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The advent of 5G and blockchain technologies has signified rapid growth in the digital space. Their unique capabilities have already redefined communication and decentralized networks; however, their possible combined role is the subject of this research article. Objective: The article investigates the implications of 5G technology being used for integration with blockchain and how its disruptive nature would affect Binance's cryptocurrency trading platform. A mixed-methods research strategy was followed that draws upon data analytics, in-depth case study investigations and expert consultations. The key point was finding out how unparalleled low latencies and high-throughput capacities from 5G would be a procedures-changer for blockchain operations. The results indicate that the network capabilities of 5G can compensate for some blockchain major difficulties, including scalability and energy consumption. Quicker data transfer speeds up validation processes which leads to faster blockchain transaction times. This combination of advantages makes it possible to use microtransactions and facilitates scaling in terms of volume per transaction while retaining good levels of security and decentralization. Furthermore, with opportunities such as rapid cross-border payments and extensive smart contract capabilities available too — Binance certainly seems well-positioned to lead the charge in leveraging this convergence. But then again, the integration is accompanied by other challenges as well – mainly regulatory and technological challenges of innovation brought along by two cutting-edge technologies. The integration of 5th-generation mobile networks and blockchain has massive potential for cryptocurrency projects such as Binance. This partnership is poised to transform the existing blockchain landscape, ushering in a whole new era of fintech experience with an increased capability for speed and operational efficiency along safety systems.

This paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PDF

This paper examines the impact of market related events and investor base on the spread of Bitcoin prices between two exchange platforms, Coinbase and Binance. Based on high‐frequency data samples collected from 2019 to 2021, we show how investors from different bases react differently to market related events, which create the price spreads between exchange platforms. We also identify the arbitrage opportunities these spreads create and establish arbitrage strategies for all identified events to exploit the variations in Bitcoin prices traded on both platforms. Findings indicate arbitrage offers profits that are higher overall than holding Bitcoin on either platform. [ABSTRACT FROM AUTHOR]

This article aims at investigating the extent to which the algorithmic nature (i.e., mining process) of cryptocurrencies might influence their dynamics and interaction with some major economic indicators. Our study observes that proof‐of‐stake based cryptocurrencies are less correlated with other crypto‐assets offering more opportunities for diversifying portfolio strategy. We also observe a positive correlation between the proof‐of‐work based cryptocurrencies and the oil price. This article discusses these matters and suggests that the differences in cryptocurrencies' dynamics are more related to their service or purpose rather than their mining protocol. This claim contributes to the current debates on the intrinsic value of cryptocurrencies and it is illustrated with a discussion of the Stellar (XLM) and Ether (ETH) cases. Beyond our empirical results, our article suggests that, the liquidity and the returns dynamics of cryptocurrencies might be affected by two different aspects. Precisely, the former appears to be influenced by the economic service for which these cryptocurrencies are used, while cryptocurrencies' returns are more reactive to the way their cryptographic validation is operated. Our findings also suggest that an analysis through the economic service/purpose of cryptocurrencies is actually appropriate to understand their dynamics in relation to economic indicators. This perspective implicitly questions the monetary aspect often associated with cryptocurrencies and it calls for a more categorized research (by economic purpose) of cryptocurrencies whose potential intrinsic value would then be related to their economic purpose. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a relatively new phenomenon that is attracting a lot of interest. On the one hand, it is built on a brand-new technology whose full potential has yet to be realized. On the other hand, it performs similar services to other, more traditional assets, at least in its current form. The development of theoretical models of cryptocurrencies has received a lot of academic attention. Many elements have been mentioned in the theoretical literature on cryptocurrencies as potentially important in cryptocurrencies' pricing. The cryptocurrencies with a market value of over $100 million between January 1, 2018 and May 12, 2021 were selected for this research. Time-series analysis was done to investigate the price relationship among the cryptocurrencies. The results pointed out as major cryptocurrencies' prices are linked to Bitcoin prices. [ABSTRACT FROM AUTHOR]

Perpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple "u‐shaped" curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations. [ABSTRACT FROM AUTHOR]

This paper investigates a special and already well-documented topic within market efficiency, the put–call parity relationship. The analysis is carried out to the cryptocurrency options market, where market microstructure effects are treated. A new concept of the forward and backward PCP breaking for European options is introduced. This is examined in detail for Ethereum options listed on Binance using a one-second resolution data set. The results show that significant put–call parity violations remain even after controlling for bid–ask spread, trading costs and funding costs. • The put–call parity is tested on cryptocurrency options collected from Binance.com. • Quoted price data of ETHUSDT European options is used in one-second resolution form. • The whole distribution of the PCP breaking is studied. • Forward and backward breaking reflects bid–ask spread, transaction fee and interest. • Appreciable put–call parity breaking remains after considering all arbitrage costs. [ABSTRACT FROM AUTHOR]

This paper investigates the causes and the consequences of the FTX digital currency exchange's failure in November 2022. Analysing on-chain data, we report that FTX heavily relied on leveraging and misusing its native token, FTT, and we show how this behaviour exacerbated the company's fragile financial situation. To gain further insights into the downfall, we employ state-of-the-art network science instruments to model the evolutionary dependency structures of 199 cryptocurrencies on an hourly basis, and we investigate tick-by-tick public trades at the time of the events. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that triggered a significant decrease in the exchange's liquidity. Results suggest that the crash was actively accelerated by Binance tweets causing a systemic reaction in the cryptocurrency market. Finally, identifying the actors who mostly benefited from the FTX's collapse and highlighting a generalised trend toward centralisation in the crypto space, we emphasise the importance of genuinely decentralised finance for a transparent, future digital economy. • We investigate the causes and the consequences of the FTX exchange's failure. • We use hourly closing price, on-chain data and public transaction data. • We use state-of-the-art instruments from network science (i.e., TMFG). • We identify the collapse of the Terra-Luna as the trigger for the FTX's crash. • We highlight a generalised trend toward centralisation in the crypto space. [ABSTRACT FROM AUTHOR]

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns--and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)