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Efficient (advanced) approach

An investment approach that uses smart rules for stock selection and portfolio construction. The aim of this is to increase returns and to lower both risks and costs.

Robeco has developed an efficient approach of its own, which distinguishes between rewarded and unrewarded risk. Clearly, we will want to avoid the unrewarded risks.

In addition, we select not only individual factors, but also include others in the assessment to prevent the positive effect of one factor being eradicated by the negative effect of another.

Example: when following a pure momentum strategy, investors pick mainly stocks with a high valuation. As a result, the Value factor will have a negative effect on returns. However, by also taking this factor into account, investors can avoid paying too much for momentum stocks.

A number of things stand in the way of actually harvesting factor premiums, and transaction costs form one of the biggest obstacles. An efficient approach is one that limits the number of transactions. Another aspect is to obtain effective distribution over sectors in order to prevent excessive dependency on any one specific sector.

Ultimately, this approach will lead to much higher expected returns at a lower level of risk. The table below shows the results of this efficient approach using three factors relative to the market-weighted index.

Quantitative investing: invisible layers surface to deliver attractive returns
Quantitative investing: invisible layers surface to deliver attractive returns
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Table 1. Substantial improvements: Returns 5-6% per year; Sharpe ratio 0.3 - 0.5
Source: Robeco Quantitative Strategies; Blitz, Huij, Lansdorp & van Vliet (2013): 'Efficient factor investing strategies'. The random test covers the period June 1988 through December 2011. Returns are net of transaction costs. For low volatility, Conservative Equity data have in fact been used as of September 2006.

Academic insights into using machine learning for valuation
Academic insights into using machine learning for valuation
Machine learning (ML) mispricing models are designed to detect hidden nonlinearities that are important in predicting the fundamental value of stocks.
26-09-2022 | Research
Nowcasting growth to enrich the factors used for government bond selection
Nowcasting growth to enrich the factors used for government bond selection
We have added a quality measure for the selection of government bonds.
31-08-2022 | Insight
Investing across deflation, inflation and stagflation
Investing across deflation, inflation and stagflation
Real returns on equities and multi-asset portfolios are typically poor when inflation is high, especially in times of stagflation.
29-08-2022 | Insight
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