Trying to build an ML model to predict stock returns using financial ratios — which features should I focus on?

Hey everyone,

I’m working on a small ML project where I’m using yearly financial statement data (multiple companies across different sectors) to predict future stock returns / price movement.

Right now I have features like:

• EPS
• PE ratio
• Total assets
• Total debt
• Shareholders’ equity
• Debt/Equity
• Cash ratio
• Inventory
• Receivables
• Shares outstanding

I’m planning to:

• Create future return as target (shifted price)
• Use time-based train/test split
• Try tree models like RandomForest / XGBoost

From your experience, which financial ratios tend to be more useful for this kind of model?

Should I focus more on:

• Profitability metrics?
• Leverage?
• Liquidity?
• Growth-related features instead of raw values?

Also, is it generally better to use raw balance sheet values or engineer more ratios?