Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Authors: Lakshay Chauhan, John Alberg, Zachary Lipton
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In retrospective analysis, we leverage an industry-grade portfolio simulator (backtester) to show simultaneous improvement in annualized return and Sharpe ratio. Specifically, the simulated annualized return for the uncertainty-aware model is 17.7% (vs 14.0% for a standard factor model) and the Sharpe ratio is 0.84 (vs 0.52). |
| Researcher Affiliation | Collaboration | 1Euclidean Technologies, Seattle, USA 2Carnegie Mellon Uni versity, Pittsburgh, USA 3Amazon AI, Seattle, USA. Correspondence to: Lakshay Chauhan <lakshay.chauhan@euclidean.com>, John Alberg <john.alberg@euclidean.com>, Zachary Lipton <zlipton@cmu.edu>. |
| Pseudocode | No | The paper describes the 'simulation algorithm' in paragraph form within Section 5 but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper states: 'Our features consist of reported financial information as archived by the Compustat North America and Compustat Snapshot databases.' Compustat is a commercial database, not a publicly available or open dataset. |
| Dataset Splits | Yes | Data in the in-sample period range from Jan 1, 1970 to Dec 31, 1999 (1.2M data points), while out-of-sample test data range from Jan 1, 2000, to Dec 31, 2019 (1M data points). ... we hold out a validation set by randomly select ing 30% of the stocks from the in-sample period. |
| Hardware Specification | Yes | It took 150 epochs to train an ensemble on a machine with 16 Intel Xeon E5 cores and 1 Nvidia P100 GPU. |
| Software Dependencies | No | The optimizer Ada Delta (D. Zeiler, 2012) is used with an initial learning rate of 0.01. ... Glorot Uniform Intialization (Glorot & Bengio, 2010)... batch normalization (Ioffe & Szegedy, 2015). While specific optimizers and normalization techniques are mentioned, no version numbers for software frameworks (e.g., TensorFlow, PyTorch) or the optimizer itself are provided. |
| Experiment Setup | Yes | Table 1. MLP, LSTM Hyperparameters: Batch Size 256, Hidden Units 2048 (MLP) / 512 (LSTM), Hidden Layers 1, Dropout 0.25 (MLP) / 0.0 (LSTM), Recurrent Dropout n/a (MLP) / 0.25 (LSTM), Max Gradient Norm 1, Max Norm 3, α1 0.75 (MLP) / 0.5 (LSTM), α2 n/a (MLP) / 0.7 (LSTM). |