Ripe Factors in Focus

RIPE factors is an acrostic for the Robust, Intuitive, Persistent, and Empirical prerequisites for inclusion as a risk premia factor:


factors must perform effectively in a constantly changing environment;


factors should behave as one would expect e.g. a company with improving financial metrics such as increasing profit margins and asset turnover should, everything else being equal, perform better than a company with decreasing profit margins and asset turnover;


factor returns will be cyclical but show persistence and generate excess returns over medium to long-term; and


factors should be based on/supported by empirical academic research that explains a reasonable basis for the existence of a premium.

The RIPE factors are Value, Quality, Momentum, Small Size, and Low Volatility:

5 RIPE factors - Value, Quality, Momentum, Small Size and Low Volatility

Factor Premise Common constructions Our enhanced approach Rationale

Stocks priced low (high) relative to fundamental measures of value outperform (underperform) so bias portfolio towards cheap stocks

Metrics based on P/B, P/E, D/P or composites of these and other fundamental value metrics

We use forward looking data which is particularly relevant in periods of changing markets and company financials

Reflects forward looking company valuations (ie when banks cut dividends this is reflected in our factor design)


Stocks of higher quality companies tend to outperform so bias portfolio towards stocks with a strong measure of quality

Metrics such as ROE, ROA, earnings stability, dividend growth stability, financial leverage

In addition to the common metrics used we also focus on capital expenditures and efficiency of capital utilised

By focusing on capital efficiency and improvements in quality metrics we aim to capture improvements rather than just levels


Price trends tend to persist so bias portfolio towards stocks that have recently performed well

Metrics based on past returns (eg. 6 or 12 months, often excluding a recent period), sometimes normalised for volatility

We capture momentum not only at the stock level but also at an industry level. We also look at earnings momentum using forward estimates

Academic research has shown that momentum at the industry level persists and explains most of the excess returns

Small Size

Small companies tend to outperform larger ones so bias portfolio towards stocks with a smaller market capitalisation

Metrics based on market capitalisation (full or free float)

We capture the small size effect at the portfolio level. Doing so allows us to benefit from the correlations between stocks

Return enhancing due to (i) small company illiquidity and credit risk premia and (ii) the rebalancing effect of selling stocks that have risen in price

Low Volatility

Contrary to predictions of financial theory, less risky stocks tend to outperform risker ones so bias portfolio towards stocks with historically low absolute variability of returns

Metrics based on beta and realised volatility, eg standard deviation (one year, two years, three years), downside standard deviation, standard deviation of idiosyncratic returns, beta

We capture the low volatility effect at the portfolio level. Doing so allows us to benefit from the correlations between stocks

Capturing the effect at the fund level allows us to hold stocks with average volatility which might display other desirable characteristics