Jan 1, 2023·Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Hangzhou, China
Quantitative trading plays a pivotal role in financial markets. Over the past decade, quantitative trading has made remarkable improvements. Due to instability and nonlinearity in financial markets, it is still challenging to formulate high-return trading strategies to address the problem of long-t
Olawole Akomolafe, Babajide Oluwaseun Olaogun, Michael Olumuyiwa Adesuyi, Victor Ukara Ndukwe · 5 authors
Effective liquidity management is critical for the reliability and efficiency of international remittance and cross-border payment systems. Delays, settlement failures, and currency conversion inefficiencies can significantly impact SMEs, corporates, and individual remitters, leading to operational disruptions, increased costs, and reduced financial inclusion. This study proposes a Predictive AI Model for Remittance Liquidity Optimization, designed to forecast liquidity requirements in real time, optimize fund allocation, and enhance the overall performance of international payment networks. The model integrates multi-source data, including historical transaction volumes, foreign exchange (FX) rates, settlement schedules, and network congestion metrics, to generate predictive insights and automated liquidity management recommendations. The conceptual framework of the model incorporates advanced machine learning and time-series forecasting techniques, combined with an optimization engine that dynamically allocates available funds to minimize delays, reduce transaction costs, and manage FX risks. Real-time anomaly detection mechanisms identify potential liquidity shortfalls, network congestion, or settlement failures, triggering alerts and corrective actions. The model also includes integration layers with banking platforms, fintech providers, and remittance networks, enabling seamless execution of liquidity redistribution and settlement optimization. Predictive outputs are visualized through interactive dashboards, supporting operators in decision-making and ensuring transparency in fund flows. By leveraging AI-driven forecasting and optimization, the model reduces settlement delays, improves FX efficiency, and enhances operational reliability across multi-currency, multi-jurisdictional payment corridors. Its applications extend to SMEs, corporate treasuries, and high-volume remittance corridors, promoting financial inclusion and operational continuity. Future extensions include adaptive learning algorithms for self-optimizing liquidity strategies, integration with distributed ledger technologies for real-time settlements, and expansion to multi-party global supply chains. Ultimately, this predictive AI model provides a scalable, intelligent solution for enhancing liquidity management in international payment systems, fostering greater efficiency, resilience, and transparency in global financial networks.
As the markets for cryptocurrencies have risen rapidly in recent years, there is more interest in forecasting their prices.The use of Long Short-Term Memory (LSTM) neural networks for cryptocurrency price prediction is examined in this paper.The model is trained to predict the price using historical data on the cryptocurrency exchange rate with the currencies of G20 nations.The pre-processed data collection is divided into training and testing sets.Then, using the training set and testing set, the LSTM model is trained.To assess the model's performance, it is put up against the time series model known as Autoregressive Integrated Moving Average (ARIMA).The outcomes show that the LSTM model performs better in terms of accuracy and offers more trustworthy forecasts of the cryptocurrency exchange rate than the ARIMA model.These results imply that LSTM neural networks are a potential method for predicting the price of cryptocurrencies and may be used to help traders and investors make wise choices.
Due to economic uncertainty and the financial crisis of 2008, a desire for an unregulated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of unknown individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their prices. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Data. Based on the results of our research, comprising three experiments, autoregres-sive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.
In recent years, machine learning has achieved good results in the field of asset prices. Compared with traditional data analysis and technical analysis, using machine learning methods can show unique advantages in various aspects. In this paper, we combine the correlation between bull and bear mark
Well labeled natural language corpus data is essential for most natural language processing techniques, especially in specialized fields. However, cohort biases remain a significant challenge in machine learning. The narrow origin of data sampling or human annotators in cohorts is a prevalent issue for machine learning researchers due to its potential to induce bias in the final product. During the development of the CryptoLin corpus for another research project, the authors became concerned about the potential influence of cohort bias on the selection of annotators. Therefore, this paper addresses the question of whether cohort diversity improves the labeling result through the implementation of a repeated annotator process, involving two annotator cohorts and a statistically robust comparison methodology. The utilization of statistical tests, such as the Chi-Square Independence test for absolute frequency tables, and the construction of confidence intervals for Kappa point estimates, facilitates a rigorous analysis of the differences between Kappa estimates. Furthermore, the application of a two-proportion z-test to compare the accuracy scores of UTAD and IE annotators for various pre-trained models, including Vader Sentiment Analysis, TextBlob Sentiment Analysis, Flair NLP library, and FinBERT Financial Sentiment Analysis with BERT, contributes to the advancement of knowledge in this field. The paper utilizes Cryptocurrency Linguo (CryptoLin), a corpus containing 2683 cryptocurrency-related news articles spanning more than three years,and compares two different selection criteria for the annotators. CryptoLin was annotated twice with discrete values representing negative, neutral, and positive news respectively. The first annotation was done by twenty-seven annotators from the same cohort. Each news title was randomly assigned and blindly annotated by three human annotators. The second annotation was carried out by eighty-three annotators from three cohorts. Each news title was randomly assigned and blindly annotated by three human annotators, one in each different cohort. In both annotations, a consensus mechanism using simple voting was applied. The first annotation used the same cohort with students from the same nationality and background. The second used three cohorts with students from a very diverse set of nationalities and educational backgrounds. The results demonstrate that manual labeling done by both groups was acceptable according to inter-rater reliability coefficients Fleiss’s Kappa, Krippendorff’s Alpha, and Gwet’s AC1. Preliminary analysis utilizing Vader, Textblob, Flair, and FinBERT confirmed the utility of the data set labeling for further refinement of sentiment analysis algorithms. Our results also highlight that the more diverse annotator pool performed better in all measured aspects.
In this paper, we compare the predictive power of Auto Regressive Integrated Moving Averages (ARIMA) and Multi-Layer Perceptron Artificial Neural Networks (MLP ANN) model to short-term forecast the monthly returns of Bitcoin cryptocurrency. We evaluate the performance of two models using time series with monthly data from January 2018 to December 2021. The key parameters for the final assessment of prognostic models are the values of Root Mean Square Error-RMSE and Forecast Error-FE. The results of the short-term BTC return forecast showed better properties of composite compared to univariate time series forecasting models, i.e., higher prognostic power of the MLP ANN model compared to the selected ARIMA (1,1,3) model (lower RMSE and FE). The results point to further comparative research of prognostic models and the possibility of forming more complex and hybrid structures of neural network models in order to predict economic phenomena as accurately as possible.
Bitcoin is a high-risk and high-yield investment, and in recent years, more and more investors have begun to pay attention to Bitcoin, but its riskiness has deterred many.However, the data over the past few years has continued to show that Bitcoin is exceptionally profitable, and holding Bitcoin for a long time is an investment option.This paper analyzes how to use Bitcoin and gold for venture capital and uses LSTM neural network to demonstrate its capability in trading.Furthermore, this paper delineates the novel strategy making combined with LSTM and a traditional technical indicator which yield more winning rate.
Meiryani Meiryani, Marco Marco, Albert, Nada Ayuanda
The purpose of the study is to compare the performance between bitcoin Cryptocurrency, S&P500 stocks and Gold which can be a consideration for investors in determining investment decisions. . This research is quantitative research using comparative methods. The population and sample used in this study are the monthly closing prices of the three investment instruments that are the subject of research with saturated sampling in the period 01 January 2019 – 31 December 2022 as many as 144 data. In this study, the type of data used is secondary data in the form of historical data of instruments. Then researchers used a non-parametric statistical test, the Kruskal-Wallis’s test. The results show that there is no significant difference between Treynor and Jensen’s returns from Bitcoin, Gold and S&P500. While the risk and Sharpe Ratio show that there is a significant difference. The best investment instrument in 2019-2022 is bitcoin because when viewed from the average return bitcoin is able to have the highest percentage compared to other instruments. Gold is an asset that is often considered a refuge in situations of economic uncertainty. Meanwhile, stocks in that period experienced a huge price decline, this was caused by the COVID-19 pandemic.
Sarat Chandra Nayak, Subhranginee Das, Satchidananda Dehuri, Sung‐Bae Cho
Cryptocurrencies have carved out a significant presence in financial transactions during the past few years. Cryptocurrency market performs similarly to other financial markets with considerable nonlinearity and volatility and its prediction is a growing research area. It is challenging to capture the inherent uncertainties connected with cryptocurrency using the currently used conventional methodologies. The popularity of random vector functional link networks (RVFLN) is attributed to its simple structural layout, quick rate of learning, and enhanced generalization ability. It computes the output layer weights using non-iterative techniques like least square methods or iterative techniques like gradient methods, and assigns hidden neuron parameters at random. Random initialization of non-optimal hidden neuron settings, however, degrades the performance. Population-based metaheuristics are a superior option to random initialization for determining the ideal parameters and avoiding the problem of local optima stagnation. In the current article, an elitist artificial electric field algorithm (eAEFA) for training RVFLN is proposed. Here, eAEFA is utilized to create an ideal RVFLN by determining the weights and biases of the hidden layer connections. The elitism method is used by AEFA to maximize its strength. Here, the most suitable entities are directly inserted to create the population of the following generation. By predicting the closing values of six widely used cryptocurrencies, including Bitcoin, Litecoin, Ethereum, ZEC, XLM, and Ripple, one may determine how well the eAEFA+RVFLN model is performing. For comparison study, models including ARIMA, multi-layer perceptron (MLP), basic RVFLN, support vector regression (SVR), LSTM, GA trained RVFLN, and AEFA trained RVFLN are also constructed concurrently. In terms of performance and statistical significance testing, the suggested eAEFA+RVFLN findings outperform the comparator models. On an average, it achieves a MAPE (mean absolute percentage of error) value of 0.0573, R2(coefficient of determination) of 0.9589, POCID (prediction of change in direction) of 0.9676, RMSE (root mean squared error) of 0.0685, MAE (mean absolute error) of 0.0727 and an average rank of 1.346; as a result, it is possible to recommend it as a useful financial forecasting tool.
Sherin M. Omran, Wessam H. El-Behaidy, Aliaa A. A. Youssif
Bitcoin is the first and most famous cryptocurrency. It is a virtual currency that is operated in a decentralized form using cryptographic strategies called blockchains. Although it has experienced significant market acceptance by traders and investors in recent years, it also suffers from volatility and riskiness. Technical analysis is one of the most powerful tools used for trading signals’ allocation using some algorithmic strategies called technical indicators. In this research, a newly proposed multi-objectives decomposition-based particle swarm optimization algorithm is used to find the best parameter values for some technical indicators, which in turn generates the best trading signals for Bitcoin trading. In this context, three conflicting objectives have been used, i.e., the return on investment, the Sortino-ratio, and the number of trades. The proposed algorithm is compared to the original MOEA/D algorithm as well as the indicators using their original parameters. Results showed the superiority of the proposed algorithm during the training and testing periods over the other benchmarks.
In this paper, we introduce a new approach to multivariate forecasting cryptocurrency prices using a hybrid contextual model combining exponential smoothing (ES) and recurrent neural network (RNN). The model consists of two tracks: the context track and the main track. The context track provides additional information to the main track, extracted from representative series. This information as well as information extracted from exogenous variables is dynamically adjusted to the individual series forecasted by the main track. The RNN stacked architecture with hierarchical dilations, incorporating recently developed attentive dilated recurrent cells, allows the model to capture short and long-term dependencies across time series and dynamically weight input information. The model generates both point daily forecasts and predictive intervals for one-day, one-week and four-week horizons. We apply our model to forecast prices of 15 cryptocurrencies based on 17 input variables and compare its performance with that of comparative models, including both statistical and ML ones.
The prediction of cryptocurrency prices is a hot topic among academics. Nevertheless, predicting the cryptocurrency price accurately can be challenging in the real world. Numerous studies have been undertaken to determine the best model for successful prediction. However, they lacked correct results because they avoided identifying the critical features. It is important to remember that trends are critical features in time series to obtain data information. A dearth of research demonstrates that the cryptocurrency trend comprises linear and nonlinear patterns. Therefore, this study attempted to fill this gap and focused on modelling and forecasting trends in cryptocurrency. This study examined the linear and nonlinear dependency trend patterns of the top five cryptocurrency closing prices. The weekly historical data of each cryptocurrency were taken at different periods due to the availability of data on the system. In achieving its goal, this study examined the results by plotting based on residual trend and diagnostic statistic checking using three deterministic methods: linear trend regression, quadratic trend, and exponential trend. Based on the minimum Akaike Information Criterion (AIC), the result showed that the top five cryptocurrency closing price data series contained nonlinear and linear trend patterns. The information of this study will assist traders and investors in comprehending the trend of the top five cryptocurrencies and choosing the suitable model to predict cryptocurrency prices. Additionally, accurately measuring the forecast will protect investors from losing their investment.
An Pham Ngoc Nguyen, Martin Crane, Marija Bezbradica
Abstract The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods like Simple Moving Average, Random Forest and Support Vector Regression. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations.
Cryptocurrency is a popular digital currency due to its security and peer-to-peer transferability. Predicting cryptocurrency prices is crucial for investors and traders to make informed decisions on buying, selling, or holding cryptocurrencies based on their expected value, potential risks, and returns. This study aims to identify the optimal model for predicting the prices of cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), using Deephaven for Data curation. The study involves extracting data from both cryptocurrencies by Deephaven and selecting the most correlating parameters through time lag adjustment. We use correlating cryptocurrency data to train models, such as Artificial Neural Networks (ANN), Long-Short Term Memories (LSTM), and Gated Recurrent Units (GRU). Where the trial-and-error technique was applied for selecting optimized hyper-parameters for each model. The models are then evaluated by statistical evaluators, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), separately for training and testing datasets. For Bitcoin, the results showed that the LSTM model outperform ANN and GRU models in both training and testing data with MAE, RMSE, and MAPE average values of 0.079, 1.16, and 0.0006, respectively. While for Ethereum, the results also revealed that LSTM model performance is superior with MAE, RMSE, and MAPE average values of 0.0025, 0.124 and 0.0002, respectively. While GRU (MAE 0.012, RMSE 0.117, MAPE 0.002) performs robustly against ANN (MAE 0.035, RMSE 0.149, MAPE 0.003) model.
<abstract><p>In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., <italic>F-LSTM</italic> utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of <italic>F-LSTM</italic>, we ran different empirical simulations. Our findings demonstrate that <italic>F-LSTM</italic> outperforms conventional approaches and machine learning techniques by achieving a loss minimal of $ 2.3 \times 10^{-4} $. Furthermore, the <italic>F-LSTM</italic> uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the <italic>F-LSTM</italic> requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (<italic>F-LSTM</italic>). In terms of data privacy and accuracy, <italic>F-LSTM</italic> addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction.</p></abstract>
P. V. Nagamani, Gowri Anand, Srinivasa Prasanna, Basava Raju · 5 authors
The past several years have seen an increase in interest in trading that is supported by machine learning and artificial intelligence.Utilize automated trading with the aid of machine learning and artificial intelligence to reap the maximum rewards from the cryptocurrency market.For a specific time, we keep the daily data.We achieve excellent results by utilising tactics supported by cutting-edge algorithms.The results produced the expansion in the crypto currency industry with the aid of straight forward architecture and algorithms.The rise in market capitalization has led to a rise in popularity for the cryptocurrency in 2017.Today's market involves more than 1500 crypto currencies.For usage in online transactions, the crypto currency can be created.A crypto money technology is bitcoin.Bitcoin's value changes constantly, second by second.As a result, we apply machine learning architecture to forecast the value of the bitcoin price in this case.We are working to demonstrate that, in comparison to previous techniques and architectures, this ML architecture produces results that are more accurate.Our study use the Support Vector Machine(SVM) and K Nearest Neighbor(KNN)algorithms to successfully forecast bitcoin prices.The findings demonstrate that the Support Vector Machine(SVM) method outperforms the K Nearest Neighbor(KNN) method as it is currently being used.
The volatile and unpredictable nature of the cryptocurrency market makes it particularly challenging to make profitable investment decisions. different machine learning-based techniques have been employed for forecasting cryptocurrency value. However, although some works have addressed incorporating the Blockchain transactions’ data into the analysis, none of them has provided a hybrid solution, including features obtained through complex network modeling. In this paper, we investigated the use of machine learning and complex network techniques to improve the profitability of a cryptocurrency portfolio during a downtrend period. We extracted features through a complex network-building methodology based on the Bitcoin blockchain transactions, merged them with the historical cryptocurrency values, and generated the predictions using different machine-learning models. The results indicated that incorporating complex network features improved the performance in retaining the initial capital at the end of the experiment, leading to an increment of 7.09% and 4.33% for the CNN and LSTM models, respectively. Our findings suggest that the proposed method enhanced the performance of cryptocurrency investment strategies during downtrend periods.