Publications
Selected papers in algorithmic trading, machine learning, and market microstructure.
Bitcoin: A Peer-to-Peer Electronic Cash System
Satoshi Nakamoto
A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work.
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar +5 more
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
Forecasting Financial Time Series Using LSTM Networks
Enoch Nii Boi Quansah, Guohua Chen
In this paper, we investigate the application of Long Short-Term Memory (LSTM) recurrent neural networks for financial time series forecasting. We compare LSTM networks against traditional statistical models including ARIMA and classical machine learning approaches. Our experiments on equity and cryptocurrency markets demonstrate that LSTM networks capture temporal dependencies and non-linear patterns more effectively, yielding improved directional accuracy and reduced mean-squared error across multiple prediction horizons.
Momentum Strategies in Cryptocurrency Markets
Ester Felez-Vinas, Brian Lucey, Samuel Vigne
We study the profitability of momentum strategies in cryptocurrency markets across a broad cross-section of assets. Using data spanning over 200 cryptocurrencies, we document significant momentum effects at daily and weekly horizons. Cross-sectional momentum portfolios generate substantial risk-adjusted returns that are robust to transaction costs and alternative weighting schemes. We further examine the role of liquidity, volatility, and market microstructure in driving these momentum premia.