Introducing The RavenPack Sentiment Index

RavenPack | December 01, 2011

To capture the overall sentiment of the market, we construct a news sentiment index based on the Event Sentiment Score (ESS) developed by RavenPack.

The index is constructed using a simple, intuitive, and robust approach capturing the sentiment momentum on the US market over a three month period.

The RavenPack Sentiment Index Moves Closely with Financial Markets

  • From January 2000 to September 2011, the contemporaneous correlation between the RavenPack Sentiment Index and the S&P 500 Index is 79%,
  • The RavenPack Sentiment index is consistently highly correlated with the S&P 500 Index across different market trends. Especially, we find an average correlation of almost 90% during bear markets

The RavenPack Sentiment Index is both Statistically and Economically Significant

  • A causal relationship exists from market sentiment to stock market returns
  • The sentiment trading strategy based on monthly VAR(2) yields an annualized return of 10.2%
  • The recursive monthly VAR(2) model is able to generate an outof-sample annualized return of 6.7% between April 2006 and September 2011
  • The sentiment based trading strategy based on weekly VAR(10) yields an annualized return of 13.4%
  • The recursive weekly VAR(10) model is able to generate an outof-sample annualized return of 17.5% with an Information Ratio of 0.81

Sentiment Index

1. Introduction

This is the second edition of the new RavenPack Research Series. In this report, we take a closer look at the RavenPack Sentiment Index and its relationship to financial market returns.

Although historically quantitative investing is able to beat the market by using simple factors like size, momentum, or value to earn excess risk adjusted returns, the performance of traditional factors has shown significant decay after large amounts of assets have been drawn into the quantitative asset management business.

One way to discover alpha opportunities is to identify new data sources that are able to capture the mispricing in the market and that are not exploited too extensively.

One of the most recent developments in the pursuit of the new “Holy Grail” lies in the field of behavioral finance. Researchers are striving to come up with non-economic factors, such as investor sentiment, as a possible predictor of asset prices. Although we have already seen some encouraging results, they are far from satisfactory for at least two reasons: (1) Most of the commonly-used proxies for market sentiment are based on indirect measures inferred from financial markets using a top-down approach. Such measures include the discount on closed-end funds, the Put-Call Ratio, the optionimplied volatility, or IPO volume, just to name a few. The major drawback for this type of sentiment indicator is that it's not orthogonal to asset price, which is usually the variable that a sentiment index should try to explain. (2) Most sentiment indicators including survey based measures such as the University of Michigan Consumer Confidence Index or the Conference Board Consumer Confidence Index can only be updated at very low frequency such as monthly or quarterly and will be of little use to quantitative asset management where timeliness is crucial.

In this paper, we introduce a new measure of market sentiment - the RavenPack Sentiment Index. Unlike other sentiment proxies, the RavenPack Sentiment Index is comprehensive, direct, and up-to-date. It's constructed using a bottom-up approach and exhibits a high correlation with overall market movements. In the next section, we will briefly introduce the sentiment index construction methodology and examine the causality relationship between the RavenPack Sentiment Index and stock market performance. We will discuss the economic significance of the RavenPack Sentiment Index by studying the profitability of a sentiment-based trading strategy. Finally, we provide some interesting conclusions and recommendations for further research.

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