Quantitative Research Paper
Forecasting 101: Neural Networks
Summary
We are thrilled to present the final report in our Technical Series in Statistics and Econometrics from the Quantitative Research Division!
In this paper, we explore neural networks, a versatile machine learning tool capable of capturing complex relationships between features and target variables. Specifically, we examine their performance in forecasting median house prices in California.
This report concludes this semester’s focus on forecasting methods. Our series began with traditional techniques like LASSO and Ridge regression, progressed to Bayesian regression, and now concludes with machine learning approaches such as neural networks.
Authors
- Federico LazzariniHead of Quantitative Research
- Filippo GombacQuantitative Research Analyst
Details
- Published
- 2 December 2024
- Division
- Quantitative Research
- Type
- Quantitative Research Paper
- Pages
- 13
- Authors
- 2
Cite this report
Minerva Investment Management Society (2024). Forecasting 101: Neural Networks. Quantitative Research. https://minervaims.org/archive/forecasting-101-neural-networks-2024
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