Project Summary:
As part of a hackathon at Dane Street, a leading investment firm, our team focused on developing a robust predictive model for stock market performance. Our goal was to forecast the future closing prices of a specific stock that showed promising trends. This project was crucial for providing actionable insights to our High Net-worth Individual (HNI) clients, who were keen on making informed investment decisions to build long-term wealth.
Key Objectives:
Stock Price Prediction:
Utilized advanced machine learning algorithms to predict the closing prices of the targeted stock. Incorporated various global economic indicators and market data to enhance the accuracy of our predictions. Investment Strategy Recommendation:
Analyzed predicted stock prices to suggest optimal investment strategies. Provided daily recommendations to clients, advising them on whether to Buy, Hold, or Sell the stock to maximize their returns. Tools and Techniques:
Data Analysis and Preprocessing: Identified and handled null values to ensure data integrity. Visualized data using scatterplots and boxplots to understand distributions and identify outliers. Conducted seasonality checks to understand periodic patterns in the data. Forecasting and Modeling: Used ARIMA (AutoRegressive Integrated Moving Average) for predicting closing prices. Calculated the 50-day moving average to smooth out short-term fluctuations and highlight longer-term trends. Employed Random Forest and K-Nearest Neighbors (KNN) classifiers to train and test models for improved predictive accuracy. Outcome:
Delivered a predictive tool that significantly improved the accuracy of stock price forecasts. Enabled clients to make data-driven investment decisions, enhancing their portfolio performance. Role and Contributions:
Performed comprehensive data preprocessing, including null value identification and data visualization. Conducted seasonality checks to capture trends and patterns in stock price movements. Implemented ARIMA for time series forecasting and calculated the 50-day moving average for trend analysis. Developed and fine-tuned machine learning models (Random Forest and KNN) for training and testing predictions. Designed a decision support system to translate model outputs into actionable investment advice. This project showcases our ability to combine financial expertise with advanced data analytics to support high-stakes investment decisions, reflecting our commitment to client success and innovative financial solutions.