Masoud Soleimani
arXiv 22 Sep 2026 · Econometrics
arXiv:2609.26303 · PDF · Extracted main text
Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error correlation largely mirrors forecast correlation and is therefore a poor measure of diversity; an equally weighted combination beats a no-information forecast only when average alignment is large relative to the combination's dispersion; and the gain from adding a forecaster separates into genuine improvement and mere dilution, which equal-weight admission can mistakenly reward. We develop a cautious selection rule, study it in simulations, and apply it to language-model forecasts of US equity rankings and mechanical signals ranking exchange-traded funds. Selection removes most dilution losses, but no combination beats the no-information forecast.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Diebold, Francis X. and Shin, Minchul (2019) Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives | 0.737 | 3 | 2 | 100% |
| 2 | Asness, Clifford S. and Moskowitz, Tobias J. and Pedersen, Lasse Heje (2013) Value and momentum everywhere | 0.644 | 2 | 2 | 100% |
| 3 | Jegadeesh, Narasimhan and Titman, Sheridan (1993) Returns to buying winners and selling losers: Implications for stock market efficiency | 0.644 | 2 | 2 | 100% |
| 4 | Bates, J. M. and Granger, C. W. J (1969) The combination of forecasts | 0.511 | 2 | 1 | 100% |
| 5 | Brown, Gavin and Wyatt, Jeremy and Harris, Rachel and Yao, Xin (2005) Diversity creation methods: A survey and categorisation | 0.511 | 2 | 1 | 100% |
| 6 | Claeskens, Gerda and Magnus, Jan R. and Vasnev, Andrey L. and Wang,… (2016) The forecast combination puzzle: A simple theoretical explanation | 0.511 | 2 | 1 | 100% |
| 7 | Kim, Minsu and Ray, Evan L. and Reich, Nicholas G (2026) Beyond forecast leaderboards: Measuring individual model importance based on contribution to ensemble accuracy | 0.511 | 2 | 1 | 100% |
| 8 | Krogh, Anders and Vedelsby, Jesper (1995) Neural network ensembles, cross validation, and active learning | 0.511 | 2 | 1 | 100% |
| 9 | Romano, Joseph P. and Wolf, Michael (2005) Stepwise multiple testing as formalized data snooping | 0.511 | 2 | 1 | 100% |
| 10 | Stock, James H. and Watson, Mark W (2004) Combination forecasts of output growth in a seven-country data set | 0.511 | 2 | 1 | 100% |
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