The portfolio flows of international investors☆
Introduction
How do international portfolio flows behave? Do flows affect asset returns? Are emerging market stock prices and exchange rates particularly vulnerable to such flows? These questions have been of perennial interest to investors, economists, and policy makers, and are posed with greater urgency during times of financial upheaval. Frequently, the answers to these questions cast international investors in a poor light. It is often argued that foreign outflows lead to price overreaction and price contagion. An opposing view, espoused most often by financial economists, is that trading is merely the process by which information is incorporated into asset prices. Outflows do not create crises, they merely reflect the underlying state of fundamentals.
While there are numerous strongly held views, there is surprisingly little information on the behavior of international portfolio flows and their relation to local asset returns. Indeed, what little information there is on aggregate investor purchases in major capital markets comes from quarterly, or at best monthly, data. For example, Tesar and Werner (1995a), Tesar and Werner (1995b), Warther (1995), Bohn and Tesar (1996), and Brennan and Cao (1997) examine estimates of aggregate international portfolio flows. They find evidence of positive, contemporaneous correlation between inflows and returns. Bohn and Tesar (1996) also find evidence that flows are positively correlated with lagged flows, and with contemporaneous and lagged measures of expected returns. However, the low frequency of previously available data is a severe limitation, given the poor statistical precision it permits. Partly as a result of this limitation, few researchers have explored topics related to international flows, such as the frequency and presence of herding or trend-following behavior among investors, or the dynamic interaction of international flows and local asset returns.1
In this paper, we exploit a new and potentially superior source of flow data to help answer these questions. The data come from State Street Bank & Trust, one of the world's largest custodian banks. Custodians keep detailed records of worldwide securities holdings, trades, and transaction settlements. State Street's clients are predominantly large institutional investment pools from developed countries, including pensions, endowments, mutual funds, and governments. Their clients can be thought of as a large sample of sophisticated international investors. State Street's aggregated, international settlement data provide us with net and gross international trades on a daily basis, by country, from mid–1994 through year-end 1998. We are able to track daily gross purchases into, and sales out of, as many as 76 countries, although we follow only 44 countries in this paper.
Of course, every transaction can be viewed from the perspective of the buyer or the seller. This duality makes the behavior of any flow data inherently ambiguous. A randomly selected subsample of buys or sells, is, by definition, uncorrelated with similarly obtained subsamples, as well as with returns. So portfolio flows in general, and our flows in particular, are interesting only to the extent that they identify a group that differs from other investors. For us, large institutional investors domiciled outside of the local market are that group. In our data, an inflow into the local market is defined as any purchase by a non-local investor that settles in the local currency. (Typically, local-market securities settle in the local currency. The most commonplace exceptions are depository receipts that trade and settle in a currency different than the underlying shares.) This definition of flow is useful because the profile of these transactions corresponds closely to the generic definition of cross-border flows. Such flows are often thought to respond to similar information and misinformation, and, as already mentioned, to give rise to contagion and excessive volatility in local-market asset prices.
We put the flow data to work in a number of ways. First, we examine the behavior of flows across countries. We find that there is a small, but significant, correlation in contemporaneous cross-country flows, and that this correlation is larger within regions. We also show how these regional flow factors have grown over time.
Second, we characterize the flow data by their persistence. A variety of market microstructure models predict that traders with private information reach their desired positions slowly, in order to mitigate transaction costs.2 Thus, the order flow of informed traders is conditionally, and positively, autocorrelated. Institutional factors can also give rise to flow persistence. For example, structural shifts in asset allocation can be undertaken on a phased basis. Empirically, we find substantial evidence that flows are persistent. We also find that gross outflows are more persistent than gross inflows.
Third, we examine the covariance of equity returns with cross-border flows. A major disadvantage of previous studies that use quarterly or monthly data is that they cannot be precise about whether measured covariance is truly contemporaneous. The daily data allow for greater precision in determining contemporaneous versus non–contemporaneous components of quarterly covariance. We decompose the covariance of quarterly flows and quarterly returns into three components: (a) covariance of flows and lagged returns; (b) the covariance of contemporaneous flows and returns; and (c) the covariance of flows and future returns.
We find a statistically positive contemporaneous covariance between net inflows and both dollar equity and currency returns.3 The data also reveal strong evidence of correlation between net inflows and lagged equity and currency returns, with the sign generally positive. This pattern suggests that international investors engage in positive feedback trading, also called “trend chasing.” Indeed, positive feedback trading behavior, interpreted to mean that an increase in today's returns leads to an increase in future flows, without holding current and past inflows constant, seems to explain 60–85% of the quarterly covariance between net inflows and returns. The flows are also correlated with future equity and currency returns in emerging markets. The predictability of future equity returns explains between 15% and 35% of the covariance of quarterly returns and flows. This prediction is consistent with international investors having valuable private information on emerging markets. It is also consistent with a story in which price pressure by international investors, combined with the persistence of their flows, generates return predictability.
Fourth, we examine the conditional relationships between flows and returns. This exercise is worthwhile, because the finding that returns predict future inflows may follow from the fact that returns are correlated with current inflows and, as noted above, inflows are persistent. In other words, in a world in which flows are autocorrelated and current flows move current prices, returns will predict flows. In this setting, a more stringent definition of trend-chasing would look for predictability of future inflows over and above that implied by past inflows. Alternatively, if current flows move current prices and if prices are positively autocorrelated, as we demonstrate to be true of emerging markets, then inflows are likely to predict returns. This reasoning gives rise to the question of whether inflows can predict returns after conditioning for the effects of past returns.
Using a bivariate VAR model to test these relationships, we find that returns help to predict flows over and above the predictability of past flows. So the trend-chasing characteristic of the data meets the more stringent test. Past flows also remain important for predicting future flows once lagged returns are included. However, the statistical significance of lagged returns falls considerably. On the prediction of returns, we find that emerging market returns are predicted by the flows, after taking into account past returns. The direction of this effect is the same for developed countries, but with little statistical significance. One possibility is that the noise in flows allows lagged for developed country returns to pick up any predictive element in the flows that is incorporated into past return data.
Of course, by using the data alone, we can only verify association, not causality. To understand the implications of a specific causal structure, we lay out a simple model. In this model, inflows are driven by past flows and past returns, while returns are driven by current and past flows. This specification seems reasonable and useful, and allows us to incorporate the commonly observed autocorrelation properties of index returns as an endogenous feature of the model. Using this tool, we can trace out the dynamic impact on prices and portfolio holdings of exogenous shocks to inflows and returns.
Our main finding here is that the impact of contemporaneous flows on returns is strongly significant. Furthermore, we find that if the exogenous flow is transitory, prices tend to decline once the inflow recedes. In other words, a shock to flows appears to generate expectations of additional future flows. The current price increase seems to reflect this expectation, leading to larger increases in anticipation of further future flows. If the future inflows do not materialize, then prices decline. No actual net outflow is required.
Finally, our data have implications for the recent crisis in Asia. The data reveal that international investors did not abandon emerging markets during the crisis. In fact, they remained net buyers of emerging market equities over the July 1997–July 1998 period, though at a reduced rate. Daily inflows into all emerging markets averaged 40% of their pre-crisis (1994–1997) levels, while for Asia the ratio was 30%. This fact may appear puzzling in view of the steep decline that took place in the equity prices of emerging markets. However, it dovetails with our interpretation of the structural model above. The persistence that characterizes flows suggests that prices in the region had been bid up in anticipation of future inflows. When these inflows failed to materialize, prices declined.
The rest of the paper is organized as follows. Section 2 provides a brief summary of related literature. Section 3 discusses the data in more detail, and provides summary statistics and variance ratios of flows. Section 4 examines the correlation of returns and flows. It begins by distinguishing several hypotheses of interest, then presents covariance ratios used to test these hypotheses. Our bivariate, vector auto-regressions are then presented in Section 5. Section 6 concludes.
Section snippets
Related literature
There are two main areas of work on which this paper builds. The closest is probably the small literature focused on international portfolio flows, which includes Tesar and Werner (1995a), Tesar and Werner (1995b), Warther (1995), Bohn and Tesar (1996), and Brennan and Cao (1997). These papers document positive contemporaneous correlations between inflows and dollar stock returns. There is mixed evidence of correlation between inflows and developed country exchange rates in Brennan and Cao
Data
Our flow data differ in a number of respects from those used in previous studies. The data are derived from proprietary information provided by State Street Bank & Trust (SSB). SSB is the largest U.S. master trust bank, the largest U.S. mutual fund custodian, with nearly 40% of the industry's funds under custody, and one of the world's largest global custodians. It has approximately $6 trillion of assets under custody. SSB records all transactions in these securities. From this database, we
The behavior of portfolio flows
In this section, we examine the univariate behavior of the flows.
The Interaction between flows and returns
In this section we investigate the bivariate behavior of flows and returns. Are flows and returns correlated? Do flows forecast returns, and vice versa? We begin our exploration by looking at the unconditional co-movement between the two data series at various horizons. We then examine their conditional covariation within a vector autoregression framework.
Our first evidence on the relationship between flows and prices is simply visual. Fig. 6 shows how the detrended emerging-market flows
Conclusions
We have used a new source of high frequency data on international portfolio flows to learn about how inflows behave and how they interact with returns. Our findings can be summarized as follows:
International portfolio inflows are slightly positively correlated across countries, and are more strongly correlated within regions. The correlation of flows in most regions, and particularly within Asia, rises strongly during the Asian crisis subsample, but not during the Mexican crisis subsample.
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Cited by (0)
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We are grateful to Stan Shelton, Mark Snyder, Matt Conroy, Brian Garvey, Maurice Heffernan, and Lenny Keyser of State Street Bank for their help and support in obtaining data. We are also indebted to Tom Glaessner, André Perold, Linda Tesar, René Stulz, and the referee for helpful comments and conversations. The views expressed here are ours, and we alone bear responsibility for any mistakes and inaccuracies.