In the financial market, investors often do not consider the market environment, but only invest based on the investment behavior of others. This is commonly known as Herd behavior. The herd behavior not only takes part in traditional financial markets but also in cryptocurrency markets. This paper aims to build a model which is applicable in cryptocurrency markets. To research the herd behavior in cryptocurrency markets and the dynamic relationship between the investors and the value of cryptocurrency with the model.
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.
Purpose The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns. Design/methodology/approach The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020. Findings The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research. Practical implications The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market. Originality/value The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.
In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.
The main purpose of this study is to examine the effects of Elon Mask's Twitter posts about cryptocurrencies on cryptocurrency markets within the scope of herding behavior bias. For this purpose, the daily price values and transaction volumes of Bitcoin and Dogecoin are analyzed by applying the EGARCH models. The results show that Elon Musk's positive Twitter posts increase dogecoin's volatility more than bitcoin in terms of price and trading volume. In addition, the effect of positive tweets has been found to increase Bitcoin and Dogecoin prices and their market transactions. According to the results, while negative tweet sharing negatively affects bitcoin returns, it manifests itself with an increase in volatility after a certain period of time. Another result is that the Dogecoin return and negative tweet interaction vary according to time intervals, but the presence of the effect on volatility cannot be determined. It is also concluded that after the negative tweet, both bitcoin and dogecoin transaction volumes increased in the first days, but their volatility was not affected. The results are important in terms of showing the effects of an influential person's social media posts on the financial markets by creating a herd behavior effect. Revealing the "influential person effect" as a behavioral finance bias is seen as the originality of the study. It is thought that the findings can be evaluated in terms of pointing out a factor that may pose a potential risk to financial stability in the global sense.
Cristian Pinto‐Gutiérrez, Sandra Gaitán, Diego Jaramillo, Simón Velasquez
Non-fungible tokens (NFTs) can be used to represent ownership of digital art or any other unique digital item where ownership is recorded in smart contracts on a blockchain. NFTs have recently received enormous attention from both cryptocurrency investors and the media. We examine why NFTs have gotten so much attention. Using vector autoregressive models, we show that Bitcoin returns significantly predict next week’s NFT growth in popularity, measured by Google search queries. Moreover, wavelet coherence analysis suggests that Bitcoin and Ether returns are significant drivers of next week’s attention to NFTs. These results indicate that the remarkable increases in prices of major cryptocurrencies can explain the hype around NFTs.
Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.
Sitara Karim, Muhammad Abubakr Naeem, Nawazish Mirza, Jéssica Paule-Vianez
Purpose This study quantified the hedge and safe haven features of bond markets for multiple cryptocurrency indices from June 2014 to April 2021 to highlight whether bond markets offer hedging facilities to uncertainty indices of cryptocurrencies. Design/methodology/approach The authors employed the methodology of Baur and McDermott (2010) and AGDCC-GARCH model to measure the hedge and safe-haven characteristics of three bond markets (BBGT, SPGB and SKUK) for three uncertainty indexes of cryptocurrencies (UCRPR, UCRPO and ICEA). Findings The authors find that bond markets are neither hedge nor safe havens except for SKUK which is a safe haven investment for cryptocurrency indices and offers substantial diversification during the periods of economic fragility. In addition, the hedge effectiveness of SPGB outperforms other bonds during crisis periods and provides sufficient diversification potential for cryptocurrency indices. Practical implications The findings are important for policymakers, regulatory bodies, financial firms and investors in assessing hedge and safe haven characteristics of bond markets against cryptocurrency indices. Originality/value Employing the novel methodology of AGDCC-GARCH with three different bond markets and three uncertainty indices of cryptocurrencies, the current study adds to the existing strand of literature in terms of quantifying hedge and safe-haven attributes of bond markets for cryptocurrency uncertainty indexes.
Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.
Segun Kamoru Fakunmoju, Olawale Banmore, Abiodun Gbadamosi, Olajide Idowu Okunbanjo
Digital financial trading has brought a new dimension of financial technology transactions to the globe. Cryptocurrency trading is one of the new dimensions. However, cryptocurrency trading is plagued with unlawful and monetary corrupt practices, unregulated foreign currency markets, and unknown party participants. Thus, it creates the unpredicted challenge of instigating fear in the investors’ minds and scaring away economic agents, and in turn, it adversely affects economic activities. The research investigated the effects of cryptocurrency on the performance of the Nigerian economy. The specific objective was to examine the effect of cryptocurrency trading and monetary and monetary corrupt practices on Nigerian economic performance. The research used primary data through 98 copies of the questionnaire. Tobit regression method of analysis was applied to analyze the data. The finding reveals that cryptocurrency and monetary and monetary corrupt practices have a negative but significant effect on Nigerian economic performance with marginal effects of -0,172 and -0,734 with P < 0,05 as the significance level. The research concludes that cryptocurrency and monetary corrupt practices affect Nigerian economic performance. The research recommends that the government, through the Central Bank of Nigeria (CBN), should regulate and control cryptocurrency trading by using global digital financing system software. The software will monitor and control cryptocurrency trading in Nigeria to enhance cryptocurrency trading to contribute to and increase Nigerian economic activities.
The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.
Mudassar Hasan, Muhammad Abubakr Naeem, Muhammad Arif, Syed Jawad Hussain Shahzad · 5 authors
We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271-296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time-frequency connectedness.
We consider the performance of cryptocurrencies in the light of fundamental asset pricing and portfolio theory. We observe how a traditional focus on reducing asset return volatility with Markowitz diversification misses the significance of such volatility for growth. The recognition that asset growth is more likely subject to exponential or continuously compounding growth characteristics reveals that asset volatility can be exploited both across assets and across investment periods to deliver superior returns.
In equity trading, internalization is the predominant execution method for uninformed order flow, allowing retail brokers to realize cost savings and thereby offer price improvements to customers. In cryptocurrency trading, there are doubts as to whether informed and uninformed traders can be distinguished in the same way, leading brokers to seek cost savings through internal order matching instead. Using the historical order flow of the German cryptocurrency broker BISON, we present a prediction-based approach to internal order matching: Upon receiving a customer order, our model forecasts whether future order flow will be sufficient to neutralize the order before the settlement date. With a prediction accuracy of 85%, it enables brokers to match three-quarters of order volume internally, which is three times as much as a traditional static approach, and realize meaningful cost savings, even after accounting for common minimum price improvements.
Momentum is one of the most widespread, persistent, and puzzling phenomenon in asset pricing. The prevailing explanation for momentum is that investors under-react to new information, and thus asset prices tend to drift over time. We use a unique feature of cryptocurrency markets: the fact that they are open 24/7, and report returns over the last 24 hours. Thus, the one-day return is subject to predictable fluctuations based on the removal of lagged information. We show that investors respond positively to changes in reported returns that are unrelated to any new release of information, or change in the asset fundamentals. We call this behavioral anomaly "Pure Momentum".
This study uses the DCC-GARCH model to compare the correlation between two types of cryptocurrencies in two different fields.In the context of the popularity of NFTs and the metaverse, new cryptocurrencies based on the metaverse have been favored by investors.Through empirical analysis of mana cryptocurrencies in the NFT market, we find that the new cryptocurrencies in the NFT market have high volatility to Bitcoin, Ethereum, and traditional cryptocurrencies in the past year.Therefore, we conclude that new cryptocurrencies are more likely to be one of the factors for portfolio diversification.
Cryptocurrencies are deemed to be highly influenced and driven by investors' sentiments flowing across social media platforms. Consequently, researchers are attracted to investigate investors' behavioral biases in investing in cryptocurrencies. The existing related research majorly focuses on the investigation with the implementation of questionnaires and surveys. However, to what extent the feedbacks to these questionnaires or surveys truthfully reflect the investors' actual practices in investing in cryptocurrencies is uncertain and dubious. Therefore, in this study, we inspect and appraise the behavioral biases and portfolio properties of cryptocurrency investors by utilizing the on-blockchain (on-chain) information of wallet records directly from the Ethereum network. By retrieving and analyzing the unique wallet addresses and related transactions, we have obtained three behavioral bias proxies of the investors behind the wallets and five different properties of the wallets. Furthermore, we distinguish and analyze the wallets of human investors and trading bots. The results of statistical tests indicate the significant differences between human investors and trading bots on most behavioral biases and wallet properties.
Decentralized Finance (DeFi) is a new financial infrastructure with applications similar to traditional financial products, such as exchange, lending, derivatives, and asset management. This paper empirically investigates Yearn finance, one of the fastest-growing and largest in DeFi yield aggregator protocols for on-chain asset management, to demonstrate the flow-performance relationship and compare it with mutual funds in traditional finance. According to the findings, there is a positive non-linear relationship between fund flows and recent performance for using stablecoin deposited. In contrast, we cannot find this relationship for using cryptocurrency.�Then, we look further into stablecoin holder behaviour and our findings show that, on average, they prefer the leverage strategy, which offers a chance of higher returns. Finally, we examine the event study of internal and external changes to see how investors respond. For the internal changes, the publication of deploying new strategies for both stablecoin and cryptocurrency vault does not affect investors' immediate reaction. However, only stablecoin holders have directly responded to protocol partners' announcement of the partnership�with Yearn finance for external changes.
Shimon Kogan, Igor Makarov, Marina Niessner, Antoinette Schoar
Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors.Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets.Retail traders in our sample are contrarian in stocks and gold, yet the same traders follow a momentum-like strategy in cryptocurrencies.Individual characteristics do not explain the differences in how people trade cryptocurrencies versus stocks, suggesting that our results are orthogonal to differences in investor composition or clientele effects.Furthermore, our findings are not explained by inattention, differences in fees, or preference for lotterylike stocks.We conjecture that retail investors hold a model of cryptocurrency prices, where price changes imply a change in the likelihood of future widespread adoption, which in turn pushes asset prices further in the same direction.