A transparent alternative to neural networks with an application to predicting volatility
Megan Czasonis, Mark Kritzman, and David Turkington
| Year of publication: |
[2024] ; This version: September 12, 2024
|
|---|---|
| Authors: | Czasonis, Megan ; Kritzman, Mark ; Turkington, David |
| Publisher: |
[Cambridge, MA] : [MIT Sloan School of Management] |
| Subject: | Activation Function | Adjusted Fit | Bias | Cross Validation | Epoch | Fit | Fold | Grid Prediction | Hidden Layer | Hyperparameter | Information Theory | Informativeness | Learning Rate | Linear Regression Analysis | Mahalanobis Distance | Mean Squared Error | Model Free | Neural Network | Node | Patience | Relevance | Relevance-based Prediction | Similarity | Neuronale Netze | Neural networks | Prognoseverfahren | Forecasting model | Schätztheorie | Estimation theory | Lernprozess | Learning process | Volatilität | Volatility |
| Extent: | 1 Online-Ressource (circa 27 Seiten) |
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| Series: | Sloan working papers. - Cambridge, Mass. : [Verlag nicht ermittelbar], ZDB-ID 2446924-5. - Vol. [no. 7149 (24)] |
| Type of publication: | Book / Working Paper |
| Type of publication (narrower categories): | Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper |
| Language: | English |
| Other identifiers: | 10.2139/ssrn.4961917 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://ebvufind01.dmz1.zbw.eu/10015101048