COB Faculty Works
Permanent URI for this collectionhttps://hdl.handle.net/1969.6/87079
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Browsing COB Faculty Works by Subject "Bitcoin"
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Item Cryptocurrency risks, fraud cases, and financial performance(2023-02-23) Kerr, David; Loveland, Karen; Smith, Katherine; Smith, Lawrence MurphyIn this study, we examine major cryptocurrencies, present notable fraud cases, describe fraud risks, and analyze cryptocurrency financial performance. People debate whether cryptocurrency is an investment opportunity, the new Dutch Tulip Bubble, or a giant Ponzi scheme. There have been a number of high-profile fraud cases associated with cryptocurrencies, such as the FTX scandal in late 2022, thereby making fraud a real concern to current and potential future investors. Regarding financial performance, cryptocurrencies experienced a major collapse in value in the most recent period of the study, about three times worse than the major stock market indices. While in prior periods, cryptocurrencies have significantly outperformed stock market indices, recent fraud cases and the extreme volatility of cryptocurrencies indicate that investing in cryptocurrencies comes with much higher risk than traditional stock market investments. The debate over the investment potential of cryptocurrencies continues, whether they have long term value or are simply the new Dutch Tulip Bubble. The study’s findings will be useful to investors, regulators, and academic researchers regarding the cryptocurrency industry.Item Cryptocurrency trading using machine learning: A Technical note(8/10/2020) Koker, Thomas E.; Koutmos, DimitriosWe present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.Item Forecasting Bitcoin volatility using hybrid GARCH models with machine learning(2022-12-13) Zahid, Mamoona; Iqbal, Farhat; Koutmos, DimitriosThe time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.