The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there. Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.
Abstract This study investigates whether gold, USD, and Bitcoin are hedge and safe haven assets against stock and if they are useful in diversifying downside risk for international stock markets. We propose a combined GO‐GARCH‐EVT‐copula approach to examine the hedge and safe haven properties of gold, USD, and Bitcoin. We then examine the attractiveness of these assets in reducing stock portfolio risk by using downside risk measures estimated by the proposed approach and other competing models. We also evaluate the relative performance of the proposed model in reducing downside risk with the competing models. The findings of the study indicate that the USD is the most valuable hedge and safe haven asset closely followed by gold, while Bitcoin is the least valuable. It is also observed that the proposed combined approach performs best in reducing the portfolio downside risk. The findings of this study are of significance for portfolio managers and individual investors who wish to protect the portfolio value during market turmoil.
Bu çalışmanın amacı COVID-19 pandemi döneminde kripto para fiyatlarında balon oluşup oluşmadığının araştırılmasıdır. Bu amaçla piyasa değeri en yüksek 3 kripto para olan Bitcoin (BTC), Ethereum (ETH) ve Binance Coin (BNB) fiyatlarına ilişkin, 10/03/2020-06/07/2021 tarihlerini kapsayan veri seti GSADF testiyle analiz edilmiştir. Yapılan analizler sonucunda incelenen her üç kripto paranın da fiyatlarında balon olduğu tespit edilmiştir. Buna ek olarak verileri analiz edilen kripto paralarda tespit edilen fiyat balonlarının dönemlerinin benzer olması, balon tespit edilen dönemlerde piyasanın tamamını etkileyen fiyat hareketleri olduğu yönünde güçlü kanıtlar sunmuştur. Çalışmanın düzenleyici otoriteler ve yatırımcılar açısından önemli sonuçlar ortaya koyduğu düşünülmektedir. COVID-19 pandemisi ya da piyasalar üzerinde benzer etkiler yapabilecek finansal kriz ortamlarında yatırımcılar oluşabilecek fiyat balonlarına dikkat etmeli ve yatırım kararlarında bu durumu göz önünde bulundurmalıdır. Son olarak finansal piyasaları düzenleyici taraflar söz konusu dönemlerde yatırımcıları oluşabilecek olumsuz durumlardan korumak adına gerekli adımları atmalıdırlar.
This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
Cryptocurrencies show tremendous growth by market capitalization, however Bitcoin cross-country holdings are still in question. The purpose of the paper is to show that inflation discontent with the rule of law failures can explain why residents of different countries are prone to cryptocurrency holdings. The level of financial development is also considered. A hypothesis is proposed for more complex and segmented motives of Bitcoin holdings, tested by the OLS method. Single- and multi-factor regressions with independent variables are used, which can validate cross-country Bitcoin holdings in terms of inflation discontent, quality of institutions and financial development. Regression results confirm the idea of more segmented motives to hold Bitcoins. First, the hedge against inflation motive is rooted in the institutional weakness of central banks, and the regression results show that inflation variables are the most significant. Second, the hedge against institutional risks of asset ownership motive, based on the lack of rule of law and the relevant variable, is best performing among other institutional variables. Third, it is wrong to neglect financial development. However, it only plays a role in interaction with better innovation performance, meaning that crypto investors try not only to diversify their portfolios, but also to profit from involving in a sector with promising technological perspectives. The main takeaway is that institutional factors help explain why people in countries with worsened inflation and institutional performance tend to hold a large fraction of Bitcoins in assets. Obviously, monetary and institutional fragility is underestimated in the general discussion about the nature of digital money.
In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.
Purpose Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy. Design/methodology/approach This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness. Findings Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy. Originality/value To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.
The globally acknowledged accelerating crypto hype has put a lot of work on researchers and analysts. This new marvel of digital currencies requires a lot of attention, for it to become conventional worldwide. Virtual coins or cryptographic forms of money utilize encryption framework, so called the Block Chain technology, that manage the formation and supply of coins and exchanges must be recognized from a financial examination point of view. Subsequently, it is essential to inspect which social, money related & macroeconomic components decide its cost with a specific end goal to know the degree and outcomes of the economy. This paper aims to study different internal and external factors that affect cryptocurrencies’ prices. A sample of four digital coins with largest market capitalization has been selected. Daily price data from the years 2015 to 2020 of Bitcoin with other altcoins such as Ethereum, Ripple and Litecoin has been taken. Internal factors consist of demand and supply variables and also the attractiveness associated with its increasing hype. Other factors include KSE-100 Index (Karachi Stock Exchange), USD-PKR (Dollar to Pakistani Rupee) exchange rate and oil prices from PSO (Pakistan State Oil). ARDL analysis has been done to study the effect of these factors on the prices of crypto coins. Our analysis shows that circulating supply has a significant effect on Ethereum and Ripple prices in the long run. Attractiveness has been significant on the prices of Ethereum only.
The decentralization of cryptocurrency has decreased the level of central control, which has impacted international trade and ties. There is also an urgent need for a credible way of projecting the price of cryptocurrencies, which is currently unavailable. A novel method to predict cryptocurrency price is proposed in this paper, which makes use of deep learning techniques such as the recurrent neural network (RNN), gated recurrent unit (GRU), convolution 1D, and the long short-term memory (LSTM). This method considers a variety of factors such as market capitalization, volume, circulating supply, and maximum supply. It is more accurate at recognizing long-term relationships than the LSTM. Developed in Python, the proposed approach was tested on a range of real-world data sets. The findings demonstrate that the proposed method may be used to properly predict the price of cryptocurrencies.
Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens
Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisor’s performance during equity market downturn. Finally, The Robo-Advisor’s actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisor’s portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisor’s portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.
Kripto paralar artan dijitalleşme ve merkeziyetsiz finans düşüncesinin bir ürünü olarak ortaya çıkmış ve yeni projelerle birlikte büyümeye devam etmektedir. Kripto paralar, alım veya satım gibi her türlü işlemlerde kullanılmasının yanı sıra değişim aracı ve yatırım aracı olarak da kullanılabilmektedir. Ayrıca madencilik yoluyla söz konusu para birimi üretimi yapılabilmektedir. Kripto para denilince ilk olarak ortaya çıkan ve tüm kripto paraların öncüsü olarak kabul edilen Bitcoin, piyasa hacmi açısından da en yüksek kripto paradır. Bu bağlamda çalışmamızın amacı, piyasanın öncül parası olan Bitcoin ve kısaca “Altcoin” diye ifade ettiğimiz diğer kripto paralar arasındaki ilişkiyi incelemektir. İlişkiyi incelemek için zaman serisi analiz yöntemi kullanılarak 01.01.2018-31.12.2020 dönemi günlük veriler Trandingview ve Coinmarketcup aracılığıyla toplanmış ve Johansen eşbütünleşme testi, Vektör Hata Düzeltme Modeli (VECM) ve Granger nedensellik testi yapılmıştır. Johansen eşbütünleşme sonucuna göre ele alınan dönemlerde kullanılan değişkenler arasında uzun dönemli bir ilişki bulunmaktadır. Granger nedensellik sonucuna göre ise Cardona'dan Bitcoin'e, Bitcoin'den Etheryum'a ve Cardano'dan Binance Coin' doğru tek yönlü nedensellik tespit edilmiştir.
We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.