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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Abstract

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
v
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
URI: https://ueaeprints.uea.ac.uk/id/eprint/66551
DOI:

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