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on Forecasting |
| By: | Narum, Benjamin S. (Dept. of Business and Management Science, Norwegian School of Economics); Berentsen, Geir D. (Dept. of Business and Management Science, Norwegian School of Economics) |
| Abstract: | The need for joint forecasting of parasitic lice and associated preventative treatments stems from large monetary losses associated with such treatments, and the distribution of potential future treatments can be used in operational planning to hedge their associated risk. We present a spatio-temporal forecasting model that accounts for the joint dynamics between lice and treatments where spatial interaction between sites is derived from hydrodynamic transportation patterns. The model-derived forecasting distributions exhibit large heterogeneity between sites at significant levels of exposure which suggests the forecasting model can provide great value in assisting operational risk management. |
| Keywords: | Long-term forecasting; GARMA models; density forecasts; aquaculture; salmon lice |
| JEL: | C53 Q22 |
| Date: | 2024–05–27 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nhhfms:2024_007&r= |
| By: | Ali Mohammadjafari |
| Abstract: | Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.08089&r= |
| By: | Yiluan Xing; Chao Yan; Cathy Chang Xie |
| Abstract: | Forecasting stock prices remains a considerable challenge in financial markets, bearing significant implications for investors, traders, and financial institutions. Amid the ongoing AI revolution, NVIDIA has emerged as a key player driving innovation across various sectors. Given its prominence, we chose NVIDIA as the subject of our study. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.08284&r= |
| By: | Xiaowei Chen; Hong Li; Yufan Lu; Rui Zhou |
| Abstract: | This paper proposes a probabilistic machine learning method to price catastrophe (CAT) bonds in the primary market. The proposed method combines machine-learning-based predictive models with Conformal Prediction, an innovative algorithm that generates distribution-free probabilistic forecasts for CAT bond prices. Using primary market CAT bond transaction records between January 1999 and March 2021, the proposed method is found to be more robust and yields more accurate predictions of the bond spreads than traditional regression-based methods. Furthermore, the proposed method generates more informative prediction intervals than linear regression and identifies important nonlinear relationships between various risk factors and bond spreads, suggesting that linear regressions could misestimate the bond spreads. Overall, this paper demonstrates the potential of machine learning methods in improving the pricing of CAT bonds. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00697&r= |