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Stock Portfolio System FAQ

股票投资组合系统常见问题

Definitions

Fair Value Unbiased – This is generally the market neutral price given the fundamentals. Market neutral in this case means, if you invest at this price, you expect a “market” return. In other words, you’d be better off investing in the S&P 500 than buying at this price.

Fair Value – This is my best attempt to replicate the Market-Maker fair value. There are no official or formal ways to do this, and THIS VALUE DOES NOT REPRESENT FAIR VALUE. Only Fair Value Unbiased does. This takes into account things like sentiment, “brand value” or loose words like “moat”. All these things sound cool until one day they are not.

During a correction, these are the first things to disappear. Companies that have a huge % of their market cap in this historically go down the hardest. A good example of the above is LULU.

LULU stock drawdown showing sentiment collapse
Figure 1: LULU – High sentiment premium evaporates during correction

Tom Ind Value – This is a custom oscillating indicator that I’ve made. It’s good at capturing bottoms (don’t use it for finding tops). It only works for stocks with ~5+ years of data. Hence my universe of long stocks is stocks with longer history. The value will typically range from 0–100, with 0 being rock bottom. It’s an exponential indicator, meaning that the path from 0 to 20 can be 50%–100%, and from 20 to 40 another 50%–100%, etc. Values can occasionally go under 0, but only during extreme market conditions (super buy zones).

Putting it all together

When all 3 align:

1) Fair Value Unbiased > 40%
2) Fair Value > 40%
3) Tom Ind Value < 10

We have typically found the bottom of a stock’s drawdown and can invest. And a green tree will appear.

Green Tree Signal - Buy Confirmation
Figure 2: Green Tree Signal – All conditions met, buy confirmed

During extreme market conditions (e.g. China / US tech stocks in 2022–2023), or during the Great Financial Crisis, both upsides can reach 100%+, even 200%+ with negative Indicator Values. At these rare points in history, we recommend selling everything you own and buy stocks.

This is an automated recommendation engine. If you follow these exact rules, you expect to generate the portfolio historical return (which does not include dividends — for example, buying Oil and Bank stocks in 2020 yielding 8–12%, as well as selling covered calls to close positions). In the real world, and if you time the recommendations differently (e.g. you have extra info or just time it better), you can easily outperform the recommendations. The portfolio generates ~5–10% above historical portfolio shown. The historical portfolio only shows price change. Hence the years we invest in tech only, the returns appear higher than when we invest in dividend stocks.
Charts

When looking at charts we recommend Log Scale view. Looking at something that compounds exponentially (e.g. stocks) through a linear scale is nonsense as all old data gets squeezed out due to current “higher” prices.

Linear scale chart - misleading
Figure 3: Linear Scale – Old data gets compressed, misleading

Looks more reasonable in this form:

Log scale chart - accurate compounding
Figure 4: Log Scale – Accurate representation of compounding

Log scale captures the time it takes for anything to double (or 10x). So the gap between 1 to 10 is the same as the gap between 10 to 100 and the same as 100 to 1000 etc. Whereas in a regular chart the large numbers today make the earlier actions of the stock irrelevant (which they clearly are not!)