---
title: Equity Price Model
url: https://www.ml-quant.com/papers/ssrn/4979203/
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 4979203
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4979203
featured: 2024-10-09
citations: unknown
topic: Econometrics & Forecasting
---


# Equity Price Model

The 3MR Reactive Price Model uses linear regression and yield prediction to forecast future values of the S&P 500 index, rejecting the martingale hypothesis and allowing for a retrospective yield estimate.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4979203
- Identifier: SSRN 4979203
- Released: 2024-10-07
- First featured: Quant Letter No. 69 (2024-10-09): https://www.ml-quant.com/issues/2024-10-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Accelerating Training with Neuron Interaction and Nowcasting Networks](https://www.ml-quant.com/papers/arxiv/2409.04434/): The article explores the enhancement of weight nowcaster networks (WNNs) through neuron interaction and nowcasting (NiNo) networks, resulting in a 50% speed increase in neural network training for vision and language tasks.
- [Soft Measures for Extracting Causal Collective Intelligence](https://www.ml-quant.com/papers/arxiv/2409.18911/): A study uses large language models to extract fuzzy cognitive maps from text, but emphasizes the need for specific soft similarity measures for this process.
- [Assumption violations in causal discovery and the robustness of score matching](https://www.ml-quant.com/papers/arxiv/2310.13387/): The paper evaluates the performance of recent causal discovery methods on observational data, revealing that score matching-based methods excel in difficult scenarios, setting a new evaluation standard for causal discovery methods.
- [Can GANs Learn the Stylized Facts of Financial Time Series?](https://www.ml-quant.com/papers/arxiv/2410.09850/): The study examines the capability of Generative Adversarial Networks in learning complex financial time series patterns, highlighting that their performance is greatly influenced by the generator architecture chosen.
