Cross Section Analysis of the KBW Nasdaq Financial Technology Index
Bibliographic record
Abstract
Financial Technology (FinTech) has emerged as a potentially transformative force in the various financial segments. To track this new sector garnering investor attention, Keefe, Bruyette & Woods and Nasdaq, came up with KBW Nasdaq Financial Technology Index (KFTX) on July 18, 2016 comprising of 49 constituents. The objective of this paper is to compare KFTX performance with the leading market indices, including S&P 500 and Dow Jones Industrial Average index. The data is collected for a period of 12 months, 24 months and 34 months starting from July 18, 2016. The findings of the analysis suggest that the returns for KFTX are consistently higher for 12 months, 24 months and 34 months over S&P 500 and Dow Jones Industrial Average. The cross section analysis of the 48 KFTX index constituents sub-classified into eight categories representing several different financial industry groups and businesses indicate that for the 34 months period networks and payments gave returns of 82.1% and 71.6% whereas asset management business gave an average negative return of – 51.0% and the specialty marketplace lenders gave a return of 26.6%. This indicates a significant non-uniform growth within the FinTech industry. The findings motivate for an in-depth analysis of the various industry groups and businesses within the FinTech industry and to explore further the reasons and attributes which differentiate these sectors. The study has implications for policy makers, asset management companies and investors in terms of understanding and framing policies for FinTech investments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".