The model increases accuracy in predicting which firms most/least likely to come under financial distress
Thomson Reuters, announced that it has introduced the first commercial credit risk model to measure corporate financial health by quantitatively analyzing text. The StarMine Text Mining Credit Risk Model (TMCR) assesses the credit risk of publicly traded companies by analyzing the text in various documents. StarMine TMCR systematically assesses the language in Reuters news, StreetEvents conference call transcripts, corporate filings and select broker research reports to predict which firms are likely to come under financial distress and which are likely to thrive.
The model is a complement to StarMine’s other two credit risk models, the StarMine Structural Credit Risk Model and the StarMine SmartRatios Credit Risk Model. StarMine TMCR builds on Thomson Reuters aims to help professional investment managers generate more profitable investment ideas and mitigate risks, giving them an edge over their peers. See previous post: Thomson StarMine SmartRatios
“Mining text is the new frontier in quantitative analysis,” said Ryan Roser, principal quantitative research analyst at Thomson Reuters and the chief architect of StarMine TMCR. “What we have created is a very powerful and flexible framework for analyzing and making predictions from text. We have successfully applied the model to default prediction and foresee applying it in other applications as well.”
In creating the StarMine TMCR, the StarMine quant group developed a new specialized dictionary of credit and financial-health related language to enable robust predictions of credit quality beyond what is possible when using a standard dictionary in a classic “bag-of-words” text mining framework. The StarMine TMCR provides transparency into the origin of its final scores, ratings and default probabilities by creating overall component scores for each class of document it processes: news, transcripts, filings, and select broker research. The StarMine TMCR also provides insight into the types of language the model picks up on, categorizing the language into four groups – income statement related, balance sheet and debt structure related, legal obligations and terms, and external & market events – and producing scores for each of the four categories. In addition, the StarMine TMCR scores each individual document for a company, allowing analysts to quickly zero in on the documents that matter.
“The Text Mining model is probably the most innovative model our StarMine team has ever created,” said Dr. George Bonne, director of quantitative research at Thomson Reuters. “Not only is StarMine TMCR powerful in predicting default and generating alpha, it will also increase the efficiency of analysts and financial professionals by helping to identify the documents that matter most out of the potentially hundreds or thousands of pages they may be responsible for reading every day.”
The model produces daily updated estimates of the probability of default or bankruptcy within one year for over 20,000 companies globally, including financials. The default probabilities are also mapped to traditional letter ratings and ranked to produce 1-100 percentile scores (with 100 being comparable to an AAA rating). StarMine TMCR is provided as a daily data feed, as well as through a range of Thomson Reuters desktops.
Thomson Reuters provides equities and credit based alpha and risk factors for investment managers as well as corporate investor relations teams. Thomson Reuters has long been known for its enhanced sell-side forecasts; one example is StarMine SmartEstimates, which predict the direction of earnings surprises with a success rate of around 70% when the SmartEstimate significantly differs from the consensus estimate. The StarMine Text Mining Credit Risk model sits alongside the StarMine Structural Credit Risk model and the StarMine SmartRatios Credit Risk Model, both form part of StarMine’s credit risk offering.
BIIA comment: This a good example of how the BIG Data concept (data mining and analytics) is used to better predict risk and to add significant value to product offerings. What does this mean for traditional commercial credit information companies: It adds another up-scale substitute product offering to an already crowded market.