Large (and Deep) Factor Models
<|reference_start|>Large (and Deep) Factor Models: 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 large factor model (LFM): A linear factor pricing model that uses many non-linear characteristics. The nature of these characteristics depends on the architecture of the DNN in an explicit, tractable fashion. This makes it possible to derive end-to-end trained DNN-based SDFs in closed form for the first time. We evaluate LFMs empirically and show how various architectural choices impact SDF performance. We document the virtue of depth complexity: With enough data, the out-of-sample performance of DNN-SDF is increasing in the NN depth, saturating at huge depths of around 100 hidden layers.<|reference_end|>
@article{kelly2024large,
    title={Large (and Deep) Factor Models},
    author={Bryan Kelly, Boris Kuznetsov, Semyon Malamud, Teng Andrea Xu},
    journal={arXiv preprint arXiv:2402.06635},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06635},
    primaryClass={q-fin.ST cs.CE cs.LG}
}