Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Malamud, Semyon"'
We open up the black box behind Deep Learning for portfolio optimization and prove that a sufficiently wide and arbitrarily deep neural network (DNN) trained to maximize the Sharpe ratio of the Stochastic Discount Factor (SDF) is equivalent to a larg
Externí odkaz:
http://arxiv.org/abs/2402.06635
The recent discovery of the equivalence between infinitely wide neural networks (NNs) in the lazy training regime and Neural Tangent Kernels (NTKs) (Jacot et al., 2018) has revived interest in kernel methods. However, conventional wisdom suggests ker
Externí odkaz:
http://arxiv.org/abs/2301.11414
Recent progress in Generative Artificial Intelligence (AI) relies on efficient data representations, often featuring encoder-decoder architectures. We formalize the mathematical problem of finding the optimal encoder-decoder pair and characterize its
Externí odkaz:
http://arxiv.org/abs/2210.00637
We introduce a methodology for designing and training deep neural networks (DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE ha
Externí odkaz:
http://arxiv.org/abs/2203.05417
Autor:
Glebkin, Sergei1 (AUTHOR) glebkin@insead.edu, Malamud, Semyon2 (AUTHOR), Teguia, Alberto3 (AUTHOR)
Publikováno v:
Review of Financial Studies. May2023, Vol. 36 Issue 5, p2131-2173. 43p.
Autor:
Malamud, Semyon, Schrimpf, Andreas
How should an agent (the sender) observing multi-dimensional data (the state vector) persuade another agent to take the desired action? We show that it is always optimal for the sender to perform a (non-linear) dimension reduction by projecting the s
Externí odkaz:
http://arxiv.org/abs/2110.08884
We study the general problem of Bayesian persuasion (optimal information design) with continuous actions and continuous state space in arbitrary dimensions. First, we show that with a finite signal space, the optimal information design is always give
Externí odkaz:
http://arxiv.org/abs/2102.10909
Autor:
KELLY, BRYAN (AUTHOR) bryan.kelly@yale.edu, MALAMUD, SEMYON (AUTHOR), ZHOU, KANGYING (AUTHOR)
Publikováno v:
Journal of Finance (John Wiley & Sons, Inc.). Feb2024, Vol. 79 Issue 1, p459-503. 45p.
Autor:
Glebkin, Sergei1 sergei.glebkin@insead.edu, Malamud, Semyon2 semyon.malamud@epfl.ch, Teguia, Alberto3 alberto.mokakteguia@sauder.ubc.ca
Publikováno v:
INSEAD Working Papers Collection. 2023, Issue 65, p1-83. 84p.
Autor:
Eren, Egemen, Malamud, Semyon
Publikováno v:
In Journal of Financial Economics May 2022 144(2):571-589