Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly …

We survey the recent literature on learning in financial markets. Our main theme is that many financial market … uncertain and subject to learning. We discuss phenomena related to the volatility and predictability of asset returns, stock …

We survey the recent literature on learning in financial markets. Our main theme is that many financial market … uncertain and subject to learning. We discuss phenomena related to the volatility and predictability of asset returns, stock …

Prediction of financial market data with deep learning models has achieved some level of recent success. However … learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation … this study we propose a framework for training deep reinforcement learning models in agent based artificial price …

The dissertation consists of three chapters, each focusing on a different application of learning in financial markets …-term economic outlook (the Fed information channel). The last chapter presents an interesting case of memory decay in learning about … exponential rate. Learning leads to endogenous boom-bust cycles and optimal hedging demands in asset allocation problems. …

-reaction occurs, thus rational agents may strategically want to bias their learning process. Our analysis points out that the …