Merging Advanced Data Science with Financial Insights for Strategic Investment Decisions In this post, I introduce an approach that combines data science and financial analysis to more accurately predict stock prices. Here's a quick rundown of our method:
Spotting Trends: We smooth out stock prices to clearly see trends, using a technique called cubic spline interpolation. Grouping Stocks: Similar stocks are grouped together based on these trends, making it easier to analyze them as a collective. Building Models: Select a group, we create models that predict future stock returns. Explain Key Drivers: We use SHAP values to understand what factors most influence our predictions. Choosing the Best Stocks: Through Response Surface Methodology (RSM), we pinpoint the stocks with the highest potential.
Additionally, this method offers new insights into key financial concepts: Addressing Time Series Limitations: Our method navigates the complexities of time series analysis, providing a stable and reliable alternative that accounts for the Efficient Market Hypothesis. Sharpe Ratio Reimagined: We explore an enhanced version of the Sharpe Ratio, improving how we measure returns against risk.
My goal is to create a thorough and effective system for predicting stock prices, blending in-depth analysis with informed decision-making strategies.