Data for: Trends, Reversion, and Critical Phenomena in Financial Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
These data accompany the publication "Trends, Reversion, and Critical Phenomena in Financial Markets". They contain daily data from Jan 1992 to Dec 2019 on 24 financial markets, namely - 6 equity indices: S&P 500, TSE 60, DAX 30, FTSE 100, Nikkei 225, Hang Seng - 6 Interest rates for government bonds: US 10-year, Canada 10-year, Germany 10-year, UK 10-year, Japan 10-year, Australia 3-year - 6 FX rates: CAD/USD, EUR/USD, GBP/USD, JPY/USD, AUD/USD, NZD/USD - 6 Commodities: Crude Oil, Natural Gas, Gold, Copper, Soybeans, Live Cattle The data are provided in 13 columns: - Column 1: date - Column 2: market - Column 3: daily log return of futures on that market, normalized to have mean 0 and standard deviation 1 over the 28-year time period - Columns 4-13: trend strengths in that market over 10 different time horizons of (2,4,8,16,32,64,128,256,512,1024) business days. The trend strengths are defined in the accompanying paper. They are cut off at plus/minus 2.5. The daily log returns were computed from daily futures prices, rolled 5 days prior to first notice, which were taken from Bloomberg. The following mean returns and volatilites were used to normalize the daily log returns in column 3: Market Mean St. Dev. S&P 500 2.217% 1.100% TSE 60 2.416% 1.067% DAX 30 1.199% 1.366% FTSE 100 1.053% 1.103% Nikkei 225 -0.483% 1.486% Hang Seng 0.768% 1.674% US 10-year 3.734% 0.366% Can. 10-year 3.637% 0.376% Ger. 10-year 4.141% 0.337% UK 10-year 2.983% 0.419% Jap. 10-year 4.453% 0.249% Aus. 3-year 3.029% 0.074% CAD/USD 0.048% 0.479% EUR/USD -0.222% 0.619% GBP/USD 0.316% 0.597% JPY/USD -0.761% 0.667% AUD/USD 0.851% 0.725% NZD/USD 1.563% 0.724% Crude Oil 0.093% 2.243% Natural Gas -2.649% 2.985% Gold 0.580% 0.987% Copper 0.936% 1.586% Soybeans 0.631% 1.360% Live Cattle 0.483% 0.894%
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it