agent they consider is characterized by an imperfect learning model. An interesting question that emerges is if, and to what …

misspecified models. Under model misspecification, standard Bayesian learning loses its formal justification and biased learning … processes may provide a selection advantage. However, considering two cases of model misspecification and four learning … processes, our analysis reveals a general difficulty in ranking learning behaviors with respect to their long-run performances …

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 …