Stock market correlation and investor attention

Symitsi, Efthymia (2017) Stock market correlation and investor attention. Doctoral thesis, University of East Anglia.

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    This thesis deals with three separate problems in �nance related to covariance. First, I
    assess the forecasting performance of popular multivariate GARCH, hybrid implied and
    realised covariance models in terms of statistical and economic criteria. I perform a rigorous
    analysis across major equity indices using di�erent forecasting horizons, market regimes,
    loss functions and tests. A Vector Heterogeneous Autoregressive speci�cation is the best
    among competing models. Less complex models that rely on high-frequency data yield
    superior forecasts and reduce the portfolio risk. Hybrid estimators that combine optionimplied
    and high-frequency information also have merit when option-implied volatilities are
    corrected for the volatility risk-premium. During �nancial turmoil the ranking does not
    change signi�cantly but forecast accuracy deteriorates.
    Second, I investigate comovement in investor attention as a determinant of excess stock
    market comovement proposing a novel proxy, \co-attention". Co-attention is estimated as
    the correlation in demand for market-wide information across stock markets approximated
    by the Google Search Volume Index (SVI). My results reveal signi�cant co-attention driven
    to some extent by correlated news and fundamentals. Most importantly, I �nd that coattention
    is positively related to excess comovement. This e�ect is more pronounced in
    developed economies and during recessions. I fail to document signi�cant e�ects of correlated
    news supply on stock markets, lending support to the idea that information demand
    governs investing decisions. Co-attention is not only induced through international investors,
    but domestic investors as well. My results provide evidence of attention-induced �nancial
    contagion in unrelated economies. However, international investors' co-attention appears to
    facilitate volatility transmission indirectly across markets.
    Third, I solve the optimal budget allocation problem across keywords for paid search adiv
    vertising accounting for the risk induced by maintaining a portfolio of volatile and correlated
    keywords. In a mean-variance context, I maximise the growth rates in keyword popularities.
    Advertising costs and conversion rates are shown to be irrelevant. I demonstrate practical
    implementation using readily available data from Google Trends database estimating averages,
    variances and co-variances as growth rates in SVIs. Based on keyword sets for major
    sectors, I form e�cient frontiers consisting of optimal combinations of keywords. Optimal
    keyword portfolios o�er statistically higher risk-adjusted performance against portfolios constructed
    using popular heuristics. A proposed heuristic based on risk-adjusted performance
    reduces the computational cost and provides competing results.

    Item Type: Thesis (Doctoral)
    Faculty \ School: Faculty of Social Sciences > Norwich Business School
    Depositing User: Stacey Armes
    Date Deposited: 22 Mar 2018 12:46
    Last Modified: 22 Mar 2018 12:46

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