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The Biggest Lie About Stocks That You Probably Believe

If you’re like many investors, you learned early on that one of the most important things to know about a stock’s value is the price-to-earnings (aka…

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If you’re like many investors, you learned early on that one of the most important things to know about a stock’s value is the price-to-earnings (aka “P/E”) ratio.

I hate to be a party pooper, but if that’s how you were “brought up” as an investor — or if you only like to buy cheap stocks with low P/E ratios — you’ve been missing out on a lot of profitable growth opportunities.

The good news is that in today’s article, I’m going to show you how to make a lot more in stocks than you’re making now. Here’s the story…

For generations, investors have used P/E ratios to determine if a stock is cheap and should be purchased or if it’s expensive and should be sold (or avoided).

Typically, stocks are considered bargains when they sell for P/E ratios of less than 12. Many people think a stock with a P/E of 25 is too expensive. (The long-term average P/E of the broad market is about 16.)

Now, I’m a bargain hunter in “normal” life. I’m always looking for deals when it comes to things like cars and real estate. However, decades of investing — and my studies of the greatest stock market winners in history — have taught me that a slavish devotion to low P/E stocks will cause you to miss out on pretty much every massive stock market winner of the next 100 years.

Put simply, a devotion to low P/E stocks will doom you to a life of underperformance.

Why is this the case?

Basically, you have to pay up to own the best, in both the stock market and in life. There’s a reason why Ferraris cost more than Hondas… and oceanfront property costs more than inland property.

Full disclosure: I’m a big fan of Ferraris, and even own a few of them, though I certainly don’t have anything against Hondas. But the reality is that Ferraris are in a whole different world than Hondas when it comes to quality and performance. That’s why Ferraris are more expensive than Hondas. The very best doesn’t come cheap.

This dynamic is at work in the stock market as well. The stocks of businesses with superior products, superior services, superior sales growth, and superior prospects outperform the stocks of lower-quality businesses. So, investors are willing to afford the higher stock market valuations of the best businesses.

My studies of the top stock performers of the past 100 years show that most mega stock winners trade for more than 25 times earnings during their huge runs.

What Really Matters When It Comes to Capturing Triple-Digit Returns

In my years of investing and studying the market’s all-time biggest winners, I’ve found that it’s much, much more important to focus on a stock’s earnings and sales growth than its P/E ratio. So much so that they are key factors in my proprietary stock grading system.

If you aren’t willing to buy stocks at more than 30 times earnings, you’ll automatically eliminate yourself from owning the world’s best growth stocks.

It’s not uncommon to see mega winners trade for 30-, 40-, and 50-times earnings during their mega runs. I’m talking about massive winners like Netflix (NASDAQ:NFLX), Amazon (NASDAQ:AMZN), Meta Platforms, formerly known as Facebook, (NASDAQ:FB) and Google, now Alphabet (NASDAQ:GOOG).

I could list mega winner after mega winner after mega winner. But I’m sure you get the idea. You can’t buy a Ferrari for a Honda price. You can’t buy oceanfront real estate for an inland price. And you can’t buy the absolute best growth stocks at laggard valuations.

As winners like these soared hundreds of percent, many investors sat on the sidelines because they felt these stocks were too expensive given their P/E ratio. Those folks just didn’t understand that the very best doesn’t come cheap.

Now this doesn’t mean you should run out and buy any expensive stock. It’s important to keep in mind that a company with a fad product or service can get bid up to absolutely ridiculous valuations — and ultimately burn investors. You don’t want to get swept up in the hype and end up paying a ridiculous valuation that leaves you holding the bag in the end.

Tesla (NASDAQ:TSLA) is a good example here. It recently had a poor earnings result that fell below analysts’ forecasts and CEO Elon Musk has been aggressively selling some of his shares.

In all bull markets, bubbles emerge. Tesla has been trading with a whopping P/E ratio of 355, so this stock’s bubble has been “pricked.” And yet, the stock trades at over $1,096 per share after its 5:1 stock split in August 2020.

All that said, the best investors know that top-shelf stocks often trade for seemingly-rich valuations of 30, 40, and 50 times earnings.

Earnings and sales growth are the major drivers of a stock’s price. The more a company grows its earnings, the more its shares will be worth.

That’s how the market works. It’s the “iron law” of the stock market.

And that’s why you should focus on the companies with massive revenue and earnings growth if you’re looking for stocks with massive upside potential, stocks that can bring you triple-digit returns.

The bottom line: High-quality stocks sporting high P/E ratios scare off the folks who are devoted to buying cheap, lower-quality business… but they reward those of us who understand market history… and know the very best doesn’t come cheap.

Sincerely,

Signed:
Louis Navellier

P.S. Ultimately, spotting the right investment is simple. You buy when the company achieves a Quantum A … and you sell when it disappears.

You don’t fall in love. You certainly don’t fall for hype. You may return to that stock someday — but with my system, you know exactly when it’s time to take profits off the table.

That’s why I say there’s no luck and very little skill behind my own success. Just hard work…and the result: a proprietary system for picking the best investments of the day. It’s made it easy to nab market-beating returns — for double your money (or better).

I’m eager to show you what my system is picking up now. Click here to find out more.

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Amazon.com, Inc. (AMZN), Meta Platforms, Inc. (FB), Alphabet, Inc. (GOOG)

Louis Navellier, who has been called “one of the most important money managers of our time,” has broken the silence in this shocking “tell all” video… exposing one of the most shocking events in our country’s history… and the one move every American needs to make today.

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Economics

Japan’s “curious” lack of inflation

Commenter Cove77 directed me to an article in The Economist, discussing the “curious” lack of inflation in Japan:

Entrenched expectations built…

Commenter Cove77 directed me to an article in The Economist, discussing the “curious” lack of inflation in Japan:

Entrenched expectations built up through decades of little to no inflation play a big role in explaining why rising producer costs have not fed through to consumer prices. Domestic companies are notoriously unwilling to pass on increases in the prices of imports to consumers. At a press conference in October Kuroda Haruhiko, the governor of the Bank of Japan, attributed this reluctance to habits picked up during the country’s periodic bouts of deflation. Companies have a good reason to resist increases. Last week Kikkoman, a producer of soy sauce, announced a 4-10% increase in its prices from February. Such an event might barely be noticed in America. But in Japan it made the national news.

Another crucial factor is the weakness of Japan’s consumer recovery. Private spending fell in the third quarter of the year, and is now 3.5% below where it was at the end of 2019. Spending on durable goods, the source of much American inflation, has been practically flat for the past eight years in Japan.

The second paragraph is correct; a lack of consumer spending is the cause of Japan’s low inflation. (I prefer to focus on NGDP, but the two aggregates tend to move together.)

Given the lack of NGDP growth in Japan, low inflation is inevitable. The supposed “reluctance” of firms to raise prices (mentioned in the first paragraph) plays no role on the low Japanese inflation. To claim it does is to confuse causes and symptoms.  (Conversely, in America you see people complain about “price gouging” by oil companies, an equally erroneous claim.)

It is theoretically possible that an unwillingness of firms to raise prices would lead to lower inflation, at least for a period of time.  Thus suppose the BOJ increased Japanese NGDP at 5%/year over the next few years.  If Japanese firms refused to raise prices then real GDP would also rise at 5%/year.  At some point, however, you run out of workers; the growth in real output could not continue at that pace.

But this is not what is happening in Japan, where NGDP growth since the late 1990s has been negligible.  Slow NGDP growth (i.e. tight money) fully explains the lack of Japanese inflation since 1996.  After accounting for near-zero NGDP growth, there’s nothing left to explain from the pricing behavior of Japanese firms.

PS.  Take a second look at the graph.  It shows levels of NGDP, not growth rates.  This is one of the most mind-boggling graphs in the entire history of modern macroeconomics.  And by the way, Japan’s total population in 2020 is about the same as in 1996; so per capita NGDP is also flat.  Imagine no raise in a quarter century!  (In real terms Japan has done OK, but even there I’d say its performance has been a bit disappointing compared to countries such as the US, Australia, and Germany.)

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Economics

With VVIX In Full “Yikes” Mode, Negative Convexity “Accelerant Flows” Are Now In Play

With VVIX In Full "Yikes" Mode, Negative Convexity "Accelerant Flows" Are Now In Play

The holiday-shortened trading day – or rather bloodbath…

With VVIX In Full “Yikes” Mode, Negative Convexity “Accelerant Flows” Are Now In Play

The holiday-shortened trading day – or rather bloodbath – is almost over so time for a quick post-mortem from Nomura’s Charlie McElligott who notes that the close the week with a hard “risk-off” on Nu Variant concerns laying into a super-illiquid post-holiday US market, which is hammering growth- and inflation- sensitives (Russell -4.4%, WTI Crude -10.7%, Copper -3.8%, 5Y Breakevens -13.3bps) and crypto (XBT -7.3%, ETH -9.7%) as a read on speculative sentiment, while front VIX fut is +8 vols and VVIX (vol of vol) skying through 138 in full “yikes” mode (largest 1d move VVIX since Jan 27th).

Meanwhile, Rates is even more of a calamity, because as McElligott puts it, “that’s what Rates trading is in 2021…PAIN; Funds positioned for bear-flattening after the recent run-up / pull-forward in Central Bank “accelerated taper to hasten hiking liftoff” trades…but now you’re getting a bull-steepening, as the market suddenly realizes that IF we were to get more border closures (on account of Nu) which hit growth and further contribute to supply-chain woes, you’re sure as hell not getting 2.5 / 3 Fed hikes in ’22 on account of perpetually scared of their own shadow CB’s and their asymmetric policy (at one point, belly / UST 5Y yields were -19bps today…WOW)”

So with regard to this downtrade’s impact or sentiment especially in light of Goldman’s repeated calls for a year-end meltup, there is an obvious psychological impact: people who’ve been playing for the year-end seasonality surge for Equities— the Nomura quant included  — are getting increasingly frustrated because after a rollicking bull-move in October, the S&P has gone absolutely nowhere since Nov 1, while late-comers have had their hands blown-off…which then further bleeds desire to put risk capital to work into the rest of the year end—i.e. buyside transitions into “protect year” mode, where “return of capital” > “return on capital.

This is a problem since such a psychological revulsion goes for all trades with any crowding, i.e. “bearish fixed-income”/“flattener” expressions, which have had a heck of a run and could see hastened monetization in order to protect PNL into year-end—especially with Dealer balance-sheet and market illiquidity certain to get worse with every passing day which incentizes folks to tap-out on trades.

So, putting it all together, the Nomura strategist calls today a “reversal” type of day from the general halcyon of recent months for most assets, especially with the recent hawkishness in Rates reversing and seeing Bonds squeeze/rally massively today on the risk-off, which means completely opposite price-action in Equtiies “Value / Growth” dynamics of last week–ouch

Hence, what we are seeing according to the Nomura quant, are “shock outlier moves” in US stocks today in a mega “reversal of the reversal” seen last week, with “Bond / Duration proxies” exploding higher, while “Economically Sensitives” get smashed—see “1-Day Ret Z-Score (1yrRel)” column:

And with this broad momentum/trend reversal, McElligott warns that we must pay a lot of attention to “negative convexity” strategies who have again accumulated substantial exposures in recent weeks of low realized Volatility.

Meanwhile, an even larger risk here residing within systematic strategies is the volatility component as it relates to the de-leveraging or re-leveraging of positions, because position sizing is set to be inversely proportional to the instrument’s volatility input—or as Charlie puts it, “Volatility is the exposure toggle.

So looking at US Equities Vol Control, we see a more substantial “local” risk of selling—in other words, a 1.5% change in SPX today would be -$8.0B of de-allocation from US Equities; a 2.0% change would be -$14.0B; a 2.5% change would be -$21.1B of exposure to reduce, and so on (and mind you, Vol Control is now back up to $228.4B of US Eq Exposure, a solid 77%ile rank)

If this feels very deja vu, it’s because it is – much like late August, we’ve seen a resumption of the same pattern in the equity vol space (which Goldman addressed just a few days ago) where we have ridiculously low realized Vol pinned by dispersion/low corr and options sellers (overwriters, strangle sellers) which have been stuffing Dealers full of Gamma which has stabilized us into pullbacks …yet despite so many other Vol metrics screaming “crash.”

According to the Nomura strategist, the only way then to rinse this “imbalance” is to actually see a proper “shock down” and get that realized Volatility “true up” to implied Volatility; but as always, it will take more than just 1 day of vol spike…and require multiple days of follow through of said “high rVol” to drag up trailing averages before getting that larger mechanical deleveraging…but today is a good head-start… unless of course we get an all clear from the authorities in the next few hours in which case the meltup will continue.

McElligott’s punchline is similar to that from SpotGamma – namely that we are nearing max pain “short Gamma vs Spot” for Dealers in SPX / SPY options – below 4574 in spoos –  with market slipping away from prior stabilizing “long Gamma” (as Vol runs higher) and nearing Dealer “short strikes” on client hedges, while already there “short Gamma vs Spot” in QQQ (Nasdaq) and EXTREMELY short in IWM (Russell)…so negative convexity “accelerant flows” are very much in-play.

Tyler Durden
Fri, 11/26/2021 – 12:48




Author: Tyler Durden

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Precious Metals

Pondering The Economic Significance Of Aggregated Price Indices

Blair Fix wrote an interesting article “The Truth About Inflation.” There is a lot of material in there, which overlaps some of my thinking that would go into a section of my inflation primer. One key observation is that variance in price changes for i…

Blair Fix wrote an interesting article “The Truth About Inflation.” There is a lot of material in there, which overlaps some of my thinking that would go into a section of my inflation primer. One key observation is that variance in price changes for individual items swamps the change in the aggregate CPI. I ran into a comment that “everybody knows” about variance in CPI components, but this points to the problem that conventional approaches to macro have a theoretical blind spot — what if aggregate CPI is not economically meaningful because we are literally and figuratively adding up apples and oranges?

I have started up working on my agent-based model again. Although the modelling work is extremely preliminary (I have a working model, but it is arguably silly), it suggests to me a natural way to discuss topics like this. In the case of inflation aggregates, there has been a lot of ink spilled about aggregation problems, both by heterodox authors (post-Keynesians, and even Austrians) as well as the mainstream. If I were attempting to write an advanced text on inflation (my plan is to write one after dealing with the easy stuff in a primer), I would have to wade through that literature. The problem with that literature is straightforward: my eyes glaze over when reading it, and I suspect that many readers of my summary would end up in the same boat.

The way to do an end run around the dead hand of obscure ancient academic arguments is to look at concrete examples of the real problems that crop up in analysis. Blair Fix’s statistical analysis is one way to do it. Another way would be to use models that illustrate how problems develop. The advantage of using models is that people can endlessly nitpick about statistical methodology, whereas models are transparent. Unfortunately, my framework is not yet at a stage to offer simulation data, but I can explain the issues involves in behavioural rule development (which are currently in progress).

What I am Not Referring To

Since these arguments are extremely preliminary, I want to keep this article as short as possible. I can see a number of misinterpretations of the thesis, so I will run through them first.

Aggregate CPI is (possibly) useful in the following contexts.

  1. Attempting to measure the change of something resembling “the cost of living.” How well the CPI does this is an entirely different argument, but it is arguably why most people are interested in the CPI.

  2. The reader’s job involves forecasting or trading the CPI. Sure, that’s nice. The thing is that some people spend their working lives trying to forecast the outcomes of sports events, but those outcomes are not normally considered important for the macro-economy.

  3. Central banks target consumer price indices. This drags the inflation aggregates into the macro-economy discussion by brute force. However, central banks could do something like target gold prices, which turns the gold price from an insignificant piece of trivia (other than for gold traders or producers) into a variable of wider concern.

With those out of the way, the concern is: is the change in the aggregate CPI otherwise of concern? The classic example of such logic is the use of “real” interest rates: we judge whether the policy interest rate are “restrictive” or not by subtracting the aggregate inflation rate from the nominal rate.1 This is not a trivial issue: conventional thinking results in the expectation that the economy is constantly about to spin off into a inflationary/deflationary spiral. For example, if the aggregate CPI rises, then “real” interest rates fall if nominal interest rates do not go up, which allegedly causes more inflationary pressures.

Single Good Model

We can start with a model with a single traded good. For example, imagine an ahistorical video game simulation of a city state from 4000 years ago with a fiat currency, and monetary transactions mainly consist of paying workers with money, and they buy some foodstuff. Everything else would be non-modelled exchange transactions (e.g., family members make clothing).

In this case, the price of food is obviously important for the simulation, and is interchangeable with an aggregate consumer price measure. Such a single commodity economy is entirely standard in conventional aggregated macro models. (I am currently working with a single good model in my agent-based framework, so as to allow “calibration” of the model versus conventional ones.)

What happens as we add new traded commodities?

Another Foodstuff: No Problem

If we added another type of food — for example, have “grain” and “meat” — it would slot in fairly easily. We might have a class division of consumption (rich households eat more meat), but if we have households who eat both, there would be a consumption function that would trade off consumption based on price. We could then imagine creating an aggregate measure of “food prices” for which the aggregate makes some sense.

Adding A Multi-Use Good: Problems

Instead, let us imagine that we add wood as the second good. It would be consumed by households to provide heating and to cook food. But it also is used in production processes, as well as a capital good (build trading ships, buildings).

Now we can ask: what do the decision rules of the agents look like? For firms within the model, they are going to look at the state of the “food” and “wood” markets separately. If the simulation wanted to capture the reality of agrarian societies, the “food market” would be sensitive to variable crop yields (although real societies would have diversified food sources). “Wood” output might be somewhat more stable, but presumably sensitive to new supply sources, etc.

The aggregated price might only matter to the consumption decisions of households — if we believed neoclassical consumption functions. But if we assumed that out model households behaved like modern Canadians, even there we run into problems. Wood would also be used to build houses, and so aggregate savings decisions would be driven by the housing market. And what drives the housing market? Once again, if the society is like modern Canada, that would depend on whether foreign students are coming in (from countries with capital controls) and buying expensive houses.

(My wood example is matched by the real world situation of energy prices. Although we do not think of energy sources as being capital goods, they are critically important for almost all production processes. Moreover, things like the oil market is a global affair, and thus not sensitive to things like monetary policy in a small country.)

In summary, the only place where the aggregate price index might “normally” show up in agent decision rules is in the household consumption function — and it is unclear whether that behaviour matches the way that households behave in the real world, or it an assumption that is forced into the model because it gives the desired results.

I used scare quotes around “normally” since there is an important exception — indexation.

The Indexation Exception

One easy way to embed the aggregate price index into agent behaviour is to have them index prices. This could either be literally indexing prices, or “indirect indexing”: adjusting prices to match the changes in the local currency versus a “hard currency” (or gold, in the Gold Standard era).

In commodities markets, a standard trading adage is a variant of the following “high prices are the cure for high prices.” If we look at individual markets, any price spikes due to shortages is met by changes of behaviour of producers and consumers. If we are to believe the clergy of the free market, price signals are key component of capitalism. As such, different markets will follow different paths. However, indexing all transactions has the effect of dragging them all in a coherent fashion.

As such, it might not be a complete surprise that inflation was highest in the developed countries when cost of living adjustments were the most potent, and developing countries that have high foreign exchange pass through into prices have the most difficulties with inflation. (It is usually safe to ignore emerging market strategists’ discussions of developed country inflation since they over-estimate exchange rate pass through by at least two orders of magnitude.)

One of the advantages of agent-based modelling is that we can see the effect of adding indexation to some contracts, and see whether my guess about those dynamics holds up in model economies. With an aggregated model, the feedback effects of indexation would be trivially baked into the model.

Concluding Remarks

Outside of the few obvious exceptions, it is probably safest to treat aggregated price indices as the output of economic dynamics, but its usefulness as an input is far more questionable. The prices underlying the index have their own dynamics, and so the only way to deal with them is to simulate each component separately.

1

Monetarists have an objection to that conventional description that only makes sense to Monetarists.

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(c) Brian Romanchuk 2021










Author: Brian Romanchuk

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