CN's Third Quarter Results Reflect Strong Top-Line Growth and Renewed Focus on Scheduled Operation

CN's Third Quarter Results Reflect Strong Top-Line Growth and Renewed Focus on Scheduled Operation

Railroad Now Expecting to Deliver Adjusted Diluted EPS Growth of Approximately 25% in 2022

MONTREAL, Oct. 25, 2022 (GLOBE NEWSWIRE) -- CN (TSX: CNR) (NYSE: CNI) today reported its financial and operating results for the third quarter ended September 30, 2022. Diluted earnings per share (EPS) of C$2.13 increased by 40% on an adjusted basis, which represents a quarterly record. ⁽¹⁾ Diluted EPS decreased by 10%, mainly due to a merger termination fee received in the same quarter of 2021. CN delivered strong top-line growth and yield, as operational advancements continued to drive improvements to key metrics, including car velocity, dwell, and train speed.

“Our back to basics approach continues to drive strong results. CN's team of railroaders is doing a great job in delivering service to our customers and value for our shareholders. We remain focused on disciplined execution of our integrated operating plan to maximize the effectiveness and efficiency of our incredible three-coast network. We have a busy fourth quarter, with a strong start in the Canadian grain crop, and we are resourced for the months ahead. We are pleased to be raising our 2022 outlook to reflect our performance.”
– Tracy Robinson, President and Chief Executive Officer, CN

Financial results and operating highlights
Third -quarter 2022 compared to third -quarter 2021

• Record revenues of C$4,513 million, an increase of C$922 million or 26%, mainly due to higher fuel surcharge revenue driven by higher fuel prices, freight rate increases and the positive translation impact of a weaker Canadian dollar.
• Record operating income of C$1,932 million, an increase of 44%, or an increase of 31% on an adjusted basis. ⁽¹⁾
• Diluted EPS of C$2.13, a decrease of 10%, mainly due to a merger termination fee received in the third quarter of 2021.
• Diluted EPS increased by 40% on an adjusted basis, which represents a quarterly record. ⁽¹⁾
• Operating ratio, defined as operating expenses as a percentage of revenues, of 57.2%, an improvement of 5.5-points, or an improvement of 1.8-points on an adjusted basis. ⁽¹⁾
• Free cash flow for the first nine months of 2022 was C$2,924 million compared to C$2,034 million for the same period in 2021. ⁽¹⁾
• Injury frequency rate ⁽³⁾ decreased by 29% and the accident rate ⁽⁴⁾ decreased by 19%.
• Car velocity improved by 5% and dwell improved by 9%.
• Fuel efficiency improved by 1% to 0.838 US gallons of locomotive fuel consumed per 1,000 gross ton miles (GTMs).
• Origin train performance averaged 87%, an improvement of 12% compared to 78% for the same period in 2021. ⁽⁵⁾

New 2022 financial outlook ⁽²⁾
CN is now expecting to deliver approximately 25% of adjusted diluted EPS growth (compared to its April 26, 2022 target of 15-20%) and free cash flow of approximately C$4.2 billion in 2022 (compared to its April 26, 2022 target range of C$3.7 billion - C$4.0 billion). ⁽¹⁾ CN continues to target an operating ratio below 60% and a ROIC of approximately 15% in 2022. ⁽¹⁾

Third -quarter 2022 revenues, traffic volumes and expenses
Revenues for the third quarter of 2022 were C$4,513 million, an increase of C$922 million, or 26%, when compared to the same period in 2021. The increase was mainly due to higher fuel surcharge revenue driven by higher fuel prices, freight rate increases, higher Canadian export volumes of coal via west coast ports, higher volumes of U.S. grain and the positive translation impact of a weaker Canadian dollar.

Revenue ton miles (RTMs), measuring the weight and distance of freight transported by CN, increased by 5% compared to the year-earlier period. Freight revenue per RTM increased by 22% compared to the year-earlier period, mainly driven by higher fuel surcharge revenue driven by higher fuel prices, freight rate increases and the positive translation impact of a weaker Canadian dollar.

Operating expenses for the third quarter of 2022 increased by 15% to C$2,581 million, mainly driven by higher fuel prices and the negative translation impact of a weaker Canadian dollar; partly offset by Transaction-related costs of $84 million recorded in the third quarter of 2021 resulting from the terminated CN Merger Agreement with Kansas City Southern ("KCS").

2022 key assumptions

^{CN has made a number of economic and market assumptions in preparing its 2022 outlook. The Company assumes that North American industrial production for the year will increase in the mid single-digit range and now assumes U.S. housing starts of approximately 1.6 million units and U.S. motor vehicle sales of approximately 14 million units (compared to its January 25, 2022 assumption of approximately 15.5 million units). For the 2021/2022 crop year, the grain crop in Canada was below its three-year average and the U.S. grain crop was in line with its three-year average. The Company assumes that the 2022/2023 grain crop in Canada will be above its three-year average (or in line when excluding the significantly lower 2021/2022 crop year) and that the 2022/2023 U.S. grain crop will be in line with its three-year average. CN assumes total RTMs in 2022 will increase in the low single-digit range versus 2021. CN assumes continued pricing above rail inflation upon contract renewals. CN now assumes that in 2022, the value of the Canadian dollar in U.S. currency will be approximately $0.77 (compared to its January 25, 2022 assumption of approximately $0.80) and now assumes that in 2022 the average price of crude oil (West Texas Intermediate) will be approximately US$95 per barrel (compared to its April 26, 2022 assumption of approximately US$90 - US$100 per barrel). In 2022, CN plans to invest approximately 17% of revenues in its capital program.}

This earnings news release, as well as additional information, including the Financial Statements, Notes thereto and MD&A, is contained in CN’s Quarterly Review available on the Company's website at www.cn.ca/financial-results and on SEDAR at www.sedar.com as well as on the U.S. Securities and Exchange Commission's website at www.sec.gov through EDGAR.

About CN
CN is a world-class transportation leader and trade-enabler. Essential to the economy, to the customers, and to the communities it serves, CN safely transports more than 300 million tons of natural resources, manufactured products, and finished goods throughout North America every year. As the only railroad connecting Canada’s Eastern and Western coasts with the U.S. South through a 18,600-mile rail network, CN and its affiliates have been contributing to community prosperity and sustainable trade since 1919. CN is committed to programs supporting social responsibility and environmental stewardship.
-----------------------------------------------------

Source

https://money.tmx.com/en/quote/CNR/news/6889599090361672/CNs_Third_Quarter_Results_Reflect_Strong_TopLine_Growth_and_Renewed_Focus_on_Scheduled_Operation

This Dividend Aristocrat Has A Whopping 45% Upside

This Dividend Aristocrat Has A Whopping 45% Upside

In today's market, finding upside is a challenge. YTD, the S&P 500 index is down 20.83%. And if you’re invested in growth stocks, it wouldn’t be a surprise if your portfolio has losses north of 50%. 

In the last month, the four major indexes have traded in positive territory.  

• Nasdaq Composite is up 1.39%
• S&P 500 is up 3.89%,
• Russell 2000 is up 5.59%
• Dow Jones Industrial is up 7.65%

Now, let's face it. Single-digit percentages up/down aren’t usually a huge motivator for many traders. But, any positivity is welcome after the market's dismal performance YTD. 

This article highlights one company with that, if analysts are correct, has a whopping 45% upside potential.

Linde Plc (LIN)

Linde Plc is a multinational diversified industrial gas, chemical, and engineering conglomerate. It is one of the leading companies in industrial gases and the engineering sector. The company was created in October 2018 through the union of Linde AG and Praxair. Its headquarters are in Dublin, Ireland, and Woking, Surrey, UK. Additionally, the company employs over  72,327 employees (2021).

Today, Linde has operations all over the world and are mainly divided into two segments:

• The Gases segment provides industrial gases, specialty gases, medical gases, refrigerants, and other chemicals to various clients from different industries. 
• The Engineering segment, famously known as Linde Engineering, offers design and construction services for building large-scale chemical plants used to produce industrial gases. This segment has built over 4,000 plants worldwide. It also has over 1,000 process patents.

In recent years, Linde has made a few changes to its administrative structure. On October 25, 2021, the company named Sanjiv Lamba as the Chief Executive Officer (CEO), which went into effect on March 1, 2022. On the same date, the company appointed two new directors, Joe Kaeser and Alberto Weisser.

Stockholder information

The Vanguard Group, Inc. is LIN's largest shareholder, with a 9.29% stake in the company. The company's second and third largest shareholders are - Blackrock Inc. and State Street Corporation, respectively.

Financials

On July 28, 2022, the company released its financial results for the second quarter. For the quarter ending June 30, 2022, LIN's revenue was $8.457 billion representing an increase of 12% from that of the previous year. Also, its earnings per share (EPS) came in at $3.10, representing a surprise of 4.03%. 

Linde Plc will also report its latest third-quarter earnings results on October 27, 2022. Consensus is looking for $2.94 in earnings per share, representing an increase of 7.69% over the previous year. And if the next quarter's results are to repeat themselves, there could be a surprise (to the upside), which could result in the stock gapping up.

Dividend

Linde Plc has increased its dividend for 29 consecutive years. The company is also listed in the S&P 500 index, placing this company on the coveted Dividend Aristocrats list.

Additionally, on October 24, 2022, the company announced its quarterly dividend of $1.17 per share payable on December 16, 2022, to shareholders of record on December 2, 2022. LIN’s annual dividend is $4.68 representing an annualized dividend yield of 1.66% based on the stock's latest trading price. In addition, the company has a modest dividend payout ratio of 38%, which allows for share repurchases and growth.

Dividend growth investors will note that Linde Plc typically announces dividend increases in February, and if history is to repeat itself, the company should increase its dividend ~10% to around $5.14.

What the analysts say

Linde Plc has a market cap of $143 billion and trades at a P/E ratio of 24.48, which is higher than the US Chemicals industry’s average P/E ratio of 16.7. This indicates that the stock could be overvalued.

Analysts maintain a “Strong Buy” rating across the board. Furthermore, this stock's “high target” is $404 - representing a potential >45% upside for investors.

Why I like Linde Plc

Buying companies before the earnings can be risky. But, with their products used in multiple sectors, the company has durable competitive advantages. Add to that a management team committed to delivering shareholder value (which is reflected in their financials), and I think this could be a home run in 12 months' time. 

More Stock Market News from Barchart

On the date of publication, Rick Orford did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes.

---------------------------------------

Source

https://www.barchart.com/story/news/11012483/this-dividend-aristocrat-has-a-whopping-45-upside

Lessons From A Trading Great: Stanley Druckenmiller

Lessons From A Trading Great: Stanley Druckenmiller

The “greatest money making machine in history”, a man with “Jim Roger’s analytical ability, George Soros’ trading ability, and the stomach of a riverboat gambler” is how fund manager Scott Bessent describes Stanley Druckenmiller. That’s high praise, but if you look at Druckenmiller’s track record, you’ll find it’s well deserved.

Druck averaged over 30% returns the last three decades — impressive. But what’s even more astonishing is the lack of volatility… the guy almost never loses.

He never had a single down year and only had five losing quarters out of 120 altogether! That’s absolutely unheard of. And he did all of this in size. At his peak, Druck was running more than $20 billion and he was still managing to knock it out the park.

When you study Druckenmiller you get the sense that he was built in a laboratory, deep in a jungle somewhere, where he was put together piece by piece to create the perfect trader. Every character trait that makes up a good speculator, Druck possesses in spades… things like:

• Mental flexibility
• Independent thinking
• Extreme competitiveness
• Tireless inquisitiveness
• Deep self-awareness

Maybe he’s a freak of nature or perhaps a secret Jesse Livermore / George Soros lovechild… or maybe he’s just a relentlessly determined trader who’s been on a lifelong path of mastery. Either way, it behooves us to study the thoughts and actions of one of the game’s greatest. And with that, let’s begin.

On what moves stocks

In Jack Schwager’s book The New Market Wizards, Druckenmiller said this in response to the question of how he evaluates stocks (emphasis is mine):

When I first started out, I did very thorough papers covering every aspect of a stock or industry. Before I could make the presentation to the stock selection committee, I first had to submit the paper to the research director. I particularly remember the time I gave him my paper on the banking industry. I felt very proud of my work. However, he read through it and said, “This is useless. What makes the stock go up and down?” That comment acted as a spur. Thereafter, I focused my analysis on seeking to identify the factors that were strongly correlated to a stock’s price movement as opposed to looking at all the fundamentals. Frankly, even today, many analysts still don’t know what makes their particular stocks go up and down.

The financial world is chock full of noise and nonsense. It’s filled with smart people who don’t know a damn thing about how the world really works. The financial system’s incentive structure is set up so that as long as analysts sound smart and pretend like they know why stock xyz is going up, they get rewarded. This holds true for all the talking heads and “experts” except for those who actually trade real money. They either learn the game or get competed out.

Being one of those who compete in the arena, Druckenmiller was forced to learn early on what actually drives prices. This is what he found:

Earnings don’t move the overall market; it’s the Federal Reserve Board… focus on the central banks and focus on the movement of liquidity… most people in the market are looking for earnings and conventional measures. It’s liquidity that moves markets.

Liquidity is the expansion and contraction of money, specifically credit. It’s the biggest variable that drives demand in an economy. It’s something our team at Macro Ops follows closely.

The federal reserve has the biggest lever on liquidity. This is why a trader needs to keep a constant eye on what the Fed is doing.

This is not to say that things like sales and earning don’t matter. They are still very important at the singular stock level. Here’s Druckenmiller again (emphasis mine):

Very often the key factor is related to earnings. This is particularly true of the bank stocks. Chemical stocks, however, behave quite differently. In this industry, the key factor seems to be capacity. The ideal time to buy the chemical stocks is after a lot of capacity has left the industry and there’s a catalyst that you believe will trigger an increase in demand. Conversely, the ideal time to sell these stocks is when there are lots of announcements for new plants, not when the earnings turn down. The reason for this behavioral pattern is that expansion plans mean that earnings will go down in two to three years, and the stock market tends to anticipate such developments.

The market is a future discounting machine; meaning earnings matter for a stock, but more so in the future than in the past.

Most market participants take recent earnings and just extrapolate them into the future. They fail to really look at the mechanism that drives the bottom line for a particular company or sector. The key to being a good trader is to identify the factor(s) that will drive earnings going forward, not what drove them in the past.  

Druckenmiller said in a recent interview that his “job for 30 years was to anticipate changes in the economic trends that were not expected by others, and, therefore not yet reflected in security prices.” Focus on the future, not the past.

Another thing that sets Druck apart is his willingness to use anything that works; as in any style or tool to find good trades and manage them.

Another discipline I learned that helped me determine whether a stock would go up or down is technical analysis. Drelles was very technically oriented, and I was probably more receptive to technical analysis than anyone else in the department. Even though Drelles was the boss, a lot of people thought he was a kook because of all the chart books he kept. However, I found that technical analysis could be very effective.

I never use valuation to time the market. I use liquidity considerations and technical analysis for timing. Valuation only tells me how far the market can go once a catalyst enters the picture to change the market direction.

Druckenmiller employs a confluence of approaches (fundamental, macro, technical and sentiment) to broaden his view of the battlefield. This is a practice we follow at Macro Ops. It doesn’t make sense to pigeonhole yourself into a single rigid scope of analysis… simply use what works and discard what doesn’t.

How to make outsized returns

Druckenmiller throws conventional wisdom out the window. Instead of placing a lot of small diversified bets, he practices what we call the “Big Bet” philosophy, which consists of deploying a few large concentrated bets.

Here’s Druckenmiller on using the big bet philosophy (emphasis mine):

The first thing I heard when I got in the business, not from my mentor, was bulls make money, bears make money, and pigs get slaughtered. I’m here to tell you I was a pig. And I strongly believe the only way to make long-term returns in our business that are superior is by being a pig. I think diversification and all the stuff they’re teaching at business school today is probably the most misguided concept everywhere. And if you look at all the great investors that are as different as Warren Buffett, Carl Icahn, Ken Langone, they tend to be very, very concentrated bets. They see something, they bet it, and they bet the ranch on it. And that’s kind of the way my philosophy evolved, which was if you see – only maybe one or two times a year do you see something that really, really excites you… The mistake I’d say 98% of money managers and individuals make is they feel like they got to be playing in a bunch of stuff. And if you really see it, put all your eggs in one basket and then watch the basket very carefully.

A lot of wisdom in that paragraph. To earn superior long-term returns you have to be willing to bet big when your conviction is high. And the corollary is that you need to protect your capital by not wasting it on a “bunch of stuff” you don’t have much conviction on.

This reminds me of what Seth Klarman wrote in his book Margin of Safety:

Avoiding loss should be the primary goal of every investor. This does not mean that investors should never incur the risk of any loss at all. Rather “don’t lose money” means that over several years an investment portfolio should not be exposed to appreciable loss of capital. While no one wishes to incur losses, you couldn’t prove it from an examination of the behavior of most investors and speculators. The speculative urge that lies within most of us is strong; the prospect of free lunch can be compelling, especially when others have already seemingly partaken. It can be hard to concentrate on losses when others are greedily reaching for gains and your broker is on the phone offering shares in the latest “hot” initial public offering. Yet the avoidance of loss is the surest way to ensure a profitable outcome.

You need to keep your powder dry so that when the stars align you can go for the jugular and turkey neck that son of a gun.

The importance of striking when the iron is hot is something Druckenmiller learned while trading for George Soros.

I’ve learned many things from [George Soros], but perhaps the most significant is that it’s not whether you’re right or wrong that’s important, but how much money you make when you’re right and how much you lose when you’re wrong. The few times that Soros has ever criticized me was when I was really right on a market and didn’t maximize the opportunity.

An intense focus on capital preservation coupled with a big bet approach is the barbell philosophy used by many of the greats.

Keeping your losses small and pushing your winners hard is the name of the game in profitable speculation.

The fund washout we’re seeing today is not just because of the glut of mediocrity in the money management space, but also because even decent managers are scared to take the necessary risks to have big return years. They manage too much to the benchmark and are too short-term focused. That’s a recipe for average performance. Here’s Druck on how it should be done:

Many managers, once they’re up 30 or 40 percent, will book their year [i.e., trade very cautiously for the remainder of the year so as not to jeopardize the very good return that has already been realized]. The way to attain truly superior long-term returns is to grind it out until you’re up 30 or 40 percent, and then if you have the conviction, go for a 100 percent year. If you can put together a few near-100 percent years and avoid down years, then you can achieve really outstanding long-term returns.

Once you’ve earned the right to be aggressive and can bet with the house’s money (profits), you should plunge hard when that high conviction trade arises and push for outsized returns.

The trader’s mindset and handling losses

According to Druck, to be a winning trader you need to be “decisive, open-minded, flexible and competitive”.

The day before the crash in 1987, Druckenmiller switched from net short to 130% long because he thought the selloff was done. He saw the market bumping up against significant support. But through the course of the day he realized that he made a terrible mistake. The next day he flipped his book and got short the market and actually made money. You see this type of mental flexibility in all the greatest traders. And Druckenmiller is one trader that epitomizes it perhaps better than anybody else.

The practice of having “strong opinions, weakly held” is difficult but paramount to success.

In order to attain that level of mental flexibility, you need to learn to detach ego from your immediate trade outcomes. If you allow losses to affect your judgement, you’ll inevitably make bigger mistakes. Druckenmiller learned this lesson early on from Soros.

Soros is the best loss taker I’ve ever seen. He doesn’t care whether he wins or loses on a trade. If a trade doesn’t work, he’s confident enough about his ability to win on other trades that he can easily walk away from the position. There are a lot of shoes on the shelf; wear only the ones that fit. If you’re extremely confident, taking a loss doesn’t bother you.

One of the best parts about this game is that as long as you stay alive (protect your capital) you can always make another trade. Druckenmiller said the “wonderful thing about our business is that it’s liquid, and you can wipe the slate clean on any day. As long as I’m in control of the situation — that is, as long as I can cover my positions — there’s no reason to be nervous.”

I remember watching Charlie Rose interview Druckenmiller a few years ago. Charlie asked him why, after all these years, and with all the money he’s made, does he still put in 60-hour weeks trading? Druck responded (and I’m paraphrasing here) “because I have to… I love the game and I love winning, the money isn’t even important.”

To get to Druck’s level, you have to trade because that’s just what you do. It’s what you live for.

Download our free Trading Greats guide by clicking here to learn from all of the market legends! 

-----------------------------------------------------------------

Source

https://macro-ops.com/lessons-from-a-trading-great-stanley-druckenmiller/

 

Brookfield Renewable, Cameco, partner to buy Westinghouse for US$4.5B

Brookfield Renewable, Cameco, partner to buy Westinghouse for US$4.5B

Brookfield Renewable Partners and Cameco Corp. are teaming up to buy Westinghouse Electric Company for US$4.5 billion, plus debt, from another unit of Brookfield and its institutional partners.

Under the terms of the deal, Toronto-based Brookfield will pay US$2.3 billion and Saskatoon-based Cameco will contribute the remaining US$2.2 billion for Westinghouse, one of the world’s largest providers of nuclear services. Brookfield will own 51 per cent of the business, with Cameco owning the remaining 49 per cent.

Including assumed debt, the transaction comes in at an enterprise value of US$7.875 billion.

The current dominant shareholder of Westinghouse is another arm of Brookfield –  Brookfield Business Partners – which partnered with institutional partners to buy the business back in 2018 for US$4.6 billion. Brookfield Business Partners expects to book US$4.5 billion in total profit from its four years of owning Westinghouse.  

Westinghouse employs about 9,000 people, and services approximately half of the nuclear power generation sector. The bulk of its revenue comes from recurring-revenue contracts, with about 85 per cent of its clients being signed in for the long term.

In a release, Cameco chief executive officer Tim Gitzel said the deal allows Cameco – one of the world’s largest suppliers of uranium – to capitalize on the full nuclear supply chain, rather than being a source of the base fuel.

“With our more than 30-year proven track record of providing secure and reliable fuel supplies to a global customer base, this transaction fits perfectly within Cameco’s strategy and is expected to increase our ability to meet the growing needs of existing and new customers at a time when origin and security of supply is of significant concern,”

“At the same time, we expect the recurring demand for Westinghouse’s operating plant services and nuclear fuel will generate a strong revenue stream and add stable cash flow to complement Cameco’s existing uranium and fuel services business.”

Cameco said it currently has enough liquidity to finance its part of the deal, though it will be pursuing additional flexibility in the form of cash, debt and equity in order to keep its balance sheet in order prior to closing.

Concurrently, Cameco announced plans for a bought deal to sell US$650 million worth of shares, priced at $21.95 apiece.

The transaction is expected to close in the back half of 2023.

Nuclear power has been gaining greater interest over the course of the year, as natural gas disruptions stemming from the Russian invasion of Ukraine and explosions disrupting Moscow’s Nord Stream Pipeline threaten European energy security raise concerns of power outages.

The nuclear industry has by and large been in flux since Japan essentially idled its nuclear power program in the wake of the 2011 Fukushima disaster, creating concerns over demand for uranium.

Back here at home, Ontario announced plans to keep the Pickering nuclear power generation plant active for an additional year in late September, outlining plans to keep the unit in operation until 2026.

--------------------------------------------

Source

https://www.bnnbloomberg.ca/brookfield-renewable-cameco-partner-to-buy-westinghouse-for-us-4-5b-1.1831134

Five Invaluable Lessons On Value Investing

Five Invaluable Lessons On Value Investing

There aren’t many investors compounding capital at double digits over the course of decades and those that do are already well known (i.e., that guy from Omaha). However, in a small office above a taco shop, there’s a man running a hedge fund called Arlington Value who has demonstrated the advantage in simplicity, long-term thinking, and the power of compounding when it comes to value investing.

Arlington Value doesn’t have a large team of analysts. They don’t run advanced machine-learning algorithms or exploit esoteric satellite data and there’s not a single distinguished diploma on their walls.

Yet, Arlington Value has returned 18.36% CAGR over 11.5 years and its main fund, AVM Ranger Fund, has returned a mind-boggling 37.9% return since 2008. The man behind these numbers is Allan Mecham.

I spent all of last weekend pouring over his letters (s/o to Focused Compounding for the post) and there is plenty of nuggets to share. I’ve gone ahead and whittled down Mecham’s insights into five recurring lessons from his letters (spanning from 2008 – 2017) that are worth reviewing. Let’s get started!

Value Investing Lesson #1: Less (Not More) Information is Better — Avoid Noise

“I disagree with the notion that more information is always better.” – 2008 Letter

This may sound tongue-in-cheek as you’re reading this from an “information source” on investing, but bear with me. Mecham is old school. He reads print newspapers and avoids the sensationalist financial news media found on TV and the internet. Warren Buffett, Walter Schloss and countless other value investors follow similar practices (i.e., Buffett doesn’t have a computer in his office).

We know successful investors practice the art of “less is more”, but why exactly do they do it? What’s the edge?

The edge is found in clear thinking and an uncluttered mind. Our brains only have so much decision making power capacity each day. Disposing that energy into numerous outlets — reading too many blogs, following too many investors, watching Mad Money — reduces our brain’s capacity to make full-powered decisions on important questions. Mecham addresses these issues in his 2010 annual letter, saying (emphasis mine),

The steady surge of information coupled with short-term performance pressures can push rational long-term investing to the brink of extinction. The easy access to information, and the snack-bar nature of consuming it, suggests that disciplining one’s temperament rivals the need for energy and action.

The less information you consume the more time you have to ponder the few critical bits that really matter.

Value Investing Lesson #2: Selling Great Businesses Is Almost Always a Mistake

“Selling is difficult, and my track record suggests it’s usually a mistake.” – 2010 Letter

In a perfect world, we find businesses we love with management teams that know how to allocate capital well, and then we sit. Unfortunately, a small fraction of public companies meet that criteria, and even then, it’s tremendously difficult to sit and hold. In his 2010 Letter, Mecham addressed the issue of selling, saying (emphasis mine):

My view on selling is akin to the old sports adage, ‘the best defense is a good offense’; the best sell discipline is a stingy buy discipline — which couples proper analysis with a bargain price.

Mecham highlights his disdain for selling via his example of selling Autozone in 2010 — sale he admits was a mistake.

At the time, Autozone (AZO) comprised 18% of the Fund’s portfolio (something we’ll touch on later). Mecham sold at an average price of $155.67/share. Had Mecham held his shares till year end he would’ve seen share prices climb to $272.59/share (that’s good for a 75% increase in price). Hindsight is 20/20, so it’s not the share price increase I want to highlight, but Mecham’s post-mortem analysis on AZO:

We’ve followed and owned AZO for years and admire the intrinsic qualities of the business — a leading market position, durable and counter-cyclical characteristics, strong growth prospects, and an impressive managerial record of capital allocation.

Mecham sold a business with all of these characterisitcs (albeit for another great business in BRKB) and regretted doing so. Hold on to great businesses.

Value Investing Lesson #3: Inactivity Is The Key To Success — Learn To Do Nothing

We favor infrequent action (and commentary), patiently waiting for exceptional opportunities. – 2010 Letter

Takeaway #3 is a corollary to the cousin above as you cannot have one without the other.

If you don’t have an ability to be patient, do nothing and wait for opportunities, you’ll never be able to hold on to great businesses. In order to achieve the powerful effects of compounding, inactivity isn’t a preferred skill, it’s a must-have.

Although value investors talk about the necessity for long periods of inactivity, the reasons for doing so are not always clear. Mecham (like most successful value managers) buys only at deeply discounted prices — normally expressed during bouts of extreme pessimism. In his 2014 Letter, Mecham discusses his important practice of sitting on his hands (emphasis mine):

Our office feels more like an abandoned library with a couple of bums loitering around. We have yet to be swayed by the virtues of analyst teams and investment meetings. We’re old school. We mostly just sit around reading, thinking, and waiting. A quip by Stanley Druckenmiller describes our process best: ‘I like to be very patient and then when I see something, go a little bit crazy.

Not only does frequent activity result in reduced performance, but it also translates into higher costs of doing business (i.e., commissions and taxes). But why is inactivity so hard? =Two major reasons come to mind: job security and measurement barometer.

There’s an aura of legitimacy around seeing someone (or a group of people) frantically engaged in work. If you’re paying someone to do a job, your mind will more likely be at ease seeing that person at work. This plays into the first reason why it’s so hard to stay inactive: job security. Most money managers are closet indexers. In other words, they hug the index as close as possible to keep clients’ assets flowing in. And if you put yourself in the shoes of the average money manager — this makes sense. It’s a much easier conversation to have with a client if their assets move in line with the index (going up or down). It’s a much harder talk to have when the market is going up and your portfolio is stagnant (or God forbid down) during the same time frame.

Along with this crutch of job security, most managers — whether through their own blight or from their clients — measure themselves on too short of a time frame. For example, if clients expect you to outperform quarterly or monthly, how will the manager base his/her decisions on investments? Quarterly or monthly measurements leads to overtrading, selling too soon and getting into riskier positions to chase incrementally higher returns over a short time frame. This third takeaway is best surmised with the following quote from Phil Carret:

Turnover usually indicates a failure of judgement. It’s extremely difficult to figure out when to sell anything.

Value Investing Lesson #4: Focus On The Long Term — Play For Keeps

Our ideas and policies are all structured with one goal in mind: to cultivate a culture that encourages rational decision making that ultimately leads to solid risk-adjusted returns. – 2012 Letter

If there’s one takeaway that was mentioned the most throughout Mecham’s letters, it was Number 4. Playing for keeps.

Mecham routinely stresses the importance of an owner-like mindset and its impact on long-term investing success. Not only does an owner-like mindset change the time frame as an investor, it forces you to change what you care about when looking at businesses. Focusing on long-term investing (i.e., holding businesses for decades not decimal seconds) leads to a natural decline in the level of importance you place on quarterly results (earnings “beats”), short-term headwinds and temporary compressions in earnings and margins. When you think long-term, all of that doesn’t really matter. More than that, if you keep thinking like this, you’ll start to question why others even ask for quarterly guidance.

Mecham makes this crystal clear when he discusses the long-term mind-set in his 2014 letter, writing (emphasis mine):

First and foremost, we adopt the mentality of a business owner buying for keeps. To us this means thinking about staying power, competitive threats, economics, and  comparing price to value … We don’t think quarterly “beats” are germane to intrinsic value.

In other words, changing the time frame in which you think about investments leads you to spend most of your time thinking about the above items instead of the quarterly metrics that everybody else is so focused on. Mecham drives this point home a couple pages later, claiming:

I believe the biggest difference (and our main advantage) between Arlington and the average fund is our ability to implement a framework of analyzing businesses like long-term owners.

Value Investing Lesson #5: Concentration (Not Diversification) Is Vital To Outperformance

The result is a concentrated portfolio that tends to be more volatile than the indices — a situation that’s not well tolerated by lay people and Wall Street alike. – 2010 Letter

The fifth and final takeaway is (arguably) the most important for investors looking to outperform the market over the long-term.

Concentration of assets is as counter-consensus as it gets within the investing community. As I’ve mentioned before, most money managers hug the index, investing in 30, 40 or 100 stocks. This is the recipe for average, something Allan recognized early on in his Fund’s existence.

Mecham keeps a concentrated portfolio of around 12 – 15 businesses. He isn’t afraid to allocate a large percentage of his Fund’s capital to a few select names. For example, we saw earlier where Mecham allocated 18% of his funds to Autozone. Even 18% pales in comparison to Mechem’s largest investment during the course of his letters. In 2011, Mecham made Berkshire Hathaway a 50% position in his Fund. That’s a five zero % position. In fact, Mecham went so far as to go on margin to purchase more shares of Berkshire Hathaway (1.5% margin cost) — leveraging up in his largest Fund position. Most run-of-the-mill managers would be on career suicide watch after a move like that. But, like Mecham illustrates, it made logical sense (emphasis mine):

Conventional fund management holds dogmatic disdain for highly concentrated positions. Needless to say, we hold a different view. To us, as a BRK owner, the contempt for concentration is acutely illogical as BRK provides ample diversity, with exposure to disparate businesses, sectors, and asset allocations.

This logic falls in line with Buffett’s old adage of adequate diversification in which he describes owning a few local businesses in your town as proper diversification. If you own a few of the best operations in town, wouldn’t that be considered properly diversified? Of course. Somehow when venturing into financial markets that same philosophy flies out the window. A portfolio of 10 – 15 strenuously researched companies bought at bargain-bin prices is as low of a risk investment strategy as they come. Mecham stresses this to his LP’s when he pens (emphasis mine):

While our focused portfolio is sometimes criticized by the financial mainstream, we think the judgements lack substance. We are a risk-averse fund looking for low-risk layup-type investments while other funds are akin to a run-and-gun offense that routinely takes a smattering of low-percentage shots.

If you want to beat the market over the long-term, you need to make concentrated bets in companies you believe will earn higher returns on their capital than the general market, especially when it comes to value investing. Couple these concentrated bets with a long-term time horizon and steadfast determination to do nothing and you’re almost on your way to cloning Allan Mecham.

Here’s the link to all of Arlington’s Value Investing Letters via Focused Compounding.

-----------------------------------------------

Source

https://macro-ops.com/five-invaluable-lessons-in-value-investing/

The Illusion of Knowledge

The Illusion of Knowledge

Howard Marks's (Oaktree Capital) latest memo

-----------------------------------------------------

I’ve been expressing my disregard for forecasts for almost as long as I’ve been writing my memos, starting with The Value of Predictions, or Where’d All This Rain Come From in February 1993.  Over the years since then, I’ve explained at length why I’m not interested in forecasts – a few of my favorite quotes echoing my disdain head the sections below – but I’ve never devoted a memo to explaining why making helpful macro forecasts is so difficult.  So here it is. 

----------------------------------------------------

Food for Thought

There are two kinds of forecasters: those who don’t know, and those who don’t know they don’t know.

– John Kenneth Galbraith

Shortly after putting the finishing touches on I Beg to Differ in July, I attended a lunch with a number of experienced investors, plus a few people from outside the investment industry.  It wasn’t organized as a social occasion but rather an opportunity for those present to exchange views regarding the investment environment. 

At one point, the host posed a series of questions: What’s your expectation regarding inflation?  Will there be a recession, and if so, how bad?  How will the war in Ukraine end?  What do you think is going to happen in Taiwan?  What’s likely to be the impact of the 2022 and ’24 U.S. elections?  I listened as a variety of opinions were expressed. 

Regular readers of my memos can imagine what went through my mind: “Not one person in this room is an expert on foreign affairs or politics.  No one present has particular knowledge of these topics, and certainly not more than the average intelligent person who read this morning’s news.”  None of the thoughts expressed, even on economic matters, seemed much more persuasive than the others, and I was absolutely convinced that none were capable of improving investment results.  And that’s the point.

It was that lunch that started me thinking about writing yet another memo on the futility of macro forecasting.  Soon thereafter a few additional inputs arrived – a book, a piece in Bloomberg Opinion, and a newspaper article – all of which supported my thesis (or perhaps played to my “confirmation bias” – i.e., the tendency to embrace and interpret new information in a manner that confirms one’s preexisting views).  Together, the lunch and these items inspired this memo’s theme: the reasons why forecasts are rarely helpful.

In order to produce something useful – be it in manufacturing, academia, or even the arts – you must have a reliable process capable of converting the required inputs into the desired output.  The problem, in short, is that I don’t think there can be a process capable of consistently turning the large number of variables associated with economies and financial markets (the inputs) into a useful macro forecast (the output).   

---------------------------------------------------

The Machine

The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge. 

– Daniel J. Boorstin

In my first decade or so working at First National City Bank, a word was in vogue that I haven’t heard in a long time: econometrics.  This is the practice of looking for relationships within economic data that can lead to valid forecasts.  Or, to simplify, I’d say econometrics is concerned with building a mathematical model of an economy.  Econometricians were heard from a great deal in the 1970s, but I don’t believe they are any longer.  I take that to mean their models didn’t work.

Forecasters have no choice but to base their judgments on models, be they complex or informal, mathematical or intuitive.  Models, by definition, consist of assumptions: “If A happens, then B will happen.”  In other words, relationships and responses.  But for us to willingly employ a model’s output, we have to believe the model is reliable.  When I think about modeling an economy, my first reaction is to think about how incredibly complicated it is.

The U.S., for example, has a population of around 330 million.  All but the very youngest and perhaps the very oldest are participants in the economy.  Thus, there are hundreds of millions of consumers, plus millions of workers, producers, and intermediaries (many people fall into more than one category).  To predict the path of the economy, you have to forecast the behavior of these people – if not for every participant, then at least for group aggregates. 

A real simulation of the U.S. economy would have to deal with billions of interactions or nodes, including interactions with suppliers, customers, and other market participants around the globe.  Is it possible to do this?  Is it possible, for example, to predict how consumers will behave (a) if they receive an additional dollar of income (what will be the “marginal propensity to consume”?); (b) if energy prices rise, squeezing other household budget categories; (c) if the price for one good rises relative to others (will there be a “substitution effect”?); or (d) if the geopolitical arena is roiled by events continents away? 

Clearly, this level of complexity necessitates the frequent use of simplifying assumptions.  For example, it would make modeling easier to be able to assume that consumers won’t buy B in place of A if B isn’t either better or cheaper (or both).  And it would help to assume that producers won’t price X below Y if it doesn’t cost less to produce X than Y.  But what if consumers are attracted to the prestige of B despite (or even because of) its higher price?  And what if X has been developed by an entrepreneur who’s willing to lose money for a few years to gain market share?  Is it possible for a model to anticipate the consumer’s decision to pay up and the entrepreneur’s decision to make less (or even lose) money?

Further, a model will have to predict how each group of participants in the economy will behave in a variety of environments.  But the vagaries are manifold.  For example, consumers may behave one way at one moment and a different way at another similar moment.  Given the large number of variables involved, it seems impossible that two “similar” moments will play out exactly the same way, and thus that we’ll witness the same behavior on the part of participants in the economy.  Among other things, participants’ behavior will be influenced by their psychology (or should I say their emotions?), and their psychology can be affected by qualitative, non-economic developments.  How can those be modeled?

How can a model of an economy be comprehensive enough to deal with things that haven’t been seen before, or haven’t been seen in modern times (meaning under comparable circumstances)?  This is yet another example of why a model simply can’t replicate something as complex as an economy.

Of course, a prime example of this is the Covid-19 pandemic.  It caused much of the world’s economy to be shut down, turned consumer behavior on its head, and inspired massive government largesse.  What aspect of a pre-existing model would have enabled it to anticipate the pandemic’s impact?  Yes, we had a pandemic in 1918, but the circumstances were so different (no iPhones, Zoom calls, etc. ad infinitum) as to render economic events during that time of little or no relevance to 2020.

In addition to the matter of complexity and the difficulty of capturing psychological fluctuations and dynamic processes, think about the limitations that bear on an attempt to predict something that can’t be expected to remain unchanged.  Shortly after starting on this memo, I received my regular weekly edition of Morgan Housel’s always-brilliant newsletter.  One of the articles described a number of observations from other arenas that have relevance to our world of economics and investing.  Here are two, borrowed from the field of statistics, that I think are pertinent to the discussion of economic models and forecasts (“Little Ways the World Works,” Morgan Housel, Collaborative Fund, July 20, 2022):

Stationarity: An assumption that the past is a statistical guide to the future, based on the idea that the big forces that impact a system don’t change over time.  If you want to know how tall to build a levee, look at the last 100 years of flood data and assume the next 100 years will be the same.  Stationarity is a wonderful, science-based concept that works right up until the moment it doesn’t.  It’s a major driver of what matters in economics and politics.  [But in our world,] “Things that have never happened before happen all the time,” says Stanford professor Scott Sagan.

Cromwell’s rule: Never say something cannot occur . . . .  If something has a one-in-a-billion chance of being true, and you interact with billions of things during your lifetime, you are nearly assured to experience some astounding surprises, and should always leave open the possibility of the unthinkable coming true. 

Stationarity might be fairly assumed in the realm of the physical sciences.  For example, thanks to the law of universal gravitation, under given atmospheric conditions, the speed at which an object falls can always be counted on to accelerate at the same rate. It always has, and it always will.  But few processes can be counted on to be stationary in our world, especially given the role played by psychology, emotion, and human behavior, and their propensity to vary over time.

Take, for example, the relationship between unemployment and inflation.  For roughly the last 60 years, economists relied on the Phillips curve, which holds that wage inflation will rise as the unemployment rate declines, because when there are fewer idle workers on the sidelines, employees gain bargaining power and can successfully negotiate for higher wages.  It was also believed for decades that an unemployment rate around 5.5% indicated “full employment.”  But unemployment fell below 5.5% in March 2015 (and reached a 50-year low of 3.5% in September 2019), yet there was no significant increase in inflation (in wages or otherwise) until 2021.  So the Phillips curve described an important relationship that was built into economic models for decades but, seemingly, didn’t apply over much of the last decade.

Cromwell’s rule is also relevant.  Unlike in the physical sciences, in markets and economies there’s very little that absolutely has to happen or definitely can’t happen.  Thus, in my book Mastering the Market Cycle , I listed seven terms that investors should purge from their vocabularies: “never,” “always,” “forever,” “can’t,” “won’t,” “will,” and “has to.”  But if it’s true that those words have to be discarded, then so too must the idea that one can build a model that can dependably predict the macro future.  In other words, very little is immutable in our world.

The unpredictability of behavior is a favorite topic of mine.  Noted physicist Richard Feynman once said, “Imagine how much harder physics would be if electrons had feelings.”  The rules of physics are reliable precisely because electrons always do what they’re supposed to do.  They never forget to perform.  They never rebel.  They never go on strike.  They never innovate.  They never behave in a contrary manner.  But none of these things is true of the participants in an economy, and for that reason their behavior is unpredictable.  And if the participants’ behavior is unpredictable, how can the workings of an economy be modeled?

What we’re talking about here is the future, and there’s simply no way to deal with the future that doesn’t require the making of assumptions.  Small errors in assumptions regarding the economic environment and small changes in participants’ behavior can make differences that are highly problematic.  As mathematician and meteorologist Edward Lorenz famously suggested, “The flapping of a butterfly’s wings in Brazil could set off a tornado in Texas.” (Historian Niall Ferguson references this remark in the article I discuss below.)

Thinking about all the above, can we ever consider a model of an economy to be reliable?  Can a model replicate reality?  Can it describe the millions of participants and their interactions?  Are the processes it attempts to model dependable?  Can the processes be reduced to mathematics?  Can mathematics capture the qualitative nuances of people and their behavior?  Can a model anticipate changes in consumer preferences, changes in the behavior of businesses, and participants’ reactions to innovation?  In other words, can we trust its output? 

Clearly, economic relationships aren’t hard-wired, and economies aren’t governed by schematic diagrams (which models try to simulate).  Thus, for me, the bottom line is that the output from a model may point in the right direction much of the time, when the assumptions aren’t violated.  But it can’t always be accurate, especially at critical moments such as inflection points . . . and that’s when accurate predictions would be most valuable. 

-----------------------------------------

The Inputs

No amount of sophistication is going to allay the fact that all of your knowledge is about the past and all your decisions are about the future.

– Ian H. Wilson (former GE executive)

Having considered the incredible complexity of an economy and the need to make simplifying assumptions that decrease any economic model’s accuracy, let’s now think about the inputs a model requires – the raw materials from which forecasts are manufactured.  Will the estimated inputs prove valid?  Can we know enough about them for the resulting forecast to be meaningful?  Or will we simply be reminded of the ultimate truth about models: “garbage in, garbage out”?  Clearly, no forecast can be better than the inputs on which it’s based. 

Here’s what Niall Ferguson wrote in Bloomberg Opinion on July 17:

Consider for a moment what we are implicitly asking when we pose the question: Has inflation peaked? We are not only asking about the supply of and demand for 94,000 different commodities, manufactures and services. We are also asking about the future path of interest rates set by the Fed, which – despite the much-vaunted policy of “forward guidance” – is far from certain. We are asking about how long the strength of the dollar will be sustained, as it is currently holding down the price of U.S. imports.

But there’s more. We are at the same time implicitly asking how long the war in Ukraine will last, as the disruption caused since February by the Russian invasion has significantly exacerbated energy and food price inflation. We are asking whether oil-producing countries such as Saudi Arabia will respond to pleas from Western governments to pump more crude. . . .

We should probably also ask ourselves what the impact on Western labor markets will be of the latest Covid omicron sub-variant, BA.5.  UK data indicate that BA.5 is 35% more transmissible than its predecessor BA.2, which in turn was over 20% more transmissible than the original omicron.

Good luck adding all those variables to your model.  It is in fact just as impossible to be sure about the future path of inflation as it is to be sure about the future path of the war in Ukraine and the future path of the Covid pandemic.

I found Ferguson’s article so relevant to the subject of this memo that I’m including a link to it here.  It makes a lot of important points, although I beg to differ in one regard.  Ferguson says above, “It is in fact just as impossible to be sure about the future path of inflation as it is to be sure about the future path of the war in Ukraine and the future path of the Covid pandemic.”  I think accurately predicting inflation is “more impossible” (if there is such a thing) than predicting the outcomes of the other two, since doing so requires being right about both of those outcomes and a thousand other things.  How can anyone possibly get all these things right?

Here’s my rough description of the forecasting process from The Value of Predictions:

I imagine that for most money managers, the process goes like this: “I predict the economy will do A.  If A happens, interest rates should do B.  With interest rates of B, the stock market should do C.  Under that environment, the best performing sector should be D, and stock E should rise the most.”  The portfolio expected to do best under that scenario is then assembled.

But how likely is E anyway?  Remember that E is conditioned on A, B, C and D.  Being right two-thirds of the time would be a great accomplishment in the world of forecasting.  But if each of the five predictions has a 67% chance of being right, then there is a 13% probability that all five will be correct and that the stock will perform as expected. 

Predicting event E on the basis of assumptions concerning A, B, C and D is what I call single-scenario forecasting .  In other words, if what was assumed regarding A, B, C or D turns out to have been erroneous, the forecasted outcome for E is unlikely to materialize.  All of the underlying forecasts have to be right in order for E to turn out as predicted, and that’s highly improbable.  No one can invest intelligently without considering (a) the other possible outcomes for each element, (b) the likelihood of these alternative scenarios, (c) what would have to happen for one of them to be the actual outcome, and (d) what the impact on E would be.

Ferguson’s article raises an interesting question about economic modeling: What’s to be assumed regarding the general macro environment under which economic participants will operate?  Doesn’t this question indicate an insoluble feedback loop: To predict the overall performance of the economy, we need to make assumptions about, for example, consumer behavior.  But to predict consumer behavior, don’t we need to make assumptions regarding the overall economic environment?

In Nobody Knows II (March 2020), my first memo of the pandemic, I mentioned that in a discussion of the coronavirus, Harvard epidemiologist Marc Lipsitch had said there are (a) facts, (b) informed extrapolations from analogies to other viruses, and (c) opinion or speculation.  This is standard fare when we deal with uncertain events.  In the case of economic or market forecasts, we have a vast trove of history and lots of analogous past events from which to extrapolate (neither of which was the case with Covid-19).  But even when these things are used as inputs for a well-constructed forecasting machine, they’re still highly unlikely to be predictive of the future.  They may be useful fodder, or they may be garbage. 

To illustrate, people often ask me which of the past cycles I’ve experienced was most like this one.  My answer is that current developments bear a passing resemblance to some past cycles, but there is no absolute parallel.  The differences are profound in every case and outweigh the similarities.  And even if we could find an identical prior period, how much reliance should we put on a sample size of one?  I’d say not much.  Investors rely on historical references (and the forecasts they foster) because they fear that without them they’d be flying blind.  But that doesn’t make them reliable.

-------------------------------------------------

Unpredictable Influences

Forecasts create the mirage that the future is knowable. 

– Peter Bernstein

We can’t consider the reasonableness of forecasting without first deciding whether we think our world is one of order or of randomness.  Put simply, is it entirely predictable, entirely unpredictable, or something in between?  The bottom line for me is that it’s in between, but unpredictable enough that most forecasts are unhelpful.  And since our world is predictable at some times and unpredictable at others, what good are forecasts if we can’t tell which is which?

I learned a new word from reading Ferguson’s article: “deterministic.”  It’s defined by Oxford Languages as “causally determined by preceding events or natural laws.”  The world is much simpler when we deal with things that function according to rules . . . like Feynman’s electrons.  But, clearly, economies and markets aren’t governed by natural laws – thanks to the involvement of people – and preceding events may “set the stage” or “tend to repeat,” but events rarely unfold in the same way twice.  Thus, I believe the processes that constitute the operation of economies and markets aren’t deterministic, meaning they aren’t predictable.

Further, the inputs clearly are undependable.  Many are subject to randomness, such as weather, earthquakes, accidents, and deaths.  Others involve political and geopolitical issues – ones we’re aware of and ones that haven’t yet surfaced. 

In his Bloomberg Opinion article, Ferguson mentioned the English writer G. K. Chesterton.  That reminded me to include a Chesterton quote that I used in Risk Revisited Again (June 2015):

The real trouble with this world of ours is not that it is an unreasonable world, nor even that it is a reasonable one.  The commonest kind of trouble is that it is nearly reasonable, but not quite.  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.  (Emphasis added)

Going back to the lunch described on page one, the host opened the proceedings roughly as follows: “In recent years, we’ve experienced the Covid-19 pandemic, the surprising success of the Fed’s rescue actions, and the invasion of Ukraine.  This has been a very challenging environment, since all of these developments arrived out of the blue.”  I imagine the implication for him was that the attendees should let themselves off the hook for the inaccuracy of their 2020-22 forecasts and go back to work predicting future events and betting on their judgments.  But my reaction was quite different: “The list of events that shaped the current environment is quite extensive.   Doesn’t the fact that no one was able to predict any of them convince those present that they should give up on forecasting?”

For another example, let’s think back to the fall of 2016.  There were two things that almost everyone was sure of: (a) Hillary Clinton would be elected president and (b) if for some reason Donald Trump were elected instead, the markets would tank.  Nonetheless, Trump won, and the markets soared.  The impact on the economy and markets over the last six years was profound, and I’m confident no forecast that took a conventional view of the coming 2016 election got the period since then correct.  Again, shouldn’t that be enough to convince people that (a) we don’t know what’s going to happen and (b) we don’t know how the markets will react to what happens?

----------------------------------------

Do Forecasts Add Value?

It ain’t what you don’t know that gets you into trouble.  It’s what you know for sure that just ain’t so.

– Mark Twain

As I mentioned in my recent memo Thinking About Macro , in the 1970s we used to describe an economist as “a portfolio manager who never marks to market.”  In other words, economists make forecasts; events prove them either wrong or right; they go on to make new forecasts; but they don’t keep track of how often they get it right (or they don’t publish the stats).

Can you imagine hiring a money manager (or being hired, if you are a money manager) without reference to a track record?  And yet, economists and strategists stay in business, presumably because there are customers for their forecasts, despite there being no published records.

Are you a consumer of forecasts?  Are there forecasters and economists on staff where you work?  Or do you subscribe to their publications and invite them in for briefings, as was the case with my previous employers?  If so, do you know how often each has been right?  Have you found a way to rigorously determine which ones to rely on and which to ignore?  Is there a way to quantify their contributions to your investment returns?  I ask because I’ve never seen or heard of any research along these lines.  The world seems incredibly short on information regarding the value added by macro forecasts, especially given the large number of people involved in this pursuit.

Despite the lack of evidence regarding its value, macro forecasting goes on.  Many of the forecasters are part of teams managing equity funds, or they provide advice and forecasts to those teams.  What we know for sure is that actively managed equity funds have been losing market share to index funds and other passive vehicles for decades due to the poor performance of active management, and as a result, actively managed funds now account for less than half of the capital in U.S. equity mutual funds.  Could the unhelpful nature of macro forecasts be part of the reason?

The only place I know to look for quantification regarding this issue is the performance of so-called macro hedge funds.  Hedge Fund Research (HFR) publishes broad hedge fund performance indices as well as a number of sub-indices.  Below is the long-term performance of a broad hedge fund index, a macro fund sub-index, and the Standard & Poor’s 500 Index. 

	HFRI Hedge Fund Index*	HFRI Macro (Total) Index	S&P 500 Index
5-year annualized return*	5.2%	5.0%	12.8%
10-year annualized return*	5.1	2.8	13.8

* Performance through July 31, 2022.  The broad hedge fund index shown is the Fund Weighted Composite Index.

What the table above shows is that, according to HFR, the average hedge fund woefully underperformed the S&P 500 in the period under study, and the average macro fund did considerably worse (especially in the period from 2012 to 2017).  Given that investors continue to entrust roughly $4.5 trillion of capital to hedge funds, they must deliver some benefit other than returns, but it’s not obvious what that could be.  This seems to be especially true for the macro funds.

To support my opinion regarding forecasts, I’ll cite a rare example of self-assessment: a seven-page feature that appeared in the Sunday Opinion section of The New York Times on July 24 titled “I Was Wrong.”  In it, eight Times opinion writers opened up about incorrect predictions they made and flawed advice they had given.  The most relevant here is Paul Krugman, who wrote a confession titled “I Was Wrong About Inflation.”  I’ll string together some excerpts:

In early 2021, there was an intense debate among economists about the likely consequences of the American Rescue Plan . . . .  I was on [the side that was less concerned about the impact on inflation].  As it turned out, of course, that was a very bad call. . . . 

. . . history wouldn’t have led us to expect this much inflation from overheating.  So something was wrong with my model . . . .  One possibility is that history was misleading . . . .  Also, disruptions associated with adjusting to the pandemic and its aftermath may still be playing a large role.  And of course both Russia’s invasion of Ukraine and China’s lockdown of major cities have added a whole new level of disruption. . . .

In any case, the whole experience has been a lesson in humility.  Nobody will believe this, but in the aftermath of the 2008 crisis, standard economic models performed pretty well, and I felt comfortable applying these models in 2021.  But in retrospect I should have realized that in the face of the new world created by Covid-19, that kind of extrapolation wasn’t a safe bet.  (Emphasis added)

I salute Krugman for this incredible bout of candor (although I have to say I don’t remember a lot of 2009-10 market forecasts that were optimistic enough to capture the reality of the subsequent decade).  Krugman’s explanation for his error is fine as far as it goes, but I don’t see any mention of abstaining from modeling, extrapolating, or forecasting in the future.

Humility may even be seeping into one of the world’s biggest producers of economic forecasts, the U.S. Federal Reserve, home of more than 400 Ph.D. economists.  Here’s what economist Gary Shilling wrote in Bloomberg Opinion on August 22:

The Federal Reserve’s forward guidance program has been a disaster, so much so that it has strained the central bank’s credibility.  Chair Jerome Powell seems to agree that providing estimates of where the Fed sees interest rates, economic growth and inflation at different points in the future should be junked. . . .

The basic problem with forward guidance is that it depends on data that the Fed had a miserable record of forecasting.  It was consistently too optimistic about an economic recovery after the 2007-2009 Great Recession.  In September 2014, policy makers forecast real gross domestic product growth in 2015 of 3.40% but were forced to constantly crank their expectations down to 2.10% by September 2015.

The federal funds rate is not a market-determined interest rate but is set and controlled by the Fed, and nobody challenges the central bank.  Yet the FOMC members were infamously terrible at forecasting what they themselves would do . . .  In 2015, their average projection of the 2016 federal funds rate was 0.90% and 3.30% in 2019.  The actual numbers were 0.38% and 2.38%. . . .

To be sure, many current events today have caused uncertainty in markets, but the Fed has been in there hot and heavy with its forward guidance.  Recall that early this year the central bank believed that inflation caused by frictions in reopening the economy after the pandemic and supply-chain disruptions was temporary.  Only belatedly did it reverse gears, raise rates and signal that further substantial hikes are coming.  Faulty Fed forecasts resulted in faulty forward guidance and increased financial market volatility.   (Emphasis added)

Lastly on this subject, where are the people who’ve gotten famous (and rich) by profiting from macro views?  I certainly don’t know everyone in the investment world, but among the people I do know or am aware of, there are only a few highly successful “macro investors.”  When the number of instances of something is tiny, it’s an indication, as my mother used to say, that they’re “the exceptions that prove the rule.”  The rule in this case is that macro forecasts rarely lead to exceptional performance.  For me, the exceptionalness of the success stories proves the general truth of that assertion.

------------------------------------------------

Practitioners’ Need to Predict

Forecasts usually tell us more of the forecaster than of the future.

– Warren Buffet

How many people are capable of making macro forecasts that are valuable most of the time?  Not many, I think.  And how many investment managers, economists, and forecasters try?  Thousands, at a minimum.  That raises an interesting question: why?  If macro forecasts don’t add to investment success over time, why do so many members of the investment management industry espouse belief in forecasts and pursue them?  I think the reasons probably center on these:

• It’s part of the job.

• Investors have always done it.

• Everyone I know does it, especially my competitors.

• I’ve always done it – I can’t quit now.

• If I don’t do it, I won’t be able to attract clients.

• Since investing consists of positioning capital to benefit from future events, how can anyone expect to do a good job without a view regarding what those events will be?  We need forecasts, even if they’re imperfect.

This summer, at the suggestion of my son Andrew, I read an extremely interesting book: Mistakes Were Made (but Not by Me): Why We Justify Foolish Beliefs, Bad Decisions, and Hurtful Acts, written by psychologists Carol Tavris and Elliot Aronson.  Its topic is self-justification.  The authors explain that “cognitive dissonance” arises when people are confronted with new evidence that calls into question their pre-existing positions and that when it does, unconscious mechanisms enable them to justify and uphold those positions.  Here are some selected quotes:

If you hold a set of beliefs that guide your practice and you learn that some of them are incorrect, you must either admit you were wrong and change your approach or reject the new evidence.

Most people, when directly confronted by evidence that they are wrong, do not change their point of view or plan of action but justify it even more tenaciously. 

Once we are invested in a belief and have justified its wisdom, changing our minds is literally hard work.  It’s much easier to slot that new evidence into an existing framework and do the mental justification to keep it there than it is to change the framework.

The mechanisms that people generally employ when responding to evidence that throws their beliefs into doubt include these (paraphrasing the authors’ words):

• an unwillingness to heed dissonant information;

• selectively remembering parts of their lives, focusing on those parts that support their own points of view; and

• operating under cognitive biases that ensure people see what they want to see and seek confirmation of what they already believe.

I have little doubt that these are among the factors that cause and enable people to continue making and consuming forecasts.  What specific form might they take in this case?

• thinking of macro forecasts as an indispensable part of investing;

• pleasantly recalling correct forecasts, especially any that were bold and non-consensus;

• overestimating how often forecasts were right;

• forgetting or minimizing the ones that were wrong;

• not keeping records regarding forecasts’ accuracy or failing to calculate a batting average;

• focusing on the “pot of gold” that will reward correct forecasts in the future;

• saying “everyone does it”; and

• perhaps most importantly, blaming unsuccessful forecasts on having been blindsided by random occurrences or exogenous events.  (But, as I said earlier, that’s the point: Why make forecasts if they’re so easily rendered inaccurate?)

Most people – even honest people with good intentions – take positions or actions that are in their own interests, sometimes at the expense of others or of objective truth.  They don’t know they’re doing it; they think it’s the right thing; and they have tons of justification.  As Charlie Munger often says, quoting Demosthenes, “Nothing is easier than self-deceit.  For what every man wishes, that he also believes to be true.” 

I don’t think of forecasters as crooks or charlatans.  Most are bright, educated people who think they’re doing something useful.  But self-interest causes them to act in a certain way, and self-justification enables them to stick with it in the face of evidence to the contrary.  As Morgan Housel put it in a recent newsletter:

The inability to forecast the past has no impact on our desire to forecast the future. Certainty is so valuable that we’ll never give up the quest for it, and most people couldn’t get out of bed in the morning if they were honest about how uncertain the future is.  (“Big Beliefs,” Collaborative Fund, August 24, 2022)

For my birthday several years ago, my Oaktree co-founder Richard Masson gave me one of his typical quirky gifts.  In this case, it consisted of some bound copies of The New York Times.  I’ve been waiting for an opportunity to write about my favorite sub-headline from the issue dated October 30, 1929, which followed two days on which the Dow Jones Industrial Average declined by a total of 23%.  It read, “Bankers Optimistic.”  (Less than three years later, the Dow was roughly 85% lower.)   Most bankers – and most money managers – seem to be congenitally optimistic about the future.  Among other things, it’s in their best interests, as it helps them do more business.  But their optimism certainly shapes their forecasts and their resulting behavior.

------------------------------------------

Can They or Can’t They?

I never think about the future – it comes soon enough.

– Albert Einstein

Consider the following aspects of macro forecasting:

• the number of assumptions/inputs that are required,

• the number of processes/relationships that have to be incorporated,

• the inherent undependability and instability of those processes, and

• the role of randomness and the likelihood of surprises.

The bottom line for me is that forecasts can’t be right often enough to be worthwhile.  I’ve described it many times, but just for the sake of completeness, I’m going to restate my view of the utility (or rather, futility) of macro forecasts:

• Most forecasts consist of extrapolation of past performance.

• Because macro developments usually don’t diverge from prior trends, extrapolation is usually successful.

• Thus, most forecasts are correct.  But since extrapolation is usually anticipated by security prices, those who follow expectations based on extrapolation don’t enjoy unusual profits when it holds.

• Once in a while, the behavior of the economy does deviate materially from past patterns.  Since this deviation comes as a surprise to most investors, its occurrence moves markets, meaning an accurate prediction of the deviation would be highly profitable.

• However, since the economy doesn’t diverge from past performance very often, correct forecasts of deviation are rarely made and most forecasts of deviation turn out to be incorrect.

• Thus, we have (a) extrapolation forecasts, most of which are correct but unprofitable, and (b) potentially profitable forecasts of deviation, which are rarely correct and thus are generally unprofitable.

• Q.E.D.: Most forecasts don’t add to returns.

At the lunch described at the beginning of this memo, people were asked what they expected in terms of, for example, Fed policy, and how that influenced their investment stance.  One person replied with something like, “I think the Fed will remain very worried about inflation and thus will raise rates significantly, bringing on a recession.  So I’m in risk-off mode.”  Another said, “I foresee inflation moderating in the fourth quarter, allowing the Fed to turn dovish in January.  That will allow them to bring interest rates back down and stimulate the economy.  I’m very bullish on 2023.” 

We hear statements like these all the time.  But it must be recognized that these people are applying one-factor models: The speaker is basing his or her forecast on a single variable.  Talk about simplifying assumptions: These forecasters are implicitly holding everything constant other than Fed policy.  They’re playing checkers when they need to be playing 3-D chess.  Leaving aside the impossibility of predicting Fed behavior, the reaction of inflation to that behavior, and the reaction of markets to inflation, what about all the other things that matter?  If a thousand things play a part in determining the future direction of the economy and markets, what about the other 999?  What about the impact of wage negotiations, the mid-term elections, the war in Ukraine, and the price of oil? 

The truth is that humans can hold only a few things in their minds at any given time.  It’s hard to factor in a large number of considerations and especially to understand how a large number of things will interact (correlation is always the real stumper).  

Even if you somehow manage to get an economic forecast correct, that’s only half the battle.  You still need to anticipate how that economic activity will translate into a market outcome.  This requires an entirely different forecast, also involving innumerable variables, many of which pertain to psychology and thus are practically unknowable.  According to his student Warren Buffett, Ben Graham said, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.”  How can investors’ short-run choices be predicted?  Some economic forecasters correctly concluded that the actions of the Fed and Treasury announced in March 2020 would rescue the U.S. economy and trigger an economic recovery.  But I’m not aware of anyone who predicted the torrid bull market that lifted off well before the recovery got underway.

As I’ve described before, in 2016 Buffett shared with me his view of macro forecasts.  “For a piece of information to be desirable, it has to satisfy two criteria: It has to be important, and it has to be knowable.” 

• Of course, the macro outlook is important.  These days it seems as if investors hang on every forecaster’s word, macro event, and twitch on the part of the Fed.  Unlike my early days in this business, it seems like macro is everything and corporate developments count for relatively little.

• But I agree strongly with Buffett that the macro future isn’t knowable, or at least almost no one can consistently know more about it than the mass of investors, which is what matters in trying to gain a knowledge advantage and make superior investment decisions.

Clearly, Buffett’s name goes at the top of the list of investors who’ve succeeded by shunning macro forecasts and instead focusing on learning more than others about “the micro”: companies, industries and securities.

*            *            *

In a 2001 memo called What’s It All About, Alpha? , I introduced the concept of the “I know” school and the “I don’t know” school, and in 2004, I elaborated on this at length in Us and Them .  To close the current memo, I’m going to insert some of what I wrote in the latter about the two schools:

Most of the investors I’ve met over the years have belonged to the “I know” school.  This was particularly true in 1968-78, when I analyzed equities, and even in 1978-95, when I had switched to non-mainstream investments but still worked at equity-centric money management firms.

It’s easy to identify members of the “I know” school:

• They think knowledge of the future direction of economies, interest rates, markets and widely followed mainstream stocks is essential for investment success.

• They’re confident it can be achieved.

• They know they can do it.

• They’re aware that lots of other people are trying to do it too, but they figure either (a) everyone can be successful at the same time, or (b) only a few can be, but they’re among them.

• They’re comfortable investing based on their opinions regarding the future.

• They’re also glad to share their views with others, even though correct forecasts should be of such great value that no one would give them away gratis.

• They rarely look back to rigorously assess their record as forecasters.

“Confident” is the key word for describing members of this school.  For the “I don’t know” school, on the other hand, the word – especially when dealing with the macro-future – is “guarded.”  Its adherents generally believe you can’t know the future; you don’t have to know the future; and the proper goal is to do the best possible job of investing in the absence of that knowledge.

As a member of the “I know” school, you get to opine on the future (and maybe have people take notes).  You may be sought out for your opinions and considered a desirable dinner guest . . . especially when the stock market’s going up. 

Join the “I don’t know” school and the results are more mixed.  You’ll soon tire of saying “I don’t know” to friends and strangers alike.  After a while, even relatives will stop asking where you think the market’s going.  You’ll never get to enjoy that 1-in-1,000 moment when your forecast comes true and The Wall Street Journal runs your picture.  On the other hand, you’ll be spared all those times when forecasts miss the mark, as well as the losses that can result from investing based on over-rated knowledge of the future.  But how do you think it feels to have prospective clients ask about your investment outlook and have to say, “I have no idea”? 

For me, the bottom line on which school is best comes from the late Stanford behaviorist, Amos Tversky: “It’s frightening to think that you might not know something, but more frightening to think that, by and large, the world is run by people who have faith that they know exactly what’s going on.” 

It’s certainly standard practice in the investment management business to come up with macro forecasts, share them on request, and bet clients’ money on them.  It also seems conventional for money managers to trust in forecasts, especially their own.  Not doing so would introduce enormous dissonance, as described above.  But is their belief justified by the facts?  I’m eager to hear what you think.

*            *            *

A few years ago, a highly respected sell-side economist with whom I became friendly during my early Citibank days called me with an important message: “You’ve changed my life,” he said.  “I’ve stopped making forecasts.  Instead, I just tell people what’s going on today and what I see as the possible implications for the future.  Life is so much better.”  Can I help you reach the same state of bliss?

September 8, 2022

-----------------------------------------------

Source

https://www.oaktreecapital.com/insights/memo/the-illusion-of-knowledge