---
title: Autoregressive Random Forests for Financial Research
url: https://www.ml-quant.com/papers/repec/kap-compec-v-64-y-2024-i-1-d-10-1007-s10614-023-10429-9/
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: RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10429-9
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-023-10429-9%3Bh%3Drepec%3Akap%3Acompec%3Av%3A64%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10614-023-10429-9
featured: 2024-09-05
citations: unknown
topic: ML & AI Methods
---


# Autoregressive Random Forests for Financial Research

The paper shows the effectiveness of Random Regression Forests for optimal lag selection in data series, outperforming other methods.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-023-10429-9%3Bh%3Drepec%3Akap%3Acompec%3Av%3A64%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1007_s10614-023-10429-9
- Identifier: RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10429-9
- Released: 2024-09-05
- First featured: Quant Letter No. 64 (2024-09-05): https://www.ml-quant.com/issues/2024-09-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals](https://www.ml-quant.com/papers/arxiv/2408.06679/): The study explores Explainable Case-Based Reasoning methods to clarify the results of black-box machine learning algorithms, using a technique to extract the learned distance metric from Random Forests, and assesses their explanatory power.
- [Quantile Regression using Random Forest Proximities](https://www.ml-quant.com/papers/arxiv/2408.02355/): The article introduces a new method for calculating quantile regressions from random forests, showing improved performance and efficiency in predicting the average daily volume of corporate bonds.
- [Financial Fraud Detection System Based on Improved Random Forest and Gradient Boosting Machine (GBM)](https://www.ml-quant.com/papers/arxiv/2502.15822/): The paper suggests a financial fraud detection system that uses an improved Random Forest and Gradient Boosting Machine model, offering an efficient and reliable solution for detecting financial fraud.
- [Enhanced Local Explainability and Trust Scores with Random Forest Proximities](https://www.ml-quant.com/papers/arxiv/2310.12428/): The study introduces a new method to explain the performance of random forest models by viewing them as adaptive weighted K nearest-neighbors models, offering a localized understanding of model predictions.
- [Urban Flood Mapping in New Orleans](https://www.ml-quant.com/papers/repec/spr-nathaz-v-120-y-2024-i-11-d-10-1007-s11069-024-06609-x/): The research finds that machine learning models, particularly the Random Forest model, outperform traditional statistical models in mapping flood susceptibility in New Orleans.
- [Predictor Variables with Random Forests](https://www.ml-quant.com/papers/repec/sae-jedbes-v-49-y-2024-i-4-p-595-629/): The study compares variable selection with random forests, a machine learning method, with linear models in behavioral sciences, providing practical advice.