Dataset Description: Stock Price Data

This dataset contains historical stock price data for a financial asset, including information on opening price, daily highs, lows, closing prices, and trading volume over time. It is well-suited for time series analysis, price modeling, and volatility studies.

Dataset Columns:

1. Date: The date of each record, representing the day when transactions occurred.
2. Open: The stock's opening price at the beginning of the trading session.
3. High: The highest price the stock reached during the trading session.
4. Low: The lowest price recorded during the trading session.
5. Close: The stock's closing price at the end of the trading session.
6. Adj Close: The adjusted closing price, accounting for events like dividends and stock splits.
7. Volume: The total number of shares traded during the session.

Potential Uses:

• Stock price prediction using time series models (e.g., ARIMA, Prophet, or LSTM).
• Analysis of stock price volatility.
• Market trend analysis.
• Backtesting trading strategies.

Business Problem: Predicting Stock Closing Prices (2017-2024)

Predicting the closing price of a stock is a critical task in financial markets, helping traders and investors make informed decisions and optimize their portfolios. This dataset spans from 2017 to 2024, providing a comprehensive view of stock price trends over several years. By analyzing historical data, the goal is to forecast the future closing prices of the stock, which can aid in developing effective trading strategies and managing investment risks.

Problem Statement:

The objective is to create a predictive model that accurately forecasts the stock's closing price (target variable: "Close") based on historical data from 2017 to 2024. By leveraging daily data on opening prices, highs, lows, adjusted closing prices, and trading volumes, the model will predict the stock’s next closing price.

Key Business Questions:

1. How accurately can we predict the stock's closing price for the next trading day?

   • Build predictive models to forecast future closing prices using past data.

2. How do the daily high, low, and volume impact the closing price?

   • Analyze the relationship between daily price fluctuations and the closing price to capture market behavior.

3. What are the key trends from 2017 to 2024 that impact the stock’s closing price?

   • Perform trend analysis to identify patterns in closing price movements across years.

4. How does market volatility (highs and lows) influence the accuracy of closing price predictions?

   • Explore the effect of market volatility on prediction models and identify the most impactful factors.

Potential Solutions:

• Time Series Models: Apply time series forecasting methods (e.g., ARIMA, Prophet, LSTM) to predict the stock's future closing price using historical data.
• Machine Learning Regression Models: Use models like Random Forest, XGBoost, or Linear Regression to estimate the next day's closing price based on various features (Open, High, Low, Volume, etc.).
• Backtesting Trading Strategies: Test trading strategies based on the predicted closing prices and evaluate their performance over the 2017-2024 period.

Impact:

Accurate prediction of closing prices can significantly enhance decision-making for traders and investors, allowing them to identify profitable entry and exit points, reduce risk exposure, and optimize their trading strategies. This ultimately leads to better portfolio management and potentially higher returns.