---
title: Hybrid Models for Forecasting
url: https://www.ml-quant.com/papers/ssrn/5268691/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5268691
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5268691
featured: 2025-05-30
citations: unknown
topic: Econometrics & Forecasting
---


# Hybrid Models for Forecasting

The research uses traditional econometric models, machine learning, and deep learning techniques to predict financial time series, using SP 500 index and Bitcoin data, and assesses the models based on forecast error metrics and trading performance indicators.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5268691
- Identifier: SSRN 5268691
- Released: 2025-05-26
- First featured: Quant Letter No. 99 (2025-05-30): https://www.ml-quant.com/issues/2025-05-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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