Summary of Storm: a Spatio-temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders For Financial Trading, by Yilei Zhao et al.
STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading
by Yilei Zhao, Wentao Zhang, Tingran Yang, Yong Jiang, Fei Huang, Wei Yang Bryan Lim
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new Spatio-Temporal factOR Model (STORM) that uses dual vector quantized variational autoencoders to extract features from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level. This approach allows for multiple factors to be represented as multi-dimensional embeddings, which ensures orthogonality and diversity, enabling factor selection in financial trading. The model is applied to two downstream experiments: portfolio management on two stock datasets and individual trading tasks on six specific stocks, showing superior performance over baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to understand how stocks move by combining information about the past and present. It uses special math tools called variational autoencoders to get a better picture of what’s happening with different stocks. This helps make predictions about which stocks will do well in the future, which is useful for people who want to invest their money wisely. The new approach works better than older methods at helping people pick good investments and avoid bad ones. |
