Princeton Newport Partners (PNP) was a revolutionary idea when we set it up in 1969. We specialized in the hedging of convertible securities—warrants, options, convertible bonds and preferreds, and other types of derivative securities as they were introduced into the markets. Hedging risk was not new but we took it to an extreme never before tried. To begin with, we designed each of our hedges, which combined the stock and convertible securities of a single company, to minimize the risk of loss whether the stock fell or rose. We invented hedging techniques to further protect our portfolio against changes in interest rates, changes in the level of the overall market, and the catastrophic losses that can occasionally occur from enormous unexpected changes in prices and volatility. We managed this with mathematical formulas, economic models, and computers. This nearly total reliance on quantitative methods was unique, making us the earliest of a new breed of investors who would later be called quants, and who would radically transform Wall Street.
I could see from the beginning how our wealth could grow. But when I told friends and colleagues what I was up to, Vivian was almost the only one who got it, despite what I had already done in gambling. Although she wasn’t a scientist or mathematician, she shared two qualities with the best of them: She asked the right questions, and she grasped the essentials. She had spent hours helping me film spinning roulette balls so I could make a machine to predict which number would come up, just as she had dealt thousands of blackjack hands so I could practice counting cards. And she helped me edit my books about gambling and the stock market and negotiate the contracts.
My initial plan for Princeton Newport Partners, which for the first five years we called Convertible Hedge Associates, was to find pairs of closely related securities that were priced inconsistently with respect to each other, and use them to construct investments that reduced risk. To form these hedges, we simultaneously bought the relatively underpriced security while offsetting the risk from adverse changes in its price by selling short the comparatively overpriced security. Since the prices of these two securities tended to move in tandem, I expected the combination to reduce risk while capturing extra returns. I identified these situations using the mathematical methods I had worked out for judging the proper price of a warrant, option, or convertible bond versus the common stock of the same company.
Betting on a hedge I had researched was like betting on a blackjack hand where I had the advantage. As in blackjack, I could estimate my expected return, estimate my risk, and choose how much of my bankroll to bet. Instead of a $10,000 bankroll I now had $1.4 million, and instead of a $500 maximum bet, the Wall Street casino had no limit. We started betting $50,000 to $100,000 per hedge.
To search for opportunities, early every afternoon after the market closed in New York, UC, Irvine students whom I hired went to the offices of two brokerage firms with which I traded. They collected the closing prices for hundreds of warrants, convertible bonds, convertible preferreds, and their associated common stocks. A preferred stock typically pays a regular dividend, whereas a common stock may or may not pay a dividend and, if it does, will generally vary over time. A preferred stock’s dividend is paid first—in preference—before any payments due to the common stock. In the typical case, where the dividend amount is fixed, the preferred is like a bond but more risky because the dividend payments and the claim on assets upon liquidation are only paid after the corresponding bond payments. A so-called convertible preferred is one that can be exchanged for a specified number of shares of the common. So a convertible preferred is like a convertible bond but less secure, as it is paid only if there is enough money to do so after the bondholders receive their interest. At that time they gave us numerous investment possibilities.
I started by running the business from our house in 1969, and the house itself showed how much our circumstances had already changed. Eight years earlier when we arrived at New Mexico State University we rented a single-story nine-hundred-square-foot house with four tiny bedrooms, all of which were soon needed. Our second daughter, Karen, was born a few months later, followed by our son, Jeff, the next year. Soon thereafter, gambling winnings and book royalties not only allowed me to pay for my stock market education, but also to buy our first house. When we moved to UC, Irvine a couple of years later, we found a larger and nicer two-story home in Newport Beach, where the West Coast operations of Princeton Newport Partners began.
Vivian and I hired a contractor to add an outside staircase and a large second-story room to accommodate the business. In the new room the data was plotted on mathematical diagrams that I invented. These revealed favorable situations and let me quickly specify the appropriate trades. Each day’s closing prices for a convertible and its stock were plotted as a color-coded dot on that particular convertible’s diagram. The diagrams were prepared with curves that were drawn by a computer from my formula and showed the “fair price” of the convertible. The beauty of this was that I could immediately see from the picture whether we had a profitable trading opportunity. If the dot representing the data was above the curve it meant the convertible was overpriced, leading to a possible hedge: Short the convertible, buy the stock. A data point close to or on the curve indicated the price was fair, which meant liquidate an existing position, do not enter a new one. Below the curve meant buy the convertible, short the stock. The distance of the dot from the curve showed me how much profit was available. If we thought it met our target, we tried to put on the trade the next day. The slope of the curve near the data point on my diagram gave me the hedge ratio, which is the number of shares of common stock to use versus each convertible bond, share of preferred, warrant, or option.
After suffering for a few months with the distractions from the beehive of activity at the house, Vivian made me lease an office. Moving to the second floor of a small office building, I bought computers and hired more people. I developed printed tables for trading each hedge. These tables listed the prices of the stock versus the convertible needed to achieve our target return. Besides the new hedges we wanted to add, the tables told us how to adjust existing positions that needed the hedge ratio changed (so-called dynamic hedging) because the stock price had moved or that should be closed because we reached our objective.
Our computers used so much electricity that the office was always hot. We left the windows open and blew out the heat with fans, even during the coolest part of the California winter. Our landlord didn’t charge tenants for utilities, instead paying it from his lease revenues. When the heat got my attention, I calculated that the cost of the electricity we used was more than our rent. We were getting paid to be there.
Each day after the market closed, I called Jay Regan in New York with trading instructions for the next day. He had given me the results from our trades earlier during the day, from which I had already updated my position records. The next day he executed the trades I recommended, reported the results, and the whole process was repeated.
To inform our limited partners as well as potential new partners, we periodically issued updated versions of our Confidential Private Placement Memorandum, which explained such things as the operations and objectives of the partnership, the fee structure, and the potential risks. We included simplified schematic descriptions of a few of our actual investments, without the mathematical formulas, diagrams, and calculations.
One of these trades could have been right out of the pages of Beat the Market. In 1970 the American Telephone and Telegraph Company (AT&T) sold warrants to purchase thirty-one million shares of common stock at a price of $12.50 per share. Proceeds to the company were some $387.5 million, at the time the most ever for a warrant. Though it was not sufficiently mispriced then, the history of how warrant prices behaved indicated this could happen before it expired in 1975. When it did we bet a significant part of the partnership’s net worth.
We were guided in this trade and thousands of others by a formula that had its beginnings in 1900 in the PhD thesis of French mathematician Louis Bachelier. Bachelier used mathematics to develop a theory for pricing options on the Paris stock exchange (the Bourse). His thesis adviser, the world-famous mathematician Henri Poincaré, didn’t value Bachelier’s effort, and Bachelier spent the rest of his life as an obscure provincial professor. Meanwhile a twenty-six-year-old Swiss patent clerk named Albert Einstein would soon publish in his single “miraculous year” of 1905 a series of articles that would transform physics. One of these initiated the Theory of Relativity, which revolutionized the theory of gravitation and led to the nuclear age. The second paper, on the particle nature of light, helped launch the Quantum Theory. But it is yet another of Einstein’s articles that connects with my story.
In that paper Einstein explained a baffling discovery made in 1827 by the botanist Robert Brown. Brown used his microscope to observe pollen particles suspended in water. When illuminated, their tiny points of reflected light displayed a ceaseless irregular random motion. Einstein realized that this was caused by the bombardment of the pollen particles by molecules of the surrounding liquid. He wrote down equations that correctly predicted the statistical properties of the random motion of the particles. Until that time no one had ever seen a molecule or an atom (molecules are groups of atoms of various types bound together by electrical forces), and their existence had been disputed. Here was the final proof that atoms and molecules were real. This article became one of the most widely cited in all of physics.
Unknown to Einstein, his equations describing the Brownian motion of pollen particles were essentially the same as the equations that Bachelier had used for his thesis five years earlier to describe a very different phenomenon, the ceaseless, irregular motion of stock prices. Bachelier employed the equations to deduce the “fair” prices for options on the underlying stocks. Unlike Einstein’s work, Bachelier’s remained generally unknown until future Nobel laureate (1970) Paul Samuelson came across it in a Paris library in the 1950s and had it translated into English. Bachelier’s paper appeared in 1964 in The Random Character of Stock Market Prices, edited by Paul Cootner and published by the MIT Press. Part of my early self-education in finance, this collection of articles applying scientific analysis to finance strongly influenced me and many others.
Bachelier had assumed that changes in stock prices followed a bell-shaped curve, known as a normal or Gaussian distribution. This didn’t match real prices well, especially for periods longer than a few days. By the 1960s, academics had improved on Bachelier’s work by using a more accurate description of stock price changes. Even so, these newer formulas for fair option prices, which applied as well to warrants, were not useful for trading because they included two quantities that could not be estimated satisfactorily from data. One of these was a growth rate for the stock between “now” and the warrant’s expiration date. The other was a discount factor that was applied to the warrant’s uncertain payoff at expiration in order to obtain its present value.
This discount factor, or markdown, accounted for the fact that investors tend to value an uncertain payoff less than if it was a sure thing. For example, if you toss a fair coin—which by definition has equal chances of coming up heads or tails—an investor who is paid $2 for heads and nothing for tails has an average but uncertain return of $1. This value is found by multiplying each payoff by the number of ways it occurs (one, in this example) and dividing by two, the number of possible outcomes. Most investors would rather be paid $1 for sure. For two investments with the same expected return, the less risky one tends to be preferred. Influenced by having been born during the Great Depression and by my early investment experiences, I made reducing risk a central feature of my investing approach.
Back in 1967, I had taken a further step in figuring out how much a warrant was worth. Using plausible and intuitive reasoning, I supposed that both the unknown growth rate and the discount factor in the existing warrant valuation formula could be replaced by the so-called riskless interest rate, namely that which was paid by a US Treasury bill maturing at the warrant expiration date. This converted an unusable formula with unknown quantities into a simple practical trading tool. I began using it for my own account and for my investors in 1967. It performed spectacularly. In 1969, unknown to me, Fischer Black and Myron Scholes, motivated in part by Beat the Market, rigorously proved the identical formula, publishing it in 1972 and 1973. This launched the development and widespread use of so-called derivative securities throughout the financial world. For their contributions, Myron Scholes and Robert Merton received the Nobel Prize in Economics in 1997. The Nobel committee acknowledged Fischer Black’s (1938–95) contributions, and it is generally agreed that he would have shared in the prize had he not died earlier from throat cancer.
Powered largely by the formula, Princeton Newport Partners prospered. In our first two months, November and December 1969, our investors gained 3.2 percent while the S&P 500 lost 4.8 percent, an 8 percent edge. In 1970 we were up 13.0 percent versus 3.7 percent for the S&P. In 1971 the score was 26.7 percent to 13.9 percent, which was almost 13 percent better for our limited partners. In 1972 the S&P finally did better, making 18.5 percent compared with our 12 percent. Does this mean we did badly? No. It showed we were doing exactly what we intended to do, produce steady high returns in both good times and bad times. The hedges protected us against losses but at the expense of giving up some of the gains in big up-markets. The variation in our returns from year to year was mostly due to fluctuation in the quantity and quality of hedged investments, rather than the ups and downs of the market. Our first severe test came with the big bear market of 1973–74. The downturn was driven in part by the Arab oil embargo. The resulting record oil prices, adjusted for inflation, were never surpassed until the great run-up to $140 a barrel, reached in 2008.
In 1973 the S&P fell 15.2 percent and we were up 6.5, with our partners beating the market by over 20 percent. Stock market investors were hurt even more in 1974. The S&P plummeted 27.1 percent and our partners made 9.0 percent, a gap of more than 36 percent in our favor. Over that two-year cycle, limited partners in PNP saw each $1,000 increase to $1,160, whereas investors in the S&P 500 saw their $1,000 shrink to $618. Moreover, PNP made money every month in its first six years except for one in early 1974, when it declined less than 1 percent. From the peak on January 11, 1973, to the bottom on October 3, 1974, the drop in the stock market was a savage 48.2 percent, the worst since the Great Depression. Even Warren Buffett said then that it was a good thing for his partners he’d closed down when he had.
Existing partners were adding money and prospective new partners were learning about us through word of mouth. Partnership capital had grown from the initial $1.4 million to $7.4 million, and the general partners’ compensation increased proportionately. Since the Investment Company Act limited us to ninety-nine partners, each investor’s stake would have to average over $1 million in order for our pool to reach $100 million. Therefore we wanted high-net-worth individuals and institutional investors who would make an initial investment in PNP that would be substantial for us but a small part of their overall funds. We also liked that high-net-worth investors tended to be more knowledgeable, more experienced, and better able to judge the risks of the partnership, as well as having their own advisers. To increase the amount of new capital we could get from the dwindling number of spots available for new partners, we raised the minimum to join from our initial $50,000 to $100,000, then $250,000, $1,000,000, and eventually $10,000,000. We admitted new partners only after a careful check of their backgrounds. This was generally easy to do, as they often had careers about which there was public information, or they were personally known to us.
We modified our performance fee of 20 percent of the profits, billed annually, by including a “new high water” provision. This meant that if we had a losing year, we carried forward the losses and used them to offset future profits before we were paid more fees. This helped align our economic interests with those of the limited partners. As it happened, we never had a losing year, or even a losing quarter, and this calculation was never invoked.
PNP’s offices in Manhattan and Newport Beach expanded as we hired more employees. I found talent at the nearby University of California, Irvine, where I still was a professor of mathematics. Now I had to learn how to choose and manage employees. Figuring this out for myself, I evolved into the style later dubbed management by walking around. Instead of the endless schedule of formal meetings I abhorred in academia, I talked directly to each employee and asked them to do the same with their colleagues.
I explained our general plan and direction and indicated what I wanted done by each person, revising roles and tasks based on their feedback. For this to work, I needed people who could follow up without being led by the hand, as management time was in short supply. Since much of what we were doing was being invented as we went along, and our investment approach was new, I had to teach a unique set of skills. I chose young smart people just out of university because they were not set in their ways from previous jobs. Better to teach a young athlete who comes fresh to his sport than to retrain one who has learned bad form.
Especially in a small organization, it was important that everyone work well together. As I was unable to tell from an interview how a new hire would mesh with our corporate culture, I told everyone that they were temporary for the first six months, as were we for them. Sometime during that period, if we mutually agreed, they would become regular employees.
I revised our policies as I gained experience. When my secretary signed out sick every other Friday, and I discreetly asked one of her friends in the office why, I was told she had a standing hair appointment and also caught up on accumulated personal business. She drew from her annual allotment of paid sick days because they would be lost if not used. With that system, people who used their sick leave got more days off with pay and were rewarded over those who didn’t use it. I removed this instance of what economists call a perverse incentive by giving everyone a single pool of paid leave days that accumulated based on the number of hours worked and covered paid holidays, vacations, days off, and illness. Employees could use this time in any of these ways, subject only to the limitation that time off not interfere with essential job responsibilities.
In order to attract and keep superior staff, I paid wages and bonuses well above the market rate. This actually saved money because my employees were far more productive than average. The higher compensation limited turnover, which saved time and money otherwise used to teach my one-of-a-kind investment methodology. At the higher levels, it kept people from breaking off and going into business for themselves.
Investment opportunities were expanding, too, notably in April 1973 when the new Chicago Board Options Exchange (CBOE), created and managed by the long-established Chicago Board of Trade, began trading options. Before this, options were traded only over the counter (OTC), meaning that would-be buyers or sellers had to use brokers to search on their behalf for someone to take the other side of the trade. It was inefficient, and the brokers charged the customers high fees. The CBOE offered a wide range of options, with standardized terms, which were bought and sold on its trading floor, much like stocks traded on the New York Stock Exchange. Costs to buyers and sellers dropped dramatically, and trading volume skyrocketed.
In preparation for this, I programmed our Hewlett-Packard 9830A computer using my 1967 formula to calculate theoretical fair values for these options. The computer, a beautifully crafted quality device the size of a large dictionary, instructed a plotter of the type Hewlett-Packard was famous for, using ink-filled pens to represent the results as multicolored diagrams. For each option, correct pricing according to theory was represented by a curve. Each point on one of these curves represented a possible stock price and the corresponding fair price for the option. When we plotted the actual market prices of the stock and the option as a color-coded dot, we compared its location with the curve. If the point was above the theoretical curve, the option was overpriced so it was a candidate to sell short while at the same time buying stock to hedge the risk. The distance of the point from the curve showed the amount of the mispricing. In the same way, a point below the curve showed that the option was underpriced, and by how much. This meant it was a candidate for the opposite type of hedge, long the option, short the stock. The slope of our theoretical curve at any point automatically gave the proper amounts of stock versus options for establishing a hedge that minimized risk.
The theoretical fair-value curve of option prices versus all possible stock prices was produced by the computer from the formula. This, in turn, used data such as the volatility of the stock (a measure of the recent daily percentage changes in the stock price), US Treasury interest rates, and any dividends paid by the stock during the life of the option.
A couple of months before the CBOE opened, I was ready to trade with the formula for pricing options that I thought no one else knew. Princeton Newport was going to clean up. Then I received a letter and a prepublication copy of an article from someone I hadn’t heard of named Fischer Black. He said he was an admirer of my work and that he and Myron Scholes had taken a key idea from Beat the Market, known as delta hedging, a step further and derived an options formula. I scanned the article and saw it was the same formula I was using. The good news was that their rigorous proof verified that the formula I had discovered intuitively was correct. The bad news was that the formula was now public knowledge. Everyone was going to be using it. Fortunately, this took a while. When the CBOE opened for business we appeared to be the only ones trading from the formula. Down on the floor of the exchange it was like firearms versus bows and arrows.
To exploit price aberrations as quickly as possible, before others could and before they faded, we asked the options exchange to let our traders use programmed hand calculators on the floor. Our request was denied. The newcomers were not to have an advantage over the established old-time traders. We then asked for the next best thing, to be allowed to communicate by walkie-talkies with our floor traders. Denied. It reminded me a bit of what I had run into in Las Vegas with card counting. We then supplied our floor traders with printed trading tables that covered the ever-growing number of listed options. These were run off overnight on our high-speed printers and express-mailed to our offices in Princeton and Chicago. That served nearly as well as hand calculators would have.
Since we needed the tables in both offices as well as for traders spread over the floor of the exchange, we ran five copies. Using z-fold paper interspersed with layers of carbon paper, our Printronix Corporation machines ran all night, every night. The hedging instructions and target prices, which covered every situation likely to arise over the next few days, ran several hundred pages. Each table was a handful, with pages about eleven inches by seventeen, stacked a couple of inches deep. Much of this was described in a 1974 front-page article in The Wall Street Journal. Later, when established traders felt they could compete, hand calculators programmed to value options were allowed and became a basic tool for the industry.
While I was totally engaged with the university and the business, Vivian did most of the raising of our three preteen children. Yet she found time to help reelect a decent local congressman. When she opened a campaign office in Corona del Mar, party hacks tried and failed to stop her. She raised money for the campaign, found her own volunteers, and launched a large telephone-calling campaign. When the congressman was reelected, two party hacks took credit for her entire operation and moved themselves up the party ladder. Vivian was in it for results, though, not personal advancement or plaudits. In fifty-five and a half years of marriage I don’t ever remember her bragging. The closest she came was when I would admire the way she matched the hues of her outfits or furnished our household with a designer’s eye. She would look at me and matter-of-factly explain, “I have a good eye for color.”
She also quietly organized and ran a large phone bank that helped elect the first black man to a California statewide office. She influenced people one-on-one as well. A lady she met complained about “those Jews.” Vivian had lost several relatives in Nazi World War II prison camps. When she told us about meeting the woman, we expected to hear how she tore her to shreds. Explaining why she did not, Vivian pointed out that the woman would have learned nothing and simply would have become an enemy. Vivian patiently educated this basically good person, and they became friends for the rest of their lives.
Vivian’s insights helped me cope with the cast of characters I was meeting in the investment world, many of whom seemed to be lacking a moral compass. She was fascinated by people. It had become second nature to take the bits and pieces someone told her about themselves and build a unified life story, which she analyzed and checked for consistency. As a result my wife was an almost unerring judge of character, motives, and expected future behavior. I was repeatedly amazed when she applied this to business and professional people I introduced her to for the first time.
She did this easily, based on so little evidence I couldn’t believe it. But over and over again, Cassandra-like if I didn’t listen, she was right.
After meeting one of the characters, she said, “He’s greedy, insincere, and you can’t trust him.”
“How do you know this?” I asked.
She said, “You can see he’s greedy from the way he drives. The insincerity comes out when he smiles. His eyes don’t really smile, too; they mock you. And his wife has a sad look in her eyes that doesn’t add up. The face she sees at home isn’t the one he shows the world.”
Years later, this “friend” Glen, as I’ll call him, was running a hedge fund in which we were investors. The fund had lost $2 million in one of its investments, partly through fraud. When lawyers eventually recovered $1 million of the losses, Glen allocated the money to his current partners, most of whom were not among the former partners who had suffered the original loss. As he would be deriving future economic benefit from his current partners but none from the former partners, he would gain by this injustice. When I confronted him, he claimed not to be able to locate the twenty or so former partners. I had a list and told him I had current information on all but three and knew how to find those through mutual friends. Then he said he wouldn’t pay and that, under the terms of the partnership, each partner had to go to arbitration separately. The amounts averaged $50,000 or so each, which he knew were not worth pursuing after lawyers’ fees, personal time, inconvenience, and stress. He refused my request that he agree to a single arbitration to settle this for the entire group. He suggested slyly that maybe we should try a few individual arbitrations and if he lost them all, he might change his mind. When I asked his lawyer associate how he could ratify such unethical behavior, he said, “They don’t teach ethics in law school.”
As Princeton Newport Partners prospered, I was meeting interesting people. Curiously it wasn’t our investment performance but a wrinkle in the tax code that led to a meeting with Paul Newman. The code lagged behind in its treatment of listed options as there were, until a few years later when the law was changed, transactions that made it possible to substantially reduce federal and state taxes. To explore this I was invited to join Paul and his tax lawyer for lunch on the set for the movie The Towering Inferno, at Twentieth Century–Fox in Los Angeles.
The studios were adjacent to Beverly Hills High School, the only Southern California high school with an oil well on campus. When I arrived, Paul was in blue jeans with a shirt and jacket to match, long before this was chic. I am reminded of the clean but faded Levi’s that I generally wore in the 1940s for want of money and how I was astonished fifty years later when stylish people paid up for intentionally tattered and hole-filled jeans in far worse condition than my high school pants.
I was struck by Newman’s remarkable blue eyes, even more intense in person than on film. Reserved, even shy when meeting someone for the first time, he looked me over, didn’t say anything at first, then said, “You want a beer?” I said, “Sure,” and he relaxed, deciding I was a regular guy. Over lunch, as I ate a special sandwich he recommended, he asked about my blackjack card counting system and how much I thought I could make at it full-time. Mastering disguises and playing alone, instead of running a team, I estimated $300,000 per year. “Why aren’t you doing it?” he asked. I said I expected to do better running my hedge fund. As he was making six million taxable dollars that year, which was the reason for our lunch, he appreciated the answer. Nothing came of the meeting. Paul’s lawyer believed that the ideas I presented for reducing his taxes were sound but new and thus likely to be challenged. His lawyer advised Paul, a high-profile progressive Democrat, not to risk being embroiled with a Republican IRS.
We had other interactions with Hollywood. Two of our early limited partners were Robert Evans and his brother, Charles. Bob was a relatively unknown actor and producer until 1966 when the conglomerate Gulf and Western took over Paramount and picked Evans as head of production. During the next eight years Evans returned Paramount to success with hits including The Odd Couple, Rosemary’s Baby, Love Story, Chinatown, and The Godfather. In the 1997 movie Wag the Dog, Dustin Hoffman plays a character based in detail on Evans’s appearance, habits, and mannerisms.
One day in 1971 or 1972 I went to Bob’s villa in Beverly Hills to attempt to explain the types of trades that we were doing in the partnership. While he and Charles bobbed around in the backyard pool, protected with sunglasses and hats, I sat at the edge explaining the basic ideas behind convertible hedging. At the time Robert was married to his third (of seven) wives, the actress Ali MacGraw. Of course, I hoped she would make an appearance to question me about the intricacies of the market, but she was traveling. Ali had been nominated for an Academy Award for Best Actress in 1970 for her role in the movie Love Story and, even twenty years later when she was fifty-two years old, People magazine chose her as one of the fifty most beautiful people in the world.
Screenwriter Charles A. Kaufman (1904–91), whose screenplay for Freud was nominated for an Oscar in 1963, became a limited partner and regularly referred people to us, which may have indirectly caused certain other prospective partners to call us. Kaufman had a Los Angeles–based accountant who also did the books for some of the big casinos in Las Vegas. The Kaufmans gave a dinner party for Vivian and me and the accountant and his wife. The point was for me to answer questions both on partnership trading strategies and our accounting practices. When the conversation turned to blackjack and I mentioned what I knew about casino cheating, skimming, and a double set of books, the accountant acted disbelieving and astonished. His wife, a beautiful and outspoken former showgirl, would have none of it and told us they knew otherwise. The accountant may have been more connected than he let on, because shortly after this dinner, I received inquiries about investing in the partnership from then famous and highly connected Las Vegas figures like “Moe” Dalitz (1899–1989) and Beldon Katleman (1914–88). Jay Regan quickly agreed with me that we didn’t have any available openings.
One of my stories that particularly put the accountant in denial began in the summer of 1962, when I was contacted by a special agent from the US Treasury. The Treasury was investigating possible tax fraud in the Nevada casino industry, believing that certain operators were removing large quantities of cash, which they were not declaring on their tax returns. Part of a secret undercover team, “John” resembled the actor Mike Connors, best known for starring in two television series around that time, Mannix and Tightrope, as well as several movies. We met for lunch regularly at the Hamburger Hamlet in Westwood Village, adjacent to the UCLA campus. John came as the character he played to fool the casinos, wearing a broad-brimmed Stetson, cowboy clothes and identification verifying that he was a wealthy Texan named C. Cash Anderson (a little Treasury humor). He drove a new red Cadillac convertible, its white top rolled down.
In Las Vegas, he bet big at the blackjack tables, an act that got him into the rooms where the casinos counted the money from the collection boxes that had been sealed and brought in from the blackjack tables. He reported seeing two sets of books, along with corresponding adding machines, one showing the real cash totals and the other the lesser amount that was reported to the government. On behalf of the government team, John was consulting me on how to improve their play at high-stakes blackjack and thereby keep down the cost to the Treasury while they pretended to be unskilled high-rollers.
As the partnership prospered, so did Vivian and I. When we started back in 1969 I forecast how quickly my wealth and Regan’s would grow. On a yellow legal pad, with plausible assumptions about our company’s rate of return, the rate of growth of our partnership’s net worth, and taxes, I predicted that by 1975 we would be millionaires. I sent a copy to Regan.
Sure enough, in 1975, we were indeed both millionaires and the money was changing our families’ lives. Vivian and I had extensive additions and improvements made to our home. Back in 1964 I bought a used red Volkswagen in Las Cruces from one of my students. A decade later, in 1975, I drove a new red Porsche 911S. Vivian’s inexpensive utilitarian wardrobe was evolving into coordinated designer outfits with fashionable handbags and shoes. Our vacations, which used to be low-budget trips to professional meetings, were being augmented and replaced by cruises and stays in high-end overseas hotels.
We were now living beyond the means of most of our faculty friends. This had the unintended consequence of distancing us somewhat from the smart, funny, and educated people with whom we felt the most rapport. On the other hand, we hadn’t yet made many new friends in the wealthy Orange County business community, because most of our business partners were scattered across the United States. As Vivian remarked, “We’re neither fish nor fowl.”
My shifting mathematical interests were also distancing me professionally from my colleagues in the department at UCI. As is generally true in universities, the research emphasis was on pure mathematics. Loosely speaking, this is the development of abstract mathematics, or theory for its own sake.
My PhD thesis was in pure mathematics and this continued to be my focus for the next fifteen years. But with the analysis of gambling games, I also developed a strong interest in applied mathematics, which uses mathematical theories to solve real-world problems. The financial world was presenting me, and Princeton Newport Partners, with an endless array of such puzzles to solve for fun and profit. I was becoming an applied mathematician again, and in a department of pure mathematics I was professionally neither fish nor fowl.
At the same time, the Math Department was headed for serious trouble. Both the levels of grant money for research and funds from the state of California to support the university had declined. This led to fierce struggles among various factions in the department for what was left. To mediate the infighting, an outsider was brought in as a chairman. He was forced out after three turbulent years. For want of anyone else who might be acceptable to the warring groups, and against my better judgment, I was persuaded by the administration to act as temporary chairman.
The assignment was worse than I thought. I found that one assistant professor had stopped showing up to teach, dividing his time between his girlfriend four hundred miles to the north in the San Francisco Bay Area and the casinos in Reno and Lake Tahoe. A card counter, he even called me with blackjack questions! Another assistant professor was running up departmental phone bills of $2,000 per month versus a total of $200 for the other twenty-five professors combined. When I confronted him he claimed it was mathematical research. A review of the bills showed almost all the charges were for calls to two numbers in New York City. I dialed each, speaking in turn to his mother and to a store that sold musical recordings. He was enraged at me and not at all embarrassed when exposed.
Meanwhile, a full professor had stolen the confidential employment records of another full professor from the department files. When I discovered this and confronted him, he refused to return it. It turned out that the file contained a very nasty letter that he had written about his enemy. He feared that if I, as chairman, learned what he had done, I would expose him. When I asked the administration to initiate disciplinary action against these incorrigibles, they declined to act. I was stunned and stymied.
One problem in large bureaucracies is that many of the members decide it is better not to cross people, instead of standing on principle. I asked a good friend, whom I had helped to get an appointment in our department, to become my vice chairman and help me. Although he was now a full professor with tenure, he declined, saying, “I have to live in the same cage with these monkeys.” I did understand his point. On the other hand, I was not confined to the cage. I had PNP. I thought, Why try to fix this if no one will even back me up? I was in the Math Department by choice, not by necessity. It was time to move on.
Initially, I transferred to UCI’s Graduate School of Management, where I enjoyed teaching courses in mathematical finance. But I found factionalism and backstabbing as bad there as it had been in the Math Department. Both had endless committee meetings, petty squabbles over benefits, people who wouldn’t pull their weight and couldn’t be dislodged, and the dictum of publish or perish. I decided it was time to leave academia. Even so, it was not an entirely easy decision. I had heard more than one person say that what they wanted most in life was to be a tenured professor at the University of California. It had been my dream, too. Over the years I hired students and former staff from UC, Irvine but only one faculty member, one without tenure, was willing to take a chance and join my operation. The others found it a very scary notion. Of course, a few had regrets later.
Gradually reducing my teaching load from full-time, I finally resigned my UCI full professorship in 1982. I loved teaching and research and felt a sense of loss upon giving up a position I expected to enjoy for a lifetime, but it turned out to be for the best. I took what I liked with me. I kept my friends and continued my research collaborations. Free to do anything I wished, my childhood dream come true, I continued to present my work at meetings, as well as publish it in the mathematical, financial, and gambling literature.
I intensified my focus on competing with the wave of mathematicians, physicists, and financial economists who were now flocking to Wall Street from academia.