The Market Cycle Puzzle

Preface: Explaining our market timing models 

We maintain several market timing models, each with differing time horizons. The "Ultimate Market Timing Model" is a long-term market timing model based on the research outlined in our post, Building the ultimate market timing model. This model tends to generate only a handful of signals each decade.

The Trend Asset Allocation Model is an asset allocation model that applies trend-following principles based on the inputs of global stock and commodity prices. This model has a shorter time horizon and tends to turn over about 4-6 times a year. The performance and full details of a model portfolio based on the out-of-sample signals of the Trend Model can be found here.

  

My inner trader uses a trading model, which is a blend of price momentum (is the Trend Model becoming more bullish, or bearish?) and overbought/oversold extremes (don't buy if the trend is overbought, and vice versa). Subscribers receive real-time alerts of model changes, and a hypothetical trading record of the email alerts is updated weekly here. The hypothetical trading record of the trading model of the real-time alerts that began in March 2016 is shown below. 

 

The latest signals of each model are as follows:

• Ultimate market timing model: Buy equities (Last changed from “sell” on 28-Jul-2023)*
• Trend Model signal: Bullish (Last changed from “bearish” on 27-Jun-2025)*
• Trading model: Neutral (Last changed from “bullish” on 26-Nov-2025)*

* The performance chart and model readings have been delayed by a week out of respect to our paying subscribers.

Update schedule: I generally update model readings on my site on weekends. I am also on X/Twitter at @humblestudent and on BlueSky at @humblestudent.bsky.social. Subscribers receive real-time alerts of trading model changes, and a hypothetical trading record of those email alerts is shown here.

Subscribers can access the latest signal in real time here.

 

Divergent Market Cycles 

Two weekI highlighted the relative breakouts of gold prices compared to the S&P 500 and the 60/40 portfolio and argued that the breakouts represented a transition of paper to hard asset leadership. However, the last time the gold/paper asset cycle turned, it coincided with a bottoming in other market leadership factors, namely value/growth, small cap/large cap and international/U.S. stocks. This time, the turn in factor leadership isn’t evident. 

 

What are the investment implications of the continuing divergence? Changes in market leadership often occur when a market transitions from bull to bear. Does this mean that the bull is still alive and how should investors position their portfolio allocation?

The full post can be found here.

Peering into 2026: Prepare for Momentum Tailwinds

My former colleague Fred Meissner revealed a disturbing contrarian warning in a recent weekly commentary: “The most concerning story: recently I was on a panel for the CFA Society of San Francisco. All three analysts had the same outlook, which is my base case – a yearend rally followed by problems in the first part of 2026.”

It seems that everyone is looking for a rally into year-end, followed by weakness in the new year. Here is where I differ from that consensus. I believe the stock market should rally into year-end, followed by continued bullish tailwinds, at least for the first half of the year.

 

My long-term market timing model remains on a buy signal. As a reminder, this model flashes a buy signal whenever the monthly MACD of the NYSE Composite (bottom panel) turns positive and sells whenever the 14-month RSI flashes a negative divergence.

The bull is alive, and it’s helped by three major tailwinds going into 2026, namely a stimulative monetary policy, a stimulative fiscal policy, and strong price and fundamental momentum.

The full post can be found here.

The AI Bubble Debate

Are we in an artificial intelligence investment bubble? That’s becoming the narrative in the financial media. A search on Bloomberg for “AI bubble” showed elevated number of stories, with a peak in January 2025 after the news of the DeepSeek breakthrough.

 
In practice, what does that mean for equities, and the economy?

 

The full post can be found here.

A reply to Grantham's AI warning

Well-known value investor Jeremy Grantham recently penned an essay titled, “The Great Paradox of the U.S. Market”, in which he warned, “Prices reflect near perfection yet today’s world is particularly imperfect and dangerous”.

In particular, he sounded the alarm over the bubble in AI stocks and cited the Gartner Hype Cycle as the main reason for caution:

 

But every technological revolution like this – going back from the internet to telephones, railroads, or canals – has been accompanied by early massive hype and a stock market bubble as investors focus on the ultimate possibilities of the technology, pricing most of the very long-term potential immediately into current market prices. And many such revolutions are in the end often as transformative as those early investors could see and sometimes even more so – but only after a substantial period of disappointment during which the initial bubble bursts. Thus, as the most remarkable example of the tech bubble, Amazon led the speculative market, rising 21 times from the beginning of 1998 to its 1999 peak, only to decline by an almost inconceivable 92% from 2000 to 2002, before inheriting half the retail world!

As much as I respect Grantham’s investment insights, he suffers from the value investor problem of being too early and overly reliant on valuation for his views. I reiterate my view that it’s still early in the bull cycle for AI stocks (see The Path to Magnificent Exuberance). Here’s why.

 

The full post can be found here.

If machines are human, would you let one marry your daughter?*

Several months ago, the internet was all abuzz over the victory of Google's AlphaGo program beating Go grandmaster Lee Sedol (see story here). As the game of Go is a computationally and mathematically complicated game and the number of variations in the game is an order of magnitude higher than chess, it was a great victory for the kinds of "deep learning" artificial intelligence (AI) techniques pioneered by Google's Deep Mind team.

Indeed, there have been great strides by AI research teams in the fields of pattern recognition and natural language processing. As an example, the Washington Post chronicled a startup called Viv designed to be a natural language AI bot that can order you pizza, among other tasks:

In an ordinary conference room in this city of start-ups, a group of engineers sat down to order pizza in an entirely new way.

“Get me a pizza from Pizz’a Chicago near my office,” one of the engineers said into his smartphone. It was their first real test of Viv, the artificial-intelligence technology that the team had been quietly building for more than a year. Everyone was a little nervous. Then, a text from Viv piped up: "Would you like toppings with that?"

The engineers, eight in all, started jumping in: “Pepperoni.” “Half cheese.” “Caesar salad.” Emboldened by the result, they peppered Viv with more commands: Add more toppings. Remove toppings. Change medium size to large.

About 40 minutes later — and after a few hiccups when Viv confused the office address — a Pizz’a Chicago driver showed up with four made-to-order pizzas.

The engineers erupted in cheers as the pizzas arrived. They had ordered pizza, from start to finish, without placing a single phone call and without doing a Google search — without any typing at all, actually. Moreover, they did it without downloading an app from Domino’s or Grubhub.

The full post can be found at our new site here.

* The title was inspired by an old science fiction short story entitled "If all men were brothers, would you let one marry your sister?"