Littérature scientifique sur le sujet « Expert trading system »

Ith the method of empirical analysis, based on the huge amounts of real data opened by securities market, through testing the expert system of securities trading software MTM, MACD and MA, with odds, annual returns and net profit margin as management goals, statistical theory as research basis, comparatively analyzed the advantages and disadvantages of the three systems. Except for annual transaction times of number, MA expert system is slightly better than the MACD expert system; while MA expert system, no matter in odds, net profit margin or in annual returns, trading opportunities, is better than that of MTM expert system in all-round way.

The goal of this study was to analyze the possibilities of fuzzy neural networks and evolutionary programming methods for creating the human skill based stock trading systems. In stock exchange markets, the relationships between market variables are generally too complex to make rightful trading decisions and to earn stabile profits using classical system theory approach. On the other hand, there are a lot of trading experts-practicians that successfully trade stocks and achieve good results in the stock exchange markets. A useful technique for expert-knowledge extraction is the supervised learning methods, where human-experts actions are mapped using fuzzy-neural networks. In this paper we outline this procedure. Also we discuss the possibilities for improvement the proposed human skill based stock trading systems. An efficient biological system evolves slowly over the course of hundreds and housands of generations of individuals. Later generations have more fit and are more capable than earlier ones. Similarly, we have used evolutionary techniques to .evolve. the fuzzy-neural network based stock trading system, which is capable to solve the stock trading task more efficiently. Proposed procedure was tested using virtual trading system that uses historical data from US stock markets. The first results confirmed the good opportunities of the proposed approach.

According to extensive quantity of public and factual data, this essay does tests on BIAS and W&R expert system of stock-market trading software in simulation experiment method, and compares their strength and weakness in the respect of mathematical statistics theory and Management objects, winning rate, annual return rate and net profit rate. The tests show that BIAS expert system is 2.58 times as good as W&R expert system in the items of annual return rate and net profit rate, 0.26 times in the item of the total number of trading. As a whole, BIAS expert system is superior to W&R expert system.

This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

Social trading services, which are financial services connected with social networking services, are currently in the spotlight. Users can follow and automatically imitate expert traders’ trades using social trading services. Finding expert traders who exhibit an exceptional and consistent performance for users to follow is a key challenge in this field. We propose a ranking mechanism with three measures to address this issue: performance, risk, and consistency. We estimated traders’ performance, risk, and consistency levels by comparatively analyzing their trading histories and news data. In addition, we propose a system called Whom to Follow (W2F) to help users discover expert traders by utilizing this ranking mechanism. W2F visualizes the ranking results, and provides feedback functions to help users reach decisions regarding who to follow. We conducted experiments to test and then validate the proposed ranking mechanism in terms of the ranking accuracy, profit, and ranking stability. We also conducted a user experiment to demonstrate the feasibility of W2F.**This paper is an extended version of “Whom to Follow on Social Trading Services? A System to Support Discovering Expert Traders,” 10th Int. Conf. on Digital Information Management (ICDIM) 2015, pp. 188-193.

Based on the public and actual massive data of securities market, through testing the MA and RSI expert system of securities trading software by simulation experiment method, with the winning percentage, the annual rate of return, and the net profit margin as the management objectives, and with the theory of mathematical statistics as the research basis, the article carried out the comparative analysis of strength and weakness of these two systems. Except the annual transaction number, RSI expert system is comprehensively superior to MA expert system.

Based on mass real data with the method of statistical experiment, this paper attempts to take the winning rate, annual rate of return and net profit margin as the management objectives for the comparative analysis on the strengths and weaknesses of the four systems of RSI, BIAS, KDJ and W&R among the securities trading software through testing, the results of which show that RSI expert system is the best one with its overall testing results superior to other anti-trend expert systems (MA, MACD, MTM). So it is suggested that investors should choose only RSI expert system.

2007/2008
Evolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction is added the population can begin to explore the search space and will move to individuals that have an increased selection probability and that hand down this property to their descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the “survival of the fittest”. Although evolutionary computations belong to a relative new research area, from a computational perspective they have already showed some promising features such as: • evolutionary methods reveal a remarkable balance between efficiency and efficacy; • evolutionary computations are well suited for parameter optimisation; • this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods; • evolutionary methods are easily combined with other optimization techniques and can also be extended to multi-objective optimization. From an economic perspective, these methods appear to be particularly well suited for a wide range of possible financial applications, in particular in this thesis I study evolutionary algorithms • for time series prediction; • to generate trading rules; • for portfolio selection. It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the last decades, researchers have employed intensive econometric and statistical modeling that examine the effects of a multitude of variables, such as price- earnings ratios, dividend yields, interest rate spreads and changes in foreign exchange rates, on a broad and variegated range of stocks at the same time. However, these models often result in complex functional forms difficult to manage or interpret and, in the worst case, are solely able to fit a given time series but are useless to predict it. Parallelly to quantitative approaches, other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering informed signals from management and analysts, such as share repurchases and analyst recommendations. These theories are guided by intuition and experience, and thus are difficult to be translated into a mathematical environment. Hence, the necessity to combine together these point of views in order to develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is urged. To this end, the thesis focuses on the study of methodologies that satisfy these requirements by integrating economic insights, derived from academic and professional knowledge, and evolutionary computations. The main task of this work is to provide efficient algorithms based on the evolutionary paradigm of biological systems in order to compute optimal trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies are: i) the necessity to overcome data-snooping and supervisorship bias in order to learn to predict good trading opportunities by using market and/or technical indicators as features on which to base the forecasting; ii) the feasibility of using these rules as benchmark for real trading systems; iii) the capability of ranking quantitatively various markets with respect to their profitability according to a given criterion, thus making possible portfolio allocations. More precisely, I present two algorithms that use artificial expert trading systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management. The first algorithm is an automated procedure that simultaneously selects variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making possible a better exploration of the solution space on one hand, and the use of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand. In the second algorithm, the novelty is given by the definition of evolutionary learning in financial terms and its use in a multi-objective genetic algorithm in order to generate technical trading systems. The last tool is based on a trading strategy on six assets, where future movements of each variable are obtained by an evolutionary procedure that integrates various types of financial variables. The contribution is given by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and collected. In order to compare the contribution of this work to “classical” techniques and theories, the thesis is divided into three parts. The first part, titled Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming and genetic algorithms, and points out similarities and differences among them. Moreover it gives an overview of genetic algorithms and describes classical and genetic multi-objective optimization techniques. Chapter 3 first presents an overview of the literature on the predictability of financial time series. In particular, the extent to which the efficiency paradigm is affected by the introduction of new theories, such as behavioral finance, is described in order to justify the market forecasting methodologies developed by practitioners and academics in the last decades. Then, a description of the econometric and financial techniques that will be used in conjunction with evolutionary algorithms in the successive chapters is provided. Special attention is paid to economic implications, in order to highlight merits and shortcomings from a practitioner perspective. The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial trading strategies on the basis of evolutionary algorithms, and it groups Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for variable selection by minimizing the error in a multiple regression model. Measures of errors such as ME, RMSE, MAE, Theil’s inequality coefficient and CDC are analyzed choosing models based on AIC, BIC, ICOMP and similar criteria. Two components of penalty functions are taken in analysis- level of significance and Durbin Watson statistics. Asymptotic properties of functions are tested on several financial variables including stocks, bonds, returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators are removed to solve masking and smearing problems that they may cause in estimations. Two examples complete the chapter. In both cases, models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation- window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors. In Chapter 5 the paradigm of evolutionary learning is defined and applied in the context of technical trading rules for stock timing. A new genetic algorithm is developed by integrating statistical learning methods and bootstrap to a multi-objective non dominated sorting algorithm with variable string length, making possible to evaluate statistical and economic criteria at the same time. Subsequently, the chapter discusses a practical case, represented by a simple trading strategy where total funds are invested in either the S&P 500 Composite Index or in 3-month Treasury Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the the S&P 500 Composite Index, in three market phases, up-trend, down- trend and sideways-movements, covering the period 2000–2006. In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and on the signals produced by artificial trading systems. First, market neutral strategies from an economic point of view are introduced, highlighting their risks and benefits and focusing on their quantitative formulation. Then, a description of the GA-Integrated Neutral tool, a MATLAB set of functions based on genetic algorithms for active portfolio management, is given. The algorithm specializes in the parameter optimization of trading signals for an integrated market neutral strategy. The chapter concludes showing an application of the tool as a support to decisions in the Absolute Return Interest Rate Strategies sub-fund of Generali Investments.
Gli “algoritmi evolutivi”, noti anche come “evolutionary computations” comprendono varie tecniche di ottimizzazione per la risoluzione di problemi, mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie sono accomunate dal fatto che non considerano un’unica soluzione alla volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato problema. L’idea sottostante `e che, se un algoritmo fa evolvere solamente gli individui di una data popolazione che soddisfano a un certo criterio di selezione, e lascia morire i restanti, la popolazione converger`a agli individui che meglio soddisfano il criterio di selezione. Con una selezione non ottimale, cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter` a meglio l’intero spazio di ricerca e sar`a in grado di individuare in modo pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne alle popolazioni seguono i principi Darwiniani dell’evoluzione, che si possono sinteticamente riassumere nella dicitura “la sopravvivenza del più adatto”. Sebbene gli algoritmi evolutivi siano un’area di ricerca relativamente nuova, dal punto di vista computazionale hanno dimostrato alcune caratteristiche interessanti fra cui le seguenti: • permettono un notevole equilibrio tra efficienza ed efficacia; • sono particolarmente indicati per la configurazione dei parametri in problemi di ottimizzazione; • consentono una flessibilit`a nella definizione matematica dei problemi e dei vincoli che non si trova nei metodi tradizionali; • possono facilmente essere integrati con altre tecniche di ottimizzazione ed essere essere modificati per risolvere problemi multi-obiettivo. Dal un punto di vista economico, l’applicazione di queste procedure pu`o risultare utile specialmente in campo finanziario. In particolare, nella mia tesi ho studiato degli algoritmi evolutivi per • la previsione di serie storiche finanziarie; • la costruzione di regole di trading; • la selezione di portafogli. Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque l’analisi dell’evoluzione e della complessit`a dei mercati finanziari. In tal senso, dal momento che i prezzi non seguono andamenti puramente casuali, ma sono governati da un insieme molto articolato di eventi correlati, i modelli e le teorie classiche, come i dividend discount model e le varie capital asset pricing theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato una vasta gamma di modelli econometrici e statistici in grado di esaminare contemporaneamente le relazioni e gli effetti di centinaia di variabili, come ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque, questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore dei casi, pur essendo ottimi per descrivere situazioni passate, risultano inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si `e manifestato un grande interesse sulla psicologia degli investitori e sulle conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del mercato. Questi studi sono difficilmente traducibili in modelli matematici e si basano principalmente sull’intuizione e sull’esperienza. Da qui la necessit` a di combinare insieme questi due punti di vista, al fine di sviluppare modelli che siano in grado da una parte di trattare contemporaneamente un elevato numero di variabili in modo efficiente e, dall’altra, di incorporare informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando le conoscenze economiche, sia accademiche che professionali, con gli algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro `e lo sviluppo di algoritmi efficienti basati sul paradigma dell’evoluzione dei sistemi biologici al fine di determinare strategie di trading ottimali in termini di profitto e di vincoli economici e statistici. Le ragioni che motivano lo studio di tali strategie ottimali sono: i) la necessit`a di risolvere i problemi di data-snooping e supervivorship bias al fine di ottenere regole di investimento vantaggiose utilizzando indicatori di mercato e/o tecnici per la previsione; ii) la possibilità di impiegare queste regole come benchmark per sistemi di trading reali; iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto, o di altri criteri, rendendo possibile una migliore allocazione di risorse nei portafogli. In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading artificiali per predire serie storiche finanziarie e una procedura di calcolo per strategie integrate neutral market per la gestione attiva di portafogli. Il primo algoritmo `e una procedura automatica che seleziona le variabili e simultaneamente determina gli outlier in un modello dinamico lineare utilizzando criteri informazionali come funzioni obiettivo e test diagnostici come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a del metodo sono da una parte l’implementazione automatica di condizioni econometriche nella fase di selezione, consentendo una migliore analisi dello EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3 spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u informative. Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento evolutivo in termini finanziari e dall’applicazione di un algoritmo genetico multi-obiettivo per la costruzione di sistemi di trading basati su indicatori tecnici. L’ultimo metodo proposto si basa su una strategia di trading su sei assets, in cui le dinamiche future di ciascuna variabile sono ottenute impiegando una procedura evolutiva che integra diverse tipologie di variabili finanziarie. Il contributo è dato dall’impiego di un algoritmo genetico per ottimizzare i parametri negli indicatori tecnici e dal modo in cui le differenti informazioni sono presentate e collegate. La tesi `e organizzata in tre parti. La prima parte, intitolata Background, comprende i Capitoli 2 e 3, ed è intesa a fornire un’introduzione alle tecniche di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie che si occupano di efficienza e prevedibilit`a dei mercati finanziari dall’altra. Più precisamente, il Capitolo 2 introduce i concetti base e i maggiori campi di studio della computazione evolutiva. In tal senso, si dà una breve presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le strategie evolutive, la programmazione evolutiva e gli algoritmi genetici, evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica della prevedibilit`a delle serie storiche finanziarie mettendo in luce, in particolare, quanto il paradigma dell’efficienza sia influenzato dalle pi`u recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo scopo è quello di dare una giustificazione su basi teoriche per le metodologie di previsione sviluppate nella tesi. Segue una descrizione dei metodi econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni economiche, al fine di evidenziare i loro meriti e i loro difetti da un punto di vista pratico. La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed è dedicata alla descrizione di due procedure che ho sviluppato per generare sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare, il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso la minimizzazione dell’errore in un modello di regressione multipla. Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli diagnostici, ho considerato una funzione di penalità a due componenti e la statistica di Durbin Watson. Il programma impiega variabili finanziarie di vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti dalle economie Statunitense ed Europea. Nel caso le serie storiche 4 MASSIMILIANO KAUCIC considerate presentino outliers che distorcono l’efficienza e la consistenza degli stimatori, l’algoritmo `e in grado di individuarle e rimuoverle dalla serie, risolvendo il problema di masking and smearing. Il capitolo si conclude con due applicazioni, in cui i modelli sono progettati per produrre previsioni di breve periodo per l’extra rendimento del settore MSCI Europe Energy sull’indice MSCI Europe e una procedura di tipo recursive estimation-window è utilizzata per evidenziarne le performance previsionali. Nel primo esempio, l’insieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole numero di indicatori di tipo macro e da variabili finanziarie ritardate rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una notevole capacità previsiva per l’extra rendimento, individuando gli indicatori maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento evolutivo viene definito ed applicato alla costruzione di regole di trading su indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo che integra metodi di apprendimento statistico e di boostrap con un particolare algoritmo multi-obiettivo. La procedura derivante è in grado di valutare contemporaneamente criteri economici e statistici. Per descrivere il suo funzionamento, ho considerato un semplice esempio di trading in cui tutto il capitale è investito in un indice (che nel caso trattato è l’indice S&P 500 Composite) o in un titolo a basso rischio (nell’esempio, i Treasury Bills a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori e la loro conseguente integrazione mediante un metodo di apprendimento (il plurality voting committee, il bayesian model averaging o il Boosting). L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso in tre sottoperiodi: il primo rappresenta l’indice in una fase al rialzo, il secondo in una fase al ribasso e il terzo sottoperiodo considera il mercato in una fase di trend non chiara. Nella terza parte, intitolata Portfolio Selection, spiego come si possano costruire modelli di ottimizzazione di portafogli sfruttando le tecniche della computazione evolutiva e i segnali prodotti da sistemi di trading artificiali. A tal fine, dapprima descrivo il significato economico delle strategie neutral market, evidenziandone rischi e benefici, successivamente introduco il GAIntegrated Neutral tool, un insieme di funzioni MATLAB che ho scritto per la gestione attiva di portafogli impiegando algoritmi evolutivi. La procedura calcola la configurazone ottimale dei segnali di trading per una strategia integrata neutral market. Il capitolo si conclude mostrando un’applicazione del tool come supporto alle decisioni nel fondo Absolute Return Interest Rate Strategies di Generali Investments.
XXI Ciclo
1979

Focus of master's thesis is usability of automated trading of commodities with automated trading systems – expert advisors. Thesis describe theoretical background of commodity markets, trading principles, technical analysis of market, design and implementation of strategy as expert advisor. In conclusion, results are analyzed.

This Master’s thesis deals with implementation of an automated trading system for application in the currency market. The resulted system is tested and optimized on historical data. Robustness of this strategy is verified by testing on another currency pair and a different timeframe. Efficiency of the system is compared before and after optimization. Created trading system is profitable in all environments which it was tested on.

Bibliography: leaves 462-479. "... to provide a basis for understanding countertrade practices. In particular, however, it aims to provide assistance to trading parties to identify the problems associated with various forms of countertrade and to give them guidance in drafting countertrade contracts in the light of Australian law.".

The thesis focuses on automated trading systems for trading on currency market. It describes basics of market analysis and deals with the design, optimization and identifying appropriate indicators of automatic trading system, which is based on the Fibonacci retracement. This system should serve as a decision support for trader's operations in the currency market. Furthermore, this thesis deals with the possibility of avoiding exchange rate risk by trading in the foreign exchange market.

Technical analysis as a sophisticated form of forecasting technique has a varying popularity in the academic and business world. In the past, users were sceptical about technical trading rules and their performance. This is substantiated by the acceptance of the Efficient Market Hypothesis and mixed empirical findings about technical analysis in widely cited studies. The flag pattern is seen as one of the most significant spread chart patterns amongst stock market charting analysts. The present research validates a trading rule based on the further development of flag pattern recognition. The research question concentrates on whether technical analysis applying the flag pattern can outperform international stock markets indices and prove the inefficiency of these markets. The markets observed are represented by the corresponding indices DAX (Germany), DJIA (United States) and IBEX (Spain). The design of the trading rule presents several changes with respect to previous academic works: The wide sample used when considering intraday data, together with the confiuration of some of the variables and the consideration of risk, concludes that the trading rule provides greater positive risk-adjusted returns than the buy-and-hold strategy which is used as a benchmark. The reported positive results strengthen the robustness of the conclusions reached by other researchers.
El análisis técnico es una forma sofisticada de técnica de predicción cuya popularidad ha ido variando en el mundo académico y de los negocios. En el pasado, los usuarios eran bastante escépticos respecto de las reglas técnicas de trading y su performance. Todo esto, se encuentra sustentado por la aceptación de la hipótesis del mercado eficiente y descubrimientos empíricos mixtos sobre el análisis técnico, que se mencionan en un número amplio de estudios. El patrón bandera es visto como uno de los patrones gráficos más significativo y difundido entre los analistas técnicos de mercado. El presente estudio valida una regla de trading basada en el desarrollo futuro del reconocimiento gráfico del patrón bandera. La pregunta de investigación se centra en si el análisis técnico basado en el patrón bandera puede batir los índices internacionales de mercado y probar, de esta manera, la ineficiencia de dichos mercados. Los mercados observados son representados por los correspondientes índices DAX (Alemania), DJIA (Estados Unidos) e IBEX (España). El diseño de la regla de trading presenta varios cambios y novedades con respecto a trabajos académicos previos. La amplia muestra usada al considerar los datos intradía, junto con la configuración de algunas variables y la consideración del riesgo, confirman que la regla de trading proporciona mejores, y más ajustadas al riesgo, rentabilidades positivas que la estrategia de buy-and-hold que se utiliza como referencia. Los resultados positivos corroboran la robustez de las conclusiones a las que también se llegan en otros trabajos.
L'anàlisi tècnica és una forma sofisticada de tècnica de predicció, la popularitat de la qual ha anat variant al món acadèmic i dels negocis. En el passat, els usuaris eren bastant escèptics respecte de les regles tècniques de trading i la seva performance. Tot això, es troba sustentat per l'acceptació de la hipòtesi del mercat eficient i descobriments empírics mixts sobre l'anàlisi tècnica, que s'esmenten en un nombre ampli d'estudis. El patró bandera és vist com un dels patrons gràfics més significatiu i difós entre els analistes tècnics de mercat. El present estudi valida una regla de trading basada en el desenvolupament futur del reconeixement gràfic del patró bandera. La pregunta de recerca se centra en si l'anàlisi tècnica basada en el patró bandera pot batre els índexs internacionals de mercat i provar, d'aquesta manera, la ineficiència d'aquests mercats. Els mercats observats són representats pels corresponents índexs DAX (Alemanya), *DJIA (Estats Units) i IBEX (Espanya). El disseny de la regla de trading presenta diversos canvis i novetats pel que fa a treballs acadèmics previs. L'àmplia mostra usada en considerar les dades intradia, juntament amb la configuració d'algunes variables i la consideració del risc, confirmen que la regla de trading proporciona millors, i més ajustades al risc, rendibilitats positives que l'estratègia de buy-and-hold que s'utilitza com a referència. Els resultats positius corroboren la robustesa de les conclusions a les quals també s'arriben en altres treballs.
Michniuk, K. (2017). PATTERN RECOGNITION APPLIED TO CHART ANALYSIS. EVIDENCE FROM INTRADAY INTERNATIONAL STOCK MARKETS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/78837
TESIS

The paradigm shift engaged by countries in SADC and COMESA, such as Malawi, from the use of import substitution policies which were aimed at protecting their infant industries, to export led growth strategies, necessitated these developing countries to liberalise their economies. The liberalisation of these economies meant that, for them to attain development, they needed to trade more on the international market. However, with underdeveloped industries and a lack of local entrepreneurs who could provide export supplies to fill the void created by the liberalisation policies, developing countries had to look beyond their borders for investors. In pursuit of this objective, governments have been devising ways of attracting foreign direct investment which can stimulate export growth. One of the methods employed is the granting of investment incentives to would-be investors. Unlike developed countries who provide investment incentives in the form of financial incentives, developing countries grant fiscal incentives. These are incentives that reduce tax burdens of enterprises to induce them to invest in particular projects or sectors. One of the mediums of providing the incentives adopted by the developing countries is the use of EPZ schemes. EPZs provide incentives such as exemptions of direct and indirect taxes to companies that operate in the zones. However, being Members of the WTO and SADC and/or COMESA, these countries are bound by obligations regulating trade and investment as found in these Agreements. The expectation is that the fiscal incentives employed in the EPZs do not grant subsidies that are prohibited under the SCM Agreement and rules regulating subsidies in SADC and COMESA. In addition, even though the use of EPZs is not expressly proscribed under the SADC Protocol on Trade, it may be against the objectives of the Protocol - one of which is the pursuance of the inter-jurisdictional goal of cooperation in attainment of free trade among its members. Therefore, this study assesses whether the use of EPZs by some countries in the two RTAs (particularly Malawi) is in tandem with the subsidies regulation as found in the multilateral trading system and at regional level. It also assesses whether, if there is a breach of the same, it might be justified as part of the special and differential treatment accorded to developing countries by developed countries under the WTO. The study further assesses whether the use of EPZs might be against the spirit and objects of FTAs such as SADC.

Eradicating hunger and meeting food security expectations remain global goals. In the multilateral trading system (MTS) they can only be met through cooperation among countries in the form of international trade. Yet a number of trade-related incidents have eroded confidence in the capacity of the MTS to address food security issues. The research question here is “How can global food security challenges be addressed in a MTS?” The main focus is on the role of the World Trade Organization (WTO), but the research also covers its interaction with other organisations and the context in which it operates, that is, the MTS. The study covers four topics: • What is food security? • Does the WTO have a mandate and the capability to contribute to food security? • What policy measures are used to address food security issues and are they effective? • What significant changes in the context of the debate complicate or ease the quest for a consensus on how to respond to food security challenges? The method used was to collect information on the views of delegates, researchers and officials, by an online survey and from interviews, and to analyse the data using different tools. Views on “What is food security?” were grouped according to combinations of ‘orientations’ and ‘dimensions’. The former refers to a focus on people, trade or resources. The latter refers to availability, accessibility, stability and utilisation. Views on the nature of food security, while apparently showing a systematic variation by the frequency of responses, were not found to differ significantly in statistical terms over the development levels of the respondents’ countries. Lack of progress on food security issues in the WTO is therefore not caused primarily by a lack of a common understanding of the concept. A majority view was that the WTO mandate on food security is limited, although the less developed economies supported the counterview. Respondents also confirmed that WTO rules are inadequate in addressing food security issues. Lack of policy space was an issue for the least-developed and developing countries; inadequacy in disciplining trade-distortive measures was also a concern for the developing, developed and research/official groups; and lack of transparency was especially undesirable for the developed and least-developed countries. Import/export restrictions and subsidies (including domestic support) are widely used policy instruments for food security goals, despite their trade-distortive aspects. However, respondents had mixed views about the effectiveness of these policies, especially in the context of the inadequacy of rules to discipline them. The lack of case law through the dispute settlement system is compounding that issue. The food crisis of 2006–2008 raised the profile of food security but other dynamics have made it difficult to reach a consensus for change. These include greater diversity in the interests of the developing group as a whole, the shift in the negotiating positions of emerging developing countries, and protectionist concerns related to the increase in green box spending. For all these reasons – the uncertainty about the WTO’s mandate, the inadequacy of its rules and the diversity within the developing economy group – negotiations that are relevant to food security have been hindered and little progress has been made. The MTS could contribute to food security, but resolving these issues is the next step to doing so.
Thesis (Ph.D.) -- University of Adelaide, Institute for International Trade, 2017.

AbstractThis introductory chapter examines the question whether Russia’s increased role in the international agri-food system is sustainable. Russia’s role in the international food trade system has changed from being a food importer to an importer and exporter. The first section discusses several factors that impact food imports: food production; knowledge-based innovation; politicalisation of food trade policy; and population and consumption. The second part examines factors that affect food exports: agri-food export policy; climate change; foreign competition; infrastructure; and regional foreign demand. The final section provides an outlook for the future, concluding that Russia will remain a food importer although the structure of imported commodities and trading partners will continue to evolve. Absent a major climatological disaster or significant economic downturn, we express cautious optimism that Russia will continue as a major food exporter.

The objectives of sustainable development in agriculture are emphasizing the worries related to the possibility of ensuring food security at national levels caused by the climate change and agricultural productivity. These worries reflect directly on the international trading partnerships and affect the trading especially with agricultural and food supplies. The current research aims at providing a clear image of the modifications in the trade balance of Romania, recognised mostly as an exporter of raw agricultural products, such as cereals or live animals, at low prices. Romanian agricultural productivity is characterized by an acute weather dependency, due to the low levels of irrigation systems and extensive agricultural systems, combined with extreme heat during summers and low precipitation in winters and springs. Even so, the exports and imports of a country are a key factor in its economic development so they cannot be reduced only to food security and sustainability aspects. In this context, a clear image of Romania’s trade balance offers some insight for the designing of future policy related both to the possibilities of economic and sustainable development.

Organic production in the Republic of Serbia is controlled production, officially regulated by the Law on Organic Production ("Official Gazette of the Republic of Serbia", No. 30/10, 17/2019) that provided the legal basis for the adoption of regulations - Rulebook on control and certification in organic production and methods of organic production ("Official Gazette of RS", No. 095/2020) and the Rulebook on documentation submitting for certificate issuance by the authorized organization and organic products trading requirements ("Official Gazette of RS", No. 88/16). The Law on Organic Production regulates the production of agricultural and other products, goals, principles and methods of organic production and control, certification, processing, labeling, storage, transport, trade, import and export of organic products as well as other issues of importance for organic production. The provisions of the Act are applied to products originating from all stages of organic production - plant and livestock, including aquaculture products for market. Organic products are not considered to be products obtained by hunting and fishery. The system of organic products control in the Republic of Serbia was established in accordance to the European Union regulations- Council Regulation (EC) No 834/2007, Commission Regulation (EC) No 886/2008). On January 2021, these regulations will expire. The new Regulation of the European Parliament and the Council on organic production and labeling of organic products (Regulation (EU) 2018/848) will enter into force in order to respond to growing consumers demands and expectations.

Over the recent decades, the changes in the paradigm of international trade have been observed. As the result of decreasing of trade barriers as well as the reduction in trade costs allowed companies to divide their production into stages and to locate it in different countries according to their competitive advantage. Eventually, the production process has become more fragmented, both geographically and vertically. It means that intermediate products are shipped across boarders many times and every exporting economy provides some value added according to its competitive advantage. As a result, global value chains have become one of the most important feature of international trade. Following (Gereffi & Fernandez-Stark, 2011), in this study global value chains are defined as “the full range of activities that firms and workers do to bring a product from its conception to its end use”. Humphrey and Schmitz (2002) pointed out four types of upgrading in global value chains: product, process, functional and chain. Product and process upgrading involve companies retaining their positions in global value chains by enhancing productivity gains through adopting new product processes or “new configurations of product mix”. Thus, functional upgrading involves a slicing up the global value chains into new activity which generates higher value added, e.g. own brand manufacturing. In turn, chain upgrading involves a going up to new activity, which needs higher skills and capital and value added. Milberg and Winkler (2013) offered similar classifications of upgrading. Production fragmentation has caused a rapid increase in trade in intermediate goods as often companies offshore an intermediate stage of production process. Offshoring production has been typical to manufacturing (Timmer, et al., 2012), however, services have been often overlooked, but play a major role, especially in supporting global value chains (Kommerskollegium 2013). In turn, Digital Silk Road, announced in 2015, has become a significant part of Chinese Belt and Road Initiative strategy. China has implemented this strategy as a part of its long-term technological plan, under which China provides support to its exporters, including many well-known technology companies and builds a network of cooperation with selected countries in the field of technology, including ICT infrastructure, services, 5G networks, e-commerce, etc. China's rapid technological changes must not go unnoticed by trading partners, including analysed European countries, which, to maintain international competitiveness, are increasing the technological advancement and enhancing market protection against Chinese technology. Until recently, the value added from China to European countries was concentrated mainly on medium technology industries and value added from Europe to China focused more on advanced goods and services. Nowadays, there is a redirection of Chinese value added to high-tech activities (including service activities), which reflects China's ambition to build an economy that leads to innovation and industry 4.0. The transition of the CEE states’ economic and political systems initiated in the early 1990s, earned them the EU membership in 2004. The accession to the EU’s structures meant that these countries achieved the free-market economy status and they should be treated as the full member of the global business networks. Moreover, the decline in trade costs (transport and transaction), greater openness of their market and the removal of trade barriers have all helped the CEE states to join global value chains. Hence, the CEE economies are going to be more heavily involved in global production linkages. Many empirical studies have presented the close and dynamic integration of these countries with the EU market (especially the EU-15) and in a more limited scope with the whole global economy as well (Behar and Freund 2011). Generally, democratisation, the strengthening of political and economic relations (particularly with the EU), and the modernisation of many sectors (including financial sector, more advanced industries), were common elements of the CEE countries long-term development policies. One of their priorities was the redirection of foreign trade towards the EU and joining the global production linkages where China has become the core producer. Recently, the role of the economy in global value chains is more determined by the advancement of value added that it offers. Companies move toward services and innovations in the business model (Nenenen & Storbacka, 2010) and introduce industry 4.0 (Bundesministerium fur Bildung und Forschung, 2016). A symptom of these novelty is a concept of servicification of manufacturing (Neely et al. 2011) and cross-sectoral connections, which have reconstructed traditional global value chains (Naude et al. 2019) and, together with Industry 4.0, is expected to change the landscape of global manufacturing. As a result of facilitation of manufacturing, economies placed in the downstream market can improve their role in global value chains. In Europe, this can be an opportunity for most Central and Eastern European countries. Analyzing changes in CEE’s role in technological global value chains, we should take into account its two most important value-added suppliers: China and Germany, as well as their most important value-added buyer - Germany. These three economies established a sort of value added flows triangle. The regional supply chains built by Germany in the CEE allowed it to maintain a comparative advantage in sectors important for the economy, while helping the CEE countries join global value chains, positively influencing economic growth, but also reducing them to entities operating in less advanced stages of production (Jacoby, 2010; Fortwengel, 2011). Today, Germany also cooperates strongly with China (as a result of Digital Silk Road), and the CEE economies (especially the Visegrad Group) are increasingly dependent on Chinese value added, still linked to German value added. The most visible connections can be found in automotive and electronics. Hence, the question is: how strong are these links in servicification of manufacturing and whether there are visible trends in value-added flows in between this triangle in the era of industry 4.0 and Chinese Digital Silk Road. The research question seems to be relevant, thus in the subject literature, little is known about the mentioned relations (Roland Berger, 2021). The research method based on the analysis of data from the OECD Trade in Value Added databases, containing the world input-output tables for the period 2005–2018. The system of balance equations in the input-output model for one economy has been adopted to a multi-economy model. The model is described in more detail in (Koopman et al. 2013 or Hummels et al, 2001) and is based on the decomposition of gross exports. The method includes not only estimates of total value added in global value chains, but also calculations at both the mezoeconomic level and cross-sectoral flows of value added (including servicification of manufacturing). The results of analysis showed that most relations between economies continued to deepen the imbalance in flows of value added. The CEE economies are making their manufacturing increasingly dependent on advanced services (both from Germany and China). On the other hand, the share of CEE services to Chinese and German manufacturing is decreasing or remains steady. However, some trends could be observed in the last years, especially between Germany and China. German manufacturing is starting to rely more on Chinese value added (information and communication technologies services and the subgroup computer programming, consultancy and information services activities in manufacturing, information and communication technologies services' value added in transport equipment), although previously Germany provided more of these services to China. In telecommunications in manufacturing between CEE and Germany, the trend has turned against CEE. However, there was no direct compensation between pairs of economies, but the decrease in German value-added flows to China resulted in a much larger increase in value-added from China in German manufacturing. If the presented changes in flows were to reflect the effectiveness of Chinese industry 4.0 and Digital Silk Road. These strategies serve their purposes and increases not only the advancement of Chinese value-added exports, but also makes important economies dependent on this added value. On the contrary, the industry 4.0 strategy in CEE has not improved its position in the triad. Germany has still a strong position as a provider of value added, but its dependence on foreign value added is high, which derives from the links with CEE.

The energy transition is driving governments and industries to adopt various measures to reduce their climate impacts while maintaining the stability of their economy. Hydrogen technologies are one of the central topics in the energy transition. Different nations have different stances on it. Some governments see hydrogen as a decarbonization tool or part of their energy security strategy, while some others see it as a potential export commodity. While identifying priorities for the future, Kazakhstan should clearly define the role of hydrogen in the country’s long-term energy and decarbonization strategy. This work presents the first country-scale assessment of hydrogen technologies in Kazakhstan by focusing on policy, technology and economy aspects. A preliminary analysis has shown that Kazakhstan should approach hydrogen mainly as a part of its long-term decarbonization strategy. While coping with the financial risks of launching a hydrogen economy, the country can benefit from the export potential of low-carbon hydrogen in the near term. The export potential of low-carbon hydrogen in Kazakhstan is justified by its proximity to the largest hydrogen markets, huge resource base, and potentially low cost of production (in the case of blue hydrogen). Technology options for hydrogen transportation and storage for Kazakhstan are discussed in our work. The paper also identifies target hydrogen utilization areas in emission sectors regulated by Kazakhstan’s Emissions Trading System.

This review examines evidence on criminal deforestation activity in Latin America (particularly, but not exclusively the Amazon) and draws from the literature on the lessons learned in combatting criminal deforestation activity. This review focuses on Brazil as representative of the overwhelming majority of literature on criminal activity in relation to deforestation in the Amazon. The literature notes that Illegal deforestation occurs largely through criminal networks as they have the capacity for coordination, processing, selling, and the deployment of armed men to protect operations. Bribery, corruption, and fraud are deeply ingrained in deforestation. Networks may bribe geoprocessing experts, police, and public officials. Members of the criminal groups may become council members, mayors, and state representatives. Land titles are fabricated and trading documentation fraudulent. The literature also notes some interventions to combat this criminal deforestation activity: monitoring and law enforcement; national systems for registry and monitoring; legal enforcement for compliance of environmental law; International agreements and action; and Involving indigenous communities in combatting deforestation.

For Plant Protection and Quarantine (PPQ) and our partners, 2016 was a year of remarkable successes. Not only did we eradicate 10 fruit fly outbreaks, but we also achieved 4 years with zero detections of pink bollworm, moving us one step closer to eradicating this pest from all commercial cotton-growing areas of the continental United States. And when the U.S. corn industry faced the first-ever detection of bacterial leaf streak (Xanthomonas vasicular pv vasculorum), we devised a practical and scientific approach to manage the disease and protect valuable export markets. Our most significant domestic accomplishment this year, however, was achieving one of our agency’s top 10 goals: eliminating the European grapevine moth (EGVM) from the United States. On the world stage, PPQ helped U.S. agriculture thrive in the global market-place. We worked closely with our international trading partners to develop and promote science-based standards, helping to create a safe, fair, and predictable agricultural trade system that minimizes the spread of invasive plant pests and diseases. We reached critical plant health agreements and resolved plant health barriers to trade, which sustained and expanded U.S. export markets valued at more than $4 billion. And, we helped U.S. producers meet foreign market access requirements and certified the health of more than 650,000 exports, securing economic opportunities for U.S. products abroad. These successes underscore how PPQ is working every day to keep U.S. agriculture healthy and profitable.