Blueprint for a Truly Multi-Asset Trading Desk – an Asian Perspective

I recently had the pleasure of participating on a panel at this year’s FIX South East Asia Multi-Asset Trading Conference in Singapore. Along with a highly knowledgeable panel of experts including Michael Bok (Schroders), Julien de Jaillon (PIMCO), and Matthew McLoughlin (Prospera Fund Partners), and Roland de Marsangy (Liquidnet), the session explored the topic of a “Blueprint for the Truly Multi-Asset Trading Desk”. Discussion focused on what it really takes for buy-side institutions to operate in a world where asset classes are increasingly interconnected, liquidity is more fragmented, and market structure more electronic. 

A key theme that quickly emerged was that multi-asset isn’t defined by how many asset classes a desk trades, but by how integrated the firms processes, data, technology, and decisioning truly are. A genuinely multi-asset desk has a unified view of market impact, liquidity, and risk, enabling traders to make decisions in a cross-asset context rather than in isolated silos. It’s an operating model — not an inventory list. 

Why is the Industry Shifting?

The panel discussed the key drivers pushing firms toward a more integrated approach to multi-asset trading desks. Cost and margin pressure have been a significant factor, but there are multiple other reasons why asset managers have adopted this approach: 

• Growing asset-class correlation and product interconnectedness 

• Increased regulatory scrutiny and best execution demands across all asset classes 

• The rise of systematic strategies, which depend on cross-asset consistency 

• Market electronification enabling a more unified approach to execution  

• Technology enablement, which is finally making multi-asset workflows achievable 

Against this backdrop, many firms still stumble at the first step. The common mistake? Thinking the answer is hiring “the right trader” or buying “the right system.”  The starting point for a successful approach to integrated multi-asset trading must be a unified target operating model — spanning investment strategy, workflow, data architecture, compliance, and reporting. Technology and staffing are execution decisions that fit after the operating model has been designed, not before. 

Cultural and Operational Challenges

Multi-asset transformation is as much organisational as it is technological. The panel discussion highlighted several recurring hurdles: 

• Generalist vs. specialist mindset — teams need both breadth and depth 

• Individual vs. organisational incentives — alignment matters, incentives drive behaviour and help cement change 

• Catering for high touch vs. low touch — particularly in the ASEAN region where some markets and asset classes are less liquid but offer interesting opportunities 

• Trading system interoperability and cross-desk coordination 

• Data normalisation, particularly when stitching together front-to-back architectures across different asset classes 

Without a unified data layer, even the most sophisticated execution tools will struggle to scale. Enabling seamless data flows across fragmented trading systems and applications is essential to pre-trade analytics and decision making and will reduce the cost and complexity of post-trade processes, minimising costly errors. 

Technology Barriers Still Holding the Industry Back

From Rapid Addition’s perspective, the largest roadblocks to seamless multi-asset execution remain: 

• The absence of a unified data model and security master 

• Fragmented OMS/EMS workflows with no orchestration layer 

• Siloed pre-trade analytics and inconsistent TCA across assets 

• Protocol-specific connectivity challenges when connecting across exchange traded and OTC markets and counterparties 

These are solvable — but only with modular, interoperable infrastructure rather than monolithic and siloed legacy systems.  

Fixed Income’s Electronification: Opportunity and Complexity

The panel also examined the rapid electronification of fixed income. The market is moving from opaque, episodic, dealer-driven trading to a more transparent, data-rich environment enabled by RFQ, all-to-all models, streaming markets, and ETF-related liquidity. 

Platforms like Neptune and MarketAxess are transforming pre-trade transparency by delivering structured, actionable dealer axes — improving liquidity discovery, reducing information leakage, and enabling equity-like automation. But challenges remain: protocol fragmentation, variable data quality, and the need to upskill traders and platforms for far more electronic FI workflows. 

Traders and AI: A New Human–Machine Partnership

The panel agreed that AI is an opportunity not a threat to the buy-side trader. As automation grows, the buy-side trader’s role will evolve from order executor to risk manager, workflow architect, and strategic advisor. AI will increasingly act as a co-pilot — predicting liquidity, detecting anomalies, and dynamically selecting algos — while humans handle the high-value judgment exceptions and cross-asset context. Ultimately this should drive productivity and better results. 

What’s the Next Disruption in Multi-Asset Trading?

Digital assets are gaining traction and will become a natural extension of the multi-asset trading desk. However, this will be evolutionary rather than revolutionary. AI tools will continue to evolve and drive efficiencies as they are starting to do now, but the true disruption could be the next generation of an AI-native, cross-asset execution fabric: embedded capabilities such as autonomous orchestration, real-time liquidity prediction, dynamic algos, and machine-readable trading intent integrated across the investment lifecycle with a human ‘pilot’ at the centre. That would be an exciting future. 

But whatever the future direction, firms must start by unifying their data, modernising their architecture, and reskilling their teams if they want to optimise their approach to multi asset trading.