Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/6691
Title: Pricing Behavior and Liquidity in the Cryptocurrency Market: An Empirical Analysis with Reference to Bitcoin and Altcoins
Authors: Tripathi, Bhaskar
Supervisor: Sharma, Rakesh Kumar
Keywords: Cryptocurrency;Forecasting;Altcoin;Market Liquidity;Financial Market;Blockchain Technology
Issue Date: 16-Feb-2024
Publisher: Thapar Institute of Engineering and Technology
Abstract: The thesis investigates the pricing dynamics and liquidity in the cryptocurrency market, focusing on Bitcoin and select Altcoins. It examines exchange price trends, forecasts future exchange prices, identifies the underlying factors of price formation, highlights discrepancies across various cryptocurrency exchanges, and compares liquidity of these exchanges with traditional stock exchanges. Existing cryptocurrency market literature reveals several challenges arising from significant volatility and nonlinear price dynamics. Traditional econometric and statistical methods often encounter limitations during extreme market cycles prevalent in the cryptocurrency market, owing to their inherent linear assumptions in an essentially complex market structure. While past literature suggests that machine learning and deep learning models enhance forecasting accuracy, they suffer from a lack of transparency and explainability. In the first objective of this thesis, we address these challenges by analyzing the statistical properties of cryptocurrency price trends, assessing their temporal evolution, and examining the nature of data across various market periods. Before proceeding to data preprocessing, we utilize a high-dimensional multivariate dataset to broaden the range of factors potentially influencing the prices. Subsequently, given the intricacies observed within the data, we introduce a distinctive data preprocessing mechanism. We use a combination of signal-processing techniques to enhance data quality for effective modeling. A novel three-step feature selection method is proposed within this stage, ensuring efficient dimension reduction. The primary aim of this objective is to develop a highly accurate forecasting model that is robust to different market phases and varied short-term forecasting horizons. Accordingly, this research introduces a flexible and explainable financial forecasting architecture that ensures that the resulting predictions are interpretable and tailored for volatile asset market phases. Analysis using Change Point and Multifractal Detrended Fluctuation Analysis revealed that cryptocurrency prices follow non-linear trends with extreme price changes and asymmetric price distributions. Volatility showed significant fluctuations at multiple points, and the persistence of multifractality across daily and weekly scales underscored the complex nature of cryptocurrency price movements. These insights highlighted the need for an advanced forecasting model capable of capturing these dynamics. In response, we proposed a forecasting approach, combining signal processing with hybrid neural network models that utilized fundamental and technical indicators under different market conditions. This approach demonstrated superior performance compared to the existing state-of-the-art, conclusively demonstrating that accurate cryptocurrency price prediction requires sophisticated, multifaceted modeling techniques. In the second objective of this research, we investigate the price formation factors of Bitcoin and Altcoins. While prior research primarily centers on Bitcoin, this study broadens its horizon by emphasizing major Altcoins, such as Ethereum, Ripple, Litecoin, and DASH. We also aim to understand how these Altcoins' price formation determinants differ from those of Bitcoin. Our analysis pivots on two fundamental research questions. Firstly, we aim to identify the core factors influencing Bitcoin and Altcoin prices. Secondly, we seek to ascertain how these influencing factors shift across distinct market phases and differ for each coin. Recognizing the challenge posed by varied influences on price formation, such as macroeconomic indicators, supply-demand dynamics, technological factors, and global financial trends, this study broadens the scope beyond prior research that has often been limited to a subset of these factors. Moreover, acknowledging viii the standard critique of advanced predictive models regarding their lack of interpretability, our study incorporates the SHAP (SHapley Additive exPlanations) technique. SHAP Analysis of Bitcoin and Altcoins identified key factors influencing price formation, such as blockchain metrics, global financial indicators, technical analysis tools, and public sentiment. Bitcoin's price drivers evolved from blockchain-centric in early stages to market-related and operational efficiency in later stages. For Altcoins, including Ethereum, Ripple, Litecoin, and DASH, distinct factors like Bitcoin's price, social media sentiment, and blockchain activities were significant. This highlights the specific and shifting influences in cryptocurrency price dynamics across various market periods. The third objective of this thesis focuses on identifying factors responsible for pricing inconsistencies across major cryptocurrency exchanges, namely Binance, Kraken, and Coinbase. Despite the 24/7 operations, global accessibility, and immediate access to pricing data of cryptocurrencies, actual market scenarios reveal notable pricing inconsistencies across different platforms. Despite the homogeneous nature of assets like Bitcoin and Ethereum, the market often deviates from the Law of One Price, an economic principle that suggests identical assets should have the same price across all markets. This phenomenon of price inconsistencies across different cryptocurrency exchanges, intrigues researchers and practitioners, and prompts further exploration. This research investigates the causes behind these inconsistencies, considering each exchange's unique operational characteristics, geographical locations, regulatory compliances, and market dynamics. In pursuit of this objective, the research seeks answers to two essential research questions: 1) What factors contribute to these price inconsistencies across exchanges? 2) How does the influence of these factors vary among different platforms? This analysis yields crucial findings that assist arbitrageurs, traders, and investors in making informed decisions about where to trade most profitably, ultimately enriching the knowledge base of cryptocurrency pricing dynamics. Our analysis identified key drivers of these discrepancies, including bid-ask spread, social media influence, especially Twitter sentiment, and various technical indicators like maker fees and Google Trends data. Each exchange displayed distinct influencing factors: Kraken's prices were significantly impacted by bid-ask spread and maker fees, Coinbase Pro's by Google Trends and Williams %R, and Binance's by transaction volume and market sentiment. This variation in influential factors across platforms illustrates the unique market dynamics and user behaviors characterizing each exchange, offering crucial insights for understanding pricing mechanisms in the cryptocurrency landscape. The fourth objective of this thesis examines the liquidity of cryptocurrency exchanges compared to traditional stock markets. Given the integral role of liquidity in ensuring market efficiency and stability, it becomes essential to understand how cryptocurrency exchanges fare in relation to traditional stock markets. Few studies in past literature directly compare the liquidity dynamics of cryptocurrency platforms and traditional stock markets. We employ the Martin Liquidity Index (MLI) as the baseline measure for our comparative analysis. Alongside, we evaluate liquidity through four other established measures: Amihud's Illiquidity Ratio (AIR), AR Bid-Ask Spread, Roll's Covariance Liquidity Estimator, and the CS spread estimator. This multifaceted approach guides investment decisions and sets foundational liquidity benchmarks for the evolving cryptocurrency market. We specifically address two primary questions: 1) How does liquidity in traditional stock markets, as measured by the MLI, compare to major cryptocurrency exchanges? 2) Which of the leading cryptocurrency exchanges exhibits the highest liquidity, according to the ix MLI? The investigation aims to enhance understanding of liquidity dynamics, providing a framework to measure the maturation and effectiveness of cryptocurrency exchanges. This research aims to identify the most liquid cryptocurrency exchanges and assess how their liquidity compares to traditional stock exchanges, determining if they are more, equally, or less liquid. It demonstrates a consistent method for evaluating their market efficiency relative to traditional stock indices. The investigation, revealed that traditional stock markets, including NYSE, NASDAQ, NIFTY, and BSE SENSEX, generally have higher liquidity than major cryptocurrency exchanges. Among the examined cryptocurrency exchanges, Binance displayed the highest liquidity levels. This analysis confirms that traditional stock markets surpass cryptocurrency exchanges in liquidity, with Binance leading among the latter. Overall, this research deepens our understanding of cryptocurrency markets by investigating pricing dynamics, forecasting, liquidity, and exchange discrepancies. Firstly, we present a flexible architecture designed for price forecasting using a high-dimensional, multivariate dataset. This architecture handles the non-linearity in cryptocurrency prices by employing advanced data pre- processing and signal-processing methods. Additionally, by integrating both technical and fundamental analysis, it efficiently predicts prices. Secondly, the study identifies key determinants that influence cryptocurrency prices. By understanding these factors, stakeholders can make more informed decisions, reducing the inherent risks associated with investments in this domain. Thirdly, we identify the factors for discrepancies in pricing across recognized cryptocurrency exchanges, Binance, Kraken, and Coinbase, and point to market inefficiencies, offering avenues for systematic arbitrage. Finally, by systematically comparing cryptocurrency exchanges' liquidity metrics with traditional stock markets, this research offers insights into both markets' relative liquidity and efficiency. This helps investors estimate the risk and return dynamics more proficiently. Together, these objectives enhance our understanding of cryptocurrency markets and provide tangible benefits to various stakeholders, from individual investors to financial institutions, practitioners, and academic researchers engaged in cryptocurrency market studies. The findings and outcomes of this work implications for stakeholders - investors, traders, researchers, and policymakers, aiding in strategic decision-making processes.
URI: http://hdl.handle.net/10266/6691
Appears in Collections:Doctoral Theses@SHSS

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