Browsing by Subject "Cryptocurrency"

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Cryptocurrency Market Anomaly: The Day-of-the-Week-Effect
(Indian Institute of Finance, 2023-03-31T00:00:00) Verma, Ruchita; Sharma, Dhanraj; Sam, Shiney
Cryptocurrency has emerged as a fad amongst investors, academicians and policy-makers as a financial asset, making it important to empirically test the price behaviour of this emerging market. This paper is designed to investigate the presence of a well-known day-of-the-week effect in the young and emerging cryptocurrency market returns from August 2015 to March 2019. Using varied statistical techniques, this anomaly is examined for six cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Stellar and Tether). The study applies both parametric and non-parametric statistical tests, i.e.,Bar Graph, Heat map, Student�s t-test, Analysis of Variance (ANOVA),regression analysis with dummy variables and the Kruskal Wallis Test. The study�s findings show that no sample cryptocurrency returns exhibit the day-of-the-week effect phenomenon.The statistically insignificant result of the day-of-the-week effect in thecryptocurrency returns showcases the evidence of market efficiency in the cryptocurrency market. � Indian Institute of Finance.
Does Google Trend Affect Cryptocurrency? An Application of Panel Data Approach
(SCMS Group of Educational Institutions, 2023-04-03T00:00:00) Verma, Ruchita; Sam, Shiney; Sharma, Dhanraj
Cryptocurrency has emerged globally as the most profitable investment asset of the decade. The media exposure and reportage on cryptocurrency are frequent, and it seems that prices of cryptocurrencies could only rise higher. In today's digital world, any individual's first go-to information-seeking platform is the Google search engine. Thus, it is imperative to understand how Google's search trend affects an investable asset and its market as a whole. Researchers have explored varied sentiment measurement proxies such as news coverage, Facebook and Twitter posts, and, most importantly, Google searches. Numerous research studies show increasing interest in Google search volume and its predictive ability to understand investment returns and economic outcomes. In a behavioural finance context, the present research uses Pearson's correlation and panel regression to examine the association of cryptocurrency returns (Bitcoin, Ethereum, and Ripple) and their varied characteristics with the Google search intensity. The study's findings reveal that investors searching for information on Cryptocurrency online drive the price increase in cryptocurrency and push the trading volume up and increase the volatility of the cryptocurrency returns. Furthermore, investor sentiment has a statistically significant impact on cryptocurrencies' trading volume and weekly volatility in periods of high or greedy investor sentiment. The findings imply that the 'price pressure hypothesis' given by Barber and Odean (2008) as a stock market research finding is also present in the cryptocurrency market. � 2023 SCMS Group of Educational Institutions. All rights reserved.
OG-CAT: A Novel Algorithmic Trading Alternative to Investment in Crypto Market
(Springer, 2023-03-28T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
Cryptocurrencies have emerged as a good tool for investment/trading in the last decade. The investors have achieved promising gains with the long-term investments made at reasonably good price/time. However, investment in cryptocurrencies is also exposed to extremely high volatility. Due to this, the investment may suffer from a high drawdown as the price may fall. In this work, we proposed optimized Greedy-cost averaging based trading (OG-CAT) a novel trading framework as an alternative to long-term investment in cryptocurrencies. The approach exploits the wavy structure of the price movement of cryptocurrencies, the high volatility of price, and the concept of cost averaging. Furthermore, the parameters of the approach are optimized with the simulated annealing algorithm. The approach is evaluated on the two prominent cryptocurrencies: bitcoin and ethereum. During the evaluation, OG-CAT not only outperformed the buy-and-hold investment approach in terms of profit but also demonstrated a lower drawdown. The profit percentage in the case of trading BTC with OG-CAT is 1.63 times more and the max drawdown is 1.62 times less than compared to the buy-and-hold strategy. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Revolutionize AI Trading Bots with AutoML-Based Multi-timeframe Bitcoin Price Prediction
(Springer, 2023-06-27T00:00:00) Khurana, Surinder Singh; Singh, Parvinder; Garg, Naresh Kumar
Multi-timeframe analysis/prediction provides essential information to traders. It gives a broader perspective of market trends and is used to identify significant levels of support and resistance. This will help traders/trading bots in making trading decisions. The majority of current studies focused on forecasting the closing price of daily candlesticks or high-frequency time frames, such as those of 1�min or 5�min. For artificially intelligent trading bots focusing on swing trading, price prediction related to other time frames is very significant. In this research, we present a study on developing a model to enable artificial intelligent-based trading bots to predict price components (open, high, low, and close prices) of the next 30-min, 1-h, and 4-h candlesticks of Bitcoin price. The study used two Auto-Machine Learning libraries: Tree-Based Pipeline Optimization Tool (TPOT) and AutoSklearn, to find the most suitable model for the task. The models are trained on historical price data of Bitcoin, and technical indicators are computed on these data. The performance of the trained models is evaluated in terms of R2 Score (Coefficient of Determination), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that TPOT outperformed AutoSklearn library for all three time frames. It predicted all price components of 30-min candlestick with R2 Score of 0.999. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Testing of Random Walk Hypothesis in the Cryptocurrency Market
(Sage Publications India Pvt. Ltd, 2022-05-30T00:00:00) Verma, Ruchita; Sharma, Dhanraj; Sam, Shiney
Cryptocurrency as a financial asset has emerged as a fad among investors, academicians and policymakers alike. In a financial purview, this study intends to empirically test the behaviour of the cryptocurrency return, inferring its market efficiency. For this purpose, daily data of five cryptocurrencies (Bitcoin, Ethereum, Litecoin, Tether and Ripple) have been collected from 1 January 2016 to 31 March 2021 to investigate the well-known financial theory of random walk hypothesis for this young market. To provide statistical evidence and ensure the robustness of results, analysis is performed using the variance ratio test, augmented Dickey�Fuller test, Philip�Perron test, Breusch�Godfrey serial correlation LM test and ARIMA model. The statistical results illustrated strong evidence refuting the presence of the random walk hypothesis in this emerging market, thus implying inefficiency in the cryptocurrency market. Furthermore, the absence of random walk in the cryptocurrency makes this financial asset predictable, giving investors an arbitrage edge to earn abnormal gains using trading strategies, which is euphoria. � 2022 Fortune Institute of International Business.