1800s that shapes the number of peers that a firm can learn from today. The strategic learning incentive is most salient …

optimal way to learn about the returns to scale. The optimal learning is also shown to produce overvaluation. The model is …

competing approaches used in the literature. Besides taking learning into account, it does not rely on any conditioning …

machine learning algorithm over the period 2008-2017, we construct three important uncertainty indices underlying reports in …

equilibrium prices and portfolios are obtained in closed form. Learning from option prices is characterized explicitly in terms of …

dependencies and frame semantic parses into graphs. Feature engineering is folded into the learning through convolution graph … kernel learning to explore different extents of the graph. A high-dimensional space of features includes individual nodes to …

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists … as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can … a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns …

Information-based reinforcement learning is effective for trading and price discovery in limit order markets. It helps …-run momentum of the informed market orders. The learning increases liquidity supply of uninformed and liquidity consumption of … price discovery. The results shed a light into the market practice of using machine learning in limit order markets …

This paper studies how acquisition of non-fundamental information (learning about noise) affects financial markets. We … heterogeneous quality and who optimally allocate learning capacity between fundamentals and noise. We demonstrate that learning … (imprecise) compared to their prior non-fundamental information learn only about fundamentals (noise) and ii) learning about …