Prediction of Financial Crisis with Artificial Neural Network: An Empirical Analysis on Turkey
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
Prediction of economic crisis, financial distress or bankruptcy has attracted great deal of attention in financial literature and in many other fields among the researchers over the past few decades. Although there are a variety of different methods that can be used to predict the future financial crisis, due to the complexity of the existing factors, prediction of financial crisis is a very difficult case. With the advent of Artificial Neural Networks (ANNs), researchers had the chance to solve various problems in finance. ANN approach is the application of artificial intelligence, which has been improved by the simulation of cognitive learning process of human brain. ANNs are commonly used in recent years, due to major advantages that they offer such as their ability to perform nonlinear statistical modeling that provides new alternative to other statistical methods and to learn directly from examples without needing or providing an analytical solution to the problem. In this study, a monthly dataset covering the period of 1990 and 2014 that belong to the Turkish economy will be used. The purpose of this study is to develop an early-warning system to predict financial crisis. To realize this aim, multi-layered feedforward neural networks (MLFNs) will be used. By using monthly data of 7 key macroeconomic and financial indicators of Turkish economy during 1990 and 2014, we find that predictive power of ANN is quite striking. Our out-of-sample forecasts indicate that the Turkish economy remains at high risk due to major negative developments and potential political instability between 2014 and 2016.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it