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Stochastic analysis and neural network-based yield prediction with precision agriculture

Shoshi, Humayra; Hanson, Erik; Nganje, William; … - In: Journal of risk and financial management : JRFM 14 (2021) 9, pp. 1-17

In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we...

Persistent link: https://www.econbiz.de/10012627667

Predicting stock return and volatility with machine learning and econometric models: a comparative case study of the Baltic stock market

Nõu, Anders; Lapitskaya, Darya; Eratalay, M. Hakan; … - 2021

Persistent link: https://www.econbiz.de/10012694117

Nagging predictors

Richman, Ronald; Wüthrich, Mario V. - In: Risks : open access journal 8 (2020) 3/83, pp. 1-26

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

Persistent link: https://www.econbiz.de/10012293262

Carsharing customer demand forecasting using causal, time series and neural network methods : a case study

Moein, Elnaz; Awasthi, Anjali - In: International journal of services and operations … 35 (2020) 1, pp. 36-57

Persistent link: https://www.econbiz.de/10012178607

Inference using simulated neural moments

Creel, Michael D. - 2020 - This version: November 2020

Persistent link: https://www.econbiz.de/10012431141

Modeling surrender risk in life insurance : theoretical and experimental insight

Kiermayer, Mark - In: Scandinavian actuarial journal 2022 (2022) 7, pp. 627-658

Persistent link: https://www.econbiz.de/10013370727

Predicting stock return and volatility with machine learning and econometric models : a comparative case study of the Baltic stock market

Nõu, Anders; Lapitskaya, Darya; Eratalay, M. Hakan; … - In: International journal of computational economics and … 13 (2023) 4, pp. 446-489

Persistent link: https://www.econbiz.de/10014439728

A simultaneous perturbation weak derivative estimator for stochastic neural networks

Flynn, Thomas; Vázquez-Abad, Felisa - In: Computational Management Science : CMS 16 (2019) 4, pp. 715-738

Persistent link: https://www.econbiz.de/10012126685

Conditional nonparametric variable screening by neural factor regression

Fan, Jianqing; Wang, Weining; Zhao, Yue - 2024

more generally a set of variables. Our test statistics are based on the estimated partial derivative of the regression … derivative estimator is the convolution of a deep neural network regression estimator and a smoothing kernel. We demonstrate that … necessary to smooth the partial derivative of the neural network estimator to recover the desired convergence rate for the …

Persistent link: https://www.econbiz.de/10015053806

The information content of implied volatility index (India VIX)

Shaikh, Imlak; Padhi, Puja - In: Global business perspectives 1 (2013) 4, pp. 359-378

Persistent link: https://www.econbiz.de/10010230219

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