Stock Market Analysis and Predictive Models

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Introduction

Welcome to the repository for my conference paper on stock market analysis and predictive models. In this paper, I explore various models to analyze and predict stock market trends. I have employed a combination of traditional time series models and modern machine learning techniques to provide insights into stock price movements.

Models Explored

ARIMA is a classic time series model used to forecast future values based on past observations. By identifying the auto-regressive and moving average components of a time series, ARIMA can capture important patterns in stock price data.

GARCH is a model that focuses on volatility clustering in financial time series. It helps to model the changing volatility of stock prices, which is crucial for risk assessment and portfolio management.

Combining ARMA (AutoRegressive Moving Average) with GARCH, this hybrid model leverages both the trend-capturing ability of ARMA and the volatility modeling strength of GARCH. This combination enhances the accuracy of predictions.

By incorporating Artificial Neural Networks (ANN) into the ARMA-GARCH hybrid framework, we harness the power of deep learning for stock market analysis. The ANN component captures complex relationships that might not be captured by traditional models.

Fuzzy clustering techniques combined with ARMA modeling allow us to identify distinct market regimes and adapt the model parameters accordingly. This approach acknowledges the uncertainty inherent in financial data.

LSTM is a type of recurrent neural network designed for sequential data. In stock market analysis, LSTM can capture long-term dependencies and patterns, making it suitable for modeling stock price sequences.

Integrating LSTM with ARMA-GARCH enhances the predictive power by combining the strengths of both approaches. This hybrid model provides accurate short-term and long-term predictions.

To replicate the experiments and explore the models presented in this paper, follow these steps:

1. Clone this repository to your local machine.
2. Set up the necessary environment using the provided requirements.txt file.
3. Navigate to each model's respective folder and follow the instructions provided in their README files.

In this paper, I've demonstrated a comprehensive approach to stock market analysis using a variety of models. The combination of traditional time series models with modern machine learning techniques offers a holistic view of stock price behavior. Each model has its strengths and limitations, contributing to a deeper understanding of the complexities of financial markets.

Feel free to explore the code and experiment with different datasets to gain insights into your chosen stocks. If you have any questions or feedback, please don't hesitate to reach out!

Disclaimer: The models presented in this paper are for educational and research purposes only. Stock market analysis involves risk, and the predictions provided by these models may not accurately reflect future market behavior.