Quick Start

Python
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
import statsmodels.api as sm

# Load and prepare data
data = pd.read_csv('stock_data.csv')
features = ['volume', 'sentiment_score', 'platform_mentions', 'technical_indicators']
X = data[features]
y = data['next_period_return']

# Split data maintaining temporal order
split_point = int(len(data) * 0.8)
X_train, X_test = X[:split_point], X[split_point:]
y_train, y_test = y[:split_point], y[split_point:]

# Test hypothesis with ML model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

print(f"R²: {r2_score(y_test, predictions):.4f}")
print(f"Feature Importance: {dict(zip(features, model.feature_importances_))}")

Workflow

Progress:

• Hypothesis Formation: Define testable research question
• Data Collection: Gather stock prices, user data, platform metrics
• Feature Engineering: Create predictive variables and technical indicators
• Exploratory Analysis: Examine distributions, correlations, stationarity
• Model Selection: Choose appropriate AI/ML/DL approach
• Backtesting: Test on out-of-sample data maintaining temporal order
• Statistical Validation: Perform significance tests, robustness checks
• Economic Significance: Assess practical importance beyond statistical significance
• Documentation: Record methodology, assumptions, limitations

Data Preparation:

• Handle survivorship bias using point-in-time datasets
• Address look-ahead bias in feature construction
• Winsorize outliers at 1st/99th percentiles
• Check for data snooping across multiple tests

Model Validation:

• Use walk-forward analysis for time series
• Apply Bonferroni correction for multiple testing
• Implement cross-validation respecting temporal structure
• Calculate Sharpe ratios and maximum drawdown

Examples

Example 1: Social Sentiment Impact Input: Twitter sentiment scores, Reddit mentions, stock returns Output:

Hypothesis: Social sentiment predicts next-day returns
Model: LSTM with sentiment features
Results: Significant coefficient (p<0.01), 0.12% daily alpha
Economic significance: 31.2% annual Sharpe ratio improvement

Example 2: Platform Trading Volume Input: Robinhood user holdings, trading volume, price movements
Output:

Finding: 10% increase in retail platform holdings → 2.3% price increase
Methodology: Panel regression with fixed effects
Robustness: Significant across 95% of bootstrap samples
Publication: "Retail Trading and Stock Prices" - Journal of Finance

Example 3: Deep Learning Price Prediction Input: High-frequency price data, order book, news sentiment Output:

Architecture: CNN-LSTM hybrid model
Features: 50 technical indicators + NLP sentiment scores  
Performance: 67% directional accuracy, 1.8 Sharpe ratio
Validation: 3-year walk-forward backtest, transaction costs included

Best Practices

Research Design:

• Pre-register hypotheses to avoid data mining
• Use established asset pricing factors as benchmarks
• Report both in-sample and out-of-sample results
• Include transaction costs in performance metrics

Data Quality:

• Verify data integrity with cross-references
• Handle corporate actions (splits, dividends) properly
• Use CRSP/Compustat standards for academic rigor
• Document all data preprocessing steps

Statistical Rigor:

• Apply Newey-West standard errors for autocorrelation
• Use Fama-MacBeth procedure for cross-sectional tests
• Report bootstrap confidence intervals
• Conduct robustness tests across subperiods

Model Implementation:

• Implement proper cross-validation for financial time series
• Use ensemble methods to reduce overfitting
• Apply regularization (L1/L2) for feature selection
• Monitor model stability across market regimes

Common Pitfalls

Temporal Data Leakage:

• Using future information in feature construction
• Incorrect train/test splits that break temporal order
• Forward-filling missing data inappropriately

Statistical Issues:

• Multiple testing without correction
• Ignoring heteroscedasticity in residuals
• Assuming normal distributions without testing
• Cherry-picking significant results

Economic Realism:

• Ignoring transaction costs and market impact
• Testing on unrealistic position sizes
• Overlooking short-selling constraints
• Missing market microstructure effects

Data Problems:

• Survivorship bias in stock selection
• Point-in-time data availability issues
• Inconsistent data frequencies across sources
• Missing adjustment for stock splits/dividends

Overfitting Indicators:

• Dramatic performance difference between in-sample and out-of-sample
• Models with hundreds of parameters and few observations
• Perfect or near-perfect in-sample fits
• Strategies that work only in specific time periods