Hedge Fund Analyst Quiz–NG $3 The New Normal

Your boss runs into your office and slaps this report onto your desk: Don‘t Bet Against Innovation_Sub-$3 Is the New Normal

After reading the report and using your knowledge of how capital cycles work, what would you say to your boss about using the information in that report for investing?  IF you wanted to make an outstanding investment, then how might the report help you?   The video below might give you a hint.  Remember that the JP Morgan report goes to thousands of portfolio managers and analysts, so how can YOU use the information to have an edge? Or can you? Comments needed in order to keep your hedge fnd job.

Good luck!

 

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/

Work on the YOU: Free Course on Stoic Training

Article announcing Stoic Mindfulness and Resilience Training (SMRT) 2017 with details of live webinar sessions, etc.

 

Enrolment is now open for the Stoic Mindfulness and Resilience Training (SMRT) 2017 online course.  This is a free eLearning course, which Donald Robertson has been running once or twice each year for Modern Stoicism since 2014.  You can access the preliminary area now and the four weeks of the course will officially begin on Sunday 16th July, when enrolment will close.  This year over 500 people enrolled within the first 48 hours after it was announced on social media.  Around 650 people are now enrolled and we anticipate that will have increased to nearly 1,000 by the course start date.

Sign up here: http://learn.donaldrobertson.name/p/stoic-mindfulness-resilience-training-smrt/

In the first year, over 500 people took part in SMRT and data was collected from participants, using the Stoic Attitudes and Behaviours Scale (SABS) and a battery of validated outcome measures of the kind used in research on CBT and positive psychology.  You can download a PDF of our report here showing the findings in detail: SMRT_Report_2014

The writings of Seneca! http://tim.blog/2017/07/06/tao-of-seneca/

CSInvesting: Though this philosophy takes active practice, you might find developing the ability to control your thoughts and reactions to what you encounter in daily life helpful–especially in dealing with Mr. Market. Below is a schema of Stoicism (Click on diagram, then enlarge through your browser to read text).


Learning from Grants:http://grantpub.libsyn.com/episode-1-grants-interest-rate-observer

Why “smart” people do dumb things.   Rational thought. https://www.scientificamerican.com/article/rational-and-irrational-thought-the-thinking-that-iq-tests-miss/

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/