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risk modeling

Beyond beta: Navigating the factor zoo and the evolution of risk modeling in finance

Effective risk modeling combines fundamental factors (Value, Momentum), statistical models (PCA), and exposure factors to capture systematic and hidden portfolio risks.

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Data Budget Optimization for Asset Management Firms

From waste to wealth: mastering data budget optimization in asset management

Combat rising data costs through governance, deduplication, usage monitoring and integrated platforms—transforming budget waste into competitive advantage.

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Portfolio optimization for wealth management

Portfolio optimization for wealth management: delivering personalized portfolios at scale

Wealth managers face dual-level optimization: aligning each client portfolio with the firm's house view while respecting individual constraints and preferences.

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Data Governance

Transforming data into an asset: Achieving single source of truth and data quality in investment firms

Establish a single source of truth and robust data governance to transform your data into a competitive advantage and generate alpha.

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backtesting

The critical pitfalls of backtesting trading strategies: a complete guide

Avoid the seven deadly sins of backtesting—from survivorship bias to overfitting—and bridge the gap between backtest results and live trading performance.

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Portfolio decarbonization

Decarbonization for asset managers: Shifting from reducing financed emissions to financing emission reduction

True net zero investing demands forward-looking metrics and dynamic pathways to actively finance emission reductions, moving beyond static footprints.

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Point-in-time data

Point-in-time data: The critical foundation for investment decision-making

Time-stamping prevents look-ahead bias in backtesting by capturing when information became available, not just its value—essential for accurate alpha generation and compliance.

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AI/ML Investment Strategies

Beyond automation: structuring data and technology to unleash the full potential of AI/ML investment strategies

Data quality and unified architecture are the foundation for successful AI/ML deployment—algorithmic sophistication means nothing without clean, integrated data.

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Advanced Portfolio Optimization

Portfolio optimization: From linear to conic – Democratizing advanced investment strategies

Conic optimization encompasses linear and quadratic methods while enabling advanced strategies like Sharpe Ratio maximization—now accessible without deep math.

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portfolio optimization

Portfolio optimization: 3D investing – navigating the complexities of sustainable portfolio construction

Exploring how modern portfolio optimization balances returns, risk, and sustainability through advanced mathematical techniques and multi-objective frameworks.

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Passive investment

The art of passive investment: Mastering the optimization trade-off between tracking error and cost control

Successful passive investing requires sophisticated optimization balancing tracking error and costs—using sampling, rebalancing algorithms, and factor models.

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