Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis

Osler, Carol L.

doi:10.1111/1540-6261.00588

Cited by 191 publications

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References 84 publications

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“…However, the aggregate face value of open orders is likely to be smaller, not larger, in the New York afternoon (Osler (2002)). Further, clustering patterns should be largely independent of time because orders are typically open for many hours.…”

Section: Time Of Daymentioning

confidence: 99%

“…Furthermore, executed take-profit orders cluster more strongly at the exact round numbers than do stop-loss orders, and have no tendency to cluster above or below such numbers (Osler (2002)). The two tests both exploit the observation that if stop-loss orders are sometimes triggered in waves, the response to stop-loss orders should be larger, and should last longer, than the response to take-profit orders.…”

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confidence: 93%

“…Among market participants it is common knowledge that stop- This paper attempts to show that this phenomenon exists in currency markets, where stop-loss orders are commonplace. 4 To identify when such orders are executed, I turn to evidence that stop-loss orders cluster in predictable ways (Osler (2002)): executed stop-loss sell orders cluster just below round numbers; stop-loss buy orders cluster just above round numbers. 5 The empirical analysis therefore focuses on high-frequency exchange rate behavior near round numbers.…”

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confidence: 99%

“…8 Osler (2002) discusses motives for placing stop-loss and take-profit orders. Harris (1998) presents a model of the optimal use of limit orders, which can be applied with some modifications to the case of take-profit orders.…”

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Stop-Loss Orders and Price Cascades in Currency Markets

Osler

2002

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu Terms of use: Documents in EconStor may STOP-LOSS ORDERS AND PRICE CASCADES IN CURRENCY MARKETS C. L. Osler AbstractIn this paper, I provide evidence that currency stop-loss orders contribute to rapid, selfreinforcing price movements, which I call "price cascades." Stop-loss orders, which instruct a dealer to buy (sell) a certain amount of currency at the market rate once the rate has risen (fallen) to a prespecified level, generate positive-feedback trading. Theoretical research on the 1987 stock market crash suggests that such trading can cause price discontinuities, which would manifest themselves as price cascades.My analysis of high-frequency exchange rates offers three main results that provide empirical support for the hypothesis that stop-loss orders contribute to price cascades: (1) Exchange rate trends are unusually rapid when rates reach exchange rate levels at which stoploss orders have been documented to cluster. (2) The response to stop-loss orders is larger than the response to take-profit orders, which generate negative-feedback trading and are therefore unlikely to contribute to price cascades. (3) The response to stop-loss orders lasts longer than the response to take-profit orders. Most results are statistically significant for hours, although not for days. Together, these results indicate that stop-loss orders propagate trends and are sometimes triggered in waves, contributing to price cascades. Stop-loss propagated price cascades may help explain the well-known "fat tails" of the distribution of exchange-rate returns, or equivalently the high frequency of large exchange-rate moves. The paper also provides evidence that exchange rates respond to non-informative order flow. (Key words: positive-feedback, stop-loss, order flow, portfolio insurance, information, exchange rates, high-frequency, currency market microstructure) (JEL codes: F1, G3.) June 2002The views expressed in the paper are those of the author and do not necessarily reflect views at the Federal Reserve Bank of New York or the Federal Reserve System. The author thanks Priya Gandhi for excellent research assistance. Any errors or omissions are the responsibility of the author. 2 STOP-LOSS ORDERS AND PRICE CASCADES IN CURRENCY MARKETSOn October 7, 1998, the dollar-yen exchange rate fell 11 percent. On March 7, 2002, the rate dropped over 3 percent. These moves, which dwarf the 0.7 percent standard deviation of daily returns in dollar-yen since 1990, are sympt...

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Section: Time Of Daymentioning

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Stop-Loss Orders and Price Cascades in Currency Markets

Osler

2002

SSRN Journal

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“…We follow the Johansen procedure in order to test for cointegration of the exchange rate and the different types of order flow. First, the unrestricted VAR models Third, we estimate the related vector error-correction model suppliers by submitting conditional market orders, so-called take-profit orders, which trigger buy orders when the exchange rate falls to a pre-specified level and vice versa (Osler 2003(Osler , 2005, or they try to exploit intra-day exchange rate movements by applying a buy low/sell high strategy. The above interpretation of parameter signs suggests that, indeed, financial customers drive prices of foreign exchange, whereas commercial customers react to price changes.…”

Section: The Information Content Of End-user Order Flowmentioning

confidence: 99%

End-User Order Flow and Exchange Rate Dynamics

2008

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Abstract:In this paper we provide evidence for Evans and Lyons' (2005b) model of an information aggregation process in FX markets using a German bank's end-user order flow from 2002 to 2003. Though customer order flow is unambiguously the vehicle incorporating non-public information into exchange rates over time, our empirical analysis does not support the widespread optimism in the market microstructure literature that customer order flow is the high-powered source of information easily exploitable for short-run speculation. Moreover, commercial customers' order flow produces negative coefficients in contemporaneous return regressions, stressing their role as liquidity providers. Keywords:Foreign exchange; market microstructure; end-user order flow JEL-Classification: F31 Non-technical summaryIn spite of many years of extensive research short-run exchange rate fluctuations still remain difficult to explain. The microstructure approach attempts to make progress in this respect by stressing the role of asymmetric information on foreign exchange markets.At the core of standard microstructure models a market maker is assumed to anonymously collect orders from informed and uninformed traders (Kyle, 1985). The market maker infers private information from informed traders by analyzing the incoming order flow and sets prices accordingly. As a result the microstructure approach suggests order flow as a major driving force of exchange rates dynamics. This paper empirically investigates FX dealer behavior in the Euro-US dollar market using the transaction data of a German bank in a one-year period from October 2002 to September 2003. In a first step, we estimate standard market microstructure models finding that order size and currency spreads are negatively correlated and commercial customers are faced with higher spreads than financial customers. This contrasts with standard adverse selection theory, which suggests that dealers increase spreads with a rising likelihood of private information indicated by customer type or order size (Glosten and Milgrom, 1985;Easley and O'Hara, 1987). Moreover, our dealer refrains from quote shading, i.e. he does not quote currency prices in order to manage inventory. Instead, undesired inventory is unloaded in the interdealer market.The empirical results in this section are in line with other studies such as and Osler et al. (2006), implying that the end-user order flow investigated here is similar to that of other dealing banks. This lends support to the view that the data set at hand is a representative fraction of market wide order flow. Thus, our second step is to use cointegration techniques to assess the information content of end-user order flow.In line with the price discovery process suggested by Evans and Lyons (2005b), we find that exchange rates are cointegrated with customer order flow. Within the cointegration relationship, exchange rates are positively related to financial customer orders and negatively related to commercial customer orders. In the short run, however, ne...

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Disposition Effect

Kaustia

2010

Behavioral Finance

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Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis

Cited by 191 publications

References 84 publications

Stop-Loss Orders and Price Cascades in Currency Markets

Stop-Loss Orders and Price Cascades in Currency Markets

End-User Order Flow and Exchange Rate Dynamics

Disposition Effect

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