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Beyond Excess Returns: How to Enhance Sentiment Strategies using MSCI Barra Risk Models

We have just published a white paper showcasing the benefits of hedging a sentiment signal using risk factors from several MSCI Barra Risk Models. In this post, I provide some details on the methodology used for the strategy and on the achieved results.

Excess returns: Ignores several risk factors

For simplicity, researchers often use excess returns when evaluating the efficacy of a trading signal. This is equivalent to assuming just a single risk factor (the market) with constant factor exposure (𝛽=1). However, in reality, several other risk factors exist and exposure not only differs across time but also across assets. This can lead to erroneous conclusions when it comes to signal performance, both in terms of returns and volatility.

To truly isolate the effects of a given trading signal, we need to account for the contribution from a broader set of risk factors and ensure that any observed performance cannot be explained by known risk premia.

Excess Returns vs. Specific Returns: Hedging a Sentiment Signal

In particular, we move beyond excess returns to consider the specific returns as defined by several distinct MSCI Barra risk models. This is done as a convenient shortcut to a factor-neutral portfolio implementation. In this context, we evaluate the effect of applying a regional model vs. a global model (USFAST/ USMED vs. GEM3), and faster vs. slower factors (USFAST vs. USMED).

To create our sentiment signal, we take a 1-day average of RavenPack’s Event Sentiment Score (ESS), filtering on highly relevant and novel events. The resulting sentiment indicator is then used as input to a simple linear regression model with the dependent variable being the MSCI specific returns.

Excess Returns vs. Specific Returns: Our main findings

  • RavenPack sentiment adds consistent value in a daily strategy across both U.S. large/mid-caps and small-caps – after accounting for MSCI risk factors (see Figure 4). In particular, we achieve Information Ratios of 3.8 and 6.1 (before transaction costs), respectively, across the two market-cap portfolios.
  • Excess Returns

  • Sentiment models built on MSCI’s regional risk models (USFAST) outperform, in terms of Information Ratio, the global model (GEM3) by 45% across large and mid-caps and by 18% on small-caps (see Figure 1).
  • Excess Returns

  • The performance of the prediction quantiles shows the desired monotonic behavior - with more extreme predictions leading to more extreme returns and Information Ratios (see Figure 6).

Excess Returns

For a more detailed description of our methodology and for a similar analysis across the equivalent European portfolios, refer to the full report “Hedging Sentiment Signals with MSCI Barra Risk Models”.

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