How Warren Buffett has trounced “the world’s greatest hedge fund manager”

James Harris (“Jim”) Simons was born in 1938, became a full-time market-timer-trader-speculator in 1978, founded Renaissance Technologies LLC (“RenTech”) in 1982, retired as its CEO in 2010 and as its Chairman in 2021, and died in 2024. Last year his financial net worth was ca. $US31.8 billion, and Bloomberg’s Billionaires Index ranked him #49 on its list of the world’s richest people.

Warren Edward Buffett was born in 1930, became a stockbroker in 1951 and a securities analyst to 1954, ran several investment partnerships from 1956 to 1969, and since 1970 has been Berkshire Hathaway, Inc.’s full-time chairman and CEO. In May, he announced that at the end of this year he’ll retire as CEO but will remain as chairman. His current net worth, derived almost from completely from his stake in Berkshire, is ca. $160 billion; that makes him the world’s fifth-richest person.

In The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution (Penguin, 2019), Gregory Zuckerman declared that “Simons is considered the most successful money maker in the history of modern finance. (From 1988 to 2018, RenTech’s) flagship Medallion Fund ... generated average annual returns of 66% before charging hefty investor fees – 39% after fees – racking up trading gains of more than $100 billion. No one in the investment world comes close. Warren Buffett, George Soros, Peter Lynch, Steve Cohen and Ray Dalio all fall short.”

Zuckerman titled the extract from his book, which appeared in The Wall Street Journal (2 November 2019), “The Making of the World’s Greatest Investor.” Pippa Stevens (“The secret behind the greatest modern day moneymaker on Wall Street: Remove all emotion,” CNBC, 5 November 2019) anointed him “one of the greatest investors of all time.” James Gruber (Firstlinks, 13 March 2024) dubbed him “the greatest investor you’ve never heard of.”

These characterisations are false. In this article, I demonstrate that they’re mistaken; I also explain why they’re untrue.

I’m happy to assume (I don’t have a horse in the race) that Simons ranks among the greatest speculators, and that RenTech may remain – despite reports over the past several years of huge outflows of funds under management – among the best hedge fund managers of the past few decades, or even of all time.

Crucially, however, and like practically all hedge funds, they’re NOT investors: they’re market-timers and traders, that is, speculators.

This distinction is fundamental. Zuckerman contends that during the 30 years to 2018 Medallion “(racked) up trading gains of more than $100 billion.” That’s undoubtedly more than Berkshire Hathaway has earned from trading (note the italics).

Yet as I detailed in Naysayers are wrong: You CAN emulate Warren Buffett (10 June), Berkshire Hathaway’s profit on a single investment – in Apple – is at least $150 billion. That’s greater than Medallion’s total gains – all from trading – from its inception to 2019. The claim that Buffett and Berkshire “fall short” of Simons and Medallion is thus laughable.

In Stop kidding yourself: Nobody can “time the market” (30 June), I reiterated Ben Graham’s crucial distinction between investors from speculators; I concluded that opportunities to speculate are enormous – and the number of speculators is huge – but consistently successful speculation is all but impossible. Speculators almost always lose, and they often lose heavily; hence the number of successful speculators is miniscule. Neither Simons’ nor Medallion’s results challenge – never mind overturn – this conclusion.

Berkshire’s results since the 1960s have, on a comparable (i.e., return on assets and thus unleveraged) basis, greatly exceeded Simons’ and RenTech’s heavily leveraged results since the 1990s. And a straight comparison of Berkshire unleveraged and RenTech’s Institutional Equities Fund’s heavily leveraged returns since 2017 demonstrates unequivocally: Berkshire has trounced RIEF.

On these bases, taking into account their enormous disparity of size – as well as Medallion’s and RenTech’s near-collapse during the GFC – there’s simply no question: Warren Buffett has been his era’s greatest creator and nurturer of wealth.

That’s not least because investment is “positive sum” (“win-win”). In sharp contrast, speculation is a largely “zero-sum” game: it creates very few winners and many losers.

Unlike investors like Buffett, successful speculators and hedge fund managers like Simons and RenTech don’t create new wealth; they merely transfer existing wealth from others to themselves.

A Short History of the World’s Most Successful Speculator and Hedge Fund

As a boy, Jim Simons displayed a talent for and love of mathematics. As an adolescent, he completed high school in just three years. As a young man, he received a bachelor’s degree from the Massachusetts Institute of Technology (also in just three years) and a Ph.D. from the University of California at Berkeley. While pursuing his Ph.D. he indulged his appetite for speculation, often travelling across the Bay to trade soybean futures at Merrill Lynch’s office in San Francisco.

Returning to MIT in 1961 to begin an academic career, Simons sensed – uneasily – that his path in life had already been fixed. “I remember sitting in the library one day, saying, ‘well, I guess I’ll become an assistant professor and then an associate professor and then a (full) professor and ... then die,” he recalled in an interview in 2020 with the American Institute of Physics. “And it made me think (that) maybe there are other things in the world.”

In 1964, after a short stint of teaching at Harvard, Simons moved to Princeton, New Jersey, to take a high-paying (and very secretive) job at the Institute for Defense Analyses.

This quasi-government, mysterious and reclusive organisation was hiring elite mathematicians to help the U.S. National Security Agency break Soviet codes and ciphers. This work introduced Simons to algorithms – complex sequences of instructions which enable computers to perform advanced computations. IDA permitted its employees to spend half of their work time on personal pursuits; Simons devoted much of his to algorithms which attempted to predict the share market’s short-term fluctuations.

In 1968, Simons publicly criticised IDA’s president, Maxwell D. Taylor (who’d recently been Chairman of the Joint Chiefs of Staff and Ambassador to South Vietnam), over the war in Southeast Asia. As a result, Simons lost his job. He then joined the mathematics department at the State University of New York’s campus at Stony Brook (commonly called “Stony Brook University”).

To say that he’d become an accomplished mathematician is a big understatement: in 1976, at 37 years of age, the American Mathematical Society awarded to him its Oswald Veblen Prize in Geometry. It’s the highest honour in the field, and it cemented his high academic reputation.

While chairing the mathematics department at SUNY Stony Brook, and using the connections he’d made through his work in cryptography, Simons once again dabbled in market timing and speculation. Initially he traded commodity futures. He found the experience gut-wrenching, so for help he turned to his network of cryptographers and mathematicians. “Maybe there were some ways to predict prices statistically,” he mused in a 2015 interview with Numberphile. “Gradually we built models.”

Yet Simons was restless; hence in 1978, in Zuckerman’s words, “eager for a new challenge and bursting with self-confidence,” he left academia and launched a trading firm which he named Monemetrics.

He turned to an old friend and fellow code-breaker at the IDA, Leonard Baum, whose models Monemetrics used to trade currencies. Simons also recruited James Ax, whom he’d persuaded to leave Cornell University and come to SUNY Stony Brook, to oversee Baum. Ax concluded that Baum’s models could help to predict not just the fluctuations of currencies but potentially of any commodities futures contract.

“At the time,” noted Zuckerman, “some investors and academics saw markets’ zigs and zags as essentially random, arguing that all possible information already was baked into prices, so only news, which is impossible to predict, can push prices higher or lower. Others believed price shifts reflected efforts by investors to react to and predict economic and corporate news, efforts that sometimes bore fruit.”

In Zuckerman’s words, “Simons (tried) his hand trading currencies. Forty years old, with a slight paunch and long, greying hair, the former professor hungered for serious wealth. But this wry, chain-smoking teacher had never taken a finance class, didn’t know much about trading, and had no clue how to estimate earnings or predict the economy.”

“For a while,” Zuckerman continued, “Simons traded like most everyone else, relying on intuition ... But the ups and downs left him sick to his stomach. He recruited renowned mathematicians and his results improved, but the partnerships eventually crumbled amid sudden losses and unexpected acrimony. Returns at his hedge fund were so awful he had to halt its trading and employees worried he’d close the business.”

In the late-1980s, RenTech, which had succeeded Monemetrics in 1982, began to compile algorithms and develop IT systems which digested masses of data and selected “ideal” trades. Simons and his team “built sophisticated predictive algorithms – years before Mark Zuckerberg and his peers in Silicon Valley began grade school. ‘If we have enough data, I know we can make predictions,’ Simons told a colleague.”

But success took its time to knock on Simons’ door. During RenTech’s first several years, as had been the case with Monemetrics, losses occurred as frequently as profits. In aggregate, profits hovered close to zero.

“By the end of the (1980s), Simons was on his second marriage and third business partner ... Employees worried he would close the business. Computer trading seemed folly. At the time, traders searched for an information advantage – market-moving financial news unavailable to the general public ... (Yet) Simons (still) didn’t have a clue how to estimate cash flows, identify new products, or forecast interest rates.”

In the early 1990s, mathematicians and physicists at RenTech “began to identify reliable and repeatable short-term patterns in the market. They shifted to concentrate on this kind of trading, holding positions for just a few days. The idea was to resemble a gambling casino, handling so many daily bets they’d only need to profit from a bit more than half of their wagers.”

One of Simons’ recruits discovered what appeared to be recurring and overlooked patterns. Monday’s price action often followed Friday’s, while Tuesday saw retreats and reversions. Medallion began buying late in the day on Fridays if it believed that a clear uptrend existed, and sold early Monday, taking advantage of what they called the “weekend effect” (see Sell in May, go away and lose money, 19 May).

Implementing their new, short-term and algorithmic approach, in the early-1990s Simons’ team began to generate intermittently big gains. Outsiders scoffed. When his colleagues explained the firm’s methods to business students and others, “we were viewed as flakes with ridiculous ideas,” one recounted.

One crucial weakness of RenTech’s and Medallion’s operations was and remains obvious: these “patterns” were fleeting and their magnitude was miniscule; they’re therefore little more than random fluctuations. As such – and as we’ll see, in Simons’ own words – the overall success rate of any attempt to exploit them has barely exceeded 50%; essentially, they’re tosses of a coin.

As a result, they generate mediocre returns. Only by employing heavy leverage – that is, massive debt – can these meagre gains be magnified. Yet what debt giveth, it also taketh away. The higher is the leverage, the higher becomes the risk of gut-wrenching losses – or worse.

According to Zuckerman, Medallion gained 55.9% in 1990 – a dramatic improvement on its loss of 4% the previous year. These profits greatly exceeded its hefty fees – 5% of all assets managed and 20% of all gains. “In trading, as in mathematics, it is rare to achieve true breakthroughs in midlife. Yet, Simons was convinced he was on the verge of something special, maybe even historic.”

Jim Simons clearly was and his colleagues are formidable computer scientists, mathematicians, physicists, etc. And I’m happy to assume that his IQ was (and theirs is) sky-high. But I reject the commonplace inference that mathematical prowess underlay his apparent success as a speculator. More generally, I emphatically reject – as does Warren Buffett – the tacit assumption that high-IQ people necessarily are good investors.

The opposite is closer to the truth (see, for example, Does high IQ make a better investor? 11 November 2020). Intelligence is ultimately a matter of character – and not even secondarily of mathematical and other technical aptitude.

Simons Was and RenTech Remain Speculators

According to Amy Whyte (“Famed Medallion Fund ‘Stretches ... Explanation to the Limit,’” Professor Claims,” Institutional Investor, 26 January 2020), “Medallion’s strategy involves holding thousands of short-term positions, both long and short, at any given time. The fund makes high-frequency trades, but has also held positions for up to one or two weeks ...”

In “How Did Jim Simons’ Firm Make $100 Billion? He Told His Secrets to Our Reporter” (The Wall Street Journal, 10 May 2024), Zuckerman estimates these positions’ rate of success: “Simons’ trading system is accurate just over half the time.” Whyte elaborates: “... Medallion was right 50.75 percent of the time when it came to its millions of trades ... You can make billions that way.”

“In simple terms,” Whyte adds, “the Medallion fund reportedly makes money in much the same way that a casino does. The house doesn’t always win – but enough small wins over time can add up to large profits.”

“’In Medallion’s situation they’re probably not taking larger bets — they’re taking small bets that are all about the same in terms of profitability,’ explains Campbell Harvey, a finance professor at Duke University ... Harvey ... explains that being right just over half the time could theoretically result in ‘a lot of money ... If you’re doing potentially hundreds of thousands or millions of trades, even a small amount of profitability per trade turns out to be a big amount.’”

In Table 1 of Stop kidding yourself: Nobody can “time the market” (30 June), I distinguished investors from market-timers, traders and speculators. Investors focus upon companies and their long-term operations, and seek over the years and decades to profit from these operations. Market-timers, traders and speculators care little or not at all about such matters: their focus is “the market,” prices and their fluctuations over hours, days, weeks and at most months.

On that basis, it’s obvious that Simons was and RenTech are speculators – and NOT investors. That’s neither criticism not praise; it’s an obvious fact.

Zuckerman tacitly agrees. He acknowledged that Simons, who “didn’t know much about trading,” founded a trading firm whose objective was to exploit markets’ short-term “zigs and zags.” RenTech’s shift to arcane algorithms and high-frequency trading abandoned gut instinct but left untouched the emphasis on short-term market timing: they continued to hold “positions for just a few days. The idea was to resemble a gambling casino, handling so many daily bets they’d only need to profit from a bit more than half of their wagers.”

Pippa Stevens also concurs that, in effect, Simons was a market-timer, trader and speculator. She writes: “using his mathematical background and large sets of data, Simons set out to build computer models that he believed could identify and profit from patterns in the market.”

She concludes: “his algorithms ... take advantage of even the smallest and shortest fluctuations in prices. The average holding period is two days.”

Simons, RenTech and the GFC

In The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (CrownBusiness, 2010), Scott Patterson makes three crucial points about the Global Financial Crisis. First, it was “one of the most brutal market meltdowns ever seen.” Second, “not one of the quants, despite their chart-topping IQs, their impressive Ph.D.s, their billions of wealth amassed by (allegedly) anticipating every bob and weave the market threw their way, ... saw the train wreck coming.” Moreover, once it erupted they had no idea why it had occurred.

What a joke: computer, maths and physics geniuses who tacitly claim that they can foresee and exploit markets’ smallest and briefest perturbations were utterly oblivious to one of its biggest-ever crashes!

Thirdly, and like all other quant funds, Medallion and RenTech very nearly collapsed. In particular, at the height of the “quant quake” on Monday 6 August 2007, and according to Zuckerman, “Simons ... didn’t know if his firm could survive much more pain. He was scared. If losses grew, and they couldn’t come up with enough collateral, the banks would sell Medallion’s positions and suffer their own huge losses. If that happened, no one would deal with Simons’ fund again. It would be a likely death blow, even if Renaissance suffered smaller financial losses than its bank lenders.”

“Our job is to survive,” Simons declared to his colleagues. On Thursday 9 August, “Medallion already had lost more than $1 billion – a stunning 20% – that week. (RenTech’s Institutional Equities Fund), too, was plunging, down nearly $3 billion, or about 10%. An eerie quiet enveloped Renaissance’s lunchroom, as researchers and others sat in silence, wondering if the firm would survive.”

But wait: doesn’t Pippa Stevens contend that “the secret behind the greatest modern day moneymaker on Wall Street” is to “remove all emotion”? Why, during the GFC, was Simons “scared”? Was it because the title of Zuckerman’s book is false – Simons wasn’t “The Man Who Solved the Market”?

The GFC demonstrated that, to say the least, Simons’ and RenTech’s algorithms and models weren’t robust; to say the most, they abjectly failed. They’d been designed to remove emotion from trading; yet during the GFC, they – like other “quants” – were nervous wrecks.

In contrast, whilst Simons, RenTech and other “quant” funds were anxiously selling, Warren Buffett was calmly buying. He and Berkshire saw the crisis as an opportunity to purchase the shares of quality businesses at massively discounted prices.

Other speculators’ fear is the hedge fund manager’s worst enemy – and speculators’ panic and failure is the investor’s best friend.

RenTech’s Vehicles

RenTech manages four funds: (1) the Medallion Fund; (2) the Renaissance Institutional Equities Fund (RIEF), (3) the Renaissance Institutional Diversified Alpha (RIDA), and (4) the Renaissance Institutional Diversified Global Equity Fund.

According to media reports, which ultimately rely upon Zuckermaan’s estimates, between 1988 and 2018 the Medallion Fund generated a 66.1% average gross annual return and a 39.1% average net (of fees) annual return. Sounds great, right? That’s way better than Buffett and Berkshire, right? On that basis, Simons is the greatest, right?

If it’s too good to be true, it probably is. In this case there are several crucial caveats. The first is that Medallion is EXTREMELY secretive; hence Zuckerman’s estimates of its returns are uncorroborated and unverifiable:

1. since 1993, Medallion has been closed to outside investors (it’s available only to RenTech’s senior employees and their families);
2. its results have never been disclosed to the public;
3. although it’s highly likely that (for tax purposes and in order to comply with regulatory requirements) RenTech reports details about Medallion to the U.S. Internal Revenue Service and Commodity Futures Trading Commission (in the wake of the Madoff scandal, the Securities and Exchange Commission investigated it but found nothing improper or suspicious), these agencies have never released any details about Medallion;
4. Medallion’s operations and results have never been audited – or, at any rate, no audit has ever been released to the public.

In 1993, according to Bloomberg (reprinted as “The code-breaking maths whiz who built a $A38 billion fortune,” The Sydney Morning Herald, 15 May 2024), “Simons stopped accepting new money from Medallion clients, and in 2005, he kicked out outsiders entirely, allowing only employees to invest. He returned profits every year, limiting the size of the fund to around $US10 billion.”

James Gruber is thus fundamentally mistaken.

He asserts: “hedge fund investor (sic) Jim Simons’ flagship Medallion Fund has returned an astonishing 62% per annum over 33 years. $1,000 invested in his fund in 1988 would have grown to more than $8 billion by 2021.” As a comment to his article noted, “Medallion ... is capacity constrained. So if you (expended) $1,000 in 1988 then you would still only have $1,000 today, not $42 million, and you would have received about $370 per year in distributions on average. (These are very) high returns, but you are not allowed to compound them.”

The contrast between Simons’, Medallion’s and RenTech’s secretiveness on the one hand and Warren Buffett’s and Berkshire’s openness on the other thus couldn’t be greater.

Since 1964, Berkshire’s audited financial statements have been available via the SEC; since 1995, they’ve been freely available on Berkshire’s website. Moreover, as a publicly-listed entity (which neither Medallion nor RenTech are), Berkshire’s short, medium and long-term returns are easy to calculate.

Zuckerman’s estimates of Medallion’s returns (see Appendix 1 of his book) stem from his relationships with individuals who’ve been employees of Medallion and RenTech. Interviews allowed him “to piece together a picture of the fund’s strategy and success without having access to confidential internal data.”

How do I assess Zuckerman’s estimates of Medallion’s returns? I assume that he’s been diligent and honest. His estimates are the best available, but that’s because they’re the ONLY ones available. Although they’ve not been independently verified, they’ve nonetheless been widely accepted – and unquestioningly lauded.

Despite my doubts, I’ll assume that they’re at least roughly accurate. That’s because, even under this assumption, they’re certainly not nearly as good as they appear at first glance.

Medallion’s Nosebleed Leverage

If it’s too good to be true, it probably is. Another major caveat should temper any assessment of Medallion: it’s HUGELY leveraged. It’s a logical consequence of their definitions: an unleveraged entity’s return on assets (ROA) equals its return on equity (ROE). The higher is its leverage, however, the more ROE exceeds ROA.  

According to Simons’ article, “Risk and Reward: How Leverage Amplified the Medallion Fund’s Gains (Quantified Strategies, 26 January 2025), which was published shortly after his death, Medallion’s leverage has averaged 12.5 times its equity and has occasionally zoomed as high as 20 times. In other words, each $1 of its assets has on average comprised $0.92 of debt and just $0.08 of equity – and at times each $1 of its assets has comprised as little as $0.05 of equity and as much as $0.95 of debt.

This leverage is a key aspect – indeed, in Simons’ own words it’s a “defining factor” – of Medallion’s high returns: net of leverage, its average annual return is just 3-7% – which is much less than Berkshire’s and the S&P 500 Index’s (for details, see Naysayers are wrong: you CAN emulate Warren Buffett, 10 June).

On a comparable (that is, unleveraged and risk-adjusted) basis, Berkshire’s results since the 1960s have greatly exceeded Simons’ and RenTech’s since the 1990s.

To fully appreciate this crucial point, consider Table 1. It presents the simplified balance sheet of an entity whose leverage (that is, ratio of assets to equity) is 12.5 times. Assume that its return on equity is 66%, i.e., that its earnings are $8 × 0.66 = $5.28. Its return on assets – equivalent to its unleveraged return – is therefore just $5.28 ÷ $100 = 5.3%. And if fees cut the entity’s ROE to 39%, then its ROA falls to a mere $3.90 ÷ $100 = 3.9%.

Table 1: Leverage Turns Low ROA into High ROE

Why is Medallion’s – and the typical hedge fund’s – return on assets so low? One reason has always existed: the anomalies, discrepancies and inefficiencies which their algorithms seek to locate are minute and fleeting. Another reason’s importance has grown over time: ever more hedge funds are trying to exploit the same discrepancies.

Consequently, something akin to an arms race ensues: arbitrage causes the anomalies’ magnitudes and life-spans to shrink even further. Hedge funds thereby generate ever-lower returns on assets; they also require ever more leverage – and incur ever more risk – to generate a given return on equity.

Simons’ own words are telling: “leverage has been a cornerstone of Medallion’s investment strategy, enabling it to achieve extraordinary returns. The Fund has significantly amplified its potential returns through the use of borrowed capital ... This approach resulted in a correct trade rate of 50.75%, highlighting leverage’s impact on performance. Strategically using leverage has critically amplified the Medallion Fund’s returns, contributing to its success.”

The success of Medallion’s leveraged trades is thus, in effect, the toss of a coin. Medallion’s ability through high leverage “to turn small statistical advantages into significant profits has been a key factor (of its) remarkable success.”

According to Simons, “the Medallion Fund’s ability to maintain a leverage ratio averaging around 12.5 times its equity demonstrates its aggressive approach to maximizing returns. However, leverage is a double-edged sword. While it can significantly boost returns, it also introduces substantial risks. “The Medallion Fund has managed these risks through sophisticated strategies ... to counteract the potential downsides of high leverage.”

This high level of leverage “has allowed the Medallion Fund to take advantage of even the smallest market inefficiencies, turning minor pricing discrepancies into substantial profits. High leverage ratios enable (it) to deploy vast amounts of capital in the market, increasing its ability to influence asset prices and secure profitable positions. This approach, coupled with the Fund’s innovative investment strategies and high-frequency trading techniques, has been instrumental in boosting the potential for higher returns.”

Simons concluded: “leveraging its equity to this extent has led to the Medallion Fund’s remarkable success, distinguishing it from other hedge funds.”

Renaissance Institutional Equities Fund (RIEF) also utilises leverage – albeit considerably less than the Medallion Fund. RenTech’s website states that RIEF has been designed to be “net $100 long for each $100 of equity, with leverage constraints typically averaging 2.5 to 1.0 (1.75 long/2.0 short).” For every $1 of equity, RIEF typically adds an additional $1.75-$2.00 of debt. Per $1 of assets, that’s $0.33 of equity and $0.66 of debt.

Given RIEF’s high but much lower leverage than Medallion’s, and assuming that their underlying tactics and strategies are comparable, it’s reasonable to assume that RIEF’s returns – which I analyse below – will be much lower than Medallion’s – and that this disparity is the result of leverage.

Medallion and LTCM

Among quants, Medallion and RenTech are widely admired and often revered. So was Long Term Capital Management (LTCM) – before it suddenly and ignominiously collapsed. LCTM relied and Medallion and RenTech continue to rely upon complex models and high leverage. In fairness, LCTM was much more aggressively leveraged than is Medallion; net of leverage, however, both generate(d) subpar or worse results.

LTCM was an extremely highly-leveraged hedge fund, which was formed in 1994 and whose leverage usually varied within the range 25-40:1. For each $1 of equity, it borrowed $30-$40. At its height in early-1997, its equity was approximately $5 billion and it had borrowed ca. $125 billion, and the notional value of its derivatives contracts (which didn’t appear on its balance sheet) exceeded $1 trillion. In 1997-1998, the relentless shrinkage of its assets’ market valuations and the stability of its liabilities caused its leverage to skyrocket towards a staggering 100:1 – and triggered its collapse.

RenTech, Medallion and RIEF have borrowed only one-third as much per $1 of equity as did LTCM at its peak. That’s one major reason why they haven’t imploded; another is Medallion’s shift to high-frequency trading.

According to Scott Patterson, Medallion originally retained positions “for several days, even weeks.” But as time passed holding periods often shortened “to less than a day, or even just an hour, depending how a position moved.” As a result, the average gain per trade has decreased – but the magnitude of losses, too, has been curtailed.

LTCM’s partners (led by John Meriwether, who had made his reputation as a senior trader at Salomon Brothers, and advised by Myron Scholes and Robert Merton, who in 1997 shared the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, which is ubiquitously but erroneously known as “the Nobel Prize in Economics”) believed that their complex computer models minimised – indeed, virtually eliminated – the risk of heavy leverage; but market volatility (which their models utterly failed to foresee) and extremely high leverage triggered catastrophic losses.

The Russian debt and Asian financial crises of 1997-1998 were the proximate causes of LTCM’s implosion; the ultimate cause was LTCM’s partners’ gross overconfidence in their fatally flawed models.

They believed that their models weren’t merely rough approximations of reality: they actually WERE “the truth.” As such, they borrowed as much as lenders would lend to them – which, given that their partners including illustrious academics (who, ironically, won the Bank of Sweden Prize just as LTCM began to implode) was plenty. False models, extreme leverage and hubris combined to produce LTCM’s collapse – and its failure triggered a bailout facilitated by the Federal Reserve.

Roger Lowenstein’s superb book (When Genius Failed: The Rise and Fall of Long-Term Capital Management, Random House, 2000) quantifies the magnitude of its failure: “even with the headwind of its first four highly successful years, (LTCM’s) final, cumulative loss was staggering. Through April 1998, the value of a dollar invested quadrupled to $4.11. By the time of the bailout, only five months later, just $0.33 of that total remained. After deducting the partners’ fees, the results were even sorrier: each invested dollar, having grown (at its peak) to $2.85, (ultimately) shrank to a meagre $0.23.”

“In net terms, Lowenstein concludes, “the greatest fund ever – surely the one with the greatest IQs – had lost 77% of its capital while the ordinary stock market investor had been more than doubling his money.”

At the end of 1995, LTCM’s leverage ratio was 28:1; during that year, its return on equity was 59%. “Of course,” wrote Lowenstein, “its return on total assets ... was far, far less ... approximately 2.45%. This miniscule figure is what Long Term would have earned had it invested only its own money (as opposed to the funds it had borrowed). But even this figure is too high because it doesn’t reflect Long Term’s derivatives trades, which it didn’t record on its balance sheet. But derivatives most certainly increased Long Term’s ... riskiness.”

“Taking its derivative trades into account,” LTCM’s return on assets “was probably less than 1%. The exact number is unimportant; the point is that almost all of its heady 59% return was due to the remarkable power of leverage.” High-risk leverage, not high-IQ Ph.D.s, underlay its temporary success.

RIEF’s Results versus Berkshire’s and the S&P 500’s

According to Amy Whyte (“The Famed Medallion Fund Is Crushing It. Other RenTech Funds, Not So Much,” Institutional Investor, 21 April 2020), “Medallion employs a short-term, quantitative trading strategy across multiple asset classes. These include global equities, futures, commodities, and currencies, according to a person familiar with the fund. It also tends to have high turnover and significant leverage.”

RIEF, by comparison, only trades equities, and can hold stocks for long periods of time (II doesn’t specify how long), according to fund’s registration document, which I’ve been unable to procure). “As II previously reported, (REIF) was created to generate gross annual returns of 400 to 600 basis points – or 4 to 6 percentage points – above the S&P 500 over rolling three- to five-year periods.”

“The Renaissance Institutional Diversified Alpha fund, meanwhile, trades equities, derivatives, and various instruments in the global futures and forwards markets, according to fund documents. Like REIF, the RIDA fund holds significant individual positions, usually for long periods of time. The Renaissance Institutional Diversified Global Equities Funds ... trades equities and derivatives ... According to fund documents, RIDGE seeks to be market neutral by maintaining low levels of beta, or exposure to the broader market. But as the firm admitted in the fund registration document, ‘the beta models in recent volatile markets have not performed as expected.’”

Of these funds, RIEF is most comparable to Berkshire Hathaway and the S&P 500 Index. I’ve obtained its monthly results since January 2017 from a reliable source. If in that month you invested in each of these three, what would your investments have been worth each month since? Figure 1 plots the results.

Figure 1: Value per $1 of Investments in Berkshire Hathaway, RIEF and the S&P 500 Index, Monthly, January 2017-December 2024

Each $1 invested in Berkshire in January 2017 grew to $2.51 in December 2024. That’s a compound annual growth rate (CAGR) of 13.7% per year. Each $1 invested in a portfolio which perfectly mimicked the S&P 500 Index grew to $2.49 (dividends included). That’s a CAGR of 13.6% per year. Each $1 invested in RIEF, however, grew to just $1.51. That’s a CAGR of 6.5% per year.

Over the four years to December 2021, RIEF – a major vehicle of “the world’s greatest hedge fund,” managed by “the greatest-ever speculator” earned not a penny.

Do you want to outperform what some laud as world’s greatest hedge fund manager? Since 2017 it’s been trivially easy: either buy Berkshire Hathaway’s shares or units in a low-fee S&P 500 index fund! (See also Richard Ennis, CFA, “Hedge Funds: A Poor Choice for Most Long-Term Investors?” Enterprising Investor, 26 June 2024, and Raymond Kerzérho, CFA, “6 Reasons to Avoid Hedge Funds,” Enterprising Investor, 13 March 2025).

Recall that RIEF utilises significant leverage; hence its unleveraged results are even more mediocre compared to Berkshire’s and the Index’s. Moreover, REIF was created to outperform by 4-6 percentage points the S&P 500 Index’s gross returns over rolling three- to five-year periods. In this respect RIEF – and RenTech and its “quants” – have clearly failed.

I’ve not been able to locate performance data for RIDA and RIDGE – RenTech, remember, is highly secretive – but it’s reasonable to suppose that their results have also been underwhelming. According to Bloomberg (“Renaissance Investor Exodus Nears $15 Billion Despite 2021 Gains,” 21 January 2022), RIEF’s assets under management shrank by 40% between December 2019 and November 2021, and RIDA’s and RIDGE’s shriveled 67% and 49%, respectively.

According to The Financial Times (“Renaissance’s shrinking hedge funds,” 20 September 2024),”nearly two-thirds of Renaissance’s external assets under management have evaporated over the past five years, falling from $65.1 billion to $23.2 billion today.”

“This disparity” between Medallion’s purported results and those of RenTech’s other funds (such as RIEF’s in Figure 1), writes Whyte, is “’really surprising’” according to Renaissance Technologies sceptic Bradford Cornell, a professor emeritus (in financial economics) at the University of California Los Angeles.

“Cornell previously analyzed the performance of (RenTech’s) funds in a brief paper entitled ‘Medallion Fund: The Ultimate Counterexample?’ In the paper, he wrote that the performance of the Medallion fund — which between 1988 and 2018 delivered a gross annualized return of 66 percent — was ‘extraordinary.’” REIF’s and RIDA’s returns, however, were “relatively mundane and in no way comparable to (Medallion’s).”

“Discrepancies between Medallion’s returns and those of other Renaissance funds catch the eye, but I've never seen evidence something nefarious is going on – neither has the SEC, which spent two years in their offices after the Madoff scandal,” (Cornell) said. “Medallion has short-term holding periods, RIEF and the other funds search for longer-term aberrations and own smaller stocks and other investments – things Medallion shies away from.”

“If they have such secret sauce, how could the public funds be down so much?” Professor Cornell said in an email. “I wish I had an answer.”

Why Hedge Funds Underperform – and Worse

In Stop kidding yourself: Nobody can “time the market” (30 June) I demonstrated that most speculators’ results are at best mediocre. In sharp contrast, most investors produce reasonable gains; hence investors as a whole almost invariably outperform speculators. This article has corroborated and elaborated that conclusion.

Even when I tip the scales in RIEF’s favour – that is, compare its leveraged to Berkshire’s unleveraged returns – the inference is unambiguous: in the long term, the best investors handily outperform the most successful speculators.

Why do hedge funds usually underperform – and, during crises, often crash? Thanks not least to competition among hedge funds, most of the time the anomalies which they seek to exploit are typically small and fleeting. As a result, their return on assets is low.

In response, they resort to hefty leverage. Large debt boosts return on equity relative to return on assets, but also greatly magnifies risks.

What happens when heavily-leveraged hedge funds incur losses? Table 1 provided an example. If this entity’s assets’ valuation falls a mere 5% (to $95), and its liabilities remain unchanged at $92, its equity plunges to $95 - $92 = $3 – and its leverage ratio balloons to $95 ÷ $3 = 31.6. At that point, if its assets fall another 3%, it becomes insolvent. Hedge funds’ leverage is a bomb; their low ROA is a fuse – which their flawed models ignite.

Do quants’ models contain a fatal flaw – one which predisposes heavily-leveraged hedge funds to crash? The answer isn’t just unequivocally “yes” – it’s been known (but, it seems, denied by hedge funds) since the 1960s.

In 1961, Benoit Mandelbrot, a French mathematician who was working at IBM’s research lab, wrote an internal (and hence unpublished and little-known) report entitled “The Variation of Certain Speculative Prices.” Its key insight: unexpected and extreme price volatility is much more frequent than a standard (normal) distribution predicts. Distributions of price changes have “fat tails” (see also Stop kidding yourself: Nobody can “time the market,” 30 June),

The implications are momentous. Prices’ fluctuations are usually moderate; but in a surprisingly (shockingly to quants and fanatical adherents to their models) large number of instances, volatility explodes and prices gyrate wildly – and enough to cause heavy and fatal losses to speculators who make large and heavily-leveraged bets. Nassim Nicholas Taleb, a harsh critic of quants and their models, has dubbed these unexpected, wild and catastrophic swings of price as “black swans.” He’s also contended in several books that these events are far more frequent than proponents of quant models confidently – indeed, fervently – believe.

“Fat tails” eventually doom heavily-leveraged hedge funds – and the higher is their leverage, the more they resemble ticking time bombs.

How frequently do “black swans” occur? The answer to this question has been available since the 1960s – and ignored by hedge funds since the 1990s. As a young finance academic at the University of Chicago (and in 2013 a recipient of the Bank of Sweden Prize in Economic Science in Memory of Alfred Nobel, commonly but erroneously called “the Nobel Prize in Economics”), Eugene Fama discovered that extreme movements of prices occur MUCH more commonly than quants’ models suppose. In “The Behavior of Stock-Market Prices” (Journal of Business of the University of Chicago, vol. 38, no. 1, 1965), he concluded:

“If the population of price changes is strictly normal,” as these models assume, then “an observation more than five standard deviations from the mean should be observed about once every 7,000 years.” In reality, given the price changes’ “fat tails,” “such observations seem to occur about once every three to four years.”

As G.K. Chesterton wrote in his “spiritual autobiography” and classic of Christian apologetics, Orthodoxy, in 1908, “life is not an illogicality; yet it is a trap for logicians. It looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in wait.”

Chesterton rejected the claim that logic and reason fully explain the complexities of life and the universe – and thus, by implication, quants’ assertion that their models can anticipate, never mind exploit, markets’ fluctuations.

Four factors – (1) trades whose odds barely exceed 50:50, (2) markets which are usually roughly efficient but occasionally become extremely volatile, (3) high leverage and (4) gross overconfidence in false models – condemn hedge funds to underperformance and worse. In Taleb’s terms, hedge funds are fragile – and Berkshire is “anti-fragile.”

It’s therefore absurd to compare the highly secretive, heavily leveraged and uncorroborated results of RenTech’s speculative Medallion Fund to the unleveraged, very public and thus easily verifiable results of Warren Buffett’s and Berkshire’s investments. Comparing RIEF’s leveraged to Berkshire’s unleveraged results, it’s reasonable to regard RenTech as a more disciplined and less overconfident version of the reckless and arrogant Long Term Capital Management.

That’s no small achievement; all the same, it doesn’t come close to Berkshire’s.

Implications

James Simons, Medallion – and, until recently, RenTech – qualify as members of an extremely small group: successful speculators. It’s no skin off my nose, so I’m happy to assume that Simons was the best speculator and hedge fund manager of his era, or even of all time.

Others are far less restrained. “There are just a few individuals who have truly changed how we view the markets,” Theodore Aronson, founder of AJO Vista, a quant money management firm, told Bloomberg Markets in 2008. “John Maynard Keynes is one of the few. Warren Buffett is one of the few. So is Jim Simons.” “Jim had three totally remarkable careers – as a mathematician, as a pioneer of quantitative methods in trading and as a philanthropist,” said Jeff Cheeger, the Silver Professor of Mathematics at New York University’s Courant Institute and formerly Simons’ student, told Bloomberg in 2024. “He was one of the great men of our time.”

Simons founded and built a profitable business which made him one of the planet’s wealthiest men. But it didn’t produce goods or provide services which benefit the general public: it took huge numbers of arcane and highly-leveraged bets on the day-to-day – and even the hour-to-hour – fluctuations of stocks’ and bonds’ and other securities’ prices. Simons harnessed formidable mathematicians and physicists, etc., extremely complex algorithms, very powerful computers, mountains of data and super-fast telecoms; he thereby helped to revolutionise the hedge fund industry.

Stripped of many complexities, Simons was a successful market-timer and trader. Yet the wagers of this master speculator succeeded barely half the time.

Those who label him as “the world’s greatest investor,” as Zuckerman, Stevens and Gruber do, are thus trebly mistaken: (1) Simons wasn’t an investor; (2) formidable mathematicians and their complex infrastructure haven’t underpinned Medallion’s returns: a simple and risky phenomenon, heavy leverage, has; (3) Medallion’s and RIEF’s unleveraged returns demonstrate that Simons was and RenTech is greatly overrated.

Bluntly, RenTech’s is a more disciplined – or perhaps just luckier? – version of Long-Term Capital Management. That’s saying something – but is it saying anything of enduring relevance?

Apart from enriching their principals, what’s the purpose of hedge fund managers like RenTech, and of funds like Medallion and RIEF? According to William Bernstein, whom James Gruber cites (I’ve been unable to locate the reference), “clearly, the quant hedge fund business has little to do with the primary societal purpose of capital markets – the efficient allocation of capital to productive enterprises.”

Unlike investment, which is positive-sum (i.e., a “win-win” process which benefits consumers as well as investors) and whose outcome is an increase of value or resources, speculation, says Bernstein, “is a zero-sum game that transfers (wealth from one speculator to another rather than creates) wealth … “

Bernstein posed a crucial question: “were quantitative hedge fund managers to suddenly disappear, would they be missed? Or might the world be a better place without them?”

In March 2008, Paul Wilmott, founder of the mathematical finance program at Oxford University, lambasted Wall Street’s arrogant, ignorant and massively destructive quant culture – and answered Bernstein’s query: “banks and hedge funds employ mathematicians with no financial market experience to build models that no one is testing scientifically ... (and are used) by traders who don’t understand them. And people are surprised by the losses!”

In 2009, in Berkshire’s Annual Report, Warren Buffett urged that investors worthy of the label steer well clear of quants and their models: “beware of geeks bearing formulas.” According to Scott Patterson, Charlie Munger went much further: “people assume that if they use higher mathematics and computer models they’re doing the Lord’s work. They’re usually doing the devil’s work.”

The divide between speculators like Simons and RenTech on the one side and investors like Warren Buffett and Berkshire Hathaway on the other isn’t just vast: it’s unbridgeable. The lessons for investors (as opposed to market-timers, traders and speculators) are therefore profound.

Simons and RenTech developed mostly zero-sum tactics which are vastly beyond the capabilities of ordinary people: they require Ph.D.s in mathematics, physics or computer science from a leading university; they also require tens, scores or even hundreds of millions of dollars to develop and refine the algorithms and acquire the data, computing power and super-fast telecoms which “quant” trading necessitates. Globally, only a handful of entities possess such resources.

In diametric contrast, and as I demonstrated in Naysayers are wrong: you CAN emulate Warren Buffett, 10 June), Buffett and Berkshire utilise positive-sum methods and strategies which many individual investors can – if they’re willing to take the time and effort – apply to their own circumstances. Much more fundamentally, you can emulate Buffett’s (which is essentially Benjamin Graham’s) ethos.

Doing so develops the virtues of courage, curiosity, discipline, humility, independence, patience and scepticism. Speculation, on the other hand, indulges vices such as avarice, arrogance, impatience – and thus, in most people, indiscipline and even recklessness.

When they’re compared on an “apples to apples” (i.e., return on assets) basis, Berkshire’s results since the 1960s have consistently outperformed Simons’ and RenTech’s since the 1990s. Since 2017, Berkshire and the S&P 500 Index – both of which are unleveraged – have handily outpaced RenTech’s heavily-leveraged RIEF. When you bring scale into consideration – Medallion has been capped at ca. $US10 billion; RenTech presently manages ca. $23 billion; Berkshire’s assets presently exceed $1.1 trillion – Buffett and Berkshire outshine Simons, Medallion and RenTech all the more.

For these reasons, as well as RenTech’s their near-collapse during the GFC and its trials and tribulations over the past few years, there’s simply no question: Buffett has been his era’s greatest nurturer and creator of wealth.

In contrast, Simons and his speculative, heavily-leveraged “quant” funds don’t stand on the medal podium. As market-timers, traders and speculators, morally and empirically they don’t even qualify for the competition.

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