Completed

Cost-Efficient Alpha

Citi Global Market Challenge 2026• 2026.04Portfolio Strategy, Data Analysis & Investment Pitch
Portfolio Strategy
Long/Short Fund
Asset Allocation
Transaction Costs
Commodities
Fixed Income
Risk Analytics
Scenario Analysis
Citi GMC

Multi-Asset Portfolio Strategy · Long/Short Allocation · Transaction Cost Optimisation · Risk Management

Project Overview

A multi-asset portfolio strategy created for the Citi Global Market Challenge 2026. The project proposes a transaction-optimised long/short portfolio benchmarked against Fund X. The strategy allocates capital across equities, fixed income, commodities, FX, and cash, with the objective of outperforming Fund X over a three-month horizon. The core thesis is “Cost-Efficient Alpha”: alpha is generated not only through asset selection, but through disciplined capital allocation after transaction costs. The final portfolio returned 19.95% versus Fund X’s 9.74%, generating approximately +10.2% active alpha, with a Sharpe ratio of 2.82.

Key Metrics

Active Alpha

+10.2%

Portfolio Return

19.95%

Fund X Return

9.74%

Sharpe Ratio

2.82×

AUM

$500MM

Transaction Costs

0.24%

Key Analysis Areas

Cost-Efficient Alpha

The strategy focuses on net returns after transaction costs, not just gross performance. That became the central differentiator of the pitch.

Commodity-Led Outperformance

The portfolio overweighted commodities to 70%, capturing the strongest 3-month momentum and the lowest transaction-cost alpha opportunity.

Transaction Cost Discipline

The strategy trades more aggressively where costs are lower, while avoiding high-cost segments that would dilute the net return edge.

Risk-Adjusted Performance

Despite higher volatility, the portfolio achieved stronger risk-adjusted returns, with Sharpe 2.82 versus Fund X’s 2.50.

Strategy Framework

Macro Conviction

Slowing growth, sticky inflation, rate divergence, and commodity momentum form the macro foundation of the strategy.

Active Allocation

The portfolio reallocates risk toward the strongest alpha source in the dataset while keeping the structure transparent.

Risk Control

Hedge layers protect against equity crashes, commodity reversals, and rate shocks while preserving upside.

What I Did

  • Built the core portfolio thesis around cost-efficient alpha and transaction-cost-aware capital allocation.
  • Analysed time series data for equities, fixed income, and commodities to calculate 3-month returns, volatility, correlations, drawdowns, and portfolio performance.
  • Constructed the final long/short allocation across equities, fixed income, commodities, and tactical FX overlay.
  • Benchmarked the strategy against Fund X, which used a more conventional diversified allocation.
  • Developed the performance attribution logic showing why commodities were the dominant source of alpha.
  • Created the transaction cost framework, highlighting why low-cost assets should receive higher trading activity.
  • Designed the risk management framework, including equity crash hedge, commodity reversal hedge, and rate shock hedge.
  • Structured the final pitch deck with executive summary, macro thesis, portfolio construction, alpha attribution, risk analytics, scenario analysis, and research support.

Reflection

This project strengthened my ability to combine investment strategy, quantitative analysis, and institutional-style presentation. The most important learning was that a strong portfolio pitch is not only about identifying high-return assets. It also requires understanding transaction costs, turnover, risk concentration, and whether an active bet is justified by both data and macro conviction. Through this case, I learned how to translate raw time series data into a clear investment recommendation: long commodities, reduce equity beta, short fixed income, and use FX as a tactical hedge. I also improved my ability to build a professional investment deck that explains not only what the portfolio does, but why the strategy should outperform after costs.