japanja
Quant modeling can use non-numerical data, too

Quant modeling can use non-numerical data, too

10-12-2021 | インサイト

We find that text analysis can predict the risk and return characteristics of corporate bonds.

  • Patrick  Houweling
    Patrick
    Houweling
    Co-Head of Quant Fixed Income and Lead Portfolio Manager
  • Robbert-Jan 't Hoen
    Robbert-Jan
    't Hoen
    Researcher

Speed read

  • More than 80% of all corporate information is in an unstructured form
  • Literature finds that text in SEC filings predicts return and volatility of stocks
  • We show that results from the literature carry over to corporate bonds

It is estimated that over 80% of all business-relevant information is in an unstructured form, such as text, video, or audio.1 However, financial models traditionally only use numerical data, such as market prices and company accounting data. Hence, tapping into the large pool of unstructured data has the potential of enriching existing models. We investigated the opportunities that unstructured data present for investing in corporate bonds.2

最新の「インサイト」を読む
最新の「インサイト」を読む
配信登録

Mining abundant sources of non-numerical data

Textual data is an important source of non-numerical data. Examples include news articles, social media posts, transcripts of management presentations and corporate reports. Until recently, usage of such text sources in analysis required human intervention to code attributes into numerical form – a slow and tedious process. Nowadays, due to advances in natural language processing (NLP) and the immense growth in computing power, text mining techniques can be used to systematically analyze vast amounts of text data.

Academics as well as practitioners have started analyzing text data for the purpose of predicting the risks and returns of stocks and bonds. One strand of research investigates the information content of corporate reports filed by publicly listed companies in the US with the Securities and Exchange Commission (SEC). Of these SEC filings, most attention is directed towards the annual (Form 10-K) and quarterly (Form 10-Q) reports. The reports are very extensive, owing to laws and regulations that prohibit companies from making materially false or misleading statements, and from omitting material information that would render disclosures misleading. Along with the numerical data from the financial statements, these filings contain large volumes of unstructured textual information.

The information in 10-Ks and 10-Qs should enable any investor to fully understand the state of a company. In practice, however, valuable information in these reports is easily overlooked, because of the daunting challenge of reading and grasping many pages of formal and often very technical text.3 These reports therefore provide an attractive avenue of research for the application of computer-based text analysis.

Data collection and pre-processing

We obtain all 10-Ks and 10-Qs of publicly listed US issuers of corporate bonds in the Bloomberg US Corporate Investment Grade and High Yield ex. Financials indices. The sample covers the period from 1994 to 2017 and contains a total of 212,400 filings, of which 57,952 are 10-Ks and 154,448 10-Qs.

Figure 1 | Filing size

Source: Robeco, EDGAR. Sample period 1994-2017.

To facilitate later analyses, we first clean each document so that only the text, numbers and symbols in the main body of the original filing remain. Figure 1 shows the average size of the cleaned files over time, as measured by the total number of characters. As expected, we find that 10-Ks are, on average, significantly larger than 10-Qs. Moreover, there is a strong upward trend in the size of 10-Ks and 10-Qs. This is driven largely by the gradual increase over time in required disclosures.

Text analysis

The next step in the research is to process the cleaned text data so that it becomes understandable to a computer. A commonly used method to convert text into a numerical format is the Bag-of-Words (BoW) model. BoW is an NLP technique that reduces the complexity of text data by removing information about word order and context. All that remains of each filing is a list of term frequencies, i.e., the number of times each unique word appears. The idea behind the model is that the more frequently a term is used, the more important it is.4

Changers and non-changers

A recently published academic article documents that the similarity of a company’s consecutive 10-Ks and 10-Qs is a significant predictor of stock return and stock return volatility: companies that make more changes to the text of their report compared to their previous report (which the article labels as ‘changers’) underperform companies with fewer changes (labeled as ‘non-changers’) by a wide margin.5 The rationale for this finding is that firms tend to repeat what they reported previously and that they are only required to change the text if there are material changes to the company or to its circumstances over the reporting period. Changes in the text are thus interpreted as being negative. Although extensive text changes are not necessarily a bad sign, analysis does show that these are mostly related to negative events and negative future stock returns.

In our research, we test if a similar effect exists for corporate bonds. If the degree of similarity between consecutive 10-Ks and 10-Qs is truly linked to firm performance, then we expect to see this reflected in corporate bond returns as well. To gauge the similarity between reports, we compare the text in a report with that of the same report published a year previously, i.e., a 10-K is compared with previous year’s 10-K, and a 10-Q with a 10-Q of the same quarter in the previous year.

We evaluate the performance of changers versus non-changers on our sample of US investment grade and high yield issuers over the 1997-2017 period. Our hypothetical investment strategy for this research goes long in the bonds of the companies whose reports showed the fewest changes, and goes short in the bonds of the firms with the most changes.

We find that, in investment grade as well as in high yield, non-changers have outperformed changers by over 50bps per year and have been less risky than changers, resulting in higher Sharpe ratios for non-changers. Overall, we find that the degree of similarity between consecutive reports has predictive power for corporate bond risk and return, with stronger statistical significance in investment grade than in high yield.

1 http://breakthroughanalysis.com/2008/08/01/unstructured-data-and-the-80-percent-rule/
2 This insight is based on an extract from the paper “Continuous innovation in factor credit strategies”, April 2021, by Patrick Houweling, Frederik Muskens and Robbert-Jan ‘t Hoen.
3 Loughran & McDonald, 2014, “Measuring readability in financial disclosures”, The Journal of Finance, 69(4), 1643-1671.
4 We filter out uninformative words using the popular stop word list of Loughran and McDonald: https://sraf.nd.edu/textual-analysis/resources/#StopWords
5 Cohen, Malloy & Nguyen, 2020, “Lazy prices”, The Journal of Finance, 75(3), 1371-1415.

重要事項

当資料は情報提供を目的として、Robeco Institutional Asset Management B.V.が作成した英文資料、もしくはその英文資料をロベコ・ジャパン株式会社が翻訳したものです。資料中の個別の金融商品の売買の勧誘や推奨等を目的とするものではありません。記載された情報は十分信頼できるものであると考えておりますが、その正確性、完全性を保証するものではありません。意見や見通しはあくまで作成日における弊社の判断に基づくものであり、今後予告なしに変更されることがあります。運用状況、市場動向、意見等は、過去の一時点あるいは過去の一定期間についてのものであり、過去の実績は将来の運用成果を保証または示唆するものではありません。また、記載された投資方針・戦略等は全ての投資家の皆様に適合するとは限りません。当資料は法律、税務、会計面での助言の提供を意図するものではありません。

ご契約に際しては、必要に応じ専門家にご相談の上、最終的なご判断はお客様ご自身でなさるようお願い致します。

運用を行う資産の評価額は、組入有価証券等の価格、金融市場の相場や金利等の変動、及び組入有価証券の発行体の財務状況による信用力等の影響を受けて変動します。また、外貨建資産に投資する場合は為替変動の影響も受けます。運用によって生じた損益は、全て投資家の皆様に帰属します。したがって投資元本や一定の運用成果が保証されているものではなく、投資元本を上回る損失を被ることがあります。弊社が行う金融商品取引業に係る手数料または報酬は、締結される契約の種類や契約資産額により異なるため、当資料において記載せず別途ご提示させて頂く場合があります。具体的な手数料または報酬の金額・計算方法につきましては弊社担当者へお問合せください。

当資料及び記載されている情報、商品に関する権利は弊社に帰属します。したがって、弊社の書面による同意なくしてその全部もしくは一部を複製またはその他の方法で配布することはご遠慮ください。

商号等: ロベコ・ジャパン株式会社  金融商品取引業者 関東財務局長(金商)第2780号

加入協会: 一般社団法人 日本投資顧問業協会