Academic literature on the topic 'Binance coin'

This research is the first attempt to customize a trading system that is based on second order stochastic dominance (SSD) to five known cryptocurrencies’ daily data: Bitcoin, Ethereum, XRP, Binance Coin, and Cardano. Results show that our system can predict price trends of cryptocurrencies, trade them profitably, and in most cases outperform the buy and hold (B&H) simple strategy. Our system’s best performance was achieved trading XRP, Binance Coin, Ethereum, and Bitcoin. Although our system has also generated a positive net profit (NP) for Cardano, it failed to outperform the B&H strategy. For all currencies, the system better predicted long trends than short trends.

<p><em>The results of this research in this article show that the existence of virtual money in trade as a means of investment and payment in Indonesia can be said to be illegal when viewed from Law Number 7 of 2011 concerning Currencies. Digital money users in Indonesia are widely used in terms of business, which if traced in Indonesia there are already digital money such as Bitcoin and Binance Coin and others. Then, there is also the responsibility of perpetrators of money laundering crimes who use digital money as an investment tool that has a negative impact on the State of Indonesia, especially those related to this business because the person or group who committed the crime uses technological advances with dirty goals so that the perpetrator can be caught law using Law No. 8 of 2010 concerning The Prevention and Eradication of Money Laundering.</em></p><p><strong><em>Keywords</em></strong><em>: Money laundering, digital currency, criminal liability</em></p><p> </p><p>Hasil penelitian dalam artikel ini menunjukkan eksistensi uang virtual dalam perdagangan sebagai alat investasi dan pembayaran di Indonesia dapat dikatakan tidak sah penggunaannya jika dilihat dari Undang-undang Nomor 7 Tahun 2011 Tentang Mata Uang. Pengguna uang digital di Indonesia banyak dipakai dalam hal bisnis, yang jika ditelusuri di Indonesia sudah ada uang digital seperti Bitcoin dan Binance Coin dan lain-lain. lalu, ada pun tanggung jawab pelaku tindak pidana pencucian uang yang memakai uang digital sebagai sarana alat investasi berdampak negative bagi Negara Indonesia, terutama yang menyangkut terkait bisnis ini disebabkan orang atau kelompok yang melakukukan kejahatan tersebut menggunakan kemajuan teknologi dengan tujuan yang kotor sehingga pelaku bisa di hukum menggunakan Undang-undang No. 8 Tahun 2010 tentang pencegahan dan pemberantasan tindak pidana pencucian uang.</p><p><strong>Kata Kunci: </strong>Pencucian uang, mata uang digital , pertanggung jawaban pidana</p>

This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.

Virtual currency represents a specific technological innovation on financial markets. Bitcoin and other cryptocurrencies are popular alternatives to traditional cash and investment. We indicate a research gap in the literature review. We find out that current research focused rarely on portfolio diversification using bibliographic analysis in VOSviewer. We think that portfolio diversification is extremely important on the crypto market for most investors because virtual currencies are very risky compared to traditional assets. The primary aim is to construct an optimal portfolio consisting of several cryptocurrencies without traditional assets using a modern theory portfolio. The total sample consists of 16 virtual currencies from 1 October 2017 to 13 January 2020. We mainly obtain historical data on the daily close price of cryptocurrencies from Yahoo Finance. The results show that the optimal portfolio using Markowitz approach consists of Cardano, Binance Coin, and Bitcoin. In addition, virtual currencies are moderately Correlated, with the exception of Tether based on correlation analysis. The high correlation is dangerous for cryptocurrency in portfolio diversification. However, Tether is an atypical virtual currency compared to other cryptocurrencies.

Blockchain technology has attracted more and more attention from academia and industry recently. Ethereum, which uses blockchain technology, is a distributed computing platform and operating system. Smart contracts are small programs deployed to the Ethereum blockchain for execution. Errors in smart contracts will lead to huge losses. Formal verification can provide a reliable guarantee for the security of blockchain smart contracts. In this paper, the formal method is applied to inspect the security issues of smart contracts. We summarize five kinds of security issues in smart contracts and present formal verification methods for these issues, thus establishing a formal verification framework that can effectively verify the security vulnerabilities of smart contracts. Furthermore, we present a complete formal verification of the Binance Coin (BNB) contract. It shows how to formally verify the above security issues based on the formal verification framework in a specific smart contract. All the proofs are checked formally using the Coq proof assistant in which contract model and specification are formalized. The formal work of this paper has a variety of essential applications, such as the verification of blockchain smart contracts, program verification, and the formal establishment of mathematical and computer theoretical foundations.

In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.

With this research to determine the effect of public literacy in investing, the most common are currency, paper money and coins. Now the blockchain technology that we hear more often, namely with cryptocurrencies, has made many meanings of the benefits of profit because it is without the costs of administrative services, without intermediaries, and delays. In this case, one of them is the Binance Platform, the Fintech P2P lending Ecosystem and digital transaction system are growing very quickly and become one of the cashless payment options, an alternative choice for new tariff categories because of the multiple advantages of fast and technology-based processes which are the supporting culture of the MSME generation. Cryptocurrency despite very significant progress but not many countries have legalized it as a legal medium of exchange. With the influence of information systems science, namely by knowing more about the extent of acceptance of cryptocurrencies in society in terms of social finance

Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

This master's thesis focuses on the subject Management of free capital inside the cryptocurrency market. First, selected cryptocurrencies are briefly described. Subsequently, their historical data are analysed. Finally, based on these findings, an appropriate strategy for the chosen company is formulated.

Questo elaborato si propone di analizzare il tema delle criptovalute, argomento a cui si è interessato un numero sempre maggiore di persone negli ultimi anni, di modo da offrire uno strumento informativo che consenta una più ampia comprensione della materia in esame. Le criptovalute sono oggetti informatico-digitali che stanno determinando cambiamenti sempre più radicali nella nostra quotidianità: dalla concezione del denaro a quella della realtà virtuale, con l’avvento del “Metaverso”, dal sistema sanitario alla votazione elettronica (e-voting), dall’ambito giuridico al settore finanziario, tanto da determinare la nascita di un nuovo settore, il FinTech o TecnoFinanza. Colossi dell’economia mondiale -come Apple, Wordpress, Satispay e tanti altri- hanno deciso di accettare pagamenti in criptovalute aprendo così la strada dello shopping online, e non solo, anche alle monete virtuali. L’obiettivo della tesi è, quindi, quello di formare il lettore fornendo un testo consultabile che partendo dalle origini storico-economiche e dalle tecnologie alla base di queste valute, arriva ad analizzarle individualmente, soffermandosi sul loro impatto sociale, ambientale, politico ed economico.

La reconnaissance automatique du locuteur indépendante du texte est une méthode récente dans le domaine des systèmes biométriques. Le développement de la reconnaissance du locuteur se reflète tout autant dans la participation croissante aux compétitions internationales et dans les progrès en termes de performance relevés dans ces campagnes. Cependant la précision des méthodes reste limitée par la quantité d'information discriminante du locuteur présente dans les représentations informatiques des énoncés vocaux. Cette thèse présente une étude sur ces représentations. Elle identifie deux faiblesses principales. Tout d’abord, les représentations usuelles ignorent les paramètres temporels de la voix pourtant connus pour leur pouvoir discriminant. Par ailleurs, ces représentations reposent sur le paradigme de l’apprentissage statistique et diminuent l’importance d’événements rares dans une population de locuteurs, mais fréquents dans un locuteur donné.Pour répondre à ces verrous, cette thèse propose une nouvelle représentation des énoncés. Celle-ci projette chaque vecteur acoustique dans un large espace binaire intrinsèquement discriminant du locuteur. Une mesure de similitude associée à une représentation globale (vecteurs cumulatifs) est également proposée. L’approche proposée permet ainsi à la fois de représenter des événements rares mais pertinents et de travailler sur des informations temporelles. Cette approche permet de tirer parti des solutions de compensation de la variabilité « session », qui provient de l’ensemble des facteurs indésirables, exploitées dans les approches de type « iVector ». Dans ce domaine, des améliorations aux algorithmes de l’état de l’art ont été proposées.Une solution originale permettant d’exploiter l’information temporelle à l’intérieur de cette représentation binaire a été proposée. La complémentarité des sources d’information a été attestée par un gain en performance relevé grâce à une fusion linéaire des deux types d’information, indépendant et dépendant de la séquence temporelle
Text-independent automatic speaker recognition is a recent method in biometric area. Its increasing interest is reflected both in the increasing participation in international competitions and in the performance progresses. Moreover, the accuracy of the methods is still limited by the quantity of speaker discriminant information contained in the representations of speech utterances. This thesis presents a study on speech representation for speaker recognition systems. It shows firstly two main weaknesses. First, it fails to take into account the temporal behavior of the voice, which is known to contain speaker discriminant information. Secondly, speech events rare in a large population of speakers although very present for a given speaker are hardly taken into account by these approaches, which is contradictory when the goal is to discriminate among speakers.In order to overpass these limitations, we propose in this thesis a new speech representation for speaker recognition. This method represents each acoustic vector in a a binary space which is intrinsically speaker discriminant. A similarity measure associated with a global representation (cumulative vectors) is also proposed. This new speech utterance representation is able to represent infrequent but discriminant events and to work on temporal information. It allows also to take advantage of existing « session » variability compensation approaches (« session » variability represents all the negative variability factors). In this area, we proposed also several improvements to the usual session compensation algorithms. An original solution to deal with the temporal information inside the binary speech representation was also proposed. Thanks to a linear fusion approach between the two sources of information, we demonstrated the complementary nature of the temporal information versus the classical time independent representations
El reconocimiento automático del locutor independiente del texto, es un método dereciente incorporación en los sistemas biométricos. El desarrollo y auge del mismo serefleja en las competencias internacionales, pero aun la eficacia de los métodos de reconocimientose encuentra afectada por la cantidad de información discriminatoria dellocutor que esta presente en las representaciones actuales de las expresiones de voz.En esta tesis se realizó un estudio donde se identificaron dos principales debilidadespresentes en las representaciones actuales del locutor. En primer lugar, no se tiene encuenta el comportamiento temporal de la voz, siendo este un rasgo discriminatorio dellocutor y en segundo lugar los eventos pocos frecuentes dentro de una población delocutores pero frecuentes en un locutor dado, apenas son tenidos en cuenta por estosenfoques, lo cual es contradictorio cuando el objetivo es discriminar los locutores. Motivadopor la solución de estos problemas, se confirmó la redundancia de informaciónexistente en las representaciones actuales y la necesidad de emplear nuevas representacionesde las expresiones de voz. Se propuso un nuevo enfoque con el desarrollo de unmétodo para la obtención de un modelo generador capaz de transformar la representación actual del espacio acústico a una representación en un espacio binario, dondese propuso una medida de similitud asociada con una representación global (vectoracumulativo) que contiene tanto los eventos frecuentes como los pocos frecuentes enuna expresión de voz. Para la compensación de la variabilidad de sesión se incorporóen la matriz de dispersión intra-clase, la información común de la población de locutores,lo que implicó la modificación de tres algoritmos de la literatura que mejoraronsu desempeño respecto a la eficacia en el reconocimiento del locutor, tanto utilizandoel nuevo enfoque propuesto como el enfoque actual de referencia. La información temporalexistente en las expresiones de voz fue capturada e incorporada en una nuevarepresentación, mejorando aun más la eficacia del enfoque propuesto. Finalmente sepropuso y evaluó una fusión lineal entre los dos enfoques que demostró la informacióncomplementaria existente entre ellos, obteniéndose los mejores resultados de eficaciaen el reconocimiento del locutor

La plupart des toxines de Bacillus thuringiensis perméabilisent la membrane intestinale des insectes sensibles en formant des pores qui abolissent le potentiel électrique et les gradients ioniques. Plusieurs toxines ont été étudiées avec des vésicules purifiées de la bordure en brosse intestinale des insectes. Malheureusement, la membrane intestinale de beaucoup d’insectes ne forme pas des vésicules suffisamment étanches pour les expériences de perméabilisation. Une nouvelle technique utilisant des liposomes géants et une sonde de perméabilité membranaire a été développée pour caractériser deux nouvelles toxines particulièrement prometteuses pour le biocontrôle d’un des principaux ravageurs du maïs, la chrysomèle des racines du maïs (Diabrotica virgifera virgifera LeConte), Cry6Aa1 et la toxine binaire DS10/DS11. Les deux toxines perméabilisent efficacement les liposomes. La toxine binaire forme des pores qui sont légèrement sélectifs pour les cations, comme la plupart des toxines de B. thuringiensis. Bien que la Cry6Aa1 puisse former des pores sélectifs pour les anions, les résultats suggèrent aussi qu’elle pourrait, contrairement aux autres toxines de cette bactérie, ne former des pores qu’en présence d’une force ionique élevée. La formation des pores par ces deux toxines semble être sensible à la courbure de la membrane cible étant donné qu’elle est beaucoup plus efficace dans des liposomes géants que dans des liposomes de même composition, mais plus petits. Ce travail jette les bases de la mise au point d’une technique qui permettrait l’étude des toxines dans des liposomes géants enrichis avec des protéines et des lipides provenant de la membrane intestinale des insectes cibles.
Most Bacillus thuringiensis toxins permeabilize the intestinal membrane of susceptible insects by forming pores that abolish transmembrane electrical potentials and ionic gradients. Several toxins have been studied using brush border membrane vesicles purified from the insect midgut. Unfortunately, the intestinal membrane from many insects does not form vesicles that are tight enough to be used in permeabilisation experiments. A new technique using giant liposomes and a membrane permeability probe was developed to evaluate the pore-forming ability of two particularly promising toxins for the biocontrol of a major corn pest, the Western corn rootworm (Diabrotica virgifera virgifera LeConte), Cry6Aa1 and the binary toxin DS10/DS11. Both toxins permeabilized the liposomes efficiently. However, analysis of the permeabilisation rates under different experimental conditions indicates that these toxins differ in their biophysical properties. The binary toxin forms pores which are slightly selective for cations, like most B. thuringiensis toxins. On the other hand, although the results suggest that Cry6Aa1 could form anion-selective pores, they could also indicate that, in contrast with other toxins produced by this bacterium, it could form pores only under high ionic strength conditions. Pore formation by both toxins appears to be sensitive to membrane curvature since it is much more efficient in giant liposomes than in liposomes with identical composition, but smaller in size. This study sets the bases for the development of a technique that would allow the toxins to be studied in giant liposomes enriched with proteins and lipids from the intestinal membrane of target insects.