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 “neutral” on 11-Oct-2024)*
- Trading model: Neutral (Last changed from “bearish” on 19-Sep-2024)*
Update schedule: I generally update model readings on my site on weekends. I am also on X/Twitter at @humblestudent. 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.
Diversity wins
As the S&P 500 continues to grind upwards, it’s been led by a trio of sectors, financials, industrials and technology, which has becoming an emerging leader. I interpret this as a constructive sign, as the diversity of both value and growth stocks indicate the bull move.The full post can be found here.
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