (2,1,2) model was identified as the best-fitt ed model. The significance of the model and its parameters and information criteria such as the Akaike Information Criterion (AIC), Schwarz Criterion, and log-likelihood was used to identify the best-fitted model. The model was trained using data from August 2010 to March 2024. The residuals of the model were found to be white noise. The mean absolute percentage error (MAPE) for validation data is 7.09%. The percentage errors for the validating set are all positive and varied from 3.5% to 12.9%. The predicted Bitcoin price (USD) from August to October 2024 are $59947.88, $60308.7, and $60669.53. Bitcoin price can be utilized by market demand and supply, regulatory environment, and technology development. Keywords: ACF; ARIMA models; Bitcoin price; Forecasting; PACF; Time series analysis
Muhammad Yousaf, Muhammad Imran Tariq, Abdul Jabbar, Syed Qaiser Jalil
This comprehensive review paper explores the diverse landscape of cryptocurrency forecasting, tracing its evolution from an alternative to traditional monetary systems to its significant growth in the global financial arena. It consolidates existing research by categorizing and analyzing 234 scholarly articles, organizing them into machine learning, deep learning, deep reinforcement learning, and statistical methodologies, and evaluating the related metrics. The case study titled “Examining the performance differences between backtesting and forward testing” highlights the challenges investors face, as strategies that appear effective in backtesting often fail in practical use. Another case study, “Social Data Exploration in Cryptocurrency Trends,” examines how social media data can provide insights into market movements and investor sentiment, revealing the impact of social trends on cryptocurrency prices. The findings section provides a detailed view, illuminating trends such as yearly publication rates, methodological distributions, input features, training/testing splits, the total number of data samples considered, and forecasting time horizons. This survey paper serves as a valuable resource, providing researchers and investors with a solid foundation for understanding and navigating the dynamic field of cryptocurrency forecasting.
The aim of this article is to examine the reasons why cryptocurrency volatility hinders its potential to replace fiat money as legal tender. We focus on Bitcoin and Ethereum for this analysis. By applying an augmented Dickey-Fuller stationarity test, we demonstrate that cryptocurrencies lack a long-term trend; instead, their movement is erratic and highly volatile. Furthermore, eGARCH models indicate that volatility tends to decrease and is expected to persist in this pattern. In summary, theoretical and empirical analysis suggests that, due to their nature based solely on supply and demand and their high volatility, cryptocurrencies are not suitable as primary investment instruments or stores of value.
Li Yi Thong, Ricky Chee Jiun Chia, Mohd Fahmi Ghazali
Research Question: Does uncertainty indices have impact on cryptocurrency? Motivation: Most of the previous study investigate the impact of geopolitical risk and economic policy uncertainty on Bitcoin only and less research investigate the long run and short run relationship between the uncertainty indices and cryptocurrency. Hence, this study investigates whether the economic policy uncertainty, geopolitical risk and US equity market uncertainty have an impact on Bitcoin, Ethereum and Binance Coin by the multivariate VAR Granger non-causality. Idea: This study applied three different uncertainty indices (geopolitical risk, economic policy uncertainty and US equity market uncertainty) and top three ranking cryptocurrency (Bitcoin, Ethereum and Binance Coin) to investigate and compare the impact of uncertainty indices on cryptocurrency with different uncertainty conditions and applied top three ranking cryptocurrency in cryptocurrency market to reinforce the result. Data: This study applied monthly data with 42 observations which cover the period of December 2017 until May 2021 and data for cryptocurrency extracted from investing.com, while the uncertainty indices from policyuncertainty.com. Method/Tools: This study utilize multivariate VAR Granger non-causality to examine the cointegration relationship between the cryptocurrency and uncertainty indices. Findings: The results show that the economic policy uncertainty, geopolitical risk and US equity market uncertainty cointegrated with Bitcoin, while Binance Coin cointegrated with geopolitical risk only. Hence, the economic policy uncertainty, geopolitical risk and US equity market uncertainty plays a vital role in the Bitcoin prediction and geopolitical risk plays an important role to forecast the Binance Coin. Contributions: The Bitcoin investors may focus on the changes in economic policy uncertainty, geopolitical risk and US equity market uncertainty to predict the Bitcoin return, and Binance Coin investors focus on the geopolitical risk.
Machine learning techniques have emerged as potential tools in the field of extensive research led by the growing interest in predicting the future price of Ethereum. This paper fills a major knowledge gap in the area by reviewing and analysing important literature on Ethereum price forecasting, with a focus on Ethereum and it is applicable on other cryptocurrencies as well. By using machine learning models, such as random forest and linear regression, this study fills the gap by comparing the models' ability to predict Ethereum prices properly and provides insightful information for researchers and investors. The implications of these results for the analysis of the cryptocurrency market are noteworthy, as they may reduce the risks associated with the erratic cryptocurrency market and open the door for more studies to improve prediction techniques in this ever-changing environment. The study emphasises flexibility and effectiveness in navigating complicated cryptocurrency marketplaces, which advances the understanding of machine learning applications in Ethereum price forecasting.