Building Market-Neutral Long-Short Portfolios from Ranked Assets

Summary

The document explains a basket strategy that ranks assets by an expected-return signal, buys the highest-ranked group, and shorts the lowest-ranked group with equal dollar exposure. The intended market neutrality reduces sensitivity to broad market moves, while returns depend on whether the ranking separates stronger from weaker assets. Candidate ranking inputs include momentum, technical measures, pricing models, and fundamental value factors.

A synthetic example assigns future returns partly from a generated factor and compares average returns across ranked baskets; a separate example outlines momentum research on a sample of S&P 500 stocks. The article also recommends testing whether assets exhibit momentum or mean reversion, choosing rebalance timing to match the signal horizon, and monitoring factor decay. These examples illustrate the approach but do not establish a live, cost-adjusted performance record. Equal weighting may be difficult when assets require integer quantities, and transaction costs, capital requirements, and the quality of the ranking model can materially limit results.

Key ideas

• Rank assets by an expected-return signal, then take equal-dollar long and short baskets.
• The strategy’s performance depends primarily on whether the ranking distinguishes future winners from losers.
• Test whether a factor predicts momentum or mean reversion across assets and time horizons.
• Match rebalancing frequency to the signal horizon while guarding against overfitting.
• Integer trading constraints, transaction costs, and capital capacity affect practical implementation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.