Blockchain Papers

Ching‐Hsue Cheng, Jun-He Yang, Jia-Pei Dai

No abstract is available for this record.

Ali Ben Mrad, Brahim Hnich, Amine Lahiani, Salma Mefteh‐Wali

No abstract is available for this record.

Md Rizwanul Kabir, Dipayan Bhadra, Moinul Ridoy, Mariofanna Milanova

The inherent challenges of financial time series forecasting demand advanced modeling techniques for reliable predictions. Effective financial time series forecasting is crucial for financial risk management and the formulation of investment decisions. The accurate prediction of stock prices is a subject of study in the domains of investing and national policy. This problem appears to be challenging due to the presence of multi-noise, nonlinearity, volatility, and the chaotic nature of stocks. This paper proposes a novel financial time series forecasting model based on the deep learning ensemble model LSTM-mTrans-MLP, which integrates the long short-term memory (LSTM) network, a modified Transformer network, and a multilayered perception (MLP). By integrating LSTM, the modified Transformer, and the MLP, the suggested model demonstrates exceptional performance in terms of forecasting capabilities, robustness, and enhanced sensitivity. Extensive experiments are conducted on multiple financial datasets, such as Bitcoin, the Shanghai Composite Index, China Unicom, CSI 300, Google, and the Amazon Stock Market. The experimental results verify the effectiveness and robustness of the proposed LSTM-mTrans-MLP network model compared with the benchmark and SOTA models, providing important inferences for investors and decision-makers.

Syrine Ben Romdhane, Fahmi Ben Rejab, Khadija Mnasri

No abstract is available for this record.

Surinder Singh Khurana, Parvinder Singh, Naresh Kumar Garg

No abstract is available for this record.

Qiangqiang Wang

Few studies have investigated the delivery performance of suppliers in guarantor-intermediated trade finance with information delay in a multi-stage environment. This paper examines a model of dynamic guarantee finance (DGF) where guarantee institutions can adjust guarantee rates as an order progresses through different stages in the trade process, and compare it with the uniform guarantee financing model (the guarantee rate remains constant over the process, UGF). Additionally, we analyze the impact of information delay on the supplier's optimal delivery performance. The aim of this paper is to provide a foundational understanding of the potential benefits associated with transitioning from uniform-rate pricing to dynamic-rate pricing in guarantor-intermediated trade finance. The results indicate that DGF can incentivize suppliers to enhance their delivery performance, with the incentive value increasing as the trade process becomes lengthier. However, information delay has a negative effect on the incentive value of DGF. Smart contracts can complement DGF by accelerating the process of information verification. These findings provide guidance for the effective implementation of contract innovations (such as DGF) in guarantor-intermediated trade finance and highlight how the dynamic trade process and information delay impact the supplier's delivery performance.

R. Aarthi, P. Vanitha, S Reshma, C Mounisha · 5 authors

Bitcoin (BTC) and Ethereum (ETH) price and trends prediction is performed by long short-term memory (LSTM) networks, gated recurrent unit (GRU) and Random Forest machine learning algorithm, the authors explain. Feature selection techniques were effectively and widely adopted to preprocess and feed real cryptocurrency market data as input data. LSTM performs have an accuracy of 96%, GRU performs have accuracy of 97%, and Random forest 98%, meaning they are satisfactory in predicting cryptocurrency trends theme. These models were used to construct two real worlds advert based knowledge driven investment strategies which were simulated through the period under study and show the potential of this class of models. Results of which showed across different times period cases how well your prediction works [7], and all pointed out on the huge probably availability of the presence of profit making opportunity and hence the way in which your predictive way of prediction the unpredictable market crypto currency.

Ali Yeganeh, Xuelong Hu, Sandile Charles Shongwe, Frans F. Koning

In the area of multivariate process quality control, it is sometimes important to monitor the ratio of two normal random variables denoted by RZ over time. The concept of control charts has often been harnessed in this field, leading to the application of various types of statistical models, including Shewhart, Exponentially Weighted Moving Average (EWMA), and so forth. However, there is little attention to implementation of machine learning-based control charts. To bridge this gap, a novel machine learning based model incorporating the attention mechanism approach, as an implemented Artificial Intelligence (AI) model, is proposed to monitor the RZ in Phase II applications. The proposed RZ method not only provides quicker Out-of-Control (OC) shift detection than conventional RZ control charts but also does not require the quality controller to have any prior information about the upward or downward shift patterns, which is a major assumption in most of the previous RZ models. We provide extensive performance comparison results to discuss the statistical performance of our proposed method through Monte Carlo simulations. Moreover, a comprehensive real example about surveillance of the cryptocurrency market is provided to illustrate the practical application of our proposed method. Through simulation and back-testing results, it is shown how the proposed method can lead to an automated trading strategy.

Rakesh Kumar, V. Mangaiyarkarasi, Gajula Ramesh, Sudha Arvind · 6 authors

This research has developed the theoretical framework that outlines the application of blockchain and smart contracts for enhancing supply chain transparency. The framework here builds in the concept of decentralization and immutability of the blockchain technology to improve accountability, auditability and responsiveness within supply chains. The framework helps to acquire real-time visibility over different processes, control fraud, increase the accuracy of the data, and optimize several procedures as much as possible by implementing the mentioned smart contacts. When the framework is adopted, the various advantages include the efficiency gain in the processing of different transactions and the increased accuracy in the data handling capacity as well as enhanced compliance to the set standard regulatory compliances. Despite this, questions like scalability of the concept, compatibility issues and synergism of the players in the value web remain problematic. In a broader perspective, this study adds to the current literature on the integration of blockchain technology in managing supply chain and offers all-important pointers to the practising managers.

Yevgen Kotukh, Bohdan Morklyanyk, Maryna Riabokin, Roman Chaplinskyi · 6 authors

The article examines the issue of insufficiently efficient forecasting mechanism of cash flow and liquidity status on the boiler accounts of local budgets, which led to the revision of liquidity management practices. In the context of financial decentralization, local financial authorities have faced numerous challenges, including the need to ensure sufficient cash balances in the accounts of local budgets to guarantee financing and payment of obligations with minimal associated costs. In addition, effective management of cash reserves and forecasting of the revenue base of local budgets is necessary. The authors emphasize the importance of applying modern forecasting methods, such as machine learning and neural networks, which allow faster and more accurate analysis of financial data and more accurate forecasts. special attention assigned processes previous processing and automation data, algorithm selection, training models and estimates productivity. The article also investigated the concept management liquidity developed by the Ministry of finance of Ukraine for 2020-2023, and its impact on improvement of management practices state finances. Thus, in the article is highlighted necessity implementation effective methods forecasting and management liquidity for security stability and efficiency financial systems at both the local and state levels.

Aubain Nzokem, Daniel Maposa

The paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes the maximum likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavily tailed data (Bitcoin and Ethereum returns) and two peaked data (S\&P 500 and SPY ETF returns). For each index, the estimation results show that the six-parameter estimations are statistically significant except for the local parameter, $μ$. The goodness-of-fit was assessed through Kolmogorov-Smirnov, Anderson-Darling, and Pearson's chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p-value; and outperforms the Kobol, Carr-Geman-Madan-Yor, and Bilateral Gamma distributions.

Yushuo Niu

Recently, quantitative trading techniques applied in financial research have become increasingly sought after. Quantitative trading refers to the use of statistics and computer techniques to aid trading decisions. Bitcoin has attracted a large number of investors to invest in it due to its decentralised nature, anonymity, and total number of 21 million pieces. This paper wishes to profit from investing in Bitcoin. This paper predicts the logarithmic return of Bitcoin based on the Informer model. Because of the high volatility of Bitcoin, this paper shortens the prediction period of the Informer model from the traditional 24 days to 1 day. In addition, this paper introduces the technique of migration learning, where models trained on five tech company datasets are migrated to Bitcoin's dataset for training tests. This compensates for the small Bitcoin dataset to some extent. In this paper, MSE, MAE, and R-squared were used as the evaluation metrics with MSE of 0.5678, MAE of 0.5087, and R-squared of 0.232313938. The results show that the Informer model's short-term forecasting ability is validated. The value of this paper is to provide Bitcoin investors with a possible method to aid trading decisions.

F. Albert Wang, Qiang Ye, Jiang Li, Wen Shi

No abstract is available for this record.

Олександр Кузнецов, Anton Yezhov, Kateryna Kuznetsova, Oleksandr Domin

This study presents a comprehensive theoretical and empirical analysis of Patricia tries, the fundamental data structure underlying Ethereum's state management system. We develop a probabilistic model characterizing the distribution of path lengths in Patricia tries containing random Ethereum addresses and validate this model through extensive computational experiments. Our findings reveal the logarithmic scaling of average path lengths with respect to the number of addresses, confirming a crucial property for Ethereum's scalability. The study demonstrates high precision in predicting average path lengths, with discrepancies between theoretical and experimental results not exceeding 0.01 across tested scales from 100 to 100,000 addresses. We identify and verify the right-skewed nature of path length distributions, providing insights into worst-case scenarios and informing optimization strategies. Statistical analysis, including chi-square goodness-of-fit tests, strongly supports the model's accuracy. The research offers structural insights into node concentration at specific trie levels, suggesting avenues for optimizing storage and retrieval mechanisms. These findings contribute to a deeper understanding of Ethereum's fundamental data structures and provide a solid foundation for future optimizations. The study concludes by outlining potential directions for future research, including investigations into extreme-scale behavior, dynamic trie performance, and the applicability of the model to non-uniform address distributions and other blockchain systems.

N. I. M. B. Senanayaka, H. A. Pathberiya

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.

K. Ganesh, M. Anbazhagan, Shreyas Visweshwaran

n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally con-sidered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings herald a transformative perspective in financial time series forecasting, potentially reshaping investment strategies and predictive analytics in the digital currency landscape.n the rapidly evolving world of cryptocurrency markets, the precise forecasting of Bitcoin's value against the US Dollar acquires paramount importance, catering to the interests of diverse stakeholders including investors, regulatory agencies, and academia. This study ventures into a comprehensive assessment of various time series forecasting methodologies, including but not limited to Random Forest Regression, ARIMA, Linear Regression, and XGBoost. Notably, our investigation unveils a pivotal revelation: the foundational models like Linear Regression and Random Forest Regression, traditionally considered less complex, not only contend but also surpass the forecast accuracy of ARIMA models in the realm of Bitcoin. This paper aims to demystify the underpinnings of this superior performance, especially in mitigating the inherent volatility and unpredictability characteristic of Bitcoin. Our findings herald a transformative perspective in financial time series forecasting, potentially reshaping investment strategies and predictive analytics in the digital currency landscape.I

Geeta Kapur, Sridhar Manohar, Amit Mittal, Vishal Jain · 5 authors

Purpose Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern. Design/methodology/approach The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method. Findings The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends. Research limitations/implications To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE). Originality/value This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Mahdi Goldani

The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

Fatih Ecer, Tolga Murat, Hasan Dınçer, Serhat Yüksel

Abstract Crypto assets have become increasingly popular in recent years due to their many advantages, such as low transaction costs and investment opportunities. The performance of crypto exchanges is an essential factor in developing crypto assets. Therefore, it is necessary to take adequate measures regarding the reliability, speed, user-friendliness, regulation, and supervision of crypto exchanges. However, each measure to be taken creates extra costs for businesses. Studies are needed to determine the factors that most affect the performance of crypto exchanges. This study develops an integrated framework, i.e., fuzzy best–worst method with the Heronian function—the fuzzy measurement of alternatives and ranking according to compromise solution with the Heronian function (FBWM’H–FMARCOS’H), to evaluate cryptocurrency exchanges. In this framework, the fuzzy best–worst method (FBWM) is used to decide the criteria’s importance, fuzzy measurement of alternatives and ranking according to compromise solution (FMARCOS) is used to prioritize the alternatives, and the Heronian function is used to aggregate the results. Integrating a modified FBWM and FMARCOS with Heronian functions is particularly appealing for group decision-making under vagueness. Through case studies, some well-known cryptocurrency exchanges operating in Türkiye are assessed based on seven critical factors in the cryptocurrency exchange evaluation process. The main contribution of this study is generating new priority strategies to increase the performance of crypto exchanges with a novel decision-making methodology. “Perception of security,” “reputation,” and “commission rate” are found as the foremost factors in choosing an appropriate cryptocurrency exchange for investment. Further, the best score is achieved by Coinbase, followed by Binance. The solidity and flexibility of the methodology are also supported by sensitivity and comparative analyses. The findings may pave the way for investors to take appropriate actions without incurring high costs.

George Westergaard, Utku Erden, Omar Abdallah Mateo, Sullaiman Musah Lampo · 6 authors

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

Adel Mahfooz, Joshua L. Phillips

Bitcoin is known for its high volatility, which makes it challenging to accurately predict future prices. In this study, we aim to forecast Bitcoin prices for a month by incorporating exogenous variables, specifically the interest rate and recession probability. Our primary objective is to explore whether these variables have a positive impact on the prediction of Bitcoin prices. We used two popular time series forecasting models: Long Short-Term Memory (LSTM) and Facebook Prophet. Our approach involves exploring the impact of these exogenous variables on the performance of the models and comparing their results through plots and cross-validation. We trained the models using historical Bitcoin price data along with exogenous variables and evaluated their performance on a test dataset. Our results indicate that LSTM outperforms Facebook Prophet in terms of Bitcoin price prediction accuracy. This is because, while Facebook Prophet is optimized for statistical forecasting modeling, LSTM has the capability to learn intricate patterns and relationships given the right architecture with sufficient neurons. Importantly, we demonstrate that incorporating interest rates and recession probabilities significantly enhances the predictive capability of our models. Our findings suggest that changes in interest rates and recession probabilities have an impact on Bitcoin prices, and our models perform better when equipped with this valuable information.

Asha Rani Mishra, Rajat Kumar Rathore, Sansar Singh Chauhan

No abstract is available for this record.

Omar M. Ahmed, Lailan M. Haji, Ayah M. Ahmed, Nashwan M. Salih

The field of finance makes extensive use of real-time prediction of stock price tools, which are instruments that are put to use in the process of creating predictions. In this article, we attempt to predict the price of Bitcoin in a manner that is both accurate and reliable. Deep learning models, as opposed to more traditional methods, are used to manage enormous volumes of data and to generate predictions. The purpose of this research is to develop a method for predicting stock prices using the Hybrid Convolutional Recurrent Model (HCRM) architecture. This model architecture integrates the advantages of two separate deep learning models: The 1-Dimensional-Convolusional Neural Network (1D-CNN) and the Long-Short Term Memory (LSTM). The 1D-CNN is responsible for the feature extraction, while the LSTM is in charge of the temporal regression. The developed 1D-CNN-LSTM model has an outstanding performance in predicting stock values.

Rejuwan Shamim, Badr Bentalha

Supply chain efficiency relies heavily on being able to accurately predict future demand. In this chapter, the authors offer a machine learning framework for supply chain management demand forecasting that makes use of blockchain technology. The framework improves the precision of demand forecasts while maintaining data integrity and openness through the use of machine learning algorithms and blockchain technologies. Demand data is collected and preprocessed, machine learning models are applied, and blockchain is used to validate and secure the data. Results from experiments show that the framework is useful, with significant gains in accuracy and recall compared to more conventional methods. The results show the promise of merging machine learning with blockchain in demand forecasting, giving supply chain professionals a potent instrument with which to enhance the effectiveness of inventory management and overall operations. To fully reap the benefits of this approach, more study into scalability and implementation difficulties is necessary.