Abstract from the 1st International Online Conference on Risk and Financial Management
Bibliographic record
Abstract
Education loans complement human capital development and with successful recovery, they become self-sustainable.This recovery can be enhanced if defaults can be predicted accurately, which would also optimize the capital reserve requirements.Hence, this study aims to evaluate the attitudinal factors in educational loan repayment by integrating willingness with the ability of the borrower.This study follows a multi-method approach to testing the antecedent attitudinal variables of education loan repayment intention.A search of the literature finds themes for framing the hypothesis, which is tested quantitatively using partial least squares-structural equation modeling (PLS-SEM), and the prediction accuracy is calculated using artificial neural network (ANN) and deep neural network (DNN) modeling in multiple stages.Credit reporting and perceived quality of life were the two most significant variables in the PLS-SEM model integrated with the ANN model in multiple stages that resulted in increased prediction accuracy at each stage.The prediction accuracy of the ANN model before the integration of SEM was 87%, and after the final-stage SEM-ANN integration, it increased to 90%, while it increased from 89% to 93% after single-stage deep learning (DL) integration.Therefore, multi-stage SEM-ANN-DL integration improves the prediction accuracy for defaults.Improvements in the prediction accuracy can help financial institutions to plan their loan recovery and calculate the optimum capital reserve requirements for provisioning for non-performing assets. A Comparative Analysis of Machine Learning Algorithms in Technical Trading Strategies Yeswanth SBachelor of Information Technology, Velalar College of Engineering and Technology, Erode, Tamil Nadu 638009, IndiaThis study explores the integration of machine learning (ML) techniques into technical trading strategies, evaluating their performance against traditional methods across diverse financial markets.It employs key technical indicators like Moving Averages, the Relative Strength Index (RSI), and other analytical tools to boost prediction accuracy.Historical market data, sourced from the yfinance library, forms the basis for designing and testing these strategies, enabling a detailed assessment of the profitability and effectiveness of ML-enhanced approaches.The research aims to showcase machine learning's ability to
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.185 | 0.048 |
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".