Quantitative Portfolio Management: Long-Short and Market Neutral

Module: Fintech AI | Difficulty: Advanced

Market Neutral

Long-Short

Factor Neutral

where = factor loadings.

Portfolio Construction

import numpy as np
from scipy.optimize import minimize

class LongShortPortfolio:
    def __init__(self, alpha, cov, beta, risk_aversion=1.0):
        self.alpha = alpha; self.cov = cov; self.beta = beta; self.lam = risk_aversion
    def optimize(self):
        n = len(self.alpha)
        def neg_utility(w):
            return -(w @ self.alpha - self.lam/2 * w @ self.cov @ w)
        constraints = [
            {'type': 'eq', 'fun': lambda w: np.sum(w)},  # market neutral
            {'type': 'eq', 'fun': lambda w: w @ self.beta}  # factor neutral
        ]
        bounds = [(-0.05, 0.05) for _ in range(n)]
        w0 = np.zeros(n)
        result = minimize(neg_utility, w0, bounds=bounds, constraints=constraints)
        return result.x

Research Insight: Market neutral portfolios eliminate market risk, isolating alpha. The key challenge is that factor exposures change over time, requiring ongoing rebalancing. Transaction costs can consume 50%+ of alpha in high-turnover market neutral strategies.