/Share-Market-Analysis

Clustering company and forecast the stock price using ARIMA

Primary LanguageJupyter Notebook

Nasdaq 100 Cluster Analysis and Time Series Forecasting

Problem Statement

The Nasdaq 100, a stock market index consisting of 102 equity securities issued by 101 of the Nasdaq's largest nonfinancial companies, spans various sectors such as manufacturing, technology, retail, telecommunication, biotechnology, health care, transportation, media, and service providers. To build a robust and diversified investment portfolio, the cluster trading strategy is employed. This strategy aids in identifying different segments within the market, providing investors with insights to safeguard their portfolios from potential risks.

Objective

The objective of this project is to create segments within the Nasdaq 100 index, helping customers identify segments for investment and those to avoid. To achieve this, cluster analysis techniques will be utilized. Additionally, the project involves performing time series forecasting for stock prices to enhance decision-making and portfolio management.

Project Overview

This project was implemented as a data science project using Jupyter notebooks. The primary tasks include:

  1. Cluster Analysis:

    • Utilize cluster analysis techniques to identify distinct segments within the Nasdaq 100 index.
    • Build a diverse portfolio by categorizing companies based on various factors such as industry, market cap, and financial performance.

  2. Time Series Forecasting:

    • Implement time series forecasting models to predict stock prices for the identified segments.
    • Enhance decision-making by providing insights into potential future market trends.

Technologies Used

  • Programming Language: Python
  • Data Science Libraries: NumPy, Pandas, Scikit-learn
  • Time Series Forecasting: ARIMA
  • Visualization: Matplotlib, Seaborn, Plotly

Project Structure

The project is organized into the following sections:

  1. Data Collection:

    • Retrieve Nasdaq 100 stock data for analysis.

  2. Data Preprocessing:

    • Clean and preprocess the data for further analysis.

  3. Cluster Analysis:

    • Apply clustering techniques to identify market segments.

  4. Time Series Forecasting:

    • Implement time series forecasting models for stock prices.

  5. Results and Insights:

    • Provide insights into the identified segments and the forecasted stock prices.

  6. Conclusion:

    • Summarize findings and recommend strategies for portfolio management based on the analysis.

Getting Started

To run the project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/RajaLairen/Share-Market-Analysis.git
cd Share-Market-Analysis

  2. Open and run the notebook