AI in Finance: Quantitative Analysis

Applications of AI in Finance

Stock price prediction and trading signals

Credit risk assessment

Fraud detection

Portfolio optimization

Financial document analysis

Customer churn prediction

Sentiment Analysis for Markets

python
from transformers import pipeline
import pandas as pd
Financial sentiment analysis
sentiment_pipeline = pipeline(
    "text-classification",
    model="ProsusAI/finbert"  # Trained on financial text
)
news_headlines = [
    "Apple reports record quarterly earnings",
    "Bitcoin crashes 20% amid regulatory fears",
    "Fed raises interest rates unexpectedly"
]
for headline in news_headlines:
    result = sentiment_pipeline(headline)[0]
    print(f"{headline[:50]}... -> {result['label']}: {result['score']:.3f}")

Price Prediction with LSTM

python
import yfinance as yf
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import torch.nn as nn
Download historical data
data = yf.download('AAPL', start='2020-01-01', end='2024-01-01')
prices = data['Close'].values
Normalize
scaler = MinMaxScaler()
scaled_prices = scaler.fit_transform(prices.reshape(-1, 1))
Create sequences for LSTM
def create_sequences(data, seq_len=60):
    X, y = [], []
    for i in range(seq_len, len(data)):
        X.append(data[i-seq_len:i, 0])
        y.append(data[i, 0])
    return np.array(X), np.array(y)

Portfolio Optimization

python
import scipy.optimize as opt
def optimize_portfolio(returns: pd.DataFrame, risk_tolerance: float) -> dict:
    n = len(returns.columns)
    
    def negative_sharpe(weights):
        portfolio_return = np.sum(returns.mean() * weights) * 252
        portfolio_std = np.sqrt(
            np.dot(weights.T, np.dot(returns.cov() * 252, weights))
        )
        return -(portfolio_return - 0.02) / portfolio_std  # Risk-free: 2%
    
    constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1}]
    bounds = [(0, 1)] * n
    
    result = opt.minimize(
        negative_sharpe,
        x0=np.ones(n) / n,
        bounds=bounds,
        constraints=constraints
    )
    
    return dict(zip(returns.columns, result.x))

Risk Management with AI

Value at Risk (VaR) prediction

Tail risk modeling with Monte Carlo simulation

Correlation breakdown detection

Regulatory Considerations

Model explainability requirements (EU AI Act for high-risk financial applications)

Backtesting with market stress scenarios

Model governance and documentation

Overfitting risk in financial ML