THE PORTFOLIO FLOWS: Data

Flow data

Our flow data differ in a number of respects from those used in previous studies. The data are derived from (and are proprietary to) State Street Bank & Trust (SSB). SSB is the largest US master trust custodian bank, the largest US mutual fund custodian (with nearly 40% of the industry’s funds under custody), and one of the world’s largest global custodians. It has over $4.0 trillion of assets under custody. SSB records all transactions in the securities they hold in custody. From this database we distinguish cross-border transactions by the currency in which the transactions are settled. For example, transactions that are settled in Thai baht encompass purchases and sales of Thai equities and baht-denominated debt by SSB clients. To produce our data, SSB has extracted all transactions that settle in baht, and removed from them any transactions initiated by Thai investors. Our measure of cross-border flows is therefore that of transactions by non-local SSB clients in local securities.
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THE PORTFOLIO FLOWS: Related Literature 2

Indeed, by identifying the regional factor in flows we can determine whether the remaining idiosyncratic components account for the contemporaneous correlation of returns and flows. If, once the regional factor of flows is removed, there is no remaining contemporaneous correlation between returns and inflows, it suggests that international demand shocks, not shocks to information, better explain the correlation.
Do flows move prices too much, so that they predict returns negatively, or too little, so that they predict returns positively? Here the evidence from international flows is scarce. Clark and Berko (1996) examine Mexico during the late 1980s through the crisis in 1993. They find that unexpected inflows of 1% of the market’s capitalization drive prices up by 13%. In spite of the large effect, there is no evidence of non-contemporaneous correlation: the price change is permanent and there is no further predictability.
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THE PORTFOLIO FLOWS: Related Literature


There are two main areas of work on which this paper builds. The closest is the small literature focused on international portfolio flows: Tesar and Werner (1993, 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 (1997). Because their papers use quarterly data, there is little consistent evidence of non-contemporaneous correlations.

Brennan and Cao (1997) argue that the contemporaneous correlation between inflows and returns may be attributable to international investors updating their forecasts by more than locals in response to public information about local markets. If international investors’ priors are more diffuse than those of locals, i.e., if they have a “cumulative informational disadvantage”, then positive information releases will cause asset holdings to be reallocated toward international investors. Brennan and Cao favor this hypothesis because it may also help explain home bias in investor portfolios around the world.
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THE PORTFOLIO FLOWS: Introduction 4

In the bi-variate VAR we find that returns do help in predicting 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 are unable to detect statistically that flows have incremental forecasting value over and above lagged returns, although the correlation between flows and returns tends to reduce the power of our tests.

By using the data alone, we can verify association, but not causality. To understand the implications of a specific causal structure, we lay out a simple structural model of flows and returns. In this model, inflows are driven by past flows and past returns, while returns are driven by current and past flows. This model seems reasonably realistic; for example, it endogenizes the commonly-observed autocorrelation properties of index returns. Using the model, we can trace out the dynamic impact on prices and portfolio holdings of exogenous shocks to inflows and returns.
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THE PORTFOLIO FLOWS: Introduction 3


Here we find statistically positive contemporaneous covariance between (net) inflows and both dollar equity returns excess currency returns. The data also reveal strong evidence of correlation between net inflows and lagged equity and currency returns, with the sign generally positive. This is evidence that international investors are “trend chasers.” Indeed, trend chasing—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 percent of the quarterly covariance between emerging market inflows and returns. The flows are also correlated with future equity and currency returns in emerging markets. The predictability of future currency and equity returns explains between 20 and 40 percent of the covariance of quarterly returns and flows. International investors therefore appear to act on valuable private information on emerging markets.

Interestingly, the data provide no support for the hypothesis that flows into developed countries contain private information. To the contrary, in developed countries we fmd price pressure or overreaction of price to flow to be the dominant effect: today’s inflows predict prices will ease over time. Thus, developed markets appear to have both greater liquidity and greater informational efficiency than emerging markets.
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THE PORTFOLIO FLOWS: Introduction 2

Of course, every transaction can be viewed from the perspective of the buyer or the seller and this 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 they identify a group which differs from other investors. For us, large institutional investors domiciled outside of the “local” market are that group. An inflow into the “local” market is defined as any purchase by one of these investors which settles in local currency. This 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. Using factor analysis, we then identify a regional factor that summarizes regional flows across countries. The factor explains roughly 40% of flow variation within the region. It also helps remove much individual-country noise.
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THE PORTFOLIO FLOWS: Introduction


How do international portfolio flows behave? Do flows affect asset and currency 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 for as long as capital has crossed borders. They are posed with greater urgency during times of financial upheaval (e.g. the 1997-1998 Asian and 1994-1995 Mexican currency crises.) Frequently, the answers to these questions cast international investors in a poor light. It is argued that foreign outflows lead to prices that overreact and to contagion. An opposing view—espoused most often by economists—is that trading is merely the process by which information is incorporated into asset prices. International investors do not create or exacerbate crises; their trading behavior simply reflects their assessment of underlying fundamentals.

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MORAL HAZARD IN HOME EQUITY CONVERSION: Basic Measurement Issues 2

We suspect that this basis risk, properly measured, is small Why, after all, should home prices vary very much beyond the control of the homeowner due to factors beyond the measurable ones of location, size, age, etc.? Still, further work might be done to try to learn something about the basis risk before launching contract forms. Research can also be done developing finely defined price indices, for homes of specific characteristics, to serve as the basis of settlement of home equity conversion contracts.
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MORAL HAZARD IN HOME EQUITY CONVERSION: Basic Measurement Issues

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Our calibrated model is only rough indicator of outcomes. A couple of issues that appear to be particularly salient to interpreting its relevance are whether there is substantial basis risk in home price indices and whether we have modeled moral hazard behavior accurately. We will leave these issues for further research, after describing them.

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MORAL HAZARD IN HOME EQUITY CONVERSION: Implications of this Analysis for Contract Design 2

2. Home equity insurance. Here, the contract could merely specify that the policy covers the decline in the real estate price index for the region and kind of home, and not at all the decline in price of the home itself. That was, in fact, our original proposal, Shiller and Weiss (1994). This indexing would completely eliminate the moral hazard problem.

3. Shared appreciation mortgages. With these mortgages, it is clear again that the amount owed for appreciation to the lender could be measured by a real estate price index. Obviously, the moral hazard problem is eliminated since the amount owed has nothing to do with the value of the home.
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