Nessim Mezrahi is the CEO and Co-Founder of SAR. Nessim is focused on the growth of SAR by deploying innovative public company risk management data analytics solutions for insurance carriers, issuers, and their counsel. He is a mission-driven leader dedicated to implementing the court approved event study methodology in the insurance industry to effectively value securities class actions and assess the risk of securities and corporate governance litigation impacting U.S.-listed corporations.
Stephen is the VP of Data Science at SAR. Stephen leads the Data Science team by ensuring reliable execution of cloud-based quantitative and statistical functions to implement the event study methodology through tried and true coding languages. In conjunction with the Company’s software engineers, he manages SAR’s data analytics standard operating procedures and audit process to deliver scalable, near-real time data to support clients identifying, tracking, and cataloguing adverse corporate events that expose U.S.-listed corporations to securities litigation risk.
Innovation in predictive data analytics of securities litigation enhances data-driven underwriting to deliver better risk transfer solutions to mitigate loss severity from adverse corporate events affecting issuers, directors, and officers.
As the softening rate environment engulfs the public company D&O market and “securities class action severity remains high,” company-specific, near-real time data of adverse corporate events that may trigger securities and corporate governance litigation becomes mission-critical data for carriers and insureds.
The securities and corporate governance litigation risk for each publicly traded company is not the same – even if they operate in the same industry, have similar market capitalization, compete in the same geographical market, and share equal earnings potential.
According to Mathew McLellan of Marsh, “[b]uying the appropriate amount of coverage is critical, and companies should use data analytics and personalized damages modeling, along with peer benchmarking, to assess what is right for them.”
Growing economic uncertainty and a softer D&O market, makes it critical to limit potential unfavorable loss reserve developments that may erode public company portfolio profitability. This can be achieved by implementing company and time-specific securities litigation risk assessments to mitigate high severity loss developments from real world, adverse corporate events.
The application of court-approved event study analysis on a near-real time basis is empirically proven to effectively assess the risk of securities class action litigation and corporate governance risk exposures by identifying specific adverse corporate events that expose U.S.-listed corporations and D&Os, to:
- Liability under the Exchange Act ’34 and Section 10(b) and Rule 10b-5
- Liability under the Securities Act ’33 and Sections 11 and 12(a)(2)
- Employee Retirement Income Security Act ’74 (ERISA)
- Shareholder Derivative Lawsuits
According to Subodh Mistra of Institutional Shareholder Services, “[n]o one can foresee a catastrophic event occurring or witness what goes on behind the closed doors of a publicly traded company or know that a data breach is occurring until after the event has occurred and been exposed. That exposure, sometimes a result of negligence or potentially outright fraud, can often lead to a sharp decline in the stock price and as such, impacts the investors in that stock negatively.”
The implementation of proven predictive data analytics on cloud-native ecosystems enables insurance carriers to identify adverse corporate events faster, so they can deploy capital more effectively and provide insureds with coverage solutions based on event-driven risks.
Benefits of the Court-Approved Event Study Methodology
As we navigate an upcoming economic downturn, the use of data analytics to identify, categorize, and track adverse corporate events on an on-going basis enables key stakeholders in the public company D&O industry to assess company and time-specific securities litigation risks facing U.S.-listed corporations more effectively.
Our analysis of over 1,200 securities litigation filings indicates that using court-approved event study methodology to quantify potential SCA loss severity yields metrics that strongly predict potential SCA settlement losses.
SAR relies on verifiable, back-tested, cloud-based analytical processing to identify on a near-real time basis (as of the close of the preceding trading day) three unique – yet related – conditions to identify high-risk adverse corporate events that expose issuers and their directors and officers to securities and corporate governance litigation:
- Public statements issued via a press release, an earnings call, or at a press-attended industry event.
- Filed registration statements, periodic reports and other forms filed with the SEC that are required to fulfill the duties of participating in the U.S. capital markets.
- Stock price impact by evaluating the company’s stock price return, during the salient trading session, after controlling for the impact of the S&P 500 Total Return Index and an industry-representative equity index – at the 5% confidence standard.
According to attorneys at Skadden, “plaintiffs have continued to file “event-driven” securities class actions, where the catalyst is the disclosure or occurrence of a significant event that negatively impacts the stock price, often unrelated to the company’s financial results.”
Sophisticated counsel for institutional investors are innovating at a faster pace to enhance their portfolio monitoring solutions and develop creative litigation strategies triggered by adverse corporate events that negatively affect their clients’ portfolios. For example, “the Labaton Portfolio Analysis System (LPAS), which catalogs, tracks, and analyzes our client’s current historical investment records; and the Labaton Securities Monitoring and Reporting Tool (LSMART), which tracks and analyzes large share price movements and related disclosures.”
For example, just recently, an event-driven and ESG-related securities class action was filed against Wells Fargo. The confirmatory and alleged truth-revealing adverse corporate event – a press release issued by the company – is claimed by Plaintiffs’ counsel to be an alleged corrective disclosure. As a result, investors in the company’s common stock claim they “have suffered significant losses and damages.”
The Plaintiffs’ Bar maintains a competitive advantage over insureds, in part, due to their entrepreneurial innovation in data analytics which enables them to capitalize on today’s event-driven litigation landscape that plagues carriers and insureds with high severity losses and fuels social inflation.
The application of near-real time event study analysis coupled with statistically verifiable estimates of SCA loss severity – based on a defined sample of adverse corporate events – will level the innovation playing field by empowering carriers to deliver more accurate risk-specific coverage to their insureds during a shifting rate environment where company and time-specific information matters.
Empirical Results of Predictive Analytics of SCA Severity Loss Severity
Our empirical results show that two distinct factors contribute to a statistically strong relationship with observed SCA settlement amounts, as measured by observed r-squareds. According to Professor Jan Hammond of Business Analytics at Harvard Business School, “[r]egression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship.”
- SCA Exposure (market capitalization losses)
- 2. SCA Damages (estimate of max. potentially available aggregate damages)
Using no other predictor, each 1% increase in SAR’s SCA Exposure, significantly predicts an increase of .44% in SCA settlement dollars. Likewise, each 1% increase in SAR’s estimate of SCA Damages on a claim-specific basis, significantly predicts an increase of .53% in SCA settlement dollars.
This measured effect is robust across richer specifications. Controlling for circuit court, plaintiff firm, and U.S. stock exchanges (NYSE and NASDAQ), each 1% increase in SAR’s estimate of SCA Damages predicts an increase of .43% in settlement dollars, with a strong regression that explains 82% of variation in SCA settlement dollars. SAR’s estimates of SCA Exposure perform similarly.
Our univariate regression analyses, which estimate how well SCA Exposure and SCA Damages explain SCA settlement losses, yield strong predictive results to support public company D&O underwriters and other key stakeholders that place capital at risk, to:
- Determine more accurate D&O policy limit sufficiency to cover SCA risks
- Adopt data-driven D&O policy renewal analytics to enhance risk-based pricing
- Aggregate potential SCA loss severity for enhanced capital allocation
- Execute more accurate loss reserving based on company-specific SCA risk
- Estimate SCA risk of a portfolio or industry sector on a time-specific basis
Conclusion: Empirically Proven Predictive Data Analytics Are a Profit-Driver for Carriers Facing a Softening D&O Rate Environment
Based on our strong empirical results of SCA loss severity predictability using two distinct factors, D&O carriers can enhance profitability during a softening rate environment by identifying and tracking adverse corporate events to: a) mitigate unfavorable loss reserve developments, and b) optimize risk-based pricing on claims-made D&O policies.
Event-driven securities litigation risk aggregation strengthens public company D&O underwriting by identifying industry sectors that exhibit higher-than-average adverse corporate events per insured. As a result, public company D&O underwriters can find opportunities for greater returns by placing capital at risk in sectors that exhibit lower SCA risk.
Company-specific and time-specific securities litigation risk assessments equip carriers with near-real time information to support a data-driven renewal processes to ensure that insureds have sufficient coverage to mitigate high severity losses from real world event-driven risks.
Other key stake holders in the public company D&O industry – such as internal corporate counsel and outside counsel that represent insureds and shepherd boards of directors through economic uncertainty – can deliver more value with timely, data-driven risk assessments of securities and corporate governance litigation exposure based on verifiable statistical accuracy that event study analysis provides.
Nessim Mezrahi is CEO and Stephen Sigrist is VP of Data Science at SAR.
 “Rates Continue to Decelerate in Many Areas: Q1 2022 Commercial Market Update,” Carolyn Polikoff, Woodruff Sawyer, May 16, 2022.
 “US Public Company D&O Insurance Market Sees Pricing Relief, Strong Competition,” Mathew McLellan, National Association of Corporate Directors (NACD) BoardTalk, June 16, 2022.
 “Event Driven Securities Litigation,” Subodh Mishra, Harvard Law School Forum on Corporate Governance, December 18, 2020.
 “Assessing Securities Class Action Risk With Event Analysis,” Nessim Mezrahi, Law360, January 22, 2020.
 “Despite Last Year’s Decline in Filings, Securities Litigation Will Likely Pick Up in 2022 Due to Plaintiffs’ Continued Focus on SPAC Transactions and Event-Driven Litigation,” Jay B. Kasner, Scott D. Musoff, Susan L. Saltzstein, Skadden’s 2022 Insights, January 19, 2022.
 Portfolio Monitoring and Case Evaluation – Labaton Sucharow
 “Another Example of ESG-Related Actions Leading to a Securities Lawsuit,” Kevin LaCroix, The D&O Diary, June 28, 2012.
 Adalan v. Wells Fargo & Company et al, Case No. 3:22-cv-03811
 Separate univariate analyses show that SAR’s estimates of market cap losses and maximum potential damages explain 34% and 50% of variation in claim settlements, respectively, from among the claims SAR has tracked since June of 2018. These are impressive results from models that only use a single predictor variable.
 “What is Regression Analysis in Business Analytics?” Catherine Cote, Business Insights, Harvard Business School, December 14, 2021.
 The adjusted r-squared measure this multivariate regression specification is 65%.