This info is not intended for you to only follow the accurate callers as past performance is not indicative of future results.
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The essential grading methodology is to compare forecasts for the U.S. stock market (whether quantified or qualitative) to S&P 500 index returns over the future interval(s) most relevant to the forecast horizon. However, many forecasts contain ambiguities about degree and timing, equivocations and/or conditions. In general, we:
- Exclude forecasts that are too vague to grade and forecasts that include conditions requiring consideration of data other than stock market returns.
- Match the frequency of a guru’s commentaries (such as weekly or monthly) to the forecast horizon, unless the forecast specifies some other timing.
- Detrend forecasts by considering the long-run empirical behavior of the S&P 500 Index, which indicates that future returns over the next week, month, three months, six months and year are “normally” about 0.1%, 0.6%, 2%, 4% and 8%, respectively. For example, if a guru says investors should be bullish on U.S. stocks over the next six months, and the S&P 500 Index is up by only 1% over that interval, we would judge the call incorrect.
- Grade complex forecasts with elements proving both correct and incorrect as both right and wrong (not half right and half wrong).
Weaknesses in the methodology include:
- Some forecasts may be more important than others, but all are comparably weighted. In other words, measuring forecast accuracy is unlike measuring portfolio returns.
- Consecutive forecasts by a given guru often are not independent, in that the forecast publishing interval is shorter than the forecast horizon (suggesting that the guru repetitively uses similar information to generate forecasts). This serial correlation of forecasts effectively reduces sample size.
- In a few cases, for gurus with small samples, we include forecasts not explicitly tied to future stock market returns. There are not enough of these exceptions to affect aggregate findings.
- Grading vague forecasts requires judgment. Random judgment errors tend to cancel over time, but judgment biases could affect findings. Detailed grades are available via links below to individual guru records. Within those records are further links to source commentaries and articles (some links are defunct). Readers can therefore inspect forecast grades and (in many cases) forecast selection/context.
- S&P 500 Index return measurements for grading commence at the close on forecast publication dates, resulting in some looseness in grading because forecast publication may be before the open or after the close. Very few forecast grades are sensitive to a one-day return, and we try to take looseness into account in grading any forecasts that focus on the very short term.