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.

Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks

“…The stock market is dynamic with new companies becoming public every year, changing properties and names, or being removed or suspended from the market as a result of acquisitions or regulatory action. For this reason, we consider the same companies used in [6,37] since this set of firms has been proved to be a fair approximation of the American stock market for each year in that time interval. According to the Security Exchange Commission (SEC), a company in the American market is considered bankrupt in two cases:…”

“…Despite that different research works have already demonstrated the ability of machine learning (ML) to assess the likelihood of companies' default, making a fair comparison among all the proposed approaches in the literature remains challenging for several reasons: (a) most of the datasets are not publicly available or are only related to specific economic scenarios like private companies in different countries [3,4]. For private companies, little information is generally available, which makes it difficult to exploit other sources of information that may improve bankruptcy prediction performance (e.g., textual disclosures [5], annual reports [6], stock market data) and that can be used by more complex models; (b) bankruptcy prediction actually involves different tasks: the default prediction in tasks for the next year, using past data, and the survival probability prediction task that aims to predict the probability that a company will face financial distress in k years. Most datasets cannot permit the performing of both tasks, and this is a clear limitation to the development of intelligent models that aim to generalize; (c) bankruptcy prediction models are usually trained on imbalanced data including few examples of the bankruptcy class: there is still no general accepted metric to assess bankruptcy prediction performance with machine learning.…”

A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data

“…We collected data on 8262 different public companies in the American stock market between 1999 and 2018. We selected the same companies used in [37,38] since these companies were considered a good approximation of the American stock market (NYSE and NASDAQ) in those time intervals.…”

Language Models Fine-Tuning for Automatic Format Reconstruction of SEC Financial Filings

“…The goal is to classify each paragraph as part of one of the possible 18 items (Item 1b and Item 14 have not been considered because they are often not filled in by the companies in their reports). An example of the task is presented in Figure (1). We collected all the reports from American companies published between 2011 and 2022.…”

“…In this way, investors can make informed decisions about whether to buy, hold, or sell a company's securities and obtain important disclosures about a company's financial condition and business operations. One of the most important documents is the annual report (10-K filing), which provides a better understanding of a company's financial strength, competitive position, and growth prospects ( [1]). In light of this, several research works have demonstrated the importance of automatically analyzing 10-Ks to perform knowledge extraction, measuring the year-over-year changes between two consecutive annual filings, or detecting the sentiment reported in the most important sections.…”