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In this research, we review the relevant literatures to discuss the predictability of foreign exchange rates. Besides, we collect literatures to examine the development of the fundamental models, market models, technical analysis and trading rules and compare and evaluate the precision of these models. Moreover, we make a case study of a global leading investment bank to discuss how to use these models in practice. The result shows that fundamental models can help to establish the long-term equilibrium but have some shortcomings and thus we could adopt market models to resolve the shortages and the technical analyses and rules to set the exact price levels for trading purposes.

In the last few years, it has been proved that the movements of financial asset have the property of non-linearity and show some tendency within a given period. Increasing evidence that technical trading rules can detect non-linearity in financial time series has renewed interest in technical analysis. This study evaluates the market timing ability of the moving average trading rules in twelve equity markets in the developed markets and the emerging markets from January 1990 through Match 2002. We use traditional test, bootstrap p-value test, Cumby-Modest¡¦s market timing ability test and simulation stock trade to evaluate market timing ability. The overall results indicate that the moving average trading rules have predictive ability with respect to market indices in the Asia emerging stock markets. These findings may provide investors with important asset allocation information.

This thesis investigates the predictability of trading strategies in the European and American stock market from 2001 to 2013. More specific, we examine the indices CAC40, DAX, FTSE100, NASDAQ and S&P500 first with the simple moving averages, then with trading rules based on the forecasts of time – series models and finally with the combination of the technical trading rules and time –series models. The significance of the examined trading rules tested with standard t – tests. The standard tests results show that technical trading rules are the most profitable strategy, second follows the combined and then the time – series rules as the least profitable trading strategy related to buy – and – hold strategy. / Σκοπός της παρούσας εργασίας είναι η διερεύνηση της προβλεπτικής δυνατότητας στρατηγικών επενδύσεων που εφαρμόζονται στην Ευρωπαϊκή και Αμερικάνικη χρηματιστηριακή αγορά, για τη χρονική περίοδο 2001-2013. Πιο συγκεκριμένα θα εξετασθούν οι δείκτες CAC 40, DAX, FTSE 100, NASDAQ και S&P 500, με κανόνες κινητών μέσων όρων, με κανόνες που βασίζονται σε μοντέλα πρόβλεψης χρονολογικών σειρών και με κανόνες συνδυαστικών των δύο ανωτέρω. Οι παραπάνω στρατηγικές θα συγκριθούν με την στρατηγική διακράτησης (Buy-and-Hold), που έχει ορισθεί ως benchmark στρατηγική και η σημαντικότητα των αποτελεσμάτων θα εξετασθεί με στατιστικούς ελέγχους t-statistics.

A simple Stock Market Game Model (SEGM) was introduced in 2002 by J. Mockus to simulate the behavior of several stockholders trading a single stock. In contrast, the proposed model PORTFOLIO is simulating stock exchange including a number of different stocks. The objective of PORTFOLIO is not forecasting, but simulation of stock exchange processes that are affected by predictions of the participants. The main improvements are the multi-stock extension and a number of different trading rules, which represent both the heuristics of potential investors and the well-known theoretical investment strategies. This makes the model more realistic and allows the portfolio optimization in the space of investment strategies, in both the historical and virtual environments. This is an essential improvement comparing with traditional single-stock models with direct interaction of investment agents. The "virtual" stock exchange can help in testing the assumption of rational investor behavior vs. the recent theories that explain financial markets by irrational responses of major market participants. The model has been compared with actual financial time series and found the results to be close in some cases. The model is designed as a tool to represent behavior of individual investor, which wants to predict how the expected profit depends on different investment rules using different forecasting methods of real and virtual stocks. / Paprastas akcijų rinkos žaidimo modelis (angl. Stock Market Game Model) buvo pristatytas J. Mockaus 2002 m. Šis modelis imituoja kelių akcininkų, prekiaujančių viena akcija, elgesį. Siūlomas modelis PORTFOLIO, priešingai, imituoja akcijų biržos darbą, kurioje vyksta prekyba su daugelio firmų akcijomis. PORTFELIO modelio tikslas yra ne prognozavimas, bet simuliavimas akcijų biržos procesų, kurie yra priklausomi nuo investuotojų prognozių. Pagrindinis modelio patobulinimas yra kelių akcijų ir įvairių prekybos taisyklių įvedimas, kurios atstovauja tiek potencialių investuotojų euristikas, tiek gerai žinomas teorines investavimo strategijas. Tai suteikia modeliui daugiau realistiškumo ir leidžia atlikti portfelio optimizavimą naudojant įvairias investavimo strategijas tiek su istoriniais duomenimis, tiek virtualioje aplinkoje. Tai esminis patobulinimas lyginant su tradiciniais vienos akcijos modeliais. "Virtuali" akcijų birža gali padėti tiriant racionalaus investuotojo elgesio prielaidą lyginant su pastarojo laikotarpio teorijomis, teigiančiomis, kad pagrindiniai rinkos dalyviai elgiasi neracionaliai. Modelis buvo lyginamas su realiomis finansinėmis laiko eilutėmis ir buvo rastas rezultatų panašumas tam tikrais atvejais. PORTFELIO modelis gali būti naudojamas kaip priemonė imituoti individualaus investuotojo elgesį, kuris nori prognozuoti, kaip tikėtinas pelnas priklauso nuo įvairių investavimo taisyklių naudojant skirtingus realių ir virtualių akcijų kainų prognozavimo metodus.

Paprastas akcijų rinkos žaidimo modelis (angl. Stock Market Game Model) buvo pristatytas J. Mockaus 2002 m. Šis modelis imituoja kelių akcininkų, prekiaujančių viena akcija, elgesį. Siūlomas modelis PORTFOLIO, priešingai, imituoja akcijų biržos darbą, kurioje vyksta prekyba su daugelio firmų akcijomis. PORTFELIO modelio tikslas yra ne prognozavimas, bet simuliavimas akcijų biržos procesų, kurie yra priklausomi nuo investuotojų prognozių. Pagrindinis modelio patobulinimas yra kelių akcijų ir įvairių prekybos taisyklių įvedimas, kurios atstovauja tiek potencialių investuotojų euristikas, tiek gerai žinomas teorines investavimo strategijas. Tai suteikia modeliui daugiau realistiškumo ir leidžia atlikti portfelio optimizavimą naudojant įvairias investavimo strategijas tiek su istoriniais duomenimis, tiek virtualioje aplinkoje. Tai esminis patobulinimas lyginant su tradiciniais vienos akcijos modeliais. "Virtuali" akcijų birža gali padėti tiriant racionalaus investuotojo elgesio prielaidą lyginant su pastarojo laikotarpio teorijomis, teigiančiomis, kad pagrindiniai rinkos dalyviai elgiasi neracionaliai. Modelis buvo lyginamas su realiomis finansinėmis laiko eilutėmis ir buvo rastas rezultatų panašumas tam tikrais atvejais. PORTFELIO modelis gali būti naudojamas kaip priemonė imituoti individualaus investuotojo elgesį, kuris nori prognozuoti, kaip tikėtinas pelnas priklauso nuo įvairių investavimo taisyklių naudojant skirtingus realių ir virtualių akcijų kainų prognozavimo metodus. / A simple Stock Market Game Model (SEGM) was introduced in 2002 by J. Mockus to simulate the behavior of several stockholders trading a single stock. In contrast, the proposed model PORTFOLIO is simulating stock exchange including a number of different stocks. The objective of PORTFOLIO is not forecasting, but simulation of stock exchange processes that are affected by predictions of the participants. The main improvements are the multi-stock extension and a number of different trading rules, which represent both the heuristics of potential investors and the well-known theoretical investment strategies. This makes the model more realistic and allows the portfolio optimization in the space of investment strategies, in both the historical and virtual environments. This is an essential improvement comparing with traditional single-stock models with direct interaction of investment agents. The "virtual" stock exchange can help in testing the assumption of rational investor behavior vs. the recent theories that explain financial markets by irrational responses of major market participants. The model has been compared with actual financial time series and found the results to be close in some cases. The model is designed as a tool to represent behavior of individual investor, which wants to predict how the expected profit depends on different investment rules using different forecasting methods of real and virtual stocks.

The price of stocks, futures, commodities and currency are for ever changing. Anyone interested in financial prices soon discovers that changes in prices are frequently substantial and are always difficult to forecast. This paper describes the behavior of prices from a statistical perspective. Specifically, employ several technical trading rules to uncover the trend of futures price movement and attempt to make profit out of the trend. In this paper, trading of seven technical trading systems is simulated for three futures contracts from September 1998 to March 2005 to test for market disequilibrium. The results differ by trading systems. Four systems produced positive mean net returns and five systems produced positive gross return when optimal parameters were used. These results indicate that, there exist opportunities to design profitable trading systems for futures markets.

參考Fang 2003年研究方法架構，我們檢驗了結合策略(結合技術分析法則和時間序列模型)應用在六個亞洲股票市場。由於技術分析法則和時間序列模型皆可利用過去歷史資訊來預測報酬，所以結合策略的實證結果優於技術分析法則和時間序列模型。此篇中超額報酬的計算是與買進持有相比較下未考慮交易成本的超額報酬。實證結果顯示，結合策略在完整樣本中可以成功的預測資產報酬，在六個國家的平均上，結合策略的超額報酬為0.19%優於技術交易法則下的0.13%和時間序列模型下的0.17%。並且，發現在新興國家如台灣、泰國、馬來西亞和南韓的預測能力比在已開發國家市場如香港和日本還要來的好。預測能力可被低階的自我相關係數解釋。除此之外，發現我們的預測能力受到非同步交易的影響。非同步交易所造成的衡量誤差使得超額報酬下降，但是我們的預測能力還是存在的。 / Following Fang and Xu (2003), we examine trading strategies combining technical trading rules and times series forecasts on six Asian stock markets. Since both technical trading rules and time series models can exploit predictable components as function of past prices or returns, the combined strategies outperform both technical trading rules and time series forecasts. The excess returns before transaction costs for each rule and country are compared to a passive buy-and-hold strategy. The combined strategies are quite successful in predicting asset returns in full samples. On average the buy-sell returns for combined strategies are 0.19% much higher than 0.13% for technical trading rules and 0.17% for time series models. Besides, we also find that all three rules have more explanatory power in emerging markets such as Taiwan, Thailand, Malaysia and Korea than more developed markets such as Japan and Hong Kong. The predictability can be explained by significant low-order autocorrelations in returns. Moreover, excess returns (pre-trading costs) for both time series models and combined strategies can be partially attributed to the measurement errors arising from non-synchronous trading. The non-synchronous trading bias reduces but does not eliminate the predictive power of combined strategies.

The Turtle Trading Rules was a successful trend-following trading strategy for commodities in the 1980s but has lost recognition in recent days. The strategy revolved around rules for entering and exiting trades as well as position sizing for each trade. The rules was based on the fundamental aim to capture market trends while at the same time maintaining a controlled risk exposure. This thesis aims to revise the Turtle Trading Rules, here applied on major market indexes, and to examine its viability through different financial metrics. This is done by first implementing the aforementioned trading strategy to the indexes, and later by synthesizing market data through Geometric Brownian Motions. The latter primarily to examine how the strategy perform in different financial environments, what market traits favor the strategy, and to complement the previous examination without altering the core principles of the Turtle Trading Rules. The results suggest that the revised rules for major market indexes is not viable. This because of the poor return, the highest achieved 20-year return and average annual return was 25.1 % and 1.4% respectively (without taking trading fees into account). Furthermore, the strategy applied on synthetic data suggests that favorable traits are highly cyclical data with low volatility, which seldom is the case for real financial time series. The results further indicate that the main issue lies in the rules not being able to distinguish noise from actual entry and exit triggers. Moreover, the drawdown further suggest that it is the exit rather than the entry rules that are to blame for the poor performance during the cycle of a trade. / Turtle Trading-reglerna var en framgångsrik trendföljande handelsstrategi för råvaror på 1980- talet men har sedan dess tappat i populäritet. Strategin kretsade kring regler för inträde och utträde ur ordrar samt kring positionsstorleken för varje order. Reglerna byggde på det grundläggande syftet att fånga marknadstrender och samtidigt upprätthålla en kontrollerad riskexponering. Den här avhandlingen syftar till att revidera Turtle Trading-reglerna, som här tillämpas på utvalda marknadsindex, och att undersöka dess lönsamhet genom olika finansiella mått. Detta görs genom att först implementera den tidigare nämnda handelsstrategin till indexen, och senare genom att syntetisera marknadsdata genom geometriska brownska rörelser (Geometric Brownian Motions). Det senare för att i första hand undersöka hur strategin fungerar i olika finansiella miljöer, vilka marknadsdrag som gynnar strategin, och för att komplettera den tidigare granskningen utan att ändra grundprinciperna i Turtle Trading-reglerna. Resultaten tyder på att de reviderade reglerna för marknadsindexen inte är tillräckligt lönsamma. Detta på grund av den låga avkastningen, den högst observerade 20-årsavkastningen och den genomsnittliga årliga avkastningen var 25,1 % respektive 1,4% (utan hänsyn till handelsavgifter). Dessutom antyds när strategin tillämpas på syntetiska data att tydliga cykliska variationer samt låg volatilitet är fördelaktiga egenskaper, vilket sällan är fallet för reella finansiella tidsserier. Resultatet indikerade vidare att grundproblemet ligger i att reglerna inte kan skilja brus i datan från faktiska inträde och exit triggers. Dessutom tyder drawdown-graferna på att det är exit snarare än inträdesreglerna som är orsaken till det dåliga resultatet i utförandet av köp- och säljprocessen.

Studying whether new trading rules provide higher returns than the buy-and-hold strategy is relevant for both finance theory and the asset management field. In this thesis, we examine the profitability of the newly proposed trading strategy based on the concept of price overreaction on eight developed stock indices. In comparison to other studies, we extend a definition of price overreaction with an inclusion of a minimum volatility threshold. Based on the Ordinary Least Squares model, we find that a volatility condition significantly improves the predictability of return reversals after positive price overreaction. For comparison with the buy-and-hold, we use Hansen's Superior Predictive Ability test that corrects the data snooping bias. Despite better annualised returns during in-sample and out-of-sample periods, the results show that the proposed strategy is not superior to the buy-and-hold at any stock index due to heavy reliance on the predictions of the largest declines. Nevertheless, we confirm the effect of decoupling (flight to quality) that can positively affect our strategy, but only when we do not take into account transaction costs. In the end, we summarize behavioural concepts that lie behind our strategy as the overreaction and decoupling are mostly justified with cognitive biases.