Denna studie syftar till att åskådliggöra huruvida bankernas hållbarhetsrapporter innehåller olika detaljerad information och om viss information i sådana fall prioriteras. Forskaren avser vidare studera vad hållbarhetsrapporterna har för betydelse för investerare, och om eventuella trender i aktiekurserna kan identifieras i samband med hållbarhetsrapporternas publicering. För att besvara studiens syfte och forskningsfrågor har forskaren analyserat Handelsbankens, SEB:s och Swedbanks hållbarhetsrapporter mellan 2016 och 2020.  I denna studie har forskaren tillämpat en flerforskningsmetod, vilket är en kombination av en kvalitativ och kvantitativ forskningsstrategi. Genom att tillämpa en flerforskningsmetod avser forskaren att minimera respektive forskningsstrategis svagheter och därigenom öka resultatets trovärdighet. Studien har analyserats med hjälp av CONI-modellen som belyser kvaliteten och kvantiteten i rapporterna för att underlätta en empirisk jämförelse. Resultatet indikerar att bankernas hållbarhetsrapporter innehåller olika detaljerad information samt att bankerna prioriterar viss information framför annan. Forskaren har sedermera genomfört ett ANOVA- test för att besvara huruvida signifikanta skillnader existerar mellan hållbarhetsrapporterna. Resultatet visar att det inte finns några signifikanta skillnader, vilket skulle kunna bero på att det lagstadgade kravet på hållbarhetsredovisning trädde i kraft 1 januari 2017. Vidare visar den tekniska analysen att aktiekurser sjunker i samband med att hållbarhetsrapporter publiceras. Med stöd i tidigare forskning, och med hjälp av resultatet från CONI-modellens tolkande och mekanistiska delar, drar forskaren slutsatsen att bankernas hållbarhetsarbete, och således hållbarhetsrapportering, är av betydande vikt för investerare. / This study aims to illustrate whether the banks’ sustainability reports contain various detailed information, and if certain information is prioritized. The researcher also intends to study the significance of sustainability reports for investors, and whether any trends in share prices can be identified in connection with the publication of sustainability reports. To answer the study’s purpose and research questions, the researcher has analyzed Handelsbanken’s, SEB’s and Swedbank’s sustainability reports between 2016 and 2020.  In this study, the researcher has applied mixed methods research which is a combination of a qualitative and quantitative research strategy. Mixed methods research intends to minimize the weaknesses of each research strategy and thereby increase the credibility of the results. The study has been analyzed using the CONI-model, which highlights the quality and quantity of the reports to facilitate an empirical comparison. The results indicate that the banks’ sustainability activities contain various detailed information and that the banks prioritize certain information over others. The researcher has subsequently carried out an ANOVA-test to answer whether significant differences exist between the sustainability reports. The results show that there are no significant differences, which may be due to the legal obligation for sustainability reporting coming into force on the 1st of January 2017. Furthermore, the technical analysis indicates that share prices fall in connection with the publication of sustainability reports. With support from previous research, and with the results from the CONI-model, the researcher draws the conclusion that the banks’ sustainability activities, and thus sustainability reporting, is significant to investors.

The increasing usage of algorithms and extensive collections of data have changed the discipline of finance and created new possibilities for analyzing the financial markets. To further explore the potential of developing new methods for understanding financial market behaviour, this thesis examines the first digit probability distribution of Benford's Law and its applicability within the financial markets. The research investigates various indices', equities', and technical analysis tools' conformity to Benford's Law by using relative price changes and volume traded. It was found that both indices and equities exhibit resemblance with Benford's Law, whereas technical analysis tools did not. In addition, the relevance of data frequency was explored, but it was deemed not to have any effect on conformity found. In an attempt to apply the findings, a regression analysis was conducted to forecast volatility. However, even though correlation was found, the regression model failed to predict future volatility accurately. / Den ökade användningen av algoritmer och omfattande datainsamling har förändrat det finansiella spelrummet och skapat nya möjligheter för analys av finansmarknaden. För att ytterligare undersöka potentialen i att utveckla nya metoder för att förstå finansmarknadens beteende utforskar denna avhandling Benfords lag och dess tillämpbarhet på den finansiella marknaden. Studien testar olika index, aktiers och tekniska analysverktygs överensstämmelse med Benfords lag genom att använda relativa prisförändringar och handlad volym. Det visade sig att både index och aktier följer Benfords lag medan tekniska analysverktyg inte gjorde det. Dessutom undersöktes datafrekvensens relevans, men detta ansågs inte ha någon effekt på överensstämmelsen med fördelningen. I ett försök att tillämpa resultaten genomfördes en regressionsanalys för att prognosticera volatilitet. Korrelation hittades men regressionsmodellen gav inte ett tillförlitligt resultat.

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[pt] A dificuldade de se prever movimento de ativos financeiros é objeto de estudo de diversos autores. A fim de se obter ganhos, se faz necessário estimar a direção (subida ou descida) e a magnitude do retorno do ativo no qual pretende-se comprar ou vender. A proposta desse trabalho consiste em desenvolver um modelo de otimização matemática com variáveis binárias capaz de prever movimentos de subidas e descidas de ativos financeiros e utilizar um modelo de otimização de portfólio para avaliar os resultados obtidos. O modelo de previsão será baseado no Support Vector Machine (SVM), no qual faremos modificações na regularização do modelo tradicional. Para o gerenciamento de portfólio será utilizada otimização robusta. As técnicas de otimização estão sendo cada vez mais aplicadas no gerenciamento de portfólio, pois são capazes de lidar com os problemas das incertezas introduzidas na estimativa dos parâmetros. Vale ressaltar que o modelo desenvolvido é data-driven, i.e, as previsões são feitas utilizando sinais não-lineares baseados em dados de retorno/preço histórico passado sem ter nenhum tipo de intervenção humana. Como os preços dependem de muitos fatores é de se esperar que um conjunto de parâmetros só consiga descrever a dinâmica dos preços dos ativos financeiros por um pequeno intervalo de dias. Para capturar de forma mais precisa essa mudança na dinâmica, a estimação dos parâmetros dos modelos é feita em janela móvel. Para testar a acurácia dos modelos e os ganhos obtidos foi feito um estudo de caso utilizando 6 ativos financeiros das classes de moedas, renda fixa, renda variável e commodities. Os dados abrangem o período de 01/01/2004 até 30/05/2018 totalizando um total de 3623 cotações diárias. Considerando os custos de transações e os resultados out-of-sample obtidos no período analisado percebe-se que a carteira de investimentos desenvolvida neste trabalho exibe resultados superiores aos dos índices tradicionais com risco limitado. / [en] The difficulty of predicting the movement of financial assets is the subject of study by several authors. In order to obtain gains, it is necessary to estimate the direction (rise or fall) and the magnitude of the return on the asset in which it is intended to be bought or sold. The purpose of this work is to develop a mathematical optimization model with binary variables capable of predicting up and down movements of financial assets and using a portfolio optimization model to evaluate the results obtained. The prediction model will be based on the textit Support Vector Machine (SVM), in which we will make modifications in the regularization of the traditional model. For the portfolio management will be used robust optimization. The robust optimization techniques are being increasingly applied in portfolio management, since they are able to deal with the problems of the uncertainties introduced in the estimation of the parameters. It is noteworthy that the developed model is data-driven, i.e., the predictions are made using nonlinear signals based on past historical price / return data without any human intervention. As prices depend on many factors it is to be expected that a set of parameters can only describe the dynamics of the prices of financial assets for a small interval of days. In order to more accurately capture this change in dynamics, the estimation of model parameters is done in a moving window To test the accuracy of the models and the gains obtained, a case study was made using 6 financial assets of the currencies, fixed income, variable income and commodities classes. The data cover the period from 01/01/2004 until 05/30/2018 totaling a total of 3623 daily quotations. Considering the transaction costs and out-of-sample results obtained in the analyzed period, it can be seen that the investment portfolio developed in this work shows higher results than the traditional indexes with limited risk.

Paper [I] tests the success rate of trades and the returns of the Opening Range Breakout (ORB) strategy. A trader that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of low, high, open and close over a given time horizon. Paper [II] measures the returns of a popular day trading strategy, the Opening Range Breakout strategy (ORB), across volatility states. We calculate the average daily returns of the ORB strategy for each volatility state of the underlying asset when applied on long time series of crude oil and S&P 500 futures contracts. We find an average difference in returns between the highest and the lowest volatility state of around 200 basis points per day for crude oil, and of around 150 basis points per day for the S&P 500. This finding suggests that the success in day trading can depend to a large extent on the volatility of the underlying asset. Paper [III] performs empirical analysis on short-term and long-term Commodity Trading Advisor (CTA) strategies regarding their exposures to unanticipated risk shocks. Previous research documents that CTA strategies offer diversification opportunities during equity market crisis situations when evaluated as a group, but do not separate between short-term and long-term CTA strategies. When separating between short-term and long-term CTA strategies, this paper finds that only short-term CTA strategies provide a significant, and consistent, exposure to unanticipated risk shocks while long-term CTA strategies do not. For the purpose of diversifying a portfolio during equity market crisis situations, this result suggests that an investor should allocate to short-term CTA strategies rather than to long-term CTA strategies.

The introduction of contracts for difference, CFDs, in the Nordic market has meant a tremendous freedom for both large investors and small investors. CFDs, like many other financial derivatives, allow speculation on price movements in virtually all global markets with easy access to leverage, which is the same as borrowed capital. CFDs carry high risk and hence should only be used by qualified traders.  Swedish CFD providers have been criticized for mass marketing their services with lack of regard for the industry's sustainability. The legal requirements placed on CFD providers for customer's creditworthiness and knowledge are also said to be weak, given the products complexity and high risk. Thus, the purpose with this research is to respond to the criticism by examining the level of risk management, and the level of strategic awareness among CFD traders. The epistemological orientation of this research conforms to the philosophy of positivism, and the research approach is consistent with the deductive approach. The research is based on a quantitative data collection method were a survey was carried out with CMC Markets’ customers with 233 respondents.After examining the usage of risk management tools, position sizing and monitoring; the results suggest that the level of risk management among CFD traders is moderately high. For a trading strategy to function, and hence be successful, it is important that traders are consistent with the various components that a trading strategy consists of. After examining to what extent the CFD traders positions are based on rules and signals as well as loyalty to them, to what extent they optimize their trading strategy, and lastly to what extent they use available strategy methods I came to the conclusion that there is a relatively low level of strategic awareness.Keywords: CMC Markets, Finance, CFDs, Investment, Derivative, Securities Trading, CFD Trading, Stocks, Risk management, Strategic awareness, Trading strategy, Technical analysis, Fundamental analysis

量價關係，一直以來皆為技術分析學派所廣泛運用，其主張運用過去的股價以及成交量來推測股票未來的走勢，而也有許多的研究以及投資策略皆是從量價關係所出。在國內，小型股也由於其股本小的特性，往往成為有心人士炒作之標的。此外，小型股亦較大型股具有不對稱資訊的性質，而由於成交量背後往往隱藏著許多的資訊，因此投資人利用量與價之間的關係，得到能夠有效預測小型股股價的方法以利其投資。 而本文之研究，將量價關係運用在小型股上，想檢視彼此間有無任何關係存在。本文中我們使用了因果關係檢定，三因子模型，以及縱橫迴歸模型，用來分別檢視小型股與大型股的量價關係。驗證結果發現，在不同的檢驗方式下，都會得到小型股較大型股，有顯著量價影響的關係存在。 / The relation between volume and price is widely used in technical analysis. It predicts future stock price by using past stock price and volume. There are lots of investigations and investment strategies are stemmed from it. In Taiwan, small caps are preferred to be held by the people who would like to manipulate the price because of their small number of capitalization. In addition, compared with large caps, small caps are of asymmetric information to the investors. As there is lot of information hidden behind volume, investors are likely to use the relation between volume and price to get a useful way to predict small caps’ stock price. In this paper, I use granger causality test, three-factor model, and panel data model to test the relation between price/return and volume of small caps and big caps separately. The experiment shows that use different ways, we can verify there exist more obvious relations between volume and price in small caps than in large caps.

Le but de cette thèse est de construire un modèle de détection des changements de phase -passages de marché haussier à baissier et vice versa - du marché des actions américaines cotées, en utilisant un nombre relativement important de variables à la fois fondamentales (macroéconomiques et microéconomiques) et issues de l’analyse technique.Le modèle statistique retenu est la régression logistique statique, avec un retard pour les variables explicatives allant de zéro à trois mois. Les huit variables les plus significatives parmi vingt candidatesont été sélectionnées à partir des données mensuelles du S&P500 sur la période 1963-2003. Le modèle obtenu a été testé sur 2004-2013 et sa performance a été supérieure à celles de la stratégie Buy & Holdet d’un modèle univarié utilisant la variable ayant le plus fort pouvoir de détection - ce dernier modèle ayant fait l’objet d’une étude dans la littérature.Il a également été montré que des variables non encore considérées dans la littérature - la moyenne mobile sur les six derniers mois des créations nettes d’emplois non-agricoles, la base monétaire et le Composite Leading Indicator de l’OCDE - ont un pouvoir de détection significatif pour notre problématique. D'autre part, la variable binaire indiquant la position du S&P500 par rapport à sa moyenne mobile des dix derniers mois - variable de type analyse technique - a un pouvoir prédictif beaucoup plus élevé que les variables fondamentales étudiées. Enfin, les deux autres variables les plus statistiquement significatives sont macroéconomiques : l'écart entre les taux à dix ans des T-bonds et à trois mois des T-bills et la moyenne mobile des créations d’emplois non-agricoles. / The goal of this thesis is to build a model capable of detecting the reversals - shift from bull market to bear market or vice versa - of the American stock market, by using a relatively large number of explanatory variables, both of fundamental (macroeconomic and microeconomic) and of ‘technical analysis’ types.The statistical model used is static logistic regression, with lags for the independent variables ranging from zero to three months. Starting with twenty variables, the eight most significant ones have been selected on a training set consisting of monthly data of the S&P500 between 1963 and 2003. There sulting model has been tested over the 2004-2013 period and its performance was better than those of a buy & hold strategy and of a univariate model based on the variable with the highest predictive power – the latter model being the focus of a paper in the current literature. Another contribution of the thesis is that some variables not yet studied in the literature – the six month moving average of net non-farm job creations, the monetary base and the OECD Composite Leading Indicator – are statistically significant for our problem. Moreover, the predictive power of the binary variable indicating whether the S&P500 is above or below its ten-month moving average – a technical analysis variable – is much higher than that of the fundamental variables which have been considered. Finally, the two other most significant variables are macroeconomic ones: the spread between the ten-year T-bond and three-month T-bill rates and the moving average of non-farm jobs creations.

Este trabajo se plantea la cuestión que millones de inversores se han planteado en algún momento: ¿cuál es la mejor opción para sus ahorros, fondos de inversión, inversión aleatoria o estrategias de análisis técnico? Para este propósito se describen en primer lugar las normas que regulan a las instituciones de inversión colectiva (IIC) en España, distinguiendo entre los diferentes tipos de fondos en cuanto a su forma legal. A continuación se repasan las teorías sobre eficiencia en los mercados financieros. Estas teorías se enlazan con los estilos de gestión; gestión pasiva para aquellos ortodoxos que defienden la eficiencia fuerte y gestión activa para los gestores que no toman la eficiencia como un dogma. Estos últimos creen en las anomalías de mercado y recurren a estrategias basadas en fundamentos contables (estimación de beneficios, ventas, etc.). Esta primera parte concluye con una evaluación del rendimiento de los fondos españoles según su estilo de inversión. Puesto que esta no es del todo favorable para las gestoras se intentan aportar motivos por los cuales los fondos siguen disfrutando de amplia aceptación. La segunda parte del trabajo describe la metodología empleada para estudiar el comportamiento de una cartera de inversión gestionada mediante estrategias de análisis técnico. Con este fin ha sido necesario desarrollar un software capaz de realizar la gestión de carteras y que se alimenta de cotizaciones históricas desde 1/2003 hasta 1/2012. Los datos se separan en dos estudios paralelos, uno para Europa y el otro para EE.UU con el objetivo de analizar diferencias y semejanzas. El programa permite el control completo sobre la cartera, gestión de liquidez, stop-loss, etc.; y nos abastece al mismo tiempo de una gran cantidad de información estadística. La particularidad del software es la capacidad de poder variar los parámetros de las estrategias mediante barrido, obteniendo así no solamente una única simulación sino una población de simulaciones referidas a una estrategia. En la tercera parte se recurre a este conjunto de simulaciones a las que denominaremos estudios y están compuestas por varios millones de operaciones de compra y venta. Estos estudios se aproximan a funciones normales que describen la esperanza de rentabilidades que tendría un inversor que decidiera participar en el mercado siguiendo alguna de las estrategias descritas. Para poder comparar el comportamiento de las estrategias técnicas se utilizan diferentes métodos aleatorios que pretenden simular una operativa al azar. Por último se confrontan los tres métodos de inversión: fondos, análisis técnico y aleatorio; comparados con los índices de referencia correspondientes. / Plá María, M. (2014). Evaluación del comportamiento de carteras con gestión automatizada comparada con los rendimientos de carteras aleatorias y fondos de inversión [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/38987 / TESIS

This diploma work initially conduct research in the area of descriptions and analysis of time series. The thesis then proceed to introduce the problems of technical analysis of price charts as well as indicators, price patterns and method of Pure Price Action. The method Pure Price Action is demonstrated in this work in two practical examples of its application to real businesses with a view to discovering and analyzing price patterns, as well as analysis and prediction of future price and financial evolution. This analysis is an introduction to the processes of successful business, following on from this we discuss the theme of Pattern Recognition and the Instance Based Learning method. The practical aspect of this work is carried out with the aid of a MATLAB applied algorithm for the analysis of the price pattern Correction for sale and purchase in dynamic time segments, specifically in trading price graphs, like those used for commodities or stock trading. For the analysis of time series we use the Pure Price Action method. The Instance Based Learning method is used by the algorithm to recognize price patterns. The created algorithm is verified on real data of a 5 minute time series of the US Dow Jones price charts for the years 2006, 2007, 2008. The achieved accuracy is evaluated with the aid of Equity Curves.