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An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics
In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data.
Error in the Euclidean Preference Model
Spatial models of preference, in the form of vector embeddings, are learned by many deep learning and multiagent systems, including recommender systems.
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning
Its convenience led to an exponentially increasing amount of financial data, which is however hard to use for the prediction of future prices, due to the low signal-to-noise ratio and the non-stationarity of financial time series.
New Results on Equilibria in Strategic Candidacy
For four candidates, the message is, roughly, that most scoring rules (with the exception of Borda) do not guarantee the existence of a pure Nash equilibrium but that Condorcet-consistent rules, for an odd number of voters, do.
