Sentiment, order imbalance, and co‐movement: An examination of shocks to retail and institutional trading activity

Date01 January 2019
Published date01 January 2019
DOI: 10.1111/eufm.12146
Sentiment, order imbalance, and co-movement:
An examination of shocks to retail and institutional
trading activity
Patricia Chelley-Steeley
Neophytos Lambertides
Christos S. Savva
University of Birmingham Business
School, University of Birmingham,
Edgbaston Park Road, Birmingham B15
Department of Commerce, Finance and
Shipping, Cyprus University of
Technology, 3603 Lemesos, Cyprus
Using order flow imbalance as a measure of sentiment we
show that positive and negative shocks to sentiment lead
to lower co-movement between portfolio and market returns
in the post-shock period. Furthermore, an asymmetry is
present as positive shocks to sentiment have less impact on
co-movement changes than negative shocks. Moreover,
shocks to retail sentiment and the sentiment of two types of
institutional investors lead to a reduction in co-movement.
Positive shocks to institutional order flow imbalance lead to
smaller reductions in co-movement than associated with
retail shocks. These effects exist even after controlling for
firm-specific and market-wide news.
co-movement, order flow shock and sentiment, smooth transition model
G12, G14
We would like to thank the editor and an anonymous referee for the generosity of their time and comments, which have
allowed us to improve the paper considerably.
© 2017 John Wiley & Sons, Ltd. Eur Financ Manag. 2019;25:116159.
Compelling evidence shows that the mood or sentiment of the stock market can play an important role
in influencing the extent to which individual stock returns co-move with the rest of the market.
A pioneering paper by Kumar and Lee (2006) suggests that order flow imbalance (buying activity
relative to selling activity) is a useful measure of sentiment. This connection exists because an
optimistic mood encourages more buying activity and less selling activity, while a pessimistic mood
encourages more selling and less buying activity. Kumar and Lee (2006) use a measure of order
flow imbalance adjusted for common factors to capture sentiment and show that sentiment increases
A premise of Kumar and Lee (2006) is that market sentiment is captured by the sentiment of retail
investors because these investors do not have access to the information resources that institutions have,
so they make more irrational investment decisions. However, there is growing evidence to suggest that
institutional investors also act irrationally and are influenced by sentiment (see, e.g., Bagnoli, Clement,
Crawley, & Watts, 2014; Barberis, Shleifer, & Wurgler, 2005; Brown & Cliff, 2005; DeVault, Sias, &
Starks, 2016). Institutional investors become sentiment traders because they are influenced by
reputational trading,which encourages institutions to trade in the direction of sentiment to avoid their
performance standing out from the average, while the consistent short-term predictability of sentiment
strategies and the impediments to corrective low-cost arbitrage cause institutions to take advantage of
short-term predictability driven by sentiment because it is profitable. As a result, the order flow of
institutional investors reflects sentiment. Moreover, recent analysis of the order flow of retail and
institutional investors by DeVault et al. (2016) shows that institutions are more driven by sentiment
than retail investors. We are therefore motivated to extend the analysis of Kumar and Lee (2006) and
consider whether the sentiment of retail and institutional investors can influence co-movement.
One of the key results presented in Kumar and Lee (2006) shows that the portfolio returns of small
firms is positively influenced by the order flow imbalance or sentiment of retail investors. They do not
find that there is a relationship between the portfolio returns of larger firms and retail sentiment. Large
institutional investors invest more heavily in large firms, while retail investors are more concentrated in
small firms. This suggests that for large firms to reflect investor sentiment the measure of sentiment
may need to be broadened to include institutional sentiment, as these investors are more likely to have
an influence over large firms. Moreover, the results of Kumar and Lee (2006) do not provide direct and
conclusive evidence of the relationship between sentiment and co-movement, only that sentiment
influences returns and therefore indirectly must influence co-movement.
Using an adaptation of the Lee and Radhakrishna (2000) algorithm we identify the trades
associated with three types of traders. Small trades are classified as retail trades; medium trades are
classified as institutional informed trades or stealth trades; and the largest trades are classified as large
institutional trades. For each of these three investor groups we use the tradersdaily order flow
imbalance to capture their sentiment. For each type of investor this is measured as a ratio of their dollar
value of buyer-initiated trades to seller-initiated trades adjusted for common factors. We also measure
the total sentiment of the market using all trades. Over the period examined we find that sentiment to
the different investor groups on average across all stocks is optimistic, but varies over time.
Empirically, we find that firm characteristics are also important in determining sentiment, as some
firm characteristics are associated with average order imbalances greater than unity, which suggests
greater optimistic sentiment. These characteristics are firms that are S&P500 index constituents, larger,
more liquid, have low book-to-market ratios, have higher prices, are older firms, have higher levels of
institutional ownership, have lower earnings-to-price ratios and higher earnings growth, because on
average over time buying pressure for these stocks outweighs selling pressure. Large institutional
traders on average display positive sentiment across all the stock characteristics we examine, but
have the greatest positive sentiment for smaller firms, stocks with low illiquidity, and firms
with high prices and earnings growth. Retail investors have pessimistic sentimentforsmallstocks,
low priced stocks, and young stocks, but the most positive sentiment for high priced stocks, firms
with low earnings per share (EPS) ratios, and firms with high earnings growth. Stealth traders
generally have positive sentiment for each firm characteristic which is especially strong for large
firms, and stocks with high prices, high institutional ownership, low EPS, and high earnings
We calculate the average pairwise correlation between changes in the order flow imbalance of one
stock and another to identify whether changes to order flow are systematically correlated, an indication
that trading decisions are coordinated and therefore influenced by sentiment. Our results suggest that
changes to the overall order flow imbalances across all stocks are not highly correlated, but changes
amongst retail investors appear to be correlated. We also find that firm characteristics influence the
average pairwise correlations and contribute to higher levels of coordination amongst firms with some
shared characteristics. Multivariate analysis identifies which characteristics have an independent
influence over correlations.
We next examine whether the sentiment level of the investor group is related to portfolio excess
returns. This is similar to some previous analysis undertaken by Kumar and Lee (2006). We extend
their analysis by regressing portfolio returns in excess of the risk-free rate against a set of market factors
and the sentiment of our distinctive types of investors. We find that total sentiment, retail, and large
institutional sentiment are correlated with portfolio returns, even after controlling for market risk
factors. This confirms that the sentiment of retail investors influences co-movement, but also shows
that the sentiment of institutional investors matters.
To our knowledge, we are the first paper to comprehensively examine the impact that shocks to
sentiment, measured as order flow imbalance shocks, have on co-movement. The study of shocks
rather than levels offers a number of advantages. First, shocks capture unanticipated changes in order
flow imbalance so reflect the element of order flow imbalance or sentiment that represents an
innovation or change in behavior, so may have a different impact on returns and co-movement to order
flow imbalance levels. We also show that order flow imbalance levels are non-stationary, but that
changes to order flow imbalance are stationary, which provides an additional motivation to focus on
shocks. Within the paper we consider two types of shocks. The first is the change in the order flow
imbalance level between consecutive periods. This is a useful measure as it adequately captures the
concept of a shock and can be easily calculated.
Using this concept of shock, we estimate the average pairwise correlation between the market
excess return and changes in sentiment for each of the investor groups. This analysis is similar to
some that Kumar and Lee (2006) undertook, but we extend their analysis to also include the
sentiment of stealth and institutional traders. We find that the changes in total order flow
imbalance and retail order flow imbalance are negatively correlated with changes in the market
excess return. This suggests that changes in order flow imbalance are associated with reduced
co-movement and motivates us to examine the impact of order flow shocks on stock market
correlation further.
A weakness of the analysis undertaken by Kumar and Lee (2006) is that a direct link between
co-movement and order flow imbalance is not established. This motivates us to examine a second
type of shock to order flow imbalance, which is derived from the Silvennoinen and Teräsvirta
(2005) and Berben and Jansen (2005) Smooth Transition Conditional Correlation Generalized
Autoregressive Conditional Heteroskedasticity model (STCC GARCH model). Estimation of this
model allows us to capture the conditional return correlation between individual portfolio returns

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