Robeco logo

Disclaimer

The information contained in the website is solely intended for professional investors. Some funds shown on this website fall outside the scope of the Dutch Act on the Financial Supervision (Wet op het financieel toezicht) and therefore do not (need to) have a license from the Authority for the Financial Markets (AFM).

The funds shown on this website may not be available in your country. Please select your country website (top right corner) to view more information.

Neither information nor any opinion expressed on the website constitutes a solicitation, an offer or a recommendation to buy, sell or dispose of any investment, to engage in any other transaction or to provide any investment advice or service. An investment in a Robeco product should only be made after reading the related legal documents such as management regulations, prospectuses, annual and semi-annual reports, which can be all be obtained free of charge at this website and at the Robeco offices in each country where Robeco has a presence.

By clicking Proceed I confirm that I am a professional investor and that I have read, understood and accept the terms of use for this website.

Decline

Quantitative investing

LASSO regression

LASSO is an acronym that stands for ‘least absolute shrinkage and selection operator’. It is associated with a machine learning technique – LASSO regression – that performs both shrinkage and variable selection to simplify linear regression models and prevent overfitting.


glossary-lasso-regression.png

Where

λ is amount of shrinkage or penalty
λ = 0 implies all features are considered as no parameters are eliminated
λ = ∞ implies no feature is considered

A linear regression allows you to determine if there is a relationship between variables. For example, it can quantify the relationship between a dependent variable (crop yields) and explanatory variables (soil fertility,temperature, water quality, etc.). But in cases where there are many candidate variables to explain crop yields, the statistical model can become complex and difficult to process.

The LASSO regression is helpful in such instances as it can select variables based on their importance. This is achieved through a process called shrinkage, a method which imposes a penalty to reduce the absolute size of the regression coefficients. Although reduced in magnitude, the most important variables will continue to reflect material coefficients, while the less-contributing variables will exhibit values close to zero or even zero.

Through this process, it identifies which variables to keep and which ones to exclude, based on the size of their coefficients. Using our example, the technique would gradually select the variables which best predict crop yields, beginning with the most important one before working its way through the list. At some point, adding more variables would no longer improve the prediction accuracy of the model sufficiently, but instead it would add substantial complexity.

Therefore, the technique allows you to simplify a model by reducing the number of parameters in a regression and precluding potential data noise. It also enables you to guard against overfitting by eliminating variables with little explanatory power, potentially making the model more robust across different datasets. Additionally, it can help optimize models with high multicollinearity as it can choose between correlated explanatory variables.

In general, the LASSO regression is a basic machine learning (ML) technique that can be used for many applications. It is essentially a standard linear regression with a slight twist. Contrary to more sophisticated ML techniques, however, it is not able to pick up non-linear relationships between variables.

For our quant investing platform, it has the potential to help fine-tune models by assisting us with variable selection. For instance, we have used it to select company characteristics that have linear predictive value for risk and returns. We have also used it to identify which industries lead or lag others in terms of returns.


See also

Capital Asset Pricing Model
Random forest
Efficient (advanced) approach


Next-generation quant

As technology advances, so do the opportunities for quantitative investors. By incorporating more data and leveraging advanced modelling techniques, we can develop deeper insights and enhance decision-making.

Read more