Investor attention and the pricing of cryptocurrency market

Zhang, Wei; Wang, Pengfei

doi:10.1007/s40844-020-00182-1

Cited by 23 publications

(11 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They also discovered that spike in investor attention has increased cryptocurrency returns. Zhang and Wang (2020) analyzed the relationship between twenty cryptocurrencies and investor attention and stated that they observed a bidirectional causal relationship between cryptocurrency returns and investors' attention. Gemici and Polat (2021) examined the volatility spillovers between Bitcoin, Litecoin and Ethereum and reported that they observed a one-way causality running from Bitcoin to both Litecoin and Ethereum.…”

Section: Ui̇i̇i̇d-ijeas 2022 (34):129-142 Issn 1307-9832mentioning

confidence: 99%

Kri̇pto Para Pi̇yasalarinda Nedenselli̇k Ve Eşbütünleşme

GÜL

2022

Uluslararası İktisadi Ve İdari İncelemeler Dergisi

View full text Add to dashboard Cite

This paper investigates the causality and cointegration relationships between seven major cryptocurrencies, namely Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Polkadot (DOT) and Ripple (XRP), using Johansen Cointegration and Granger Causality tests over the period from August 21, 2020 to April 19, 2021. Results indicate that there exists cointegration among cryptocurrencies in the long run. Findings also show that there is a bi-directional causal relationship between BNB and ETH. Additionally, BNB appears to be Granger cause of ADA, DOGE and DOT. On the other hand, analyses provide evidence of one-way causality running from XRP to both DOGE and DOT. These results might have some important implications for investors in terms of portfolio management.

show abstract

Section: Ui̇i̇i̇d-ijeas 2022 (34):129-142 Issn 1307-9832mentioning

confidence: 99%

Kri̇pto Para Pi̇yasalarinda Nedenselli̇k Ve Eşbütünleşme

GÜL

2022

Uluslararası İktisadi Ve İdari İncelemeler Dergisi

View full text Add to dashboard Cite

show abstract

“…Rothman et al [55] used videos and posts on YouTube, Facebook, Telegram, and Reddit for sentimental analysis of cryptocurrencies. Kraaijeveld et al [56] analyzed public Twitter sentiment, including Twitter bots, to predict the prices of nine different cryptocurrencies, and Zhang et al [57] examined the relationship between investor attention and cryptocurrencies using Google Trends. Burggraf et al [58] examined the relationship between investor sentiment on Bitcoin return by considering householdlevel and market-level sentiment using Google's search engine.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Tanwar¹,

Patel

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.

show abstract

“…In addition, Tantaopas et al (2016) reemphasize that search at a broad index level rather than at a security level is a representative of retail investors by studying stock market in the Asia-Pacific region. Uses of SVI as a proxy of retail investor attention appear in bond (Pham & Huynh, 2020), foreign exchange (Goddard et al, 2015;Han et al, 2018), futures (Saxena & Chakraborty, 2020), oil (Xiao and Wang,), and cryptocurrency (Zhang & Wang, 2020) markets. In general, the findings underpin the impact of investor attention on asset prices.…”

Section: Public Interest Statementmentioning

confidence: 99%

Chasing for information during the COVID-19 panic: The role of Google search on global stock market

Padungsaksawasdi

Treepongkaruna

2021

Cogent Economics & Finance

View full text Add to dashboard Cite

This paper examines the causal relationship between global stock market performance and Google search volume index (SVI) surrounding the disastrous event of the coronavirus (COVID-19) outbreak. Based on 6,106 stock index-day observations of 71 countries during the period from 1 January 2020 to 29 May 2020, we find that both the SVI and the growth in confirmed cases lower the global stock market returns. Consistent with the information discovery theory, we find when the confirmed cases increase, retail investors search for more information, improving their returns on stock indices during the outbreak. Finally, our further instrumentalvariable analysis shows that our results are unlikely confounded by endogeneity.

show abstract

Investor attention and the pricing of cryptocurrency market

Cited by 23 publications

References 58 publications

Kri̇pto Para Pi̇yasalarinda Nedenselli̇k Ve Eşbütünleşme

Kri̇pto Para Pi̇yasalarinda Nedenselli̇k Ve Eşbütünleşme

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Chasing for information during the COVID-19 panic: The role of Google search on global stock market

Contact Info

Product

Resources

About