DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions.

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions.

Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets.

We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment and utilize it to simulate a realistic trading environment for NASDAQ equities based on historic order book messages. To train a trading agent that learns to maximize its trading return in this environment, we use Deep Dueling Double Q-learning with the APEX (asynchronous prioritized experience replay) architecture.

On Sequential Bayesian Inference for Continual Learning.

Sequential Bayesian inference can be used for to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous task's posterior as a prior for a new task can prevent catastrophic forgetting in Bayesian neural networks. Our first contribution is to perform sequential Bayesian inference using Hamiltonian Monte Carlo.

Time-series forecasting with deep learning: a survey.

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting-describing how temporal information is incorporated into predictions by each model. Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category.

Sentiment correlation in financial news networks and associated market movements.

In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period of 7 years. We investigate the propagation of such sentiment in company networks and evaluate the associated market movements in terms of stock price and volatility.

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning.

Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation.

A Direct Mapping of Max k-SAT and High Order Parity Checks to a Chimera Graph.

We demonstrate a direct mapping of max k-SAT problems (and weighted max k-SAT) to a Chimera graph, which is the non-planar hardware graph of the devices built by D-Wave Systems Inc. We further show that this mapping can be used to map a similar class of maximum satisfiability problems where the clauses are replaced by parity checks over potentially large numbers of bits. The latter is of specific interest for applications in decoding for communication.

Maximal violation of the Collins-Gisin-Linden-Massar-Popescu inequality for infinite dimensional states.

We present a much simplified version of the Collins-Gisin-Linden-Massar-Popescu inequality for the 2x2xd Bell scenario. Numerical maximization of the violation of this inequality over all states and measurements suggests that the optimal state is far from maximally entangled, while the best measurements are the same as conjectured best measurements for the maximally entangled state. For very large values of d the inequality seems to reach its minimal value given by the probability constraints.