Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. An excellent example of the use of this "wisdom of crowds" is a prediction market. The purpose of our Twitter-based prediction market is to suggest that carefully designed market...

This paper studies the effects of information exchange and social networks on the performance of prediction markets with endogenous information acquisition. We provide a game-theoretic framework to resolve the question: Can social networks and information exchange promote the forecast efficiency...

Stock market variation data is collected in the form of breaking news from various business websites. The stock market trend changes with key financial reforms, weather conditions, and political events. The dataset is created using financial news text data. The dataset features consist of TF-IDF...

Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations...

We compare forecasts from different adaptive learning algorithms and calibrations applied to US real-time data on inflation and growth. We find that the Least Squares with constant gains adjusted to match (past) survey forecasts provides the best overall performance both in terms of forecasting...

Behavioral and experimental literature on financial instability focuses on either subjective price expectations (Learning-to-Forecast experiments) or individual trading (Learning-to-Optimize experiments). Bao et al. (2018) have shown that subjects have problems with both tasks. In this paper, I...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...