SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest

Paper: arXiv 2410.07143

Abstract

Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called “Sentiment-Augmented Random Forest” (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses established machine learning methods (Random Forest, FinGPT) without novel mathematical derivations, keeping math complexity low, but includes detailed empirical work with specific datasets, APIs, and comparative metrics.

Research Flowchart

  flowchart TD
  Start(["Research Goal: Enhance Stock Trend Prediction"]) --> Inputs
  subgraph Inputs ["Data & Models"]
      direction LR
      I1["(Historical Market Data)"]
      I2["(Financial News via FinGPT)"]
  end
  Inputs --> Method
  subgraph Method ["Key Methodology: SARF"]
      direction TB
      M1["Sentiment Feature Extraction"] --> M2["Feature Fusion<br>Sentiment + Technical Data"]
      M2 --> M3["Random Forest Model Training"]
  end
  Method --> Outcome
  subgraph Outcome ["Findings & Outcomes"]
      direction LR
      O1["9.23% Accuracy Improvement"]
      O2["Lower Prediction Errors"]
      O3["Superior to LSTM/RF"]
  end

Cited References (23)

1. FinGPT: Democratizing Internet-scale Data for Financial Large Language Models (2023) — link
2. Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets (2022) — link
3. An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model (2021) — link
4. The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty (2020) — link
5. Financial Trading Strategy System Based on Machine Learning (2020) — link
6. Stock market prediction using machine learning classifiers and social media, news (2020) — link
7. A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing (2019) — link
8. Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices (2019) — link

0 of 23 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.