Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty

Gurdgiev, Constantin; O’Loughlin, Daniel

doi:10.1016/j.jbef.2020.100271

Cited by 133 publications

(41 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Accordingly, some researchers, such as Stavroyiannis and Babalos ( 2019 ), study the hypothesis of non-rational behavior, such as herding, in the cryptocurrencies market. Gurdgiev and O’Loughlin ( 2020 ) explore the relationship between the price dynamics of 10 cryptocurrencies and proxies for fear (VIX index), uncertainty (U.S. Equity Market Uncertainty index), investors’ sentiment toward cryptocurrencies (measured based on investors’ opinions expressed in a bitcoin forum) and investor perceptions of bullishness/bearishness in the overall financial markets (measured by CBOE put-call ratio). They highlight that investor sentiment is a good predictor of the price direction of cryptocurrencies and that cryptocurrencies can be used as a hedge during times of uncertainty; but during times of fear, they do not act as a suitable safe haven against equities.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

View full text Add to dashboard Cite

This study examines the predictability of three major cryptocurrencies—bitcoin, ethereum, and litecoin—and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

2021

View full text Add to dashboard Cite

show abstract

“…additionally, decision making may have a plethora of implications associated with factors such as money and investment because behavioral biases are inaccurate and potentially harmful to investors' behavior due to erroneous decisions (Bhatia et al, 2020). although significant attention has been devoted to the evaluation of investment properties, to date, academic literature on behavioral drivers remains less developed (Gurdgiev & o'loughlin, 2020).…”

Section: Introductionmentioning

confidence: 99%

Untitled

Ullah¹,

Elahi²,

Ullah³

et al. 2020

View full text Add to dashboard Cite

The trend of the last decade is to achieve sustainable development of society. The reasons of the development of this tendency are the global processes of industrial growth, the level of consumption of products, urbanization, and the development of globalization processes, the formation of the impact of non-profit organizations in the fight against environmental problems. The problems of environmental responsibility are urgent for many researchers, as they represent a way to solve complex environmental and economic problems facing the representatives of modern business, society and the state. The article deals with the analysis of the environmental component of social responsibility and its impact on the sustainable development of European countries. The article focuses on prioritizing sustainable development goals, namely Partnership for Sustainable Development. The factors that most influence on the environmental sustainability of European countries (Lithuania, Hungary, Slovakia, France, and Ukraine) were analyzed. The correlation between GDP changes, populations and the level of environmental pollution has been proved. The definition of the concept of responsible consumption is considered and recommendations of reducing the level of influence of the agricultural sector on the environmental component were proposed. The necessity to increase environmental social responsibility in order to prevent a negative impact on the economy of European countries is substantiated.

show abstract

“…Jadi ini sesuatu yang kita harapkan, dan kemungkinan dengan melihat pandemi yang akan memakan beberapa tahun untuk bisa terkendali, inovasi di bidang digital dan investasi akan bersama-sama terdorong bertumbuh. Gurdgiev dan O'Loughlin [6] menemukan bahwa perasaan sentimen investor dapat menjadi ramalan kenaikan atau penurunan harga uang kripto, yang merupakan dampak langsung bagaimana persepsi atau bias mempengaruhi perilaku para investor dan akhirnya mendorong perubahan dalam harga uang kripto di pasar. Untuk ke depannya terdapat potensi untuk mengamati pengaruh dari perasaan sentimen investor ini dengan menggunakan tool Artificial Intelligence.…”

Section: Abstract-cryptocurrency Gold Investments Stocks Webinarunclassified

Pengayaan Pengetahuan Investasi Digital Melalui Webinar Investasi dalam Era High Tech

Edi¹,

Djajalaksana²,

Kasih³

2021

Sendimas VI 2021

View full text Add to dashboard Cite

Tren investasi telah berubah secara signifikan akibat imbas keberadaan pandemi COVID-19. Investasi tradisional seperti tabungan dan deposito tampak tidak lagi semenarik sebelumnya. Ledakan teknologi yang diakibatkan oleh adanya pandemi, mempromosikan berbagai investasi gaya baru menggunakan teknologi seperti saham, cryptocurrency, dan emas yang bisa dikelola melalui aplikasi daring. Meski investasi untuk ketiga jenis ini sudah menjadi perbincangan viral di Tanah Air, banyak orang Indonesia yang masih memiliki pengetahuan dan keterampilan yang terbatas di era baru investasi. Fakultas Teknologi Informasi Universitas Kristen Maranatha Indonesia bermaksud melaksanakan Corporate Social Responsibility melalui Program Pengabdian Masyarakat dengan mendidik masyarakat Indonesia tentang topik di era baru investasi digital, dan secara khusus ingin mempertimbangkan faktorfaktor apa pun yang terkait dengan investasi di saham, cryptocurrency, dan emas dengan cara yang benar. Niat ini telah diwujudkan dengan mengadakan Webinar Gratis yang terbuka untuk semua orang di Indonesia meskipun tidak terbatas hanya untuk mereka yang ada di Indonesia dengan topik “The Investment Puzzle: Stocks, Cryptocurrency, or Gold In The High Tech Era”. Webinar ini telah terlaksana pada hari Sabtu, 19 Juni 2021 melalui Aplikasi Zoom dan disiarkan juga melalui YouTube Channel Fakultas Teknologi Informasi Universitas Kristen Maranatha. Jumlah pendaftar mencapai 898 orang namun yang hadir dalam realisasinya adalah 462 peserta dari segala jenis kalangan dan masyarakat luas. Selain telah terpublikasi melalui YouTube, acara Webinar ini juga telah terpublikasi dalam bentuk berita di MCU News dan artikel di Kompasiana.

show abstract

Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty

Cited by 133 publications

References 38 publications

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Untitled

Pengayaan Pengetahuan Investasi Digital Melalui Webinar Investasi dalam Era High Tech

Contact Info

Product

Resources

About