Three Ways to Gain an 'Edge' as an Investor

A gold rush in early 1980... This elite trading firm didn't always shine... More than just being smart... Three ways to gain an 'edge' as an investor... Why panic-selling often spreads through the market... The ideal time horizon... How to achieve the best results...

In early 1980, selling gold seemed like a no-brainer to many folks...

The precious metal traded for around $225 an ounce at the start of 1979. It rose to about $500 by the end of the year. And it spiked to more than $800 in January 1980 – more than tripling in less than a year.

Many people took advantage of the surge in prices by selling their gold jewelry. Lines formed at jewelry stores around the country with folks wanting to cash out.

But not Lenny Baum...

Baum was a trader at hedge fund Renaissance Technologies (called Limroy at the time). He had been long gold futures contracts, so he was sitting on millions of dollars in profits.

Renaissance founder Jim Simons knew the jewelry selling would increase the gold supply... and in turn, it could crush prices. So as author Gregory Zuckerman recounted in The Man Who Solved the Market, Simons implored Baum to sell, yelling, "Sell the f---ing gold, Lenny!"

However, Baum thought the uptrend would continue and stubbornly held on to the position.

One day in mid-January 1980, Simons forced the issue and called the firm's broker. Then, he pressed the phone to Baum's ear. Finally, Baum relented and took profits.

Simons was right... Gold fell to around $500 an ounce by the spring of 1980. And it didn't eclipse its 1980 peak price of more than $865 until 2008... 28 years later.

At first, Simons didn't fully trust the 'black boxes' that would ultimately make him a billionaire...

Renaissance has grown into one of the world's most successful quantitative trading firms. Yet in the beginning, Simons hesitated to let the computers make all of the decisions.

Early on, Renaissance's traders relied primarily on intuition and instinct. It worked with the gold trade, which resulted in big profits. But others ended in disaster...

In 1984, Baum made large, ill-timed bets on U.S. Treasurys and the Japanese yen. Baum's positions lost 40%, triggering a liquidation of his holdings. It was a horrible year for the firm. In The Man Who Solved the Market, Zuckerman summarized Simons' mindset...

The losses had been so upsetting that Simons contemplated giving up trading to focus on his expanding [venture capital] technology business. Simons gave clients the opportunity to withdraw their money. Most showed faith, hoping Simons could figure out a way to improve the results, but Simons himself was racked with self-doubt.

You see, in the early days, Simons and his traders lacked an advantage (or "edge") in the markets. They couldn't produce consistent gains relying on gut feel alone.

They were all smart, but as you'll see in today's Digest, that's not always enough... Every trader needs an edge. And investors without an edge should just stick to buying index funds.

Eventually, Simons and his traders developed significant market edges. The firm even devised the "greatest money-making machine in financial history," as Zuckerman called it.

In today's Digest, I (Alan Gula) will examine the three main types of market edges – informational, analytical, and behavioral. And more important, I'll describe some enduring edges that retail investors like you can exploit to maximize your overall performance.

If anyone could trade on pure intellect, it would be Simons...

Simons earned a Bachelor of Science degree in mathematics from MIT in just three years before becoming a world-renowned mathematician. In 1976, he was awarded the American Mathematical Society's Oswald Veblen Prize in Geometry – the highest honor in the field.

In the 1970s, Simons also built an elite math department at Stony Brook University. But then, in 1978, he left a successful career in academia because he saw investing as the ultimate challenge. And collecting data was the first key to solving the market's puzzle.

Renaissance employees collected commodity, currency, and interest rate data going back decades. Some of it wasn't available electronically yet, so it wasn't an easy process. They also found data on intraday price movements (or "ticks") for futures contracts.

These data seem commonplace now. But back in the 1980s, it gave Renaissance an informational edge. They had access to data almost no one else did. And that allowed them to spot "anomalies" that few others could see.

An anomaly is basically a predictable price movement. If these repeating patterns exist, then prices don't follow a "random walk," as the efficient market hypothesis asserts. In other words... the past can help you predict the future.

Sometimes, prices "trend." Sometimes, they "revert to the mean." Renaissance's data assets allowed it to build models that determined when to bet on each outcome, rather than relying on human judgment.

Informational edges are hard to get in our increasingly digital world. And if Renaissance – or any hedge fund – can access unique data, it wouldn't want anyone to know about it.

Simons may be a gifted mathematician. But that's not why Renaissance has been so successful...

An "algorithm" is a procedure that solves a problem or performs a task. And Simons has an algorithm for operating his business. In a 2015 interview with math website Numberphile, he said...

My algorithm has always been: You get smart people together, you give them a lot of freedom, create an atmosphere where everyone talks to everyone else... and you provide the best infrastructure, the best computers and so on... and make everyone partners.

Those smart people that Simons has hired are often PhD mathematicians. But he has also hired computer science experts.

Simons learned about computer algorithms at the Institute for Defense Analyses ("IDA"). From 1964 to 1968, Simons worked as a code-breaker at the IDA, a nonprofit that helps the U.S. government address national security issues. He and the other mathematicians deciphered hidden messages within the communications of foreign adversaries, such as the Soviet Union.

Simons was awful at writing the actual computer code. But he excelled at developing code-breaking computer algorithms that found hidden patterns in the data. And it proved to be ideal training for finding similar hidden patterns in financial market data years later...

Despite Renaissance's early reliance on human instinct – like the gold example I detailed at the start of today's Digest – it would eventually become 100% systematic. By that, I mean the computer models – and only the computer models – generate trades. The math behind those models mostly involves statistics, along with some probability theory.

Renaissance wasn't the first financial firm to use quantitative methods to manage money. But it was one of the first to successfully apply "machine learning" to the markets...

Most financial market anomalies are subtle...

If anomalies are obvious, traders exploit them... and they quickly disappear. Even the most subtle anomalies can disappear over time. So traders must constantly work to find more.

Renaissance has a knack for spotting signals in the noise. That's its analytical edge.

The firm's algorithms parse the huge data sets that it has collected over the years, teasing out anomalies. The algorithms can even spot anomalies that elude human comprehension.

Basically, the computer models make predictions about future prices. But as it turns out, predicting the future isn't enough...

Renaissance is also good at estimating market impact, which is a cost of trading. By buying and selling securities, fund managers move prices around. This "slippage" eats into profits (or totally negates profits for small anomalies).

Renaissance has also built a cohesive, automated trading system... Algorithms select the optimal trades. They determine how much money to allocate to each trade. And they hedge the portfolio, attempting to minimize the volatility based on prevailing market conditions.

Renaissance's employees don't play a role in the trading decisions. But they all have an opportunity to see the millions of lines of code and improve them.

Despite Renaissance's informational and analytical edges, it's estimated that the firm only profits on barely more than 50% of its trades. Still, given enough trades (and leverage), the edges are magnified.

The Medallion fund is Renaissance's crown jewel...

Simons started the Medallion fund in 1988. The hedge fund had its worst year in 1989... eking out a small, positive gross return (before fees). And then its performance took off...

Over the next five years, Medallion averaged 63% annual gross returns.

Renaissance found its early success by focusing on futures and currencies. Around the late 1990s, the firm "figured out" equities. Then, the firm began to incorporate stock market anomalies into the trading models. And the results speak for themselves...

In 2000, the benchmark S&P 500 Index fell 9%. That year, the Medallion fund racked up a gross return of 130% – an unbelievable return for a $1.9 billion fund.

Medallion's best year happened in 2008... It made a 152% gross return for investors. Meanwhile, the S&P 500 plunged 37% that year due to the financial crisis.

The average annual total return for the S&P 500 from 1988 to 2018 equaled 11.6%. Over that same time frame, Medallion produced average annual gross returns of around 66%. Its net returns (the returns after management and performance fees) are still an astounding 39%. This is the most impressive track record in financial market history.

Only Renaissance employees can invest in the Medallion fund. That's how special the fund is. (Renaissance manages other funds for institutional investors.)

And Renaissance has limited Medallion's fund size to around $10 billion for the past few years. It returns the excess capital to its employee investors each year.

Analytical edges aren't just for the computers...

At Stansberry Research, we follow quantitative models. But our models are just tools...

Our analytical edge is primarily based on fundamental research. We analyze companies' financial statements, estimate cash flows, and assess risks. And when we've identified securities that we believe are sufficiently mispriced, we make a recommendation.

In our flagship newsletter, Stansberry's Investment Advisory, the recommendations fall into different categories of businesses. Another aspect of our edge over other investors is our deep understanding of these types of businesses.

For example, our most recent recommendations – which you can access in our July issue, if you're a subscriber – are two property and casualty (P&C) insurers. We've written extensively about P&C insurance... and have even called it the "world's best business."

And we recently created a new Software as a Service ("SaaS") category in the Investment Advisory. Our first recommendation, made in October 2019, is already up an astounding 217%. And our most recent SaaS pick is outperforming nicely, too... It's up 38% in about four months.

Of course, we write a lot about "capital efficient" businesses in the Investment Advisory. Regular Digest readers know these highly scalable businesses don't require significant capital expenditures to grow. As a result, they tend to have wide free cash flow margins.

The market still doesn't fully understand or appreciate capital efficiency. So the outperformance of the stocks of these special businesses is an anomaly that persists.

In early April, we recommended one of the largest, most capital-efficient businesses the world has ever seen... The company's shares had declined as much as 36% from their peak. But we saw an opportunity amid the COVID-19 pandemic fallout. And our recommendation is paying off so far... Subscribers who followed our advice are up 23% in three months.

Now, buying when most investors are panicking requires discipline. That brings me to the third type of investor advantage – the behavioral edge.

We all have cognitive biases...

These biases are systematic "errors" in our decision-making processes.

In 1979, psychologists Daniel Kahneman and Amos Tversky identified the bias of "loss aversion." They found that the psychological effects from gains and losses are asymmetrical.

Let's say you gain $200 on a trade. You have a psychological benefit.

Start from scratch again. Now, let's say you lose $200. You have a psychological cost.

The loss is more "painful" than the gain is "pleasurable." In fact, Kahneman and Tversky discovered that, on average, losses were around twice as painful as gains were gratifying.

Behavioral economist Richard Thaler built upon the bias of loss aversion...

Thaler studied how investors tend to evaluate their portfolios frequently, even if they have long-term investment goals. He dubbed the combination "myopic loss aversion."

The research of these social scientists helps to explain why there's often panic-selling in the stock market.

Myopic loss aversion was on full display during the crash in March...

Just look at data from brokerage behemoth Fidelity Investments. The firm has nearly 31 million brokerage accounts and holds more than $7 trillion in customer assets.

In the period from mid-February to mid-May, 18% of individual investors with Fidelity accounts sold all of their stocks at some point. And more than 30% of Fidelity clients aged 65 and older sold all of their equity holdings during that span.

Imagine selling every single share of stock that you own and going to cash.

Some of these investors had too much equity exposure to begin with... And most of them suffered from myopic loss aversion, panicking as the market crashed during the pandemic.

Plain and simple... if you're not continually prepared for a bear market, you shouldn't be investing in stocks. You're going to lose the behavioral investing "game."

Investors who sold into the bear market are now faced with a difficult dilemma as stocks have come roaring back... Do they buy back their shares at a higher price than they sold at?

You should aim to lengthen your time horizon, not shorten it...

Short-term horizons – ranging from seconds to days – are the domain of systematic trading strategies. Short-term traders are up against Medallion and other quant funds.

Medallion's algorithms select trades... along with their timing and the size of the bet. There are no emotions involved. In fact, Medallion has likely found many ways to systematically profit from all of the various errors (biases) of human investors... hence its success.

We want to be investors, not traders. And the good news is, you can have a behavioral edge if you invest for the long term.

Through practice and discipline, we can overcome our natural biases. That will give us a behavioral edge over most investors, who let their biases hurt their performance.

And we'll achieve the best results if we stack our edges...

Whenever you're making an investment decision, ask yourself, "What is my edge?"

You don't have access to unique data. And you don't use machine learning to model the financial markets. So you better have some high-quality research and be disciplined.

The strategy of buying shares of high-quality companies when they go on sale will never go stale. It combines analytical and behavioral edges... and that's why it can be so profitable.

We recently created a product called Stansberry's Forever Portfolio based on these edges...

These are stocks that you can – and should – hold for ultra-long-term periods to minimize the risk of myopic loss aversion. Just buy shares when we recommend them and hang on.

Back in March, when the markets were crashing amid the COVID-19 panic, Porter went on-camera for a rare interview. Not only did he accurately predict the market's bottom within just a few days, but he also detailed which stocks folks should start loading up on – these so-called "forever stocks."

So far, all 20 recommendations in the Stansberry's Forever Portfolio are showing gains. Three positions are up more than 50% in less than four months. But it's still not too late to follow this strategy for the long haul...

That's why we're re-releasing Porter's critical market update for a limited time. And more important, we're reopening our charter offer for Stansberry's Forever Portfolio. Find out how you can get instant access to the complete portfolio of these forever stocks right here.

New 52-week highs (as of 7/10/20): Amazon (AMZN), Calibre Mining (CXB.TO), DB Gold Double Long ETN (DGP), Electronic Arts (EA), New Oriental Education & Technology (EDU), Alphabet (GOOGL), Green Thumb Industries (GTBIF), Hecla Mining (HL), KraneShares MSCI All China Health Care Index Fund (KURE), Lonza (LZAGY), NetEase (NTES), ProShares Ultra Technology Fund (ROM), Seabridge Gold (SA), Global X Silver Miners Fund (SIL), Spotify Technology (SPOT), Trulieve Cannabis (TCNNF), and Take-Two Interactive Software (TTWO).

Not much going on in the mailbag today. As always, we love to hear what's on your mind. Share your questions, comments, and observations with us at feedback@stansberryresearch.com.

Regards,

Alan Gula
Baltimore, Maryland
July 13, 2020