Raphael Stevens

JPMorgan Chase sued its former investment banking chief Jes Staley over his ties to disgraced former financier Jeffrey Epstein, alleging that Staley is to blame for any legal fallout from a pair of lawsuits against the bank.
The company on Wednesday filed a suit against Staley that sought to claw back his last eight years of pay at JPMorgan and make him responsible for potential payouts in lawsuits faced by the New York-based bank.
As pressure on the bank increased, JPMorgan went from defending its former executive in recent weeks to shifting the blame for any Epstein fallout to him.

Jes Staley, CEO of Barclays
Justin Solomon | CNBC

JPMorgan Chase sued its former investment banking chief Jes Staley over his ties to disgraced former financier Jeffrey Epstein, alleging that Staley is to blame for any legal fallout from a pair of lawsuits against the bank.
The company on Wednesday filed a suit against Staley that sought to claw back his last eight years of pay at JPMorgan and make him responsible for potential payouts in lawsuits faced by the New York-based bank. The compensation alone amounts to more than $80 million.

The legal maneuver is the latest twist in cases that have embroiled the biggest U.S. bank by assets. Late last year, the U.S. Virgin Islands and a group of alleged Epstein victims sued the bank, accusing it of facilitating the sex offender’s crimes. JPMorgan kept Epstein as a private wealth client until 2013, in part because Staley vouched for him, despite internal concerns after Epstein’s 2008 conviction on sex crimes.
As pressure on the bank increased, JPMorgan went from defending its former executive in recent weeks to shifting the blame for any Epstein fallout to him.
One of the internal emails released in the recent lawsuits mentioned a review of the Epstein account expected to be done by JPMorgan CEO Jamie Dimon; the bank said it hadn’t seen evidence the review happened. Plaintiffs have sought to question Dimon on the case, an effort the bank is resisting.
“To the extent that Staley knew of, participated in, or witnessed sexual abuse associated with Epstein and did not report it to, or actively concealed it from JPMorgan,” it is Staley, and not the bank, who is responsible for injuries Epstein caused, JPMorgan said in its Wednesday filing.

‘Powerful exec’

JPMorgan also identified Staley as the “powerful financial executive” accused of sexually assaulting one of Epstein’s alleged victims in one of the suits it faces.

Staley’s attorney, Kathleen Harris of Arnold & Porter, declined to comment. Staley, who left JPMorgan in 2013 and later became CEO of Barclays before stepping down from the London-based bank in 2021, has denied knowledge of Epstein’s crimes.
Still, JPMorgan said in the filing that it didn’t admit that the two plaintiffs’ allegations were accurate, and in a statement called the lawsuits “misplaced and without merit.”
“The plaintiffs have made troubling allegations concerning the conduct of our former employee Jes Staley, and if true he should be held responsible for his actions,” a JPMorgan spokeswoman said.
“We expect all of our employees at every level of the firm act with honesty and integrity,” she added. “If these allegations against Staley are true, he violated this duty by putting his own personal interests ahead of the company’s.”

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Pavlo Gonchar | Lightrocket | Getty Images

Check out the companies making headlines before the opening bell: 
Etsy — Shares fell more than 6% in premarket after Jefferies double-downgraded the online marketplace to underperform from buy. The firm cited the company’s need to spend more on marketing as buyer churn increases.

Silvergate Capital — Shares of the crypto lender tumbled 50% after the company announced it will wind down operations and liquidate Silvergate Bank. The news comes about a week after the bank warned it may not be able to continue operating and follows a series of financial challenges and government investigations in the aftermath of the collapse of FTX, which was a customer of the bank.
Uber — Shares of the ride-hailing company rose about 2% in premarket trading following a Bloomberg report that Uber is considering spinning off its freight logistics division. The freight unit had $1.5 billion of revenue in the fourth quarter.
MongoDB — Shares of the database platform provider slid over 10% in premarket. The decline came after MongoDB offered weak guidance on revenue that disappointed investors. The company did post earnings and revenue that beat expectations for the fourth quarter.
SVB Financial — The financial services company’s stock dropped 30% after the firm announced that it intends to offer $1.25 billion of its common stock and $500 million of depositary shares.
Credit Suisse —The U.S.-traded shares of the Swiss bank fell more than 4% in premarket trading after the company announced it would delay its annual report after receiving comments from the Securities and Exchange Commission. The regulator’s comments were about cash flow statements in 2019 and 2020, the bank said.

LoanDepot — The mortgage lender’s shares shed over 10% after its fourth-quarter earnings report missed analysts’ expectations. The company reported a loss of 46 cents per share and revenue of $169.7 million. Analysts polled by FactSet had estimated an earnings loss of 27 cents per share and revenue of $190.9 million.
Hilton — Shares of the hotel chained inched up 0.5% in premarket after Barclays upgraded the stock to overweight from equal weight, saying the company can weather macro challenges better than its peers. 
 — CNBC’s Alex Harring and Jesse Pound contributed reporting.

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Workers pass a Credit Suisse Group AG bank branch in Geneva, Switzerland, on Thursday, Sept. 1, 2022.
Jose Cendon | Bloomberg | Getty Images

Credit Suisse on Thursday announced that it will delay the publication of its 2022 annual report after a late call from the U.S. Securities and Exchange Commission on Wednesday night.
In a statement, the embattled Swiss lender said the conversation related to SEC comments about the “technical assessment of previously disclosed revisions to the consolidated cash flow statements in the years ended December 31, 2020, and 2019, as well as related controls.”

“Management believes it is prudent to briefly delay the publication of its accounts in order to understand more thoroughly the comments received. We confirm the 2022 financial results as previously released on February 9, 2023, are not impacted by the above,” the bank said.
The annual report was scheduled for release on Thursday morning. On Feb. 9, Credit Suisse reported a massive 2022 full-year net loss of 7.3 billion Swiss francs ($7.8 billion) and telegraphed another “substantial” full-year loss for this year.

The bank in October announced a plan to simplify and transform its business in a bid to return to stable profitability, following chronic underperformance in its investment bank and a litany of risk and compliance failures.
In late February, Swiss regulator FINMA concluded that Credit Suisse “seriously breached its supervisory obligations” regarding a business relationship with collapsed supply chain finance firm Greensill Capital.
Credit Suisse shares closed Wednesday’s trade at around 2.68 Swiss francs per share, down 3.22% since the start of the year, and are expected to fall further at market open on Thursday.
This is a developing story and will be updated shortly.

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Who are the earliest adopters of new technologies? Cutting-edge stuff tends to be expensive, meaning the answer is often the extremely rich. Early adopters also tend to be incentivised by cut-throat competition to look beyond the status quo. As such, there may be no group more likely to pick up new tools than the uber-rich and hyper-competitive hedge-fund industry.This rule appears to hold for artificial intelligence (ai) and machine learning, which were first employed by hedge funds decades ago, well before the recent hype. First came the “quants”, or quantitative investors, who use data and algorithms to pick stocks and place short-term bets on which assets will rise and fall. Two Sigma, a quant fund in New York, has been experimenting with these techniques since its founding in 2001. Man Group, a British outfit with a big quant arm, launched its first machine-learning fund in 2014. aqr Capital Management, from Greenwich, Connecticut, began using ai at around the same time. Then came the rest of the industry. The hedge funds’ experience demonstrates ai’s ability to revolutionise business—but also shows that it takes time to do so, and that progress can be interrupted.Ai and machine-learning funds seemed like the final step in the march of the robots. Cheap index funds, with stocks picked by algorithms, had already swelled in size, with assets under management eclipsing those of traditional active funds in 2019. Exchange-traded funds offered cheap exposure to basic strategies, such as picking growth stocks, with little need for human involvement. The flagship fund of Renaissance Technologies, the first ever quant outfit, established in 1982, earned average annual returns of 66% for decades. In the 2000s fast cables gave rise to high-frequency marketmakers, including Citadel Securities and Virtu, which were able to trade shares by the nanosecond. Newer quant outfits, like aqr and Two Sigma, beat humans’ returns and gobbled up assets. By the end of 2019, automated algorithms took both sides of trades; more often than not high-frequency traders faced off against quant investors, who had automated their investment processes; algorithms managed a majority of investors’ assets in passive index funds; and all of the biggest, most successful hedge funds used quantitative methods, at least to some degree. The traditional types were throwing in the towel. Philippe Jabre, a star investor, blamed computerised models that had “imperceptibly replaced” traditional actors when he closed his fund in 2018. As a result of all this automation, the stockmarket was more efficient than ever before. Execution was lightning fast and cost next to nothing. Individuals could invest savings for a fraction of a penny on the dollar.Machine learning held the promise of still greater fruits. The way one investor described it was that quantitative investing started with a hypothesis: that of momentum, or the idea that stocks which have risen faster than the rest of the index would continue to do so. This hypothesis allows individual stocks to be tested against historical data to assess if their value will continue to rise. By contrast, with machine learning, investors could “start with the data and look for a hypothesis”. In other words, the algorithms could decide both what to pick and why to pick it.Yet automation’s great march forward has not continued unabated—humans have fought back. Towards the end of 2019 all the major retail brokers, including Charles Schwab, e*trade and td Ameritrade, slashed commissions to zero in the face of competition from a new entrant, Robinhood. A few months later, spurred by pandemic boredom and stimulus cheques, retail trading began to spike. It reached a peak in the frenzied early months of 2021 when day traders, co-ordinating on social media, piled into unloved stocks, causing their prices to spiral higher. At the same time, many quantitative strategies seemed to stall. Most quants underperformed the markets, as well as human hedge funds, in 2020 and early 2021. aqr closed a handful of funds after persistent outflows.When markets reversed in 2022, many of these trends flipped. Retail’s share of trading fell back as losses piled up. The quants came back with a vengeance. aqr’s longest-running fund returned a whopping 44%, even as markets shed 20%.This zigzag, and robots’ growing role, holds lessons for other industries. The first is that humans can react in unexpected ways to new technology. The falling cost of trade execution seemed to empower investing machines—until costs went to zero, at which point it fuelled a retail renaissance. Even if retail’s share of trading is not at its peak, it remains elevated compared with before 2019. Retail trades now make up a third of trading volumes in stocks (excluding marketmakers). Their dominance of stock options, a type of derivative bet on shares, is even greater.The second is that not all technologies make markets more efficient. One of the explanations for aqr’s period of underperformance, argues Cliff Asness, the firm’s co-founder, is how extreme valuations became and how long a “bubble in everything” persisted. In part this might be the result of overexuberance among retail investors. “Getting information and getting it quickly does not mean processing it well,” reckons Mr Asness. “I tend to think things like social media make the market less, not more, efficient…People don’t hear counter-opinions, they hear their own, and in politics that can lead to some dangerous craziness and in markets that can lead to some really weird price action.”The third is that robots take time to find their place. Machine-learning funds have been around for a while and appear to outperform human competitors, at least a little. But they have not amassed vast assets, in part because they are a hard sell. After all, few people understand the risks involved. Those who have devoted their careers to machine learning are acutely aware of this. In order to build confidence, “we have invested a lot more in explaining to clients why we think the machine-learning strategies are doing what they are doing,” reports Greg Bond of Man Numeric, Man Group’s quantitative arm.There was a time when everyone thought the quants had figured it out. That is not the perception today. When it comes to the stockmarket, at least, automation has not been the winner-takes-all event that many fear elsewhere. It is more like a tug-of-war between humans and machines. And though the machines are winning, humans have not let go just yet. ■For more expert analysis of the biggest stories in economics, finance and markets, sign up to Money Talks, our weekly subscriber-only newsletter. More