Handful of giants mask bear market in most US stocks

1. Index concentration hides damage — ▶ 2:29

   Nearly 6 in 10 US stocks are down more than 20% from their highs, Pin says, yet the S&P 500 is up on the year because the index is effectively about 7 or 8 giant tech companies. Among chip makers at the center of the AI story, 9 in 10 are down more than 20% and 4 in 10 have lost half their value. The implication for index-fund holders is that apparent diversification across 500 companies is really exposure to a few correlated names. A stumble at one of them could pull the whole index down, since the rest is, as he puts it, outlier noise.

2. Fear already priced in — ▶ 4:43

   A market in which most stocks have already fallen a long way is the opposite of a bubble top, Pin argues. He defines a top as everything rising together while nobody worries. In his framing the fear has been paid for, so the live question is where money lands when it returns. He says it will not flow evenly: it will pour into a certain kind of company while others keep losing. He still expects the big 8 to take a significant wobble eventually, which is why he says holders should not feel safe in them. This is his opinion; he offers no valuation data.

3. The midterm return pattern — ▶ 7:33

   Election spending floods the system and partly bids up asset prices, Pin argues, while the vote itself removes an unanswered question that keeps money in cash. His evidence is that the S&P 500 has averaged 16% returns in the 12 months after midterms, 14% of it in the first six months. He also says it has finished higher after every midterm going back roughly 50 years. The figures are presented as a pattern. The mechanisms are asserted rather than tested, and no sample size or comparison with non-midterm years is given.

4. AI spending: fear first, payoff later — ▶ 10:04

   Big tech has spent hundreds of billions on AI over two years, which made investors nervous about payoff and financing. Pin says that nervousness is why most stocks have already fallen. His argument is that such spending propagates to the suppliers of picks and shovels (chips, power, data centers) and then to companies that use them, so fear arrives first and payoff later. He compares it to railways, electricity and the internet: overbuilding, panic, a wash-out, then survivors profit. In his words, "The internet didn't die in 2000. The nonsense died." He judges risk lower than a few months ago.

5. Liquidity as the biggest driver — ▶ 11:47

   The largest of the three forces, Pin says, is money supply. M2, which he glosses simply as money, is growing at its fastest pace in 4 years. The central bank has also stopped shrinking its balance sheet and started buying government debt again, injecting money through the short-term debt market. He likens it to a vitamin shot: it cures nothing but perks the market up. He argues this money seeks returns and tends to end up in stocks and in hard assets that hold value while cash loses. He gives no figures for the scale of the central bank purchases.

6. Skilled money and contrarian sentiment — ▶ 13:49

   Skilled money, Pin says, follows flows by rules rather than loyalty to single stocks. It favors companies growing sales and profits with decent returns on capital, or those with a compelling story, and it buys where other skilled money is buying. He pairs this with a contrarian reading of a long-running investor survey. The average share of bearish respondents is 31%, but it reached 70% in early 2009 and 60% in autumn 2022. He says the market rose 67% after the 2009 scare and climbed significantly after 2022. His logic: frightened investors have already sold, leaving cash on the sidelines.

7. Market is not the economy — ▶ 16:45

   The stock market is not a thermometer for the economy, Pin argues. It looks forward to expected earnings and, above all, to how much money is looking for a home. In 2020, with unemployment at 15%, stocks reached an all-time high by August after governments flooded the system with money. In 2022, with unemployment at a 50-year low, the market fell 19% as authorities withdrew money. Two cases are consistent with his liquidity thesis but cannot isolate it from expected earnings, which he also cites. His practical advice: avoid large cash piles and watch money flows rather than economic news.

Felix Nicholas Pin's central claim is that investors are watching the wrong number. The S&P 500 is up on the year and near highs, yet by his account nearly 6 in 10 US stocks are down more than 20% from their highs, the technical definition of a bear market. Among the chip makers tied to the AI story, 9 in 10 are down more than 20% and 4 in 10 have lost half their value. His explanation is concentration. The index, he says, is effectively about 7 or 8 giant technology companies, so a calm headline number conceals damage across the rest of the market.

That concentration changes what an index fund actually is. Someone who has averaged into one for decades believes they own 500 companies. Pin says they own about 8 stocks that largely behave alike. He concedes that over 30 years this has worked for many people. His objection is fragility: if one of those few stumbles on a bad earnings call, the index goes with it. Yet the same breadth damage underpins his optimism. A top, in his definition, is when everything rises together and nobody is worried. Here, he says, "The fear has already been paid," and the risk is therefore lower than a few months ago.

Three forces, he argues, are converging. First, the AI capital spending that unsettled markets is being digested. Big tech spent hundreds of billions over two years while investors questioned the payoff and the financing. Pin's template is railways, electricity and the internet: overbuilding, panic, a wash-out, then profits for the survivors. Fear arrives first and payoff later. Second, the midterms remove uncertainty that he says freezes money on the sidelines, while election spending seeps into asset prices. The supporting statistic is striking. The S&P 500 has averaged 16% in the 12 months after midterms, 14% of it within six months, and has finished higher after every midterm over roughly 50 years.

The third force, which he calls the biggest, is liquidity. M2 is growing at its fastest pace in 4 years, and the central bank has stopped shrinking its balance sheet and resumed buying government debt. This is the thread that ties his case together, because he treats the stock market as a function of how much money is looking for a home rather than a gauge of the economy. In 2020, with unemployment at 15%, stocks hit an all-time high by August. In 2022, with unemployment at a 50-year low, the market fell 19% as money was withdrawn. Read charitably, the midterm pattern and the AI cycle are two channels for the same driver, though Pin does not make that connection explicitly.

Sentiment adds a behavioral layer. Pin cites a long-running investor survey in which, on average, 31% of respondents expect lower markets in six months. Readings reached 70% in early 2009 and 60% in autumn 2022, and he says stocks rose strongly afterward, by 67% following the 2009 scare. The logic is that frightened investors have already sold, leaving cash waiting to return. Skilled money, he says, follows these flows by rules rather than sentiment. It favors growth, profitability and compelling stories, and it buys where other skilled money is buying. The practical lesson he draws is to read the direction of money before the news confirms it.

The evidence is thinner than the confidence. The midterm record is presented without sample sizes, baselines or comparison to other years, and its mechanisms, spending and cleared uncertainty, are asserted rather than tested. The liquidity case rests on one claim about M2 growth and two anecdotal years, with no sense of scale. The talk is also a funnel to a free live training, and Pin says he is not a financial adviser. There is an internal tension too. He warns that the dominant big 8 will suffer a significant wobble, yet urges investors not to sit in cash, without specifying how to hold equities while avoiding those names.

The sharpest unresolved issue is visibility. Pin calls the window a matter of weeks, yet also points out that a Morgan Stanley strategist is saying publicly that this is one of the best setups of his career. A pattern that visible invites the question of how much is already in prices. His own premise adds a second problem. If the giants that dominate the index stumble, the headline number would fall toward the weakness already present beneath it, and the improving breadth he expects elsewhere would have to be large enough to offset that.