Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Authors: Lakshay Chauhan, John Alberg, Zachary Lipton
ICML 2020 | Venue PDF | 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 <EMAIL>, John Alberg <EMAIL>, Zachary Lipton <EMAIL>. |
| 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). |