Alternative Data Driven Strategies

Alternative investment that aims to be decorrelated, finding different sources of alpha and coordinating them with each other is the challenge. Applying science in the service of financial advisory.

Why?

Why?

In an increasingly globalized world, any small negative event can end up causing sharp declines in stock markets around the world; that's why data is becoming even more important in finding new sources of excess return with little correlation to the market.

How?

How?

Finding different sources of alpha and coordinating them with each other to build a balanced portfolio, without exposure to market beta. Looking for patterns across the noise of alternative data.

What?

What?

Long/Short leveraged equity strategy with ESG focus, which relies on the use of alternative data to generate market-uncorrelated returns. With options trade on top.

Real track-record

Portfolio
Total Return 30.9 %
Annualized Return 10.7 %
Annualized Volatility 8.6 %
Annualized Sharpe 1.2
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Long/Short Market Neutral

Alternative Data

Alternative Data

Data-driven investment strategy that analyze complex data sets through quantitative methods to deliver a truly differentiated and uncorrelated investment solution.

Fundamental Analysis

Fundamental Analysis

Quantitative research process for identifying quality, ESG-integrated, companies for the long leg. While offseting risks embedded in the long book by shorting companies with no exposure to any factor premium.

Zero Beta

Zero Beta

Disciplined risk management algorithm that avoids over-exposure to any security, sector or factor, generating an enduring source of alpha.

Whatever the Weather

The strategy provides exposure to equities without assuming all of the associated risks. The strategy seeks to exploit investment opportunities in both bull and bear markets to generate steady returns, whatever the weather.

100% systematic methodology

Quality factor bias on the long leg

No-factor exposure on the short leg

Options trades on top

Short Book Construction

We build the short book to hedge market risk and to source additional alpha.

The Machine

The Machine

A machine learning algorithm is applied to financial data to identify securities with high probability of underperformance in the next 5-60 days.

Filter Out Risky Companies

Filter Out Risky Companies

We filter stocks with high borrow costs that tend to have high volatility and risk, as well as stocks that potentially have high ESG ratings.

Market Neutral

Market Neutral

To hedge market risk, we optimize the short portfolio weights to achieve near beta-neutrality for the portfolio.

Related ETS papers

AI case study: Long/Short Strategy
ESG 2022: “greenwashing” or alpha source?
Factor contribution