Blockchain Papers

Debachudamani Prusti, Asis Kumar Tripathy, Rahul Sahu, Santanu Kumar Rath

No abstract is available for this record.

Dwijendra Nath Dwivedi, Anilkumar Vemareddy

No abstract is available for this record.

Keshanth Jude Jegathees, Aminu Bello Usman, Michael O’Dea

No abstract is available for this record.

Akila Lourdes Miriyala Francis, E. Viswanathan, Dheeksha Jayaraman, Janani Padamanavan Ashokkumar · 5 authors

In this research paper, we'll talk about cryptocurrency with the help and development of deep learning, and AI-assisted trading has gained immense popularity. To regulate the splendid engrossment from the part of cryptology we take the assistance of retailing (Deep learning & AI-support). A specific period of data has been stored on daily bases to receive the outcomes in a company of the help of ultra-modern algos. With the references to various papers, I found out the pros and cons of cryptocurrency price prediction. Some simple algorithms & architectures helped to grow the cryptocurrency market. Crypto trading became popular in 2017 and now more than 1500 cryptocurrencies are proactively trading. Crypto currencies can be smoothly created and used for online settlement. Bitcoin is also known as cryptocurrency and its values keep varying every second. Hence for predicting the rate of bitcoin cost I will use the infrastructure of LSTM. This infrastructure will help us in proving that LSTM will provide more accuracy. RNN is a category of ANN and connectivity for this type of network is made through nodes from the direct nodes along with a time-related progressions. LSTM is a RNN infrastructure which is a part of DL which handles the entire data as well as single data points.

Sajan Kumar Kar

After the invention of Bitcoin by a man named Satoshi Nakamoto along with other blockchain-based person-to-person payment systems, the cryptocurrency market has instantly gained popularity. Because of this, that is, the volatility of the various cryptocurrency prices. This attracts much attention from both the investors and the researchers. The task of forecasting the prices of crypto-currencies because of the static prices and the arbitrary effects in the market is quite challenging. Cryptocurrency price forecasting models that are available now mainly focus on analyzing extrinsic factors, like macro-financial indicators, data linked to the blockchain, and data from social media – with the goal of enhancing the prediction accuracy. However, the intrinsic noise present in the raw data, caused by market and political conditions worldwide, is complex to interpret. In our research we propose a multiple input convolutional neural network model, specifically a convolutional neural network model for the prediction of future cryptocurrency price. Generally, RNNs and LSTMs are used for problems dealing with timeseries data. We used the concept of residual networks on 1-Dimensional convolutional networks to solve the problem of predicting the price of Bitcoin, the most popular cryptocurrency out there at the moment. Furthermore, we conduct additional experiments on ether, the cryptocurrency of Ethereum to further confirm that even CNNs can work equally well, if not better in comparison to the widely used LSTM neural network models.

Alexander I. Iliev, Malvika Panwar

No abstract is available for this record.

Thuan Dinh Nguyen, Huong Nguyen Thi Viet, Uyen Dang Vu Phuong, Nhut Nguyen Minh · 5 authors

No abstract is available for this record.

Haoyuan Ma

Price forecasting is pretty crucial in the asset management and allocation and quantitative trading industries. With the development of the global economic situation, decentralized finance has gradually entered people's field of vision, and cryptocurrency and cryptocurrency finance have become the r

Vijay Gautam, Weiping Li, David Carter

No abstract is available for this record.

Satnam Singh, Khriesavinyu Terhuja, Tarun Kumar

No abstract is available for this record.

Iskandar Muda, Jaymin Shah, Jarudin Jarudin, Gioia Arnone · 6 authors

As the number of people infected with COVID-19 continued to rise, many nations placed their entire nations under a complete lockdown. As a direct consequence of this, the entire world is currently experiencing a catastrophic financial crisis. As a result of the pandemic, unemployment rates have increased across a number of different sectors, which is having a significant negative effect on international trade. During this challenging period, Artificial Intelligence (AI) is altering the way businesses examine the statistics pertaining to their cryptocurrency holdings. Utilizing artificial intelligence (AI) in the realm of business can result in a variety of positive outcomes. The technological effects of AI make our day-To-day lives simpler because they eliminate the need for human intervention in many situations. It would be helpful to have a better understanding of artificial intelligence and the methods it uses, such as the classifier model, in the event that there was a pandemic. If people have access to real-Time data analysis and predictions that have been generated by AI and big data, they will be able to make better decisions. In anticipation of the arrival of a new world, the company, along with SMEs and start-ups, is stepping up its efforts to enhance the management of virtual businesses by establishing a presence on multiple e-Trade systems. Artificial intelligence (AI) has emerged as a key player in the quest to find effective solutions to issues that arise in the workplace. AI is being applied to improve business operations in many different areas, including marketing, fraud detection, algorithmic trading, customer service, portfolio management, and product recommendations based on what customers want. These are just some of the many problems that are being solved by AI. In addition, technological advancements could be made in order to enhance the functionality of the suggested guidelines and achieve the most precise result possible in light of the current value of cryptocurrencies.

El-Sayed M. El-Kenawy M. El-Kenawy

The rapid evolution of cryptocurrencies has brought transformative changes to the financial landscape. Cryptocurrency prices, characterized by their inherent volatility, pose challenges for precise forecasting. This study introduces a novel approach to cryptocurrency price forecasting, leveraging Long Short-Term Memory (LSTM) networks, known for discerning temporal dependencies within time series data. Motivated to enhance prediction accuracy, this research investigates the effectiveness of LSTM networks in capturing complexities inherent in cryptocurrency price movements. The proposed methodology involves meticulous data collection and preprocessing, utilizing an extensive dataset from Kaggle. This dataset forms the foundation for predictive modeling and facilitates an in-depth analysis of cryptocurrency price dynamics. Exploratory data analysis, including visualization techniques, and a dedicated Time Series Analysis precede the implementation of predictive models, such as LSTM networks. Results and evaluation showcase promising outcomes, emphasizing the models' precision, accuracy, and explanatory power. The Mean Absolute Error (MAE) of 0.0177 underscores the precision achieved in predicting cryptocurrency prices, while the Mean Squared Error (MSE) of 0.00066 and the R² Score of 0.9486 attest to our models' overall accuracy and explanatory power. This research significantly contributes to understanding cryptocurrency forecasting by incorporating LSTM networks, paving the way for advancements in this evolving domain.

Jędrzej Rudkiewicz, Marcin Hernes

The purpose of the research is to study the cryptocurrency data listed on Binance, and design a profitable strategy based on the findings. The data covers over 150 selected cryptocurrencies. The study aims to detect anomalies in the volume and number of transactions and apply an investment strategy based on deviations and sudden price fluctuations. An autoencoder and LSTM-based neural network have been used. Based on the results of the present research, it can be concluded that the model successfully identified anomalies in the data regarding the volume and number of transactions carried out. I it was also observed that price volatility in the period close to the detected anomaly was significantly higher than average volatility for the sample.

Youwu Liu, Zijiang Yang

No abstract is available for this record.

Thushantha Sanju, Hasindu Liyanage, Keshara Bandara, Dilini Kandakkulama · 6 authors

In the dynamic world of financial markets, the prediction of stock performance and bitcoin trading is undergoing a significant transformation due to the integration of advanced technologies and novel methodologies.The incorporation of Transformer models alongside Time Embeddings significantly improves the precision of stock market predictions by effectively capturing intricate temporal relationships and mitigating the presence of overly simplistic assumptions.The integration of real-time social media data with sentiment analysis based on BERT provides significant value in understanding investor sentiment.Additionally, the application of language model pre-training, as exemplified by BERT, brings about a transformative impact on text classification for predicting stock prices.Within the domain of cryptocurrency, sophisticated algorithms such as Transformers, Long Short-Term Memory (LSTM), Deep Convolutional LSTM (DC-LSTM), and Neural Networks (NN) have demonstrated enhanced capabilities in predicting price movements.These algorithms are further bolstered by the implementation of a comprehensive trading strategy.Automated systems for bitcoin trading introduce elements of personalization and adaptability to the trading process, thereby facilitating broader access to a diverse group of traders.The progress highlights the significant importance of the integration of technology and methodologies in the field of financial analysis.This integration enables investors and traders to possess the necessary resources for making well-informed choices within the ever-changing landscape of financial markets.

Abhishek Srinivas Murthy, A - Akshay, Bhagyashri R. Hanji

No abstract is available for this record.

Chenyang Liao, Kai Lu, Jiamiao Zhang

The market for cryptocurrency has thrived for more than 10 years and has experienced a drastic change. The success of cryptocurrencies was concerned and analyzed worldwide. This research discusses the way to build machine learning and statistical models to predict the future price of the cryptocurre

Muhammad Zakhwan Mohamed Rafik, Noraisyah Mohamed Shah, Nor Azizah Hitam, Faisal Saeed · 5 authors

No abstract is available for this record.

Yue Wu

The question of how to benefit from an organic combination of gold and bitcoin has become a prominent topic in the contemporary society. Hence, we've built the time series forecasting models and target planning models of gold and bitcoin, providing the best gold and bitcoin rotation investing strategy based on our methodology. We consider the connection between gold and bitcoin price fluctuations by creating the SVM-GARCH Combination Model, and at the same time, data-based nonlinear feature extraction and heteroscedasticity processing give a more accurate and dependable foundation for investment decision making.In terms of investment planning, We first utilized VaR to clarify our quantitative investment risk indicators, and then built a VaRY Model to organically integrate and balance investment returns and risks. At the same time, we include Risk Adjustment Parameters into the planning model so that, by dynamic weight adjustment, our target planning model can match the wealth utility propensity of investors with diverse risk preferences, therefore improving the model's application and flexibility. Finally, in view of the differences in trading restrictions between Trading Days and Non-trading Days, we formulate different dynamic weights - Multi-objective Programming Models for trading and non trading periods, so that our best investment decision can be more comprehensive and targeted.We present proof for the brilliance of our investment strategy in four dimensions by merging and assessing the forecasting model and the planning model: Accuracy, Rationality, Flexibility, and High Return.

Leyi Zhang

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

Nidhi Shukla, Ashutosh Kumar Singh, Vijay Kumar Dwivedi

No abstract is available for this record.

Crina Anina Bejan, Dominic Bucerzan, Mihaela Crăciun

No abstract is available for this record.

Katarzyna Kryńska, Robert Ślepaczuk

No abstract is available for this record.

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.