No Price Discovery Then No Markets; A Reader’s Question

Has the meteoric rise of passive investing generated the “greatest bubble ever”?
The better we understand the baked-in biases of algorithmic investing, the closer we can come to answers.

 

The following article was originally published in “What I Learned This Week” on June 15, 2017. To learn more about 13D’s investment research, visit website.     https://latest.13d.com/tagged/wiltw

In an article for Bloomberg View last week titled “Why It’s Smart to Worry About ETFs”, Noah Smith wrote the following prescient truth: “No one knows the basic laws that govern asset markets, so there’s a tendency to use new technologies until they fail, then start over.” As we explored in WILTW June 1, 2017, algorithmic accountability has become a rising concern among technologists as we stand at the precipice of the machine-learning age. For more than a decade, blind faith in the impartiality of math has suppressed proper accounting for the inevitable biases and vulnerabilities baked into the algorithms that dominate the Digital Age. In no sector could this faith prove more costly than finance.

The rise of passive investing has been well-reported, yet the statistics remain staggering. According to Bloomberg, Vanguard saw net inflows of $2 billion per day during the first quarter of this year. According to The Wall Street Journal, quantitative hedge funds are now responsible for 27% of all U.S. stock trades by investors, up from 14% in 2013. Based on a recent Bernstein Research prediction, 50% of all assets under management in the U.S. will be passively managed by early 2018.

In these pages, we have time and again expressed concern about the potential distortions passive investing is creating. Today, evidence is everywhere in the U.S. economy — record low volatility despite a news cycle defined by turbulence; a stock market controlled by extreme top-heaviness; and many no-growth companies seeing ever-increasing valuation divergences. As always, the key questions are when will passive strategies backfire, what will prove the trigger, and how can we mitigate the damage to our portfolios? The better we understand the baked-in biases of algorithmic investing, the closer we can come to answers.

Over the last year, few have sounded the passive alarm as loudly as Steven Bregman, co-founder of investment advisor Horizon Kinetics. He believes record ETF inflows have generated “the greatest bubble ever” — “a massive systemic risk to which everyone who believes they are well-diversified in the conventional sense are now exposed.”

Bregman explained his rationale in a speech at a Grant’s conference in October:
“In the past two years, the most outstanding mutual fund and holding- company managers of the past couple of decades, each with different styles, with limited overlap in their portfolios, collectively and simultaneously underperformed the S&P 500…There is no precedent for this. It’s never happened before. It is important to understand why. Is it really because they invested poorly? In other words, were they the anomaly for underperforming — and is it reasonable to believe that they all lost their touch at the same time, they all got stupid together? Or was it the S&P 500 that was the anomaly for outperforming? One part of the answer we know… If active managers behave in a dysfunctional manner, it will eventually be reflected in underperformance relative to their benchmark, and they can be dismissed. If the passive investors behave dysfunctionally, by definition this cannot be reflected in underperformance, since the indices are the benchmark.”

At the heart of passive “dysfunction” are two key algorithmic biases: the marginalization of price discovery and the herd effect. Because shares are not bought individually, ETFs neglect company-by-company due diligence. This is not a problem when active managers can serve as a counterbalance. However, the more capital that floods into ETFs, the less power active managers possess to force algorithmic realignments. In fact, active managers are incentivized to join the herd—they underperform if they challenge ETF movements based on price discovery. This allows the herd to crowd assets and escalate their power without accountability to fundamentals.

With Exxon as his example, Bregman puts the crisis of price discovery in a real- world context:

“Aside from being 25% of the iShares U.S. Energy ETF, 22% of the Vanguard Energy ETF, and so forth, Exxon is simultaneously a Dividend Growth stock and a Deep Value stock. It is in the USA Quality Factor ETF and in the Weak Dollar U.S. Equity ETF. Get this: It’s both a Momentum Tilt stock and a Low Volatility stock. It sounds like a vaudeville act…Say in 2013, on a bench in a train station, you came upon a page torn from an ExxonMobil financial statement that a time traveler from 2016 had inadvertently left behind. There it is before you: detailed, factual knowledge of Exxon’s results three years into the future. You’d know everything except, like a morality fable, the stock price: oil prices down 50%, revenue down 46%, earnings down 75%, the dividend-payout ratio almost 3x earnings. If you shorted, you would have lost money…There is no factor in the algorithm for valuation. No analyst at the ETF organizer—or at the Pension Fund that might be investing—is concerned about it; it’s not in the job description. There is, really, no price discovery. And if there’s no price discovery, is there really a market?”

 

We see a similar dynamic at play with quants. Competitive advantage comes from finding data points and correlations that give an edge. However, incomplete or esoteric data can mislead algorithms. So the pool of valuable insights is self-limiting. Meaning, the more money quants manage, the more the same inputs and formulas are utilized, crowding certain assets. This dynamic is what caused the “quant meltdown” of 2007. Since, quants have become more sophisticated as they integrate machine learning, yet the risk of overusing algorithmic strategies remains.

Writing about the bubble-threat quants pose, Wolf Street’s Wolf Richter pinpoints the herd problem:

“It seems algos are programmed with a bias to buy. Individual stocks have risen to ludicrous levels that leave rational humans scratching their heads. But since everything always goes up, and even small dips are big buying opportunities for these algos, machine learning teaches algos precisely that, and it becomes a self-propagating machine, until something trips a limit somewhere.”

As Richter suggests, there’s a flip side to the self-propagating coin. If algorithms have a bias to buy, they can also have a bias to sell. As we explored in WILTW February 11, 2016, we are concerned about how passive strategies will react to a severe market shock. If a key sector failure, a geopolitical crisis, or even an unknown, “black box” bias pulls an algorithmic risk trigger, will the herd run all at once? With such a concentrated market, an increasing amount of assets in weak hands have the power to create a devastating “sell” cascade—a risk tech giant stocks demonstrated over the past week.

With leverage on the rise, the potential for a “sell” cascade appears particularly threatening. Quant algorithms are designed to read market tranquility as a buy-sign for risky assets—another bias of concern. Currently, this is pushing leverage higher. As reported by The Financial Times, Morgan Stanley calculates that equity exposure of risk parity funds is now at its highest level since its records began in 1999.

This risk is compounded by the ETF transparency-problem. Because assets are bundled, it may take dangerously long to identify a toxic asset. And once toxicity is identified, the average investor may not be able to differentiate between healthy and infected ETFs. (A similar problem exacerbated market volatility during the subprime mortgage crisis a decade ago.) As Noah Smith writes, this could create a liquidity crisis: “Liquidity in the ETF market might suddenly dry up, as everyone tries to figure out which ETFs have lots of junk and which ones don’t.”

J.P. Morgan estimated this week that passive and quantitative investors now account for 60% of equity assets, which compares to less than 30% a decade ago. Moreover, they estimate that only 10% of trading volumes now originate from fundamental discretionary traders. This unprecedented rate of change no doubt opens the door to unaccountability, miscalculation and in turn, unforeseen consequence. We will continue to track developments closely as we try and pinpoint tipping points and safe havens. As we’ve discussed time and again with algorithms, advancement and transparency are most-often opposing forces. If we don’t pry open the passive black box, we will miss the biases hidden within. And given the power passive strategies have rapidly accrued, perpetuating blind faith could prove devastating.

The Greatest Bubble Ever 13D Research   (Sign-up for their updates!)

A Reader’s question that I post below so the many intelligent folks that read this can chip in their thoughts….

The part that confuses me the most is this:

From what I gather, Greenblatt typically calculates his measurement of normal EBITDA – MCX. He then puts a conservative multiple on this, typically 8 or 10 times EBITDA-MCX. He says higher quality companies may deserve 12x or more. He often says something like “this is a 10% cash return that is growing at 6% a year. A growing income is worth much more than a flat income”. He seems to do this on page 309-310 of the notes you sent me  complete-notes-on-special-sit-class-joel-greenblatt_2.

My question is: Greenblatt’s calculation of earnings (EBITDA – MCX) only includes the maintenance portion of capital expenditure. The actual cash flow may be lower because of growth capex. Yet he is assuming a 6% growing income. It seems strange to me that he calculates the steady-state income (no growth capex. Only Maintenance capex), but he assumes that the income will grow. It seems like he is assuming the income will grow 6% but doesn’t incude the growth capex in his earnings calculation. Is it logical to assume that the steady-state earnings will grow, but not deducting the cost of the growth capex from the earnings? 

Answer/reply?………….

 

 

 

Credit Bubble; Pabrai Video

What causes a credit bubble to collapse

is not a malfunctioning entrepreneurial impulse, but an artificial lengthening of production and overcapacity in fixed assets induced by the fractional reserve banking system. Everyone who keeps funds in the market or in a bank is vulnerable, since it is cash deposits that banks use to fund the reckless expansion. When the banking system blows up—as it must—conservative savers lose their savings just as surely as ardent speculators: that is the real horror and also why the existence of a dynamic sector in the economy does not change the credit bubble analysis.

Performance_Update_2017_05   A must read.

New Pabrai Video Talk at Google: https://youtu.be/kNAuELYN5X4

Also, note the research report he recommends: beyondproxy.com-My Investment Thesis on Rain Industries

I wonder how Mr. Pabrai thinks the market misprices a security by 90%.   It has been my experience that when you think you have a company priced at $10 per share but worth $100, you had better check your valuation.   For a stock to go up 10 times, you are betting on profitable growth or a change in the environment.

The value of the video is given in the reminder to go through your value lines or stock guides to give you context and ideas!  In the course I am designing, we will have access to Value-Line to constantly search.

 

An Example of an Industry Analysis; Hedge Fund Quiz

https://youtu.be/gfvAIor53Ig A 22-minute video covering the uranium industry.  An excellent example of how to approach a deeply cyclical resource industry. March Uranium Report The stock catalyst report

https://youtu.be/fw–RzrEWkQ An Industry Panel

Go where they ain’t (but patience is needed in huge dollops):

HEDGE FUND ANALYST QUIZ

Your boss calls you into his office and asks if the Fed should keep raising rates?  Then he asks if the Fed should lower rates?   What do you tell him?   There is ONLY one correct answer.  To KEEP your job you must answer correctly.

Don’t despair, you can view these excellent investing/business videos:https://www.youtube.com/channel/UCVJalJNQWimC2zWrIHR_bSQ

The Minsky Moment

June 19, 2017
Hyman Minsky was an economist who popularised the idea that “stability leads to instability”. According to Minsky and his followers, credit expands rapidly during the good times to the point where a lot of borrowing is being done by financially fragile/vulnerable entities, thus sowing the seeds of a financial crisis. That’s why the start of a financial crisis is now often referred to as a “Minsky moment”. Unfortunately, Minsky’s analysis was far too superficial.

Minsky described a process during which financing becomes increasingly speculative. At the start, most of the debt that is taken on can be serviced and repaid using the cash flows generated by the debt-financed investment. At this stage the economy is robust. However, financial success and rising asset prices prompt both borrowers and lenders to take on greater risk, until eventually the economy reaches the point where the servicing of most new debt depends on further increases in asset prices. At this stage the economy is fragile, because anything that interrupts the upward trend in asset prices will potentially set in motion a large-scale liquidation of investments and an economic bust.

This description of the process is largely correct, but rather than drilling down in an effort to find the underlying causes Minsky takes the route of most Keynesians and assumes that the process occurs naturally. That is, underpinning Minsky’s analysis is the assumption that an irresistible tendency to careen from boom to bust and back again is inherent in the capitalist/market economy.
In the view of the world put forward by Keynesians in general and Minsky in particular, people throughout the economy gradually become increasingly optimistic for no real reason and eventually this increasing optimism causes them to take far too many risks. The proverbial chickens then come home to roost (the “Minsky moment” happens). It never occurs to these economists that while any individual could misread the situation and make an investing error for his own idiosyncratic reasons, the only way that there could be an economy-wide cluster of similar errors at the same time is if the one price that affects all investments is providing a misleading signal. The one price that affects all investments is, of course, the price of credit.

Prior to the advent of central banks the price of credit was routinely distorted by fractional reserve banking, which is not a natural part of a market economy. These days, however, the price of credit is distorted primarily by central banks, and the central bank is most definitely not a natural part of a market economy. Therefore, what is now often called a “Minsky moment” could more aptly be called a “central-bank moment”.

I expect the next “central-bank moment” to arrive within the coming 12 months. I also expect that when it does arrive it will generally be called a “Minsky moment” or some other name that deftly misdirects the finger of blame, and that central banks will generally be seen as part of the solution rather than what they are: the biggest part of the problem.

www.tsi-blog.com

Does Momentum Work with Value Investing?

Does “Momentum” Investing work with “Value” Investing?

See the research paper by Nicholas Barberis below. Barberis concludes that value and momentum are driven by biases that mirror one another. Value is driven by an overreaction problem in which humans are too quick to draw conclusions from a small amount of recent data. In contrast, momentum is driven by an underreaction issue, which is the opposite of verreaction. With underreaction, humans are slow to update their views based on new evidence, which could be due to a systematic behavior bias and/or due to the fact human beings simply have limited cognitive power.

A lot to ponder.  I recommend Quantitative Investing by Wesley Gray. Momentum investing is NOT growth investing (buying price at high multiples to underlying fundamentals), because momenum investing is strictly based on recent price movements not fundamentals.

a model of investor sentiment or under and over reaction

Value and momentum everywhere

http://blog.alphaarchitect.com/2016/03/22/why-investors-should-combine-value-and-momentum/#gs.FlrDk6A

and http://blog.alphaarchitect.com/2017/06/06/the-value-momentum-trend-philosophy/#gs.7NtEcq4

Can we control our emotions and emotional responses?

http://bigthink.com/stephen-johnson/everyones-thinking-about-emotions-wrong-says-psychologist-lisa-feldman-barrett

Finding Good Capital Allocators; The Problems with Using Sentiment

Finding good capital allocators

Strategic Presentation May 2017b    What would show you that this management team allocates capital well in their resource sector?   Are their actions EXTREMELY rare in the Junior Resource Mining industry?

The Perils of Using Sentiment As a Timing Tool

The limitations of sentiment, revisited

June 12, 2017

In a blog post in March of this year I discussed the limitations of sentiment as a market timing tool. I wrote that while it can be helpful to track the public’s sentiment and use it as a contrary indicator, there are three potential pitfalls associated with using sentiment to guide buying/selling decisions. Here are the pitfalls again:

The first is linked to the reality that sentiment generally follows price, which makes it a near certainty that the overall mood will be at an optimistic extreme near an important price top and a pessimistic extreme near an important price bottom. The problem is that while an important price extreme will always be associated with a sentiment extreme, a sentiment extreme doesn’t necessarily imply an important price extreme.

The second potential pitfall is that what constitutes a sentiment extreme will vary over time, meaning that there are no absolute benchmarks. Of particular relevance, what constitutes dangerous optimism in a bear market will often not be a problem in a bull market and what constitutes extreme fear/pessimism in a bull market will often not signal a good buying opportunity in a bear market.

The third relates to the fact that regardless of what sentiment surveys say, there will always be a lot of bears and a lot of bulls in any financial market. It must be this way otherwise there would be no trading and the market would cease to function. As a consequence, if a survey shows that almost all traders are bullish or that almost all traders are bearish then the survey must be dealing with only a small — and possibly not representative — segment of the overall market.

I went on to write that there was no better example of sentiment’s limitations as a market timing indicator than the US stock market’s performance over the past few years. To illustrate I included a chart from Yardeni.com showing the performance of the S&P500 Index (SPX) over the past 30 years with vertical red lines to indicate the weeks when the Investors Intelligence (II) Bull/Bear ratio was at least 3.0 (a bull/bear ratio of 3 or more suggests extreme optimism within the surveyed group). An updated version of the same chart is displayed below.

The chart shows that while vertical red lines (indicating extreme optimism) coincided with most of the important price tops (the 2000 top being a big exception), there were plenty of times when a vertical red line did not coincide with an important price top. It also shows that optimism was extreme almost continuously from Q4-2013 to mid-2015 and that following a correction the optimistic extreme had returned by late-2016.

Sentiment was at an optimistic extreme late last year, at an optimistic extreme when I presented the earlier version of the following chart in March and is still at an optimistic extreme. In effect, sentiment has been consistent with a bull market top for the bulk of the past four years, but there is still no evidence in the price action that the bull market has ended.

Regardless of what happens from here, four years is a long time for a contrarian to be wrong.   See more at http://www.tsi-blog.com

Lesson? Always place data into context and do not rely on any one piece of information.   Sentiment can be useful as part of an over-all picture of a market or company.

Here is an example of an investor who applies that principle in his OWN method of investing. https://www.thefelderreport.com/2017/05/31/how-a-funny-mentalist-learned-to-avoid-annihilation/

He gained INSPIRATION from his investing heroes but did not try to mimic them.

This analyst of gold doesn’t just use news and sentiment but also fundamentals: https://monetary-metals.com/the-anatomy-of-browns-gold-bottom-report-4-june-2017/

And finally, consider the slow crash: https://mishtalk.com/2017/06/12/buy-the-faangs-baby-slow-torture/#more-46281

Spin-off and Event Driven Web-site; Thomas Kaplan

https://www.oozingalpha.com

This web-site came to my attention recently.

A successful long-term deep value investor in resources discusses his approach

http://services.choruscall.ca/links/novagold20170505.html  Focus on Thomas Kaplan’s presentation at Novagold’s Annual Meeting–his view of history and how he analyzes a market–beginning at minute 17:20 or 4th slide)

in-gold-we-trust-2017-extended-version-english

In Gold We Trust 2017; Worldly Wisdom

in-gold-we-trust-2017-extended-version-english

“Doubt is not a pleasant condition, but certainty is absurd.” Voltaire

Absolute return small cap investing  https://www.thefelderreport.com/2017/05/30/podcast-eric-cinnamond-on-the-value-of-absolute-return-investing/

The Qualities of a Good Analyst; 100-to-1 Master Class

Confidence vs. Humility

1Q17 | Bill Nygren Market Commentary (Abridged)

see: http://1Q17-Bill-Nygren-Market-Commentary

March 31, 2017

At Oakmark, we are long-term investors. We attempt to identify growing businesses that are managed to benefit their shareholders. We will purchase stock in those businesses only when priced substantially below our estimate of intrinsic value. After purchase, we patiently wait for the gap between stock price and intrinsic value to close.

Oh Lord, it’s hard to be humble, when you’re perfect in every way.  It’s Hard To Be Humble by Mac Davis, 1974

What Makes a Good Oakmark Analyst?

I also like March because it is the month I get to speak to investment students at my alma mater’s Applied Securities Analysis Program and Bruce Greenwald’s value investing program at Columbia.  Typical topics include how I got interested in investing, my education and career path, and what makes Oakmark unique. Without fail, the aspiring investment professionals will eventually ask about the characteristics we look for when we hire analysts at Oakmark or, more generally, What do you think makes a good investment analyst?  Perhaps the answer might give some insight into how we think at Oakmark.

When I served as director of research, I used to joke that every analyst search we conducted started with the same list of requirements: A high GPA from a good university, a major in finance or accounting, intuitive math skills, strong oral and written communication ability, three to five years’ related work experience, intense competitive drive, and activities demonstrating leadership. MBA or CFA required.  Yet almost every hire was somewhat outside that box. We hired some analysts with low GPAs, some with different degrees and some from second-tier colleges. We hired some with over 10 years’ experience, and others with no experience at all. Some had neither an MBA nor a CFA. What we realized was that our search criteria, though representative of our typical hires, was not really defining the candidates we were looking for. Those criteria defined the candidates most investment firms are looking for, but didn’t at all get to what makes Oakmark unique.

Team Player
There are three additional characteristics that we believe are necessary to succeed at Oakmark that we either don’t think we can teach or don’t want to teach, so we require them to be present before we hire an analyst.  First is being a team player.   At many investment firms, analysts have a one-on-one relationship with portfolio managers.  They develop their stock recommendations and present them to a portfolio manager who decides whether or not the stocks will be purchased. If analysts pick good stocks, they will be paid well and their careers will progress. In that setup, it doesn’t really matter whether the analyst is a team player or not.  Oakmark is different.

Oakmark analysts succeed by helping the team succeed. Yes, we expect them to find good stocks to purchase, but that effort is collaborative. An analyst who begins working on a new buy idea seeks input from the rest of the investment team before the idea is finalized. When the work is presented, it is the job of every investment professional at our company to attempt to find flaws that would prevent us from investing. Throughout the time we hold a stock, the analysts will challenge each other as to whether or not our sell target correctly incorporates all the new information we’ve seen subsequent to our purchase. When the stock is sold, it is treated as a victory for the team if it went up, and a team defeat if it did not. We all understand that we do well financially when our shareholders do well financially. That’s in part because a major factor in our compensation review is how well an analyst helps improve the team’s stock selection.

We know that anyone who puts their personal success over Oakmark’s success will not last long at our company. So, we look for clues in resumes such as a history of playing team sports or other activities where accomplishments by a group are more important than by an individual. We know that we can’t teach someone how to be a team player.

Value Investor
More than 30 years ago, Warren Buffett wrote an article that has become a value investing classic: The Superinvestors of Graham and Doddsville (Fall 1984, Hermes’ the Columbia Business School Magazine). If you haven’t read it, or haven’t read it recently, it is well worth the time. In that article, Buffett explained the futility of trying to convert investors to a value investing philosophy:

It is extraordinary to me that the idea of buying dollar bills for 40c takes immediately with people or it doesn’t take at all. It’s like an inoculation. If it doesn’t grab a person right away, I find you can talk to him for years, and show him records, and it just doesn’t make any difference. They just don’t seem able to grasp the concept, simple as it is.  I’ve never seen anyone who became a gradual convert over a ten-year period to this approach. It doesn’t seem to be a matter of I.Q. or academic training. It is instant recognition or it is nothing.

We 100% agree with Buffett. Everything we do at Oakmark is based on value investing. We don’t know how to teach someone how to think like a value investor. You can’t succeed at Oakmark without practicing value investing. Therefore, we will only hire analysts who have developed a value philosophy prior to joining our team.

Humility
There are some characteristics for successful analysts that are simple more is better traits. Intelligence, curiosity, communication skills all are more is better.  Then you have the continuums where, like NCAA basketball teams, a strength carried to the extreme becomes a weakness. We want discipline, but we also want creativity. We demand patience, but don’t want stubbornness. We want thoroughness, but require decisions based on incomplete information. Success requires striking an appropriate balance between these traits that sound like opposites. Being at one extreme or the other is a recipe for failure.

(CSInvesting: reading the Nicomachaen Ethics by Aristotle would teach you to seek moderation.) http://classics.mit.edu/Aristotle/nicomachaen.html

One of the most important continuums for us is confidence versus humility. It is especially important for a value investor to have the confidence to take a position when the vast majority of investors are on the opposing side. But without humility, one loses the ability to admit a mistake. I’m reminded of the early 1980’s TV show Happy Days with the super-cool Fonzie who could never say the words I was wrong. Fonzie would have been an awful investor.

In a book many in our research department have enjoyed, Superforecasting: The Art and Science of Prediction, Philip Tetlock and Dan Gardner state:

The humility required for good judgment is not self-doubt, the sense that you are untalented, unintelligent, or unworthy. It is intellectual humility. It is a recognition that reality is profoundly complex, that seeing things clearly is a constant struggle, when it can be done at all, and that human judgment must therefore be riddled with mistakes.

What we are looking for in Oakmark analysts is confidence paired with the humility to remain open to evidence that shows they are wrong.

One of my investing heroes, former hedge fund pioneer Michael Steinhardt, said, “The balance between confidence and humility is best learned through extensive experience and mistakes.” Unlike being a team player or a value investor, with time, almost every investor develops humility. But it is an expensive lesson to learn. We want analysts who developed their humility by losing money somewhere else.

I can’t count the number of resumes I’ve seen or conversations I’ve had with students where they excitedly state that their personal portfolio returned X percent last year. And of course, X is always some number that is astoundingly high relative to the market or to Oakmark returns. That record is almost always accompanied by scorn for incompetent professional investors and the offer to teach us the secrets of their success. I smile as I mentally mark off the box needs to be humbled by losing money.  Then I wish them great success in their job search and suggest they check back with us in a few years.

Master Class in 100 to 1 Investing (Chris Mayer).

Sure a marketing tool, but perhaps some can learn more about patience.   I am not affiliated, but thought I would share the link.  All Mayer is doing is talking about the Phelp's book, 100 to 1 Investing.

100-baggers Analysis       and     100Baggers

 

Free masterclass: The Mayer Method: The breakthrough new formula for identifying tomorrow’s biggest stock market winners today.

As you’re about to see, you’ve made a great decision..

Because I’ll be sharing a few simple investment strategies with you that will show you how to take advantage of one of the greatest “hidden” opportunities I see in the market.

This is completely different from anything we’ve shared with you before…

Here’s the link to video #1 to get you started: The big change coming in the market

In this series of short videos, I’m going to walk you through exactly what this opportunity is… why it’s happening now… and then I’ll show you how I’m taking advantage of it and give you the tools you need to take advantage of it, too…

By signing up for this training, you’re already ahead of the curve on this.

Remember, I’m going to limit the first of these videos to short 10-minute segments. And then, finally, on Thursday night, I’ll show you how to put it all together in a webinar, where I’ll give away the names of six stocks I recommend you watch.

Get started with the first video by clicking here.

 

An Industry in Disruption, AUTOS. Notes from a Capital Junkie. Tit-for-Tat Analysis

 

Sergio Marchionne Has Seen the Auto Industry’s Future: He’s Not Interested

By Sviatoslav Rosov, PhD, CFA

Read an excellent analysis of the auto industry: SM_Fire_investor_presentation

Sergio Marchionne often raises eyebrows.

This time, the Fiat Chrysler CEO went a step further than usual by declaring that the latest plan for the company is essentially a one-way bet on cheap gas. Production of compact cars will end to free up production capacity for high-margin, low-mileage Jeeps and RAM trucks.

This, combined with Fiat’s more or less complete lack of a fuel economy or electrification strategy beyond buying emissions credits from other manufacturers “foolish” enough to produce electric and hybrid “compliance cars,” is quickly making Marchionne, if not an industry joke, then certainly yesterday’s man.

At least, that is what people are saying. I have an alternate hypothesis.
The Auto Industry Is Not Heading to a Good Place (The author, in my opinion, has the correct thesis.  Ride sharing, Uber, Tesla, more complex electronics mean less demand and more investment to run in place).

 

 
Fiat vs. Ford above

Fiat (FCAU) has done slightly better than GM and much better than Ford (F).  However, the auto industry is in a bad place that will worsen.

The context is frightening. Global fuel economy and emissions regulations are becoming so strict that it is possible to meet them only with partial or full electrification of the automobile. And the existing automobile production system, based primarily on stamping sheet metal and amortizing heartbreaking development costs and capital expenditures over millions of units, is incredibly capital inefficient.

What’s more, the industry’s move towards electric vehicles represents a significant challenge to the traditional strategic landscape an automaker faces. An electric vehicle has drastically fewer moving parts than an internal combustion vehicle and is, by design, far more modular, meaning that barriers to new entrants are significantly lower.

Electric vehicles are also far more uniform in their driving dynamics, because there is little scope for refining an electric motor with one moving part. Swathes of engineering and marketing investments become irrelevant. And both ride-sharing enterprises and developments in automation seem increasingly likely to grow beyond niche markets into something properly disruptive to the car ownership business model.

Marchionne Knows This

Last year, Marchionne presented a uniquely critical slide deck about the way the auto industry destroys capital. His argument was that, unless the industry consolidates and stops duplicating engineering costs (e.g., every car manufacturer has its own separately developed but fundamentally identical 2.0L 4-cylinder petrol engine), then the market will eventually force its hand, having gotten sick of miserly returns on billions in investments.

The industry response to this slide deck was more or less complete agreement, with the caveat that competitors would not have to outlast the market so much as merely outlast Fiat Chrysler. Marchionne then pursued an odd and ultimately unsuccessful merger with GM’s Mary Barra, who confidently rejected Fiat Chrysler’s plan, noting, “We are merging with ourselves.” (This presumably referred to GM’s decades-long quest to bring rationality to its stable of brands.)
GM is not only merging with itself, it is also “disrupting” itself — as evidenced by their recently announced Chevy Bolt long-range, affordable electric car. The company claimed the Bolt was designed to be the perfect car for ride-sharing apps. Just before launching the Bolt, GM announced a $500 million investment into Lyft, the main competitor to Uber.

This no doubt surprised competitors who have been making efforts to disabuse markets and investors of the notion that they would become mere providers of hardware to ride-sharing companies like Uber or autonomous car suppliers like Google. Dieter Zetsche, CEO of Daimler, remarked “We do not plan to become the Foxconn of Apple.”

Manufacturers Are Going to Have to Invest

In fact, the bosses of Daimler, BMW, and Audi went looking behind the couch for some spare change to buy joint ownership of Nokia’s (remember them?) mapping service HERE, and did so primarily to stop their rival bidder – Uber – from buying it. High-resolution maps are crucial to autonomous cars; Uber’s CEO has said that, if Tesla can make good on their promise of a long-range, autonomous electric car, he would buy “all” of them.

The Germans are thus investing billions into electric vehicles made out of carbon fiber that pilot themselves using super-high resolution maps, all the while fighting back against Apple and Google’s requests for access to their cars’ infotainment systems. Their global leadership of the auto industry will have to be pried from their cold, dead hands.

Meanwhile, all the difficult bits of the Chevy Bolt (“custom-built” for Lyft, remember) are built in large part by Korea’s LG. One wonders why Lyft (or Uber) would not simply buy the next model directly from LG? I guess even if there is no Foxconn for cars yet, there may be soon. Remember, electric cars are far more modular than internal combustion cars.
Marchionne Says “No Thanks”

Or, if not him, then certainly the Agnelli family. A sort of Italian royalty who control Fiat Chrysler (and Marchionne) via their ownership of the Exor holding company, the Agnellis have been showing signs that they are tiring of the endless drama surrounding Fiat and the auto industry in general. They bought a stake in The Economist in 2015 in a move towards media, but the recent de-conglomeration of Fiat has been noticeable in other ways.

First, in 2013, Fiat’s industrial division was de-merged and combined with CNH Global (maker of tractors under the Case IH and New Holland brands) into a separate company, CNH Industrial. Most recently, Ferrari, the jewel in the Fiat Chrysler stable of brands, was floated in New York.

Speaking of Ferrari, Marchionne took advantage of a recent dip in the fortunes of Ferrari’s eponymous Formula 1 team to unceremoniously eject Luca di Montezemolo as president and chairman of Ferrari and replace him with . . . himself. It should be noted that di Montezemolo was appointed by Gianni Agnelli himself after the death of the founder, Enzo Ferrari, and is a bona fide business superstar in Italy. Marchionne has been playing an increasingly active part in the politics of Formula 1 recently, something that will no doubt continue to make for a less stressful (but still stimulating) retirement when Marchionne puts on his famous blue sweater for the last time in 2018.

But for now, Marchionne has seen the future. Large subcontractors will produce partially or fully autonomous electric vehicles, with the sole differences between them being brand value and design. The car makers that survive may well simply produce cars for Google (Ford recently signed an agreement along these lines), Apple, or Uber. Some, like BMW or Mercedes-Benz, may survive because of their brand and design qualities. Fiat Chrysler does not have this.

Marchionne doesn’t care about expensive gas or electric vehicles because his plan is simple:

Sell the profitable Jeep/RAM brands to another conglomerate that does not compete in these segments (for example, Hyundai KIA).
Sell the unprofitable Fiat to anyone who will take it. Perhaps synergies in the lucrative European light commercial vehicle segment will attract another European maker, such as PSA Peugeot Citroën, whose CEO, Carlos Tavares, has ambitions that were thwarted at his previous employer, Renault.
Sell Alfa Romeo and Maserati to someone who could use a strong brand. Perhaps Volkswagen will finally get hold of its prized Italian trophy if they can sort out their global legal woes.

Retire to play with his giant Formula 1 Scalextric set.
Marchionne has been mocked for his firms’ strategy, which has been attributed to hubris. But perhaps he is the one seeing clearest of all.
Is the best way to deal with disruption simply to step out of the way?
If you liked this post, don’t forget to subscribe to the Enterprising Investor.

Tit-for-Tat Competitive Analysis

Question: Who wins when–in a perfectly competitive market–competitors fight each other?    Prize awarded for best answer.

Do Stocks Outperform Treasury Bills?



The above seems to answer the question above.  But what if the returns are heavily skewed to only a few stocks?  Look deeper:  Bessembinder Do Stocks Outperform Treasury Bills


The chart above shows gold “outperforming” stocks, but note the time period. 1971 was when President Nixon unhinged the dollar from gold.  Be careful when assessing performance over a set time period.