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2,329 results · page 37 of 98
David Y. Aharon, Hassan Anjum Butt, Ali M. Jaffri, Brian J. Nichols
No abstract is available for this record.
Dun Li, Dezhi Han, Zibin Zheng, Tien‐Hsiung Weng · 7 authors
No abstract is available for this record.
Paul Gerrans, Sherin Babu Abisekaraj, Zhangxin Liu
Abstract The “Fear of Missing Out” or FoMO has become an accepted motivator of behaviours extending from the purchase of limited-edition sneaker brands to social media use and cryptocurrency investment. As a motivator of individual financial behaviours, such as cryptocurrency and stock investment, it is unclear how FoMO relates to consumer financial literacy and other consumer traits, including risk tolerance and personality. We propose, and assess, a model of reported investment behaviour and investment behaviour intention. We find a larger association between FoMO and crypto ownership, both current and intended, compared with stocks. FoMO has a small association with current stock ownership, relative to the association of financial literacy and risk tolerance. Context matters when measuring FoMO with the more context-specific measures having the largest associations with investment behaviour and investment intentions. Finally, our results suggest financial literacy is an antecedent of FoMO, more so for stocks.
Zaghum Umar, Muhammad Usman, Sun‐Yong Choi, John Rice
No abstract is available for this record.
Nicholas Apergis
No abstract is available for this record.
Bikramaditya Ghosh, Elie Bouri, Jung Bum Wee, Noshaba Zulfiqar
No abstract is available for this record.
Donyetta Bennett, Erik Mekelburg, Tomás Williams
This systematic literature review summarizes the extant research in the Behavioral Finance (BeFi) and digital asset spaces to understand better the interactions of behavioral effects on the pricing of assets constructed, enabled, and exchanged in Decentralized Finance (DeFi) markets. We find that asset pricing in these rapidly evolving markets is better explained through BeFi than through traditional finance (TradFi) theory. Investor attention, sentiment, heuristics and biases, and network effects interact to form a highly volatile and dynamic market. We offer a deterministic research framework with propositions for future research. We further provide investors with a theoretically and empirically supported structure to better inform their decisions through an understanding of BeFi applications to DeFi.
Jiřı́ Málek, Duc Khuong Nguyen, Ahmet Şensoy, Quang Van Tran
No abstract is available for this record.
Ethereum Trader
Ethereum Trader is a crypto exchanging programming made to mechanize the trading of digital currencies. The exchanging framework utilizes Man-made brainpower (computer based intelligence) and AI (ML) calculations to recognize possibly beneficial exchanging open doors and execute them continuously. As per the data gave on the site, <strong>Ethereum Trader</strong> has an exchanging arrangement that is both exceptionally viable and speedy. It is stacked with different highlights intended to make life more straightforward for shoppers. https://www.theethereumtrader.com/
Surinder Singh Khurana, Parvinder Singh, Naresh Kumar Garg
No abstract is available for this record.
Masud Alam, Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Mansur Masih
We investigate the return and volatility spillovers among NFTs, REITs, and other major financial assets from January 2019 to November 2022, using connectedness approaches. The findings indicate that total return and volatility connectedness increased during the COVID-19 and the Russia–Ukraine war. REITs partially maintained their historical independence from shocks from other assets, while NFTs emerged as the new portfolio diversifiers. Findings suggest that investors can use REITs or a combination of NFTs, OIL, GOLD, and REITs with other assets to hedge against volatile assets during periods of financial turmoil. These findings have significant implications for heterogeneous market participants aiming to identify optimal portfolio diversifiers.
Sheng Fang, Guangxi Cao, Paul Egan
No abstract is available for this record.
Eojin Yi, Biao Yang, Minhyuk Jeong, Sungbin Sohn · 5 authors
This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.
Wanying Deng
According to the monetary theory, this paper believes that the demand for Bitcoin mainly includes two aspects: transaction demand and investment demand. This paper further discusses the impact of different demands on the price of Bitcoin based on two aspects of demand. Transaction demand and investment demand together affect the supply and demand relationship of the Bitcoin market. The empirical results show that the volatility of Bitcoin price is higher than that of international currencies and stocks as investment tools. This article emphasizes that the price of Bitcoin is primarily affected by supply and demand.
Qingsen Zhang
Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for improvement. Previous studies have introduced detecting the turning point between long-term slow time-series factor and short-term fast time-series factor contributes to predicting the trend well. Furthermore, a threshold decided by a certain machine learning model suggests better performance. In this paper. A multilayer perceptron (MLP) is utilized to learn the weights of time-series factors. The combination of cross-sectional momentum and time-series momentum shows advantages and the MLP learned weighted strategy is preferable.
Hugo Eduardo Ramirez, Julián Fernando Sanchéz
This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and linear and quadratic permanent impact. Then, using data from the order book of the BNB cryptocurrency, we estimate the functional form of the temporary and permanent price impact in three different scenarios: underestimation, overestimation and average estimation, finding different functional forms for each scenario. Using finite differences and optimal policy iteration, we solve the problem numerically and observe interesting changes in the optimal liquidation policy when applying calibrated linear and power forms for the temporary and permanent price impacts. Then, with these optimal policies, we identify optimal liquidation trajectories and simulate the liquidation of initial inventories to compare the performance among the optimal strategies under different parametrizations and against a naive strategy. Finally, we characterize the optimal policies based on the functional form of the inventory and find that policies generating the highest revenue are those starting with a low trading rate and increasing it as time passes.
Man-Fai Leung, Lewis Chan, Wai-Chak Hung, Siu-Fung Tsoi · 6 authors
The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visualization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals.
Leonardo L. Etro, Pier Luigi Sacco, Emiliano Sironi, Nicola Taccalite · 5 authors
We investigate potential factors influencing token after-market returns to explain the extreme levels of underpricing experienced in the cryptocurrencies market. This research analyses a sample of 300 tokens issued between September 2015 and May 2018, fully capturing the window of maximum ICO activity. The results confirm the presence of significant underpricing with an average of 180.66% and a median of 32.21%, which far exceed that of the IPO market. The cumulative after-market mean returns at three months, six months, nine months, and twelve months continue to be remarkably high and above 100%. However, the return distributions exhibit substantial negative median values, highlighting the poor post-ICO performance of most tokens. The research also confirms the IPO ‘fads hypothesis’ as a reasonable explanation of token underpricing, which attributes positive initial returns to investor overreactions. This hypothesis provides a rationale for ICO higher underpricing with respect to the IPO market as well. Finally, the quality of the management team positively affects token after-market returns while the project technical validity does not significantly impact on them. Blockchain entrepreneurs may use these results to design tokens and the offering campaigns with the aim of reducing negative swings in the after-market.
Kangsan Lee, Daeyoung Jeong
No abstract is available for this record.
Abhishek Sah, Biswajit Patra
This paper investigates the impact of COVID-19 on the cryptocurrency market. It empirically examines the level of volatility and the dynamic conditional correlations among cryptocurrencies pre-COVID-19 and during COVID-19. We find significant dynamic conditional correlations among cryptocurrencies and that the level of volatility is higher during COVID-19 than pre-COVID-19.
Abhijit Roy
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s— t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.
Yosra Ghabri, Marjène Rabah Gana
Purpose Using vector autoregressive modelling (VAR) and Granger causality tests, this paper attempts to empirically investigate the dynamic relationship between return and volume of transactions of two main cryptocurrencies: Bitcoin and Ethereum. Design/methodology/approach Based on a generalized autoregressive conditional heteroskedasticity (GARCH) model with a transaction volume parameter in the conditional volatility equation. Findings The results provide empirical evidence of a positive contemporaneous relationship between the variation in transaction volume and the daily return of Bitcoin and Ethereum. The results also show that the conditional volatility of the returns is affected by the past volatility, which implies weak-form inefficiency for both Bitcoin and Ethereum markets. The results of the VAR model, testing Granger causality, indicate that the volume of transactions Granger-Causes Bitcoin and Ethereum returns. Furthermore, the findings show a Granger causal relation from returns to volume. Originality/value This result suggests that cryptocurrency returns can predict transaction volumes and vice versa.
Emrah İsmail Çevik, Samet Günay, Sel Dibooğlu, Durmuş Çağrı Yıldırım
No abstract is available for this record.
Imran Yousaf, John W. Goodell
No abstract is available for this record.