Quantvest | Our business is replication of customized hedge fund portfolios
page-template,page-template-full_width,page-template-full_width-php,page,page-id-15741,ajax_fade,page_not_loaded,,qode-theme-ver-9.2,wpb-js-composer js-comp-ver-5.4.5,vc_responsive

Our business is replication of customized hedge fund portfolios

What is Replication?

Replication is a process of tracking returns of non-investable benchmarks using liquid securities. An example of replication would be the SPY exchange-traded fund which tracks the S&P500 index. In this example, replication is achieved by simply buying every constituent of the index in the right proportions.

Why is Hedge Fund Replication Difficult?

Traditional replication techniques result in playing catch-up with returns. Additional errors may arise from changes in leverage, use of derivatives and exotic instruments, and rapid trading.

Applying Disruptive Technology

At Quantvest, our research team has decades of experience in analyzing hedge fund styles. With Dynamic Style Analysis, we are using a disruptive technology that has successfully aided identification of fraudulent trading, sudden leverage changes, style drifts, and other important risk measures for over a decade. The DSA™ technology, coupled with replication-specific quantitative models, has been able to generate extremely accurate and dynamic results.

Replicating Hedge Funds

How do you replicate a benchmark where the constituents cannot be directly bought? Hedge Fund benchmarks would be one such example.

In these cases, a quantitative process known as “factor analysis” is used to discern which market factors explain benchmark returns. Once completed, a replication portfolio is assembled using liquid market factors.

Dynamic Style Analysis™ (DSA)

DSA is a true dynamic model, able to overcome the challenges of hedge fund replication. It is designed to handle rapidly trading, leveraged portfolios. It is also applicable to funds with short track records. A unique forecasted measure, Predicted R-Squared assists in factor selection, error detection, and prevents over-fitting.

For more information on DSA and its development, click here.