Name
Title | ||
|---|---|---|
Lazy Network: A Word Embedding-Based Temporal Financial Network to Avoid Economic Shocks in Asset Pricing Models |
Abstract | ||
|---|---|---|
Public companies in the US stock market must annually report their activities and financial performances to the SEC by filing the so-called 10-K form. Recent studies have demonstrated that changes in the textual content of the corporate annual filing (10-K) can convey strong signals of companies' future returns. In this study, we combine natural language processing techniques and network science to introduce a novel 10-K-based network, named Lazy Network, that leverages year-on-year changes in companies' 10-Ks detected using a neural network embedding model. The Lazy Network aims to capture textual changes derived from financial or economic changes on the equity market. Leveraging the Lazy Network, we present a novel investment strategy that attempts to select the least disrupted and stable companies by capturing the peripheries of the Lazy Network. We show that this strategy earns statistically significant risk-adjusted excess returns. Specifically, the proposed portfolios yield up to 95 basis points in monthly five-factor alphas (over 12% annually), outperforming similar strategies in the literature. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1155/2022/9430919 | COMPLEXITY |
DocType | Volume | ISSN |
Journal | 2022 | 1076-2787 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Adosoglou | 1 | 0 | 1.01 |
| Seonho Park | 2 | 0 | 0.34 |
| Gianfranco Lombardo | 3 | 9 | 5.27 |
| Stefano Cagnoni | 4 | 1096 | 155.20 |
| Panos M. Pardalos | 5 | 141 | 19.60 |