An Investigation of Changes in Returns on Peer-to-Peer Lending in Lithuania and Sweden During the COVID-19 Pandemic: Evidence from Wavelet Coherence Analysis
Bibliographic record
Abstract
FinTech has revolutionised financial services, making them more accessible and efficient. However, the COVID-19 pandemic impacted the FinTech industry, including peer-to-peer lending, in Lithuania and Sweden. This article explores the relationship between the pandemic and changes in peer-to-peer lending returns in both countries. By analysing existing literature, statistical data, and using wavelet coherence analysis, the study aims to understand the influence of COVID-19 on lending in Lithuania and Sweden. The results indicate that the pandemic had a limited impact on Lithuanian peer-to-peer lending, with average interest rates declining over time. Negative correlations between infection cases and lending returns were observed in specific quarters but were only significant in the short term. In contrast, the influence on Swedish peer-to-peer lending was more pronounced. Interest rates initially decreased, but a significant increase occurred in the first quarter of 2022, coinciding with a surge in COVID-19 infections and foreign policies. The negative correlation between cases of infection and lending returns persisted across the short, medium, and long terms alike in Sweden. These findings suggest that the Swedish peer-to-peer lending market was more affected by the economic and policy factors related to the pandemic than that of Lithuania.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".