Order Flow, fundamentals and exchange rate determination

Using data from State Street Corporation, Froot and Ramadorai (2005) attempt to investigate how institution-investor flow might affect currency values. The dataset spans 7 years and includes 19 currencies. This is after removing fixed or pegged currencies and rarely traded currencies. Specifically they examine the relationship between order flow, exchange rate returns and fundamentals. Froot and Ramadorai present three different ways in which flows could cause movements in exchange rates. The “strong flow-centric” view states that flows convey private information about fundamentals and thus induces a permanent effect on exchange rates. This view is in line with Evans and Lyons (2002a). The “weak flow-centric” view also states that flows rather convey private information about deviations from fundamentals and thus induces only transitory effects on exchange rates. However with the “fundamentals-only view”, flows are not connected to exchange rates. Fundamentals are the only determinants of exchange rates.

Initially, Froot and Ramadorai replicate the Evans and Lyons (2002) methodology by estimating the following equation:

j t j t j z j t p z p r+1, ( )=α +β , , ( )+ε , (3.8)

where rt+_1,j(p)is the p-period cumulative return on currency j against a basket of major currencies, z_t,j(p)represents the corresponding cumulative for the signed order flow size. They find a strong positive correlation of about 30% between flows and returns. At longer horizons of about 1 or 2 months, the correlation peaks at 45% and then gradually falls (until it becomes negative) as the horizon continues to increase. This substantial initial increase is due to non-contemporaneous correlation. Effects of flows appear to be transitory. Consequently, they infer that the positive correlation between order flow and exchange rate over short horizons is not linked to fundamentals but is the result of trend chasing activity of some investors.

To validate their inference, Froot and Ramadorai use a combination of Campbell-Shiller decomposition and VAR model to separate unexpected currency returns into permanent and transitory components. The decomposition allows for the analysis of how changes in flows and fundamentals interact with exchange rates. The main focus of their analysis is the impulse-response function associated with the VAR. These impulse response- functions allow them to calculate the short and long-run covariances between order flow and returns. They find that contemporaneous covariance between order flow and exchange rate return is positive. They also find that the covariance between current order flow and short-term future exchange rate returns is also positive. This means that order flow is able to positively predict short-term movements in exchange rates. On the other hand, this positive relationship turns negative over the long-term. Additionally the covariance between short-term future cumulative innovations in order flow and current exchange rate returns is positive. This could be as a result of some investors using

positive feedback trading rules over the short-term. The negative covariance between long-term future cumulative innovations in order flow and current exchange rate returns could be the result of negative feedback trading. In general, it seems investors use positive feedback trading rules to build up their speculative positions in the short-run and eventually offload these position using negative feedback trading rules in the long-term.

Furthermore current exchange rate returns are positively correlated with short-term future changes in interest rates and current order flow is positively correlated with short- term future changes in interest rates. According to Vitale (2006), this could imply that order flow is at least linked to fundamentals in the short-term.

Breedon and Vitale (2004) use six months of inter-dealer flows from EBS and Reuters to investigate whether the impact of order flow on exchange rates is due to information effects or liquidity effects. In view of the fact that it is difficult to untie these two effects, the paper adopts the Bacchetta and Wincoop (2006) framework. This framework is modified by making a few assumptions that take into account private information through customer order flow. They point out that the persistent effects of order flow on exchange rates can also be induced by liquidity effects. This is contrary to the common view in microstructure that the persistent effects of order flow on exchange rates are induced by only information effects. Using GMM techniques, they find that the high explanatory power attributed to order flow is due to liquidity effects. Specifically the impact of order flow on exchange rates is not due to aggregation of private dispersed information but rather due to FX dealers risk aversion. FX dealers are only willing to hold unwanted inventory if they are adequately compensated for bearing the risk

Also, the type of FX transactions used by Froot and Ramadorai cover only a portion of customer transactions in the FX market and does not cover all the information content in order flow. Numerous studies including Marsh and O’Rourke (2005), Carpenter and

Wang (2007), Mende and Menkhoff (2003) and Evans and Lyons (2003b, 2005a) have examined the information content of disaggregated order flow in FX market.

Marsh and O’Rourke use daily customer order flow of Royal Bank of Scotland between June 2004 and August 2004, to distinguish between the explanations for the strong positive correlations between spot exchange rates and order flow. In previous literature, private information, liquidity effects and feedback trading are the main suggestions that have been used to explain this strong correlation. The order flow data is split into various customer groups. This allows for a detailed comparison of information content of order flow between the different customer categories. The exchange rate is regressed on disaggregated order flows and thus relaxes the constraint that the effect of order flow on exchange rate are equal for all customer groups.

t Other t Lev t Unlev t Corp t t x x x x u S = + + + + + ∆

β

β

β

β

β

x represents Non-financial corporate flows, xtUnlev represents un-leveraged

financial flows, Lev

t

x represents the leveraged financial flows and x_tOther represents other

financial flows.

The results indicate that financial customers order flow is positively correlated with exchange rate movements whilst non-financial customer order flow is negatively correlated. They interpret these results as meaning that financial customer flows contain price relevant information with non-financial customers following negative feedback trading rules. The different impacts of order flow effectively discount any liquidity effects. Also they find that the probability of informed trading (PIN) is relatively higher for financial customers. This is not surprising since financial customers are more aggressive in the FX market and have a better access to price relevant signals. Non- financials, on the other hand, exploit short-term movements in spot rates to conduct their transactions for purely non-speculative reasons. Furthermore, Marsh and O’Rourke find

that order flow in one currency market could affect spot rate movements in another market. The previous result that liquidity effects are not the main reasons for the strong correlations is reinforced by the presence of cross-market flow effects.

Other studies have looked at the issue of heterogonous sample. Carpenter and Wang (2007) also report similar results. Using tick-by-tick customer data from a large Australian, they investigate the price impact from different participants in the FX market. They find that non-bank financial customer flows have a large impact on dealer pricing whilst corporate order flows have the least impact.

Employing Citibank data spanning 6.5 years, Evans and Lyons (2005a) argue that order flow of the different customer segments behave differently and the information content of their respective order flows is dollar for dollar also different.

Wei and Kim (1997) find that though the currency positions of large market participants are positively correlated with exchange rate volatility, they are not able to forecast future exchange rate movements and may be trading on noise rather than on private information. Bjonnes and Rime (2001) show that customer trading has a relatively larger price effect than inter-bank trading.

However, it should be noted that data for these studies have been obtained from single banks and as a result their customer order flow may not be a good representation of market-wide order flow.