ward that the stock index changes in India are correlated to
international capital flows. Nevertheless, the internal mechanism of the impact of hot money on the local stock market is
still not clear. Although practitioners widely accept the opinion
that the impact is a consequence of foreign investors’ technology or information advantage, there is not much empirical
evidence to support it. If the standpoint above is plausible, then
I expect a positive (negative) excess return when hot money
flows in (out) of EMs.
Since the 1990s, mainstream literature has presented
arguments on the motivation of U.S. international equity
investments (e.g., Taylor and Sarno 1997; Chuhan et al. 1998).
Different from the previous views that investors invest their
capital in EMs to rebalance their international portfolios, these
authors argue that U.S. investors are motivated by chasing
returns. Hot money, aimed at earning a short-term profit, is
characterized by high sensitivity, high mobility, and reversibility, which makes it a dangerous tool for a portfolio-balancing
strategy. In this sense, hot money is more likely to be driven by
a return-chasing strategy. Taking this into consideration, I set
up the other hypothesis, the reverse causality between hot
money and stock returns; that is, stock returns in EMs are
dominant drivers of equity inflows. For example, event study
techniques lead to the conclusion that there is a temporary
speed-up in the growth rate of private investment following
stock market liberalization for major EMs (Henry 2000).
Richards (2005) examines the trading behavior of foreign investors in six representative Asian emerging equity markets and
discovers that net foreign inflows are positively associated with
the same-day local equity returns. Edison and Reinhart (2001)
carry on the quantitative analysis of the consequences of capital
controls in Brazil, Thailand, and Malaysia based on daily data.
They conclude that capital controls result in high interest rates
and, in turn, lead to adjustment of asset prices in Malaysia, but
they fail to find a significant impact of capital controls on asset
prices in Brazil and Thailand. If the hypothesis holds, I conjecture that a positive correlation between past stock returns in
EMs and current hot-money inflows will be observed.
After obtaining data for hot money, I utilize the vector auto-
regressive modeling approach to analyze the interrelationship
between hot money and the local stock returns. The reason I
after the Asian financial crisis. Nevertheless, following their
2007 peak ($205 billion), these equity flows reversed suddenly
in 2008–2009 due to a global financial crisis that sparked a
flight to safety. During times of quantitative easing in advanced
economies, investors treat EMs as fruitful destinations with
higher interest rates. Therefore, equity inflows to EMs rebounded
strongly in 2010 and 2011. For example, the People’s Republic
of China experienced only small-scale hot money before 2003,
but the magnitude increased after 2005. In fact, the expectation
for renminbi (RMB) appreciation rose after July 2005, when
RMB exchange rate reform was implemented. This, coupled
with expansionary monetary policy and four rounds of quantitative easing in the United States, resulted in more hot money
flooding into the People’s Republic of China for arbitrage.
AND LOCAL EQUITY MARKETS
I used standard VAR tools to test whether the model agrees
with the initial assumption and economic implications so that I
can obtain reliable interpretations of the interaction between
hot money and the local stock returns.
I do not conduct seasonal adjustment on the main variables,
because these variables show little remarkable seasonal variation. I start the analysis of economic time series from the
stationarity tests to get rid of the spurious regression. In this
paper, I adopt the Augmented Dickey–Fuller test (ADF) to test
the stationarity of two time-series variables: hot money and
stock market returns.
Table 2 shows that both test statistics for hot money and stock
market returns are larger than critical values under the
90-percent, 95-percent, or 99-percent likelihood levels.
Hence, I reject the null hypothesis that these series have a unit
root. Put differently, I treat these series as stationary.
I primarily propose two hypotheses before examining the interrelationship between hot money in equity flows and the equity
returns in EMs. The first is that hot money in equity flows has
an influence on emerging stock market returns, which is
inspired by the views drawn from Kohli (2001). Kohli (2001)
conducts the empirical analysis on Indian data and puts for-
ADF UNIT ROOT TEST RESULTS
table 2 reports the Augmented dickey-Fuller (AdF) test statistic for the null hypothesis of unit root (non-stationary) behavior versus
stationarity. the last three columns report critical values at the 1-percent, 5-percent, and 10-percent levels, respectively. the sampling
frequency is monthly during January 1993–december 2013.
t-Statistic Test Critical Values
ADF test statistic 1% level 5% level 10% level
HotMoney − 9.7063 − 3.4565 − 2.8730 − 2.5729
Stock Market Returns − 12.7423 − 3.4564 − 2.8729 − 2.5729