The cryptocurrency market has become increasingly accessible and significant to the financial markets. This is understood by not only major financial firms, governments, and investors, but also the individual market participants globally. We delve into the history of cryptocurrency to begin our examination of the Bitcoin, Ethereum and Litecoin. Understanding the circumstances of their humble beginning, the purpose it served, and the path of their evolution, helps us to create a fuller understanding of its functions, its limitations, and the drivers of its value. This enables us to identify key market factors and variables for deployment within a robust approach for pricing and product offerings associated with Bitcoin, Ethereum and Litecoin. In order to fully capture the volume, variety, and velocity of data associated with these cryptocurrencies, the use of machine learning can provide an advantageous approach to model development for cryptocurrency pricing. This paper provides the development of a promising initial prototype pricing model for Bitcoin, Ethereum and Litecoin. Our proposed pricing models resulted in an average 7% difference between actual and predicted price for Bitcoin and Ethereum, and a 4% difference for Litecoin along a timeline, through the use of machine learning and deep learning, artificial neural networks using the contributing factors of key variables and how they influence and capture pricing and investor behaviour. We also identify theinclusion of additional datasets, such as sentiment market data into the model, along with larger exploration of Blockchain and raw transaction mining to increase the accuracy and forecasting ability of the model.
Julia Reynolds, Leopold SSgner, Martin Wagner, Dominik Wied
This paper applies recently developed procedures to monitor and date so-called "financial marketdislocations", defined as periods in which substantial deviations from arbitrage parities take place. In particular, we focus on deviations from the triangular arbitrage parity for exchange rate triplets from a cointegration perspective. Due to increasing attention on and importance of mispricing in the market for cryptocurrencies, we include the cryptocurrency Bitcoin in addition to fiat currencies. We do not find evidence for substantial deviations from the triangular arbitrage parity when only traditional fiat currencies are concerned, but document significant deviations from triangular arbitrage parities in the newer markets for Bitcoin. We confirm the importance of our results for portfolio strategies by showing that a currency portfolio that trades based on our detected break-points outperforms a simple buy-and-hold strategy.
Distributed ledger technologies replace central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows down cross-market trading and exposes arbitrageurs to price risk. We theoretically derive arbitrage bounds induced by settlement latency. Using Bitcoin orderbook and network data, we estimate average arbitrage bounds of 121 basis points, explaining 91% of the cross-market price differences, and demonstrate that asset flows chase arbitrage opportunities. Controlling for inventory holdings as a measure of trust in exchanges does not affect our main results. Blockchain-based settlement without trusted intermediation thus introduces a non-trivial friction that impedes arbitrage activity.
This paper examines the market for initial coin offerings (ICOs). ICOs are smart contracts based on blockchain technology that are designed for entrepreneurs to raise external finance by issuing tokens without an intermediary. Unlike existing mechanisms for early-stage finance, tokens potentially provide investors with rapid opportunities thanks to liquid trading platforms. The marketability of tokens offers novel insights into entrepreneurial finance, which I explore in this paper. First, I document that investors earn on average 8.2% on the first day of trading. However, about 40% of all ICOs destroy investor value on the first day of trading. Second, I explore the determinants of market outcomes and find that management quality and the ICO profile are positively correlated with the funding amount and returns, whereas highly visionary projects have a negative effect. Among the 21% of all tokens that get delisted from a major exchange platform, highly visionary projects are more likely to fail, which investors anticipate. Third, I explore the sensitivity of the ICO market to adverse industry events such as China's ban of ICOs, the hack of leading ledgers, and the marketing ban on FaceBook. I find that the ICO market is highly susceptible to such environmental shocks, resulting in substantial welfare losses for investors.
Alexander Brauneis, Roland Mestel, Ryan Riordan, Erik Theissen
We study trading of Bitcoin against US dollar (BTCUSD) on exchanges in three continents, Bitfinex, Bitstamp and Coinbase Pro. We use a high frequency dataset that contains transactions and order book information. The BTCUSD market is highly liquid in terms of bid-ask spreads and order book depth. While spreads are even lower than in equity markets, prices are not integrated across exchanges. Persistent differences exist between the three exchanges in terms of trade prices and posted prices often violating no-arbitrage assumptions. The liquidity of the Bitcoin exchanges is predominantly determined by local factors and is essentially independent of liquidity in equity and FX markets. This suggests that despite the virtual nature of Bitcoin, local jurisdictional factors affect the flow of capital between low and high price jurisdictions.
We examine price discovery and liquidity provision in the secondary market for bitcoin—an asset with a high level of speculative trading. Based on BTC-e’s full limit order book over the 2013–2014 period, we find that order informativeness increases with order aggressiveness within the first 10 tiers, but that this pattern reverses in outer tiers. In a high volatility environment, aggressive orders seem to be more attractive to informed agents, but market liquidity migrates outward in response to the information asymmetry. We also find support to the Markovian learning assumption often made in theoretical models of limit order markets.
Following the popularity of Bitcoin trading in recent years, Bitcoin futures were introduced in December 2017 as an effort to provide institutional and retail investors with additional trading tools for Bitcoin. This study analyses the Bitcoin futures mid-quote data from CBOE, and Bitcoin market index applying VAR and VECM process methodologies, Hasbrouck’s information share and the Gonzalo-Granger component share measurement to examine price discovery in Bitcoin markets. Furthermore, the chapter seeks to assess the Bitcoin market microstructure. The results drawn on the intra-day prices show that the futures are leading the price discovery at different frequencies even with comparably low futures trading volumes. This supports the extant literature of futures-spot market price discovery and the role of informed traders in the futures market.
Abstract This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.