Enhancing Financial Data Visualization for Investment Decision-Making

ArXiv ID: 2403.18822 “View on arXiv”

Authors: Unknown

Abstract

Navigating the intricate landscape of financial markets requires adept forecasting of stock price movements. This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics, with a focus on discerning nuanced rise and fall patterns. Leveraging a dataset from the New York Stock Exchange (NYSE), the study incorporates multiple features to enhance LSTM’s capacity in capturing complex patterns. Visualization of key attributes, such as opening, closing, low, and high prices, aids in unraveling subtle distinctions crucial for comprehensive market understanding. The meticulously crafted LSTM input structure, inspired by established guidelines, incorporates both price and volume attributes over a 25-day time step, enabling the model to capture temporal intricacies. A comprehensive methodology, including hyperparameter tuning with Grid Search, Early Stopping, and Callback mechanisms, leads to a remarkable 53% improvement in predictive accuracy. The study concludes with insights into model robustness, contributions to financial forecasting literature, and a roadmap for real-time stock market prediction. The amalgamation of LSTM networks, strategic hyperparameter tuning, and informed feature selection presents a potent framework for advancing the accuracy of stock price predictions, contributing substantively to financial time series forecasting discourse.

Keywords: LSTM, Stock Price Prediction, Time Series Analysis, Hyperparameter Tuning

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper focuses on applying an established LSTM architecture with standard hyperparameter tuning and data preprocessing, avoiding heavy theoretical derivations, but demonstrates strong empirical rigor through specific dataset usage, detailed methodology, and reported performance metrics.

  flowchart TD
    A["Research Goal:<br>Predict Stock Price Movements"] --> B["Data & Features<br>NYSE Dataset: OHLC + Volume"]
    B --> C["Methodology<br>LSTM Network with 25-Day Timesteps"]
    C --> D["Computational Process<br>Hyperparameter Tuning Grid Search"]
    D --> E["Computational Process<br>Early Stopping & Callbacks"]
    E --> F["Key Outcome<br>53% Improvement in Predictive Accuracy"]
    F --> G["Final Result<br>Robust Framework for Financial Forecasting"]