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Inalytics seeks to revolutionise manager selection with skills-based data model

Inalytics, the leading experts in analysing investment performance, is seeking to revolutionise the manager selection process with a new data-driven model that helps asset owners see the difference between luck and skill.

The model, developed by Inalytics through analysis of $22 trillion of trades in a database it has built over 20 years, uses data science to quantify the impact of the decisions that asset managers make when running the concentrated, bottom-up portfolios that dominate the investment landscape today. For the first time in the modern era, it means pension funds and other institutional asset owners can measure the investment skills that really matter when choosing managers to run mandates.

“Traditional attribution models were introduced in the 1980s when portfolios typically had around 160 holdings and managers would allocate to a sector or region and then select the ‘best’ stocks within it,” says Rick Di Mascio, CEO of Inalytics.

“Fund management has moved on since then but attribution has not. Today, the average portfolio holds less than 40 stocks and managers choose companies purely because they want to own them. Traditional attribution is simply not designed to measure this way of running money, which means manager selection has become a risky pursuit – leaving pension funds and other institutional asset owners without the critical analysis needed to help them select the right managers for the job.

“A new model that reflects the way decisions are now being taken is desperately needed. Our new data-based framework not only analyses the specific skills that drive returns today, it also quantifies their precise contribution to overall performance, showing asset owners exactly how good managers are at making the decisions that matter in modern investment portfolios.”

The model, ‘DECSIS’, analyses the four core investment decisions and processes – or ‘alpha drivers’ – Inalytics has found to have the potential to generate alpha in equity portfolios: stock picking, sizing positions, trading activity and holding periods.

By analysing these four drivers and showing how they contribute to the total performance of a portfolio, the model allows asset owners to see precisely where asset managers add or detract value, facilitating more efficient manager searches and better-informed due diligence and monitoring exercises.

“Asset owners want to pay for skill not luck and we have seen a notable shift in demand for tools that help them more effectively and efficiently screen and monitor asset managers,” says Di Mascio. “They want control and to have access to the metrics that tell them how a track record was actually generated, allowing them to ask managers the questions that really matter – and in some cases the ones they would rather not be asked.”

The model can also be used as an evaluation and coaching tool by fund managers, providing them with granular insight into the decisions they need to improve to enhance performance.

“Active fund managers are looking to fight back against the competitive pressures they face from passive funds offering cheap exposure to equity markets,” says Di Mascio. “If active managers can’t compete on price, they must compete on performance, but their ability to do so is limited unless they know what they need to improve.” 

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