Category Archives: Economics & Politics

Bulls Rampage

Increasing debt for equity swap

Sentiment bullish and prices high relative to the past.

Hedge Fund Analyst FINAL EXAM QUESTIONS

Investing might be considered decision-making under uncertainty. Therefore the following exam.

You must answer BOTH questions correctly to be hired.  You are now in the final pool of candidates to work for a big hedgie fund. Now comes

Question 1:

Imagine playing the following game,  At a casino table is a brass urn containing 100 balls, 50 red and 50 black.  You’re asked to choose a color.  Your choice is recorded but not revealed to anyone, after which the casino attendant draws a ball randomly out of the urn. If the color you chose is the same as the color of the ball, you win $10,000.  If it isn’t, you win nothing-$0.00.

You are only allowed to play once–which color would you prefer, and what is the maximum bid you would pay to play? Why?

Question 2:

Now imagine playing the same game, but with a second urn containing 100 balls in UNKNOWN proportions.  There might be 100 black balls and no red balls, or 100 red balls and no black balls or ANY proportion in between those two extremes.  Suppose we play the exact same game as game 1, but using this urn containing balls of unknown colors.

What is your bid to play this game IF you decide to play?   How does the “risk” in this game (#2) compare to game (#1)?

Take no more than a minute.   So are you hired?!

Answer posted this weekend.

ANSWER (9/10/2017)

A Reader provides a clearer distinction in Question2:

Your second problem is ill-specified for your desired effect . You write that all combinations of red/black balls within the 100 ball population ARE possible; you don’t say they are equally probable. You need to assume them to be equally probable in order for the reader to infer that the expectations are identical between problem 1 and problem 2.

The reason being is that without defined probabilities on the possible ratios the long run frequency of draws from the second bag isn’t calculable. Hence the expected value cannot be computed and therefore cannot be used in comparison to the EV of problem 1 (you need probabilities in a probability weighted average after all).

You could suggest that the offeree has a 50/50 chance of choosing the correct colour (even if the long run frequencies are not known). But this not an argument born from expected value. This is an argument of chance and it assumes the offeree has no additional information from which to make their decision (which is hardly ever the case).

There are 100 possible choices for the proportion of red/black: 100 red balls/0 black balls, 99 red balls/1 black ball etc., 98/2, 97/3     with 100%, 99%, 98%, 97% probability of choosing a black ball all the way………… to 2 black balls/98 red balls, 1/99, 0/100. Put equal weight on them since random.  When computed, the average of the expected payoffs across all these alternative realities, one got an expected value of $5,000, the same as Game 1.

The two games describesthe Ellsberg Paradox, after the example in Ellsberg’s seminal paper.  Thinking isn’t the same as feeling.  You can think the two games have equal odds, but you just don’t feel the same about them.   When there is any uncertainty about those risks, they immediately become more cautious and conservative.  Fear of the unknown is one of the most potent kinds of fear there is, and the natural reaction is to get as far away from it as possible.

So, if you said the two games were exactly similar in probabilities, then A+.  The price you would bid depends upon your margin of safety/comfort.   You would be rational to bid $4,999.99 since that is less than the expected payoff of $5,000.  But the loss of $4,999.99 might not be worth it despite the positive pay-off.  A bid of $3,000 or $1,000 might be rational for you.   The main point is to understand that the two games were similar but didn’t appear to be on the surface.

The Ellsberg paradox is a paradox in decision theory in which people’s choices violate the postulates of subjective expected utility. It is generally taken to be evidence for ambiguity aversion. The paradox was popularized by Daniel Ellsberg, although a version of it was noted considerably earlier by John Maynard Keynes.  READ his paper: ellsberg

Who was fooled?

Anyone not answering correctly or NOT answering has to go on a date with my ex:

The Stock Market: Risk vs. Uncertainty

Life is risky. The future is uncertain. We’ve all heard these statements, but how well do we understand the concepts behind them? More specifically, what do risk and uncertainty imply for stock market investments? Is there any difference in these two terms?

Risk and uncertainty both relate to the same underlying concept—randomness. Risk is randomness in which events have measurable probabilities, wrote economist Frank Knight in 1921 in Meaning of Risk and Uncertainty.1 Probabilities may be attained either by deduction (using theoretical models) or induction (using the observed frequency of events). For example, we can easily deduce the probabilities of the possible outcomes of a game of dice. Similarly, economists can deduce probability distributions for stock market returns based on theoretical models of investor behavior.

On the other hand, induction allows us to calculate probabilities from past observations where theoretical models are unavailable, possibly because of a lack of knowledge about the underlying relation between cause and effect. For instance, we can induce the probability of suffering a head injury when riding a bicycle by observing how frequently it has happened in the past. In a like manner, economists estimate probability distributions for stock market returns from the history of past returns.

Whereas risk is quantifiable randomness, uncertainty isn’t. It applies to situations in which the world is not well-charted. First, our world view might be insufficient from the start. Second, the way the world operates might change so that past observations offer little guidance for the future. Once bicyclists were encouraged to wear helmets, the relation between riding the bicycle—the cause—and the probability of suffering a head injury—the effect—changed. You might simply think that the introduction of helmets would have reduced the number of head injuries. Rather, the opposite happened. The number of head injuries actually increased, possibly because helmet wearing bikers started riding in a more risky manner due to a false perception of safety.2

Typically, in situations of choice, risk and uncertainty both apply. Many situations of choice are unprecedented, and uncertainty about the underlying relation between cause and effect is often present. Given that risk is quantifiable, it is not surprising that academic literature on stock market randomness deals exclusively with stock market risk. On the other hand, ignorance of uncertainty may be hazardous to the investor’s financial health.

Stock market uncertainty relates to imperfect information about how the world behaves. First, how well do we understand the process that generated historical stock market returns? Second, even if we had perfect information about past processes, can we assume that the same relation between cause and effect will apply in the future?

The Highs and Lows of the Market

Warren Buffett, the world’s second-richest man, distinguishes between periods of comparatively high and low stock market valuation. In the early 1920s, stock market valuation was comparatively low, as measured by the inflation-adjusted present value of future dividends. The attractive valuation of stocks relative to bonds became a widely held belief after Edgar Lawrence Smith published a book in 1924 on stock market valuation, Common Stocks as Long Term Investments. Smith argued that stocks not only offer dividends, but also capital appreciation through retained earnings. The book, which was reviewed by John Maynard Keynes in 1925, gave cause to an unprecedented stock market appreciation. The inflation-adjusted annual average growth rate of a buy-and-hold investment in large-company stocks established at the end of 1925 amounted to a staggering 32.13 percent at the end of 1928.

On the other hand, over the next four years, this portfolio depreciated at an average annual rate of 17.28 percent, inflation-adjusted. Taken together, over the entire seven-year period, the inflation-adjusted average annual growth rate of this portfolio came to a meager 1.11 percent. Buy-and-hold portfolios in allegedly unattractive long-term corporate and government bonds, on the other hand, grew at inflation-adjusted average annual rates of 10.18 and 9.83 percent, respectively. This proves Buffett’s point: “What the few bought for the right reason in 1925, the many bought for the wrong reason in 1929.” One conclusion from this episode is that learning about the stock market may feed back into the market and, by changing the behavior of the market, render our “learning” useless or—if we don’t recognize the feedback effect—hazardous.
Is Tomorrow Another Day?

Risk and uncertainty are two concepts that stem from randomness. Neither is fully understood. Although risk is quantifiable, uncertainty is not. Rather, uncertainty arises from imperfect knowledge about the way the world behaves. Most importantly, uncertainty relates to the questions of how to deal with the unprecedented, and whether the world will behave tomorrow in the way as it behaved in the past.

This article was adapted from “The Stock Market: Beyond Risk Lies Uncertainty,” which was written by Frank A. Schmid and appeared in the July 2002 issue of The Regional Economist, a St. Louis Fed publication.

(Source: St Louis Federal Reserve)

Bitcoin and the Theory of Money; Hedge Fund Quiz

Bitcoin is not only irredeemable, but also unbacked. That is a big difference—in favor of the dollar. (Keith Weiner of Monetary-Metals)

Read an analysis of Bitcoin as money (Bitcoin has no backing.  I think of Bitcoin as “Token” money. What are your thoughts?

Also, the developer of Bitcoin provides his understanding of the theory of money.  As a review read: On the Origins of Money_5 Menger

For those who are interested and are in NYC:
Blockchain Technology Versus Fiat Currency

The next CMRE event will be held on October 3 at the University Club in New York City: Blockchain Technology Versus Fiat Currency.  Speakers will include noted author George Gilder, co-founder of Etherium Joe Lupin, thought-leader Saifedean Ammous, and more.

Topics will range from an introduction of blockchain technology, economic implications, the politics surrounding private currencies, and the role of gold. Full program to come.

Check back on www.cmre.org for more information and to purchase tickets.

http://www.cmre.org/

TIMING THE CRASH: Performance_Update_2017_07

QUIZ: What has caused or one of the MAIN reasons that companies like Amazon keep gaining strength?  Hint: What Bezos does is meaningless.

A Lesson on Economics and the Monetary System or Interest on Gold

Even if the mention of a “Gold Standard” makes your eyes glaze over, the video above and the article below show you how a monetary system SHOULD WORK.  More importantly, you learn how the US can extract itself from ever-compounding debt.  Currently, the FED is destroying savers in the name of “helping” the economy.   Learn how credit can expand and contract WITHOUT booms and busts.

The Unadulterated Gold Standard Part I

The Horror of Being a Deep Value Money Manager–KGGAX is Kopernik Global vs. SPY and FANG Stocks

Case Study on Natural Gas/Shale Industry; Buffett Reads

Shale gas is not a revolution. It’s just another play with a somewhat higher cost structure but larger resource base than conventional gas.

The marginal cost of shale gas production is $4/mmBtu despite popular but incorrect narratives that it is lower. The average spot price of  gas has been $3.77 since shale gas became the sustaining factor in U.S. supply (2009-2017). Medium-term prices should logically average about $4/mmBtu.

A crucial consideration going forward, however, will be the availability of capital. Credit markets have been willing to support unprofitable shale gas drilling since the 2008 Financial Collapse.  If that support continues, medium-term prices for gas may be lower, perhaps in the $3.25/mmBtu range. The average spot price for the last 7 months has been $3.13.

Gas supply models over the last 50 years have been consistently wrong. Over that period, experts all agreed that existing conditions of abundance or scarcity would define the foreseeable future. That led to billions of dollars of wasted investment on LNG import facilities.

Today, most experts assume that gas abundance and low price will define the next several decades because of shale gas. This had led to massive investment in LNG export facilities.

(CSInvesting: You should read Mr. Berman’s full report at the link below.  He uses history to debunk long-term prediction models and shows the common sense of looking at markets through the long lens of history.  The assumption of abundant natural gas could be wrong–many “experts” are not even thinking of vastly different outcomes to their models.)

http://www.artberman.com/shale-gas-not-revolution/

Excellent interview:  https://www.youtube.com/watch?v=RY4kM1kWaGM


warren-buffett-favorite-books-2015-10/

Excellent investment letters from Moran Creek

Are these sustainable competitive advantages ?http://www.collaborativefund.com/blog/sustainable-sources-of-competitive-advantage/

A great read on investing:http://www.collaborativefund.com/blog/what-i-believe-most/

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

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/

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?
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Tit-for-Tat Competitive Analysis

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