本研究之目的在檢驗國際銀行於外匯市場中競爭力的來源。研究的資料來自於歐元雜誌每年針對外匯市場作的調查，並且採用1996年至2006年各個銀行量性或質性變數。實證結果發現分行分佈的廣度、公司規模大小、併購事件和母公司的當地貨幣強勢與否，與國際銀行在外匯市場的競爭力有顯著的正向關係，然而法律的影響力較不顯著。此外發現金融控股公司和投資銀行比商業銀行在外匯市場更具有競爭力。 / The aim of this research is to examine the sources of bank competitiveness inthe foreign exchange market. Using Euromoney’s annual survey of foreignexchange market competitiveness, we test the influence of various factors of bank competitiveness using data from 1996 to 2006. Results show thatinternational branch networks in major financial centers, firm size, M&A activities,and home currency have positive effects on international banks’competitiveness in the foreign exchange market. The impact of law origin contributes less to bank competitiveness. We also find that bank holding companies and investment banks are more competitive than commercial banks in the foreign exchange market.

本篇研究利用2004年1月至2007年12月的日資料探討台北外匯市場的波動性與交易量關係，實證結果與過去的研究相符，本文發現交易量與波動性呈現正的關係。本文亦將交易量拆成「預期到」與「未預期到」的部分，實證結果發現未預期到的交易量與波動性呈現正的關係，此結果與混合分配假說的預期相符，表示當新的資訊流入市場時，交易量與波動性會同時受到衝擊；此外，預期到的交易量與波動性的關係不顯著，因此沒有證據可推測台北外匯市場是否有效率。 / In this study, I examine the interaction of volume and volatility in the Taipei foreign exchange market over January 2004 to December 2007. Consistent with empirical results of previous research, I find a positive relation between total trading volume and volatility. I also decompose total volume into expected and unexpected components. I find a positive relation between unexpected volume and volatility. This result is consistent with MDH, which supposes that volume and volatility are both driven by a common and unobservable factor that reflects the arrival of new public information. Regarding the expected volume, a weakly positive correlation with the volatility was observed. There is weak evidence that the Taipei foreign exchange market is efficient.

During the past 30 years, the proliferation of electronic trading has catalysed profound structural change in the global foreign exchange (FX) spot market. Today, more than 60% of the market's volume occurs via electronic trading platforms, which provide traders with round-the-clock market access from anywhere in the world. Such platforms offer several practical benefits that have encouraged market participation from a broad new class of financial institutions and have thereby spurred market growth. The most widely used electronic trading platforms in the FX spot market incorporate several features that differentiate them from those used in other financial markets. These features raise many important questions about order flow, market state, price formation, trader behaviour, and volatility. Despite the enormous trade volumes that such platforms facilitate, these questions have received almost no attention to date. In this thesis, we study a recent, high-quality data set from a large electronic trading platform in the FX spot market in order to investigate several aspects of trading via this mechanism. We calculate a wide range of statistics regarding order flow and market state, and we highlight how our findings contrast to those reported by empirical studies of electronic trading platforms in other markets. We study the autocorrelation properties of returns, absolute returns, and order flow, and we investigate the extent to which the market's organization impacts price formation. We also introduce a model designed to reproduce the most important properties of trading via such a platform. We derive several results regarding the model's temporal evolution, and we simulate the model to investigate how the interactions between individual traders influence volatility. We conclude that electronic trading platforms in the FX spot market retain many desirable features of centralized markets while providing traders with explicit control over their personal trading partnerships.

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.

Background: The efficient market hypothesis asserts that one cannot consistently achieve returns in excess of market returns by trading on publicly available information. Since there is no collective market return in the foreign exchange (FX) market, it has generally been perceived as impossible to consistently generate a profit. There is now empirical evidence which seriously call into question the efficiency of the FX market and opens up the possibility to turn a profit on the FX market by ways of analysis.Technical analysis is a method of analysis which by using historical price data tries to deduce future price changes. Technical analysis assumes that financial markets move in sine waves. There are stronger and weaker sine waves simultaneously. An accurate identification of the dominant sine wave gives the investor a good idea about future movement. Most technical trading tools approximate the length of the sine wave by default. This static approach does not consider the specific market or the recent lengths of the dominant sine wave. Spectral analysis will help to identify the dominant cycle, and thus determine the frequency of that cycle making the applied trading rules adaptive to the market. Purpose: The purpose is to investigate whether adding spectral analysis to existing technical analysis tools can create a higher and more stable return on investment on the FX market. Method: An experiment involving four different sets of trading rules was conducted to answer the purpose. In the first test, trades were performed based on a static approach commonly used by technical traders today. In the other three tests different transforms of spectral analysis were applied, thus making the input not static, but adaptive to the market. The four sets of trading rules where coded as an automatic trading algorithm and backtested on data collected for the currency-pair EURGBP during an 11-month period. All four tests were analysed in three different areas; performance, stability of return and crash risk. Results: The study shows that the application of spectral analysis to technical analysis methods on the FX market results in higher return on investment and better stability of returns. The win/lose ratio is significantly higher and the adaptive approach increases profit as well as decreases losses. / Bakgrund: Den effektiva marknadshypotesen stadgar att det inte är möjligt att stadigt generera högre avkastning än marknadens kollektiva avkastning genom att köpa och sälja baserat på tillgänglig information. Eftersom det inte finns någon kollektiv avkastning på valutamarknaden har det länge ansetts omöjligt att generera någon stabil vinst på denna marknad. Det finns numera empiriskt bevis som tydligt ifrågasätter valutamarknadens egentliga effektivitet och som också i sin tur öppnar upp för möjligheten att generera stabil avkastning på valutamarknaden genom analys.Teknisk analys är en analysmetod som genom avläsandet av historisk prisdata försöker utläsa framtida prisförändringar. Teknisk analys antar att finansiella marknader rör sig i sinuskurvor. Det finns starkare och svagare sinuskurvor. En exakt identifikation av den dominanta cykeln ger investeraren en god idé om framtida rörelser. De flesta tekniska analysverktygen uppskattar längden på cykeln statiskt och tar varken hänsyn till den specifika marknaden eller hur den dominanta cykeln har sett ut nyligen. Spektralanalys identifierar den dominanta cykeln varigenom frekvensen av densamma kan bestämmas och analysverktyget görs adaptivt till marknaden. Syfte: Syftet med uppsatsen är att ta reda på huruvida teknisk analys på valutamarknaden kan skapa en högre och mer stabil avkastning på investerat kapital genom användandet av spektralanalys för att mäta den dominanta cykeln. Metod: Ett experiment innehållande fyra olika uppsättningar av analysverktyg gjordes för att besvara syftet. Handel i det första testet baserades på en statisk ansats som normalt används av tekniska analytiker idag. På de andra tre testerna applicerades olika transformer av spektralanalys och gjordes därigenom adaptiva till marknaden. Analysverktygen kodades som en automatisk handelsalgoritm och testades retroaktivt på insamlad data för valutaparet EURGBP under elva månader. Samtliga fyra tester analyserades i tre olika områden; prestation, avkastningsstabilitet och risk att förlora hela kapitalet. Resultat: Studien visar att applikationen av spektralanalys på tekniska analysverktyg på valutamarknaden resulterar i högre avkastning på investerat kapital och högre avkastningsstabilitet. Vinst/förlust ration är väsentligt högre och den adaptiva ansatsen ökar avkastning samtidigt som den minskar förluster.

<p><strong>Background: </strong></p><p>The efficient market hypothesis asserts that one cannot consistently achieve returns in excess of market returns by trading on publicly available information. Since there is no collective market return in the foreign exchange (FX) market, it has generally been perceived as impossible to consistently generate a profit. There is now empirical evidence which seriously call into question the efficiency of the FX market and opens up the possibility to turn a profit on the FX market by ways of analysis.Technical analysis is a method of analysis which by using historical price data tries to deduce future price changes. Technical analysis assumes that financial markets move in sine waves. There are stronger and weaker sine waves simultaneously. An accurate identification of the dominant sine wave gives the investor a good idea about future movement. Most technical trading tools approximate the length of the sine wave by default. This static approach does not consider the specific market or the recent lengths of the dominant sine wave. Spectral analysis will help to identify the dominant cycle, and thus determine the frequency of that cycle making the applied trading rules adaptive to the market.</p><p><strong>Purpose: </strong></p><p>The purpose is to investigate whether adding spectral analysis to existing technical analysis tools can create a higher and more stable return on investment on the FX market.</p><p><strong>Method: </strong></p><p>An experiment involving four different sets of trading rules was conducted to answer the purpose. In the first test, trades were performed based on a static approach commonly used by technical traders today. In the other three tests different transforms of spectral analysis were applied, thus making the input not static, but adaptive to the market. The four sets of trading rules where coded as an automatic trading algorithm and backtested on data collected for the currency-pair EURGBP during an 11-month period. All four tests were analysed in three different areas; performance, stability of return and crash risk.</p><p><strong>Results: </strong></p><p><strong></strong>The study shows that the application of spectral analysis to technical analysis methods on the FX market results in higher return on investment and better stability of returns. The win/lose ratio is significantly higher and the adaptive approach increases profit as well as decreases losses.</p> / <p><strong>Bakgrund: </strong></p><p>Den effektiva marknadshypotesen stadgar att det inte är möjligt att stadigt generera högre avkastning än marknadens kollektiva avkastning genom att köpa och sälja baserat på tillgänglig information. Eftersom det inte finns någon kollektiv avkastning på valutamarknaden har det länge ansetts omöjligt att generera någon stabil vinst på denna marknad. Det finns numera empiriskt bevis som tydligt ifrågasätter valutamarknadens egentliga effektivitet och som också i sin tur öppnar upp för möjligheten att generera stabil avkastning på valutamarknaden genom analys.Teknisk analys är en analysmetod som genom avläsandet av historisk prisdata försöker utläsa framtida prisförändringar. Teknisk analys antar att finansiella marknader rör sig i sinuskurvor. Det finns starkare och svagare sinuskurvor. En exakt identifikation av den dominanta cykeln ger investeraren en god idé om framtida rörelser. De flesta tekniska analysverktygen uppskattar längden på cykeln statiskt och tar varken hänsyn till den specifika marknaden eller hur den dominanta cykeln har sett ut nyligen. Spektralanalys identifierar den dominanta cykeln varigenom frekvensen av densamma kan bestämmas och analysverktyget görs adaptivt till marknaden.</p><p><strong>Syfte: </strong></p><p>Syftet med uppsatsen är att ta reda på huruvida teknisk analys på valutamarknaden kan skapa en högre och mer stabil avkastning på investerat kapital genom användandet av spektralanalys för att mäta den dominanta cykeln.</p><p><strong>Metod: </strong></p><p>Ett experiment innehållande fyra olika uppsättningar av analysverktyg gjordes för att besvara syftet. Handel i det första testet baserades på en statisk ansats som normalt används av tekniska analytiker idag. På de andra tre testerna applicerades olika transformer av spektralanalys och gjordes därigenom adaptiva till marknaden. Analysverktygen kodades som en automatisk handelsalgoritm och testades retroaktivt på insamlad data för valutaparet EURGBP under elva månader. Samtliga fyra tester analyserades i tre olika områden; prestation, avkastningsstabilitet och risk att förlora hela kapitalet.</p><p><strong>Resultat: </strong></p><p><strong></strong>Studien visar att applikationen av spektralanalys på tekniska analysverktyg på valutamarknaden resulterar i högre avkastning på investerat kapital och högre avkastningsstabilitet. Vinst/förlust ration är väsentligt högre och den adaptiva ansatsen ökar avkastning samtidigt som den minskar förluster.</p>

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.

This study examines the American Dollar (USD) denominated currency returns of five developing market currencies for the presence of the day-of-the-week effect. Daily data from January 1995 to February 2008 is examined, and is split into two subperiods, SP1 (1995 - 2002) and SP2 (2003 - February 2008). Currency returns are non-normally distributed across the full data set and SP1 , but tend towards normality in SP2. As such non-parametric tests are used to test the equality of the first four moments across days of the week. Tests on the first moment show that two of the currencies do not show any evidence of the day-of-the-week effect. However, evidence of the day-of-the-week effect is found in the other three currencies in SP1, although the effect disappears or weakens significantly in SP2. Little evidence of the day-of-the-week effect is found in tests on the second moment. The hypothesis of equal higher moments across currency returns is rejected for almost all of the weekday pairs for all five currencies in SP1 , but in SP2 the hypothesis of equal higher moments can only be rejected for a single pair of weekdays for one currency. This indicates the disappearance of the day-of-the-week effect across higher moments in SP2. Thus, the study finds that the day-of-the-week effect is present across the first moment and higher moments in the returns to most currencies in SP1 , but has disappeared for all five currencies in SP2. / KMBT_363 / Adobe Acrobat 9.54 Paper Capture Plug-in

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In the introduction, this dissertation about "Psychology of an investor on the Forex market" introduces to the reader the prerequisites for trading on the foreign exchange market. On a theoretical level, it deals mostly with fundamental, technical as well as psychological analysis of prediction of development of exchange rates on the Foreign Exchange Market. Theoretical part also includes an outline of basic criteria for choosing a Forex broker and introduction of a trading platform. Practical part of the dissertation focuses on comparing and choosing a broker, plus the process for opening a real trading account; creating a trading plan and strategy, which will be used to apply different tools and indicators of technical analysis of inter-day trading of EUR/USD pair. In the conclusion, trading system created by myself is evaluated and psychological phenomenon affecting investors' decision-making during real Forex trading. This dissertation deals only with Spot Forex market, because trading through FX brokers is done on the Spot market.