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News, VIX and Stock Market Returns

Posted in Sentiment Indicators, Volatility Effects

How does aggregate stock news sentiment relate to equity market return and volatility? In his October 2012 paper entitled “Time-Varying Relationship of News Sentiment, Implied Volatility and Stock Returns”, Lee Smales investigates relationships among aggregate unscheduled firm-specific news sentiment, changes in the S&P 500 Implied Volatility Index (VIX) and both contemporaneous and future S&P 500 Index returns. He measures daily aggregate unscheduled firm-specific news sentiment as an average of scores calculated by the RavenPack news analysis tool for articles with headlines specifying S&P 500 stocks published for the first time that day on the Dow Jones news wire and in the Wall Street Journal. Unscheduled means exclusion of scheduled news releases such as earnings and dividend announcements. Using daily aggregated news sentiment for S&P 500 firms and levels of the S&P 500 Index and VIX during January 2000 through December 2010, he finds that:

  • Based on the specified measurement methodology over the entire sample period, average aggregate daily news sentiment is -1.1, ranging from a low of -59.7 in 2007 to a high of +38.0 in 2003.
  • For contemporaneous measurements over the entire sample period:
    • Aggregate daily news sentiment relates negatively to change in VIX, much more strongly when VIX is high and news sentiment negative (such as 2007-2009).
    • There is also an inverse and asymmetric relationship between change in VIX and S&P 500 Index return, but the asymmetry may not hold after controlling for news sentiment.
  • Leading relationships are:
    • S&P 500 Index return (change in VIX) relates positively to itself over the next interval (autocorrelation). 
    • Aggregate news sentiment relates positively (negatively) to next-interval stock return (change in VIX).
    • Stock market return relates negatively to next-interval change in VIX.
  • Regarding potential trading strategies:
    • Extremely high (low) current levels of VIX signal attractive buy (sell) points for the S&P 500 Index. Specifically, when VIX falls in the lowest 6 (highest 3) of 21 ranks based on the lagged year of daily closes, average gross S&P 500 Index returns over the next 1, 5, 20, 60 and 250 trading days are always negative (positive). When VIX falls in rank 21 (1), average gross S&P 500 Index return over the next 20 trading days is 9.2% (-3.0%). However, when VIX falls in ranks 7-18, there is no clear pattern in stock market returns. 
    • Extremely low current levels of aggregate news sentiment signal attractive sell points for the S&P 500 Index. Specifically, when news sentiment falls in the lowest 5 of 21 ranks based on the lagged year of daily closes, average gross S&P 500 Index returns over the next 1, 5, 20, 60 and 250 trading days are always negative. When aggregate news sentiment falls in rank 1, average gross S&P 500 Index return over the next 20 trading days is -2.1%. However, when aggregate news sentiment is rank 5 or above, there is no clear pattern in stock market returns.
    • A strategy that sells the S&P 500 Index when both aggregate news sentiment and VIX are high relative to the past year is consistently profitable at a gross level, generating an average 1-month (3-month) gross return of 7.7% (10.7%). A strategy that buys the S&P 500 Index when both aggregate news sentiment and VIX are low relative to the past year is consistently profitable at a gross level, generating an average 1-month (3-month) gross return of 1.8% (5.0%).

In summary, evidence suggests that traders may be able to time the U.S. stock market based on extremely high or low values of VIX and/or extremely negative values of the sentiment embedded in aggregate unscheduled news about component stocks.

Cautions regarding findings include:

  • Trading strategy calculations ignore dividends, to the disadvantage of a buy-and-hold benchmark. Use of the S&P 500 Index also ignores any costs of forming and maintaining a tradable tracking asset.
  • Reported average trading strategy returns are gross, not net. Including reasonable frictions for entering and exiting positions and costs of shorting would reduce these returns.
  • Returns ignore the costs of generating aggregate news sentiment on a daily basis. Such costs would reduce profitability of any strategies based on this metric.
  • For holding intervals longer than one day, reported average trading strategy returns involve overlapping return measurement intervals, thereby obscuring portfolio level performance. Realistic trading would require either skipping many signals (because funds are already committed) or allocating only fractions of the portfolio to each signal (estimating position size ex ante would be problematic). Effectively, use of overlapping signals overstates sample size and may amplify impacts of extreme events.
  • The data in Table 9 of the paper do not appear to support the assertion in Section 4.2.2 of the paper that “very positive levels of news sentiment, R ≥ 20, produce forward looking returns that are always positive on average.” The table shows that average gross S&P 500 Index returns are negative for horizons of 60 and 250 trading days.
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