HedgeNordic’s Systematic Strategies & Quant Trading report explores how systematic investing continues to evolve in an increasingly complex, data-driven, and rapidly changing market environment. Built around the idea that robust processes matter more than intuition in modern markets, this issue examines the role of quantitative research, machine learning, portfolio construction, execution efficiency, and systematic risk management in generating returns and improving diversification. Through contributions from investors, researchers, and asset managers, the publication highlights how structured frameworks can help investors navigate uncertainty, harvest behavioral inefficiencies, and uncover new sources of alpha.
Please find the report here. Happy reading!
Bjarne Graven Larsen, Founder and CEO of Qblue Balanced, opens the publication by arguing that institutional investors need to move beyond the traditional 60/40 portfolio. In “Beyond 60/40: The Case for Liquid, Systematic Diversification,” Larsen contends that the traditional stock-bond portfolio has become less effective as correlations between equities and bonds have become less reliable. He presents liquid, market-neutral alternative risk premia strategies as a potential third building block that can provide diversification, transparency, and liquidity while improving risk-adjusted returns.
Christopher Reeve, Chief Investment Officer at Aspect Capital, then explains how the firm has adapted its systematic investment framework to China’s unique futures markets. In “An Innovative, Differentiated Multi-Strategy Programme Tailored to Local Markets,” Reeve discusses how shorter-term market dynamics, high retail participation, and a commodity-heavy opportunity set have led Aspect to develop a balanced combination of momentum, technical, term-structure, and value strategies specifically designed for Chinese markets.
Scott Schefrin, Portfolio Manager at AB Hedge Fund Solutions, examines why “A Rules-Based Approach Matters More Than Ever” in merger arbitrage. He argues that improving M&A activity, lower deal break rates, and a more supportive regulatory environment have created a favorable backdrop for the strategy. In a market where deal outcomes have become more predictable and dispersion between transactions has narrowed, Schefrin contends that systematic, rules-based implementation allows investors to diversify risk across a broad universe of deals, reduce concentration risk, and capture the merger arbitrage risk premium more efficiently than discretionary approaches.
Liam Hynes of S&P Global Market Intelligence contributes “Not So Lazy Prices,” exploring how advances in machine learning and natural language processing are transforming the use of unstructured data in investing. He argues that the next generation of alpha generation increasingly relies on extracting insights from earnings calls, regulatory filings, and corporate disclosures, as traditional factors and alternative datasets become more widely understood and efficiently priced.
In “The Hidden Beta in LLM Recommendations,” Hafnium Capital investigates how large language models can introduce hidden biases into investment recommendations. Victor Brassart and Dan Edelstein demonstrate that seemingly minor prompt changes can materially influence model outputs and asset preferences, suggesting that prompts themselves may represent a source of systematic exposure that investors need to understand, test, and manage.
Fredrik Langenskiöld, Senior Investment Specialist at Union Bancaire Privée, explores the growing popularity of systematic multi-strategy funds in “Systematic Multi-Strategy as a Portfolio Diversifier.” He explains how these portfolios combine multiple uncorrelated systematic strategies across liquid global markets to create more resilient return streams, offering investors diversification beyond traditional trend-following and other single-strategy approaches.
John Twomey of Abbey Capital discusses “The Benefits of Multi-Manager Portfolios in CTA Investing.” He argues that despite apparent similarities among trend-following managers, significant differences exist in signal design, execution, portfolio construction, and risk management. As a result, diversified multi-manager portfolios can help investors capture a broader opportunity set while reducing manager-specific risks and improving portfolio robustness.
Andrew Beer, Managing Partner at DBi, examines whether implementation efficiency has become the most important source of alpha generation for CTAs. In “CTAs and Alpha Generation: Is Efficient Implementation the Answer?”, Beer argues that rising trading costs and increasing strategy complexity may have eroded industry-wide Sharpe ratios. He suggests that more efficient implementation, including portfolio compression and factor-based replication techniques, may ultimately create greater value for investors than simply adding additional markets or models.
Jørgen Jordfald interviews veteran investor Peter Warren in “Edge Hunting Across Eras.” Reflecting on more than four decades in financial markets, Warren discusses how market edge has evolved from simple technological advantages and proprietary datasets to advanced quantitative models and large-scale computing. Despite these changes, he argues that curiosity, adaptability, and humility remain essential characteristics for identifying and maintaining an investment edge.
Alexander Mende and Per Ivarsson of RPM Risk & Portfolio Management challenge conventional manager-selection practices in “Finding Alpha: Strategy vs. Manager Selection.” They argue that investors should focus less on identifying supposedly superior managers and more on selecting attractive strategies. Using CTA data, they demonstrate how apparent manager outperformance often fails to persist over time and suggest that portfolio construction should emphasize strategy exposure rather than star-manager selection.
Linus Nilsson of NilssonHedge explores the debate surrounding manager dispersion in trend-following strategies in “Dispersion – High or Low? It Depends.” Using a large database of CTA managers, Nilsson shows that dispersion can appear either elevated or subdued depending on the measurement methodology and time horizon. While short-term divergence is often driven by positioning around major market events, longer-term performance differences may reveal more meaningful structural distinctions among managers.
The publication concludes with Thomas Babbedge of GreshamQuant, who explores “Horse Racing and Maretocracy.” Drawing parallels between horse racing, meritocracy, and systematic investing, Babbedge examines how investors assess skill, persistence, and performance in competitive environments. He argues that distinguishing genuine ability from luck remains one of the central challenges in investment management and highlights the importance of robust evaluation frameworks when identifying sustainable sources of alpha.
Together, the articles illustrate how systematic investing continues to evolve from simple trend-following models into a diverse ecosystem of quantitative strategies spanning alternative risk premia, machine learning, merger arbitrage, multi-strategy portfolios, and advanced portfolio construction techniques. While the tools and datasets continue to change, the central theme remains consistent throughout the publication: successful systematic investing is ultimately less about predicting the future and more about building robust frameworks for navigating uncertainty.
