Under Margin Pressure: How to Generate Alpha through Market Data Cost Optimisation

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Written by: Liqueo

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Margin pressure is increasingly challenging asset and wealth managers, significantly impacting operations, profitability, and long-term viability. This pressure is compounded by several factors, including:

  • Intense competition arising from the widespread availability of low-cost alternatives and the disruptive entry of FinTech players can significantly erode firms’ revenue margins. If firms cannot generate adequate revenue to cover their operating expenses, it can lead to a considerable decline in their profitability.
  • Regulatory pressures for fee transparency and compliance such as MiFID II impose additional burdens on firms. Compliance efforts require significant resources and investment, diverting attention and funds from revenue-generating activities. Failure to meet requirements can result in fines, reputational damage, and loss of client trust, exacerbating fee compression challenges.
  • Investing in technology to keep up with changing market dynamics can put a strain on profitability. While digitalisation can provide opportunities for improving efficiency and enhancing client experiences, the initial investment costs and ongoing maintenance expenses can be a burden for firms, especially smaller players with limited resources.
  • Investor preferences are changing and they are now more interested in value-driven fees and self-directed investing. This poses a significant threat to the revenue streams of firms. If firms fail to meet the evolving expectations of investors for personalised, cost-effective services, they may lose clients and market share. This can further erode the profitability and competitiveness of the firms.

Our latest series, Under Margin Pressure, tackles this challenge and offers actionable advice to help organisations alleviate pressure while also boosting revenue across their operations. The first installment dives into data, specifically on how to achieve alpha through market data cost optimisation.

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