Commentary
Part IV – Elaine Moore’s Financial Times article, “Did AI write this? It’s getting harder to tell,” inspired this series by documenting the growing difficulty of distinguishing human prose from machine-generated text. AI laboratories are making their output more natural, while “humaniser” tools remove familiar tells.
Part III – An audit should begin with the report’s purpose. What decision or understanding is the report intended to support? Was the analytical question framed neutrally, or was the system directed toward a preferred result?
Part II – A report can avoid prompted bias and still fail its reader. Generative systems can assemble enormous quantities of accurate and relevant information at very low marginal cost. The resulting volume may exceed the attention available from the executive, trader, director, regulator, or fiduciary expected to use it.
Part I – The easiest problem presented by AI-generated reporting may be the one attracting the most attention: detection. Formulaic language can alert an experienced reader to possible AI involvement, but identifying its origin does not establish whether the analysis is accurate, unbiased, comprehensible, or logically sound.
Staggered IPO lockup releases are reshaping securities lending by creating periods of extreme supply uncertainty and information asymmetry. Using SPCX’s August 2026 E1 lockup release as a case study, this analysis examines how earnings timing, short positioning, and constrained lendable supply combined to create a severe trap for marginal short sellers.
Time is Running out for Lenders to Prepare for T+1 and N-PX Loan Recall Wrinkles The securities industry is transitioning to a T+1 settlement cycle, where trades settle one business day after the transaction date. While this shift promises benefits like reduced market risk and lower costs, it poses significant challenges for securities lenders.
On April 2nd, 2026, an effusion of data from a daily trove of U.S. regulatory filings will create resources to drive many new use cases for artificial intelligence in capital markets. A clear opportunity exists in securities finance, where practitioners have repeatedly stated that major IT investments will be needed to comply with the many new regulatory mandates. “Black box” AI platforms may seem a ready solution but can also create nightmares for client reviews and lawsuits.
In our opinion, public data can clarify the rational limits of influence for predictive artificial intelligence. The best courtroom-ready models will display an audit trail based on the replication of critical decision parameters and vectors from past markets. Vendor data in securities finance may be more timely and deeper than the public releases but, for judicial purposes, the public data will provide foundational evidence for the “bounded rationality” of decision-makers, as defined by the late Herbert Simon, Nobel Laureate and the father of Artificial Intelligence.
When, on October 13, 2023, the Securities and Exchange Commission released its long-awaited final 10c-1 rule on reporting and public disclosure of securities loans (explained here), the most important passage, at least to the commercial data vendors who support the securities finance community, stated that, “the final rule could render existing securities lending data services less valuable, potentially leading to less revenue for the firms currently compiling and distributing these data for a fee.”[1] But is that true? Are bonuses and careers really at risk?? As shown in the table below, there is hope for vendors because the public data release will either omit or delay several data elements that are crucial to many important vendor applications today.
How Securities Lenders Unraveled the $100 billion Pump and Dump Scheme By Ed Blount and Dan Hammond “In a matter of days, the companies at the center of Archegos’s trading scheme lost more than $100 billion in market capitalization, Archegos owed billions of dollars more than it had on hand, and Archegos collapsed.”U.S. Federal Bureau […]
Is 10c-1 Regulatory Overreach? Or a Good Starting Point? [1] “The best trader I ever knew was broken when he took over a dying friend’s book. Everyone knew the book and turned on him.” Born in 1899, Henry Goldberg was the oldest trader on the floor of the New York Stock Exchange when I interviewed […]