A geração hidrelétrica é dependente da afluência, no entanto, é possível minimizar a variação da energia natural afluente por meio dos reservatórios. Por sua vez, a geração eólica tem como desvantagem a volatilidade devido a sua dependência em relação ao vento. Nesse sentido, uma carteira otimizada de projetos eólicos possibilita a redução da volatidade da energia gerada pelo conjunto, na medida em que aproveita as complementariedades do vento. No Brasil, os Estados da Bahia, Rio Grande do Norte, Ceará, Rio Grande do Sul e Piauí concentram 90% da capacidade instalada das usinas eólicas em operação, em construção ou contratada, com uma previsão da fonte atingir 11,6% de participação na matriz elétrica. A pesquisa tem como objetivo desenvolver uma metodologia de apoio baseada na teoria de portifólio de Markowitz que poderá ser utilizada pelo órgão de planejamento energético brasileiro para a definição da quantidade de energia a ser contratada por fonte e local, por meio de leilões de energia regionais e por fonte, com o objetivo de se obter uma carteira otimizada de empreendimentos, que reduza a volatilidade. O método também pode servir de apoio ao investidor para se obter um portfólio de usinas que minimize o risco de exposição financeira no mercado de curto prazo. Nenhum estudo aplicando a teoria de portifólio de Markowitz em usinas eólicas do Brasil foi encontrado na literatura. Os resultados obtidos demonstram que a carteira formada pelas usinas eólicas existentes não está na fronteira eficiente e poderia ser otimizada com aumento da expectativa de geração ou redução do risco. No mesmo sentido, a otimização da carteira também reduziu o risco de exposição ao mercado de curto prazo. / Even though the hydroelectric generation is highly dependent on the river flows, it is possible to minimize the volatility of the energy generation in a given period using the storage capacity of the reservoirs. In contrast, to minimize the volatility of the wind generation is burdensome due to its dependency on wind. Accordingly, an optimized portfolio of wind projects all together allows the reduction of the volatility of the energy generation for the complementarity of wind from different locations. In Brazil, the states of Bahia, Rio Grande do Norte, Ceara, Rio Grande do Sul and Piauí concentrate 90% of the installed capacity of wind power plants in operation, under construction or contracted with a font forecast to reach 11.6% share the electric matrix. The Thesis aims to develop a support methodology based in portfolio theory of Markowitz that can be used by the Brazilian-planning agency in future, to define the amount of energy to be contracted by source and location, through regional and source energy auctions, to obtain an optimized portfolio projects, with reduced volatility. The methodology can also serve to support the investor to obtain a portfolio of plants that minimize the risk of financial exposure to short-term market. No study applying Markowitz\'s portfolio theory in wind farms of Brazil was found in the literature. The results show that the portfolio of the existing wind farms is not on the efficient frontier and could be optimized with increased expectation of generating or reducing the risk. Similarly, the optimization of the portfolio also reduced the risk of exposure to short-term market.

Submitted by Juliano Siervo (jjulianods@yahoo.com.br) on 2017-11-22T22:39:07Z No. of bitstreams: 2 Aplicação de Programação Linear na Seleção de Carteiras de Investimento.pdf: 2024591 bytes, checksum: 1ae718bddf0383c29c91824a02979dd3 (MD5) modelo-carta-comprovanteLOGOdosPPGs.pdf: 633658 bytes, checksum: 9f56ee77aadbb677e762ff0466374d52 (MD5) / Approved for entry into archive by Milena Rubi ( ri.bso@ufscar.br) on 2017-11-23T11:31:37Z (GMT) No. of bitstreams: 2 Aplicação de Programação Linear na Seleção de Carteiras de Investimento.pdf: 2024591 bytes, checksum: 1ae718bddf0383c29c91824a02979dd3 (MD5) modelo-carta-comprovanteLOGOdosPPGs.pdf: 633658 bytes, checksum: 9f56ee77aadbb677e762ff0466374d52 (MD5) / Approved for entry into archive by Milena Rubi ( ri.bso@ufscar.br) on 2017-11-23T11:31:56Z (GMT) No. of bitstreams: 2 Aplicação de Programação Linear na Seleção de Carteiras de Investimento.pdf: 2024591 bytes, checksum: 1ae718bddf0383c29c91824a02979dd3 (MD5) modelo-carta-comprovanteLOGOdosPPGs.pdf: 633658 bytes, checksum: 9f56ee77aadbb677e762ff0466374d52 (MD5) / Made available in DSpace on 2017-11-23T11:32:05Z (GMT). No. of bitstreams: 2 Aplicação de Programação Linear na Seleção de Carteiras de Investimento.pdf: 2024591 bytes, checksum: 1ae718bddf0383c29c91824a02979dd3 (MD5) modelo-carta-comprovanteLOGOdosPPGs.pdf: 633658 bytes, checksum: 9f56ee77aadbb677e762ff0466374d52 (MD5) Previous issue date: 2017-09-29 / Não recebi financiamento / It is shown in this dissertation the applicability of Harry M. Markowitz´s Modern Theory, allied to Operation Research, in the diversification of actions in an investment portfolio, minimizing its total risk in a given expected feedback. So, Linear Programming is used in order to model the portfolio´s variance, and the Simplex Method to determine the optimized portfolio. In a second step, Quadract Programming is used in order to model the portfolio´s variance and the model is implemented in the software MATLAB. Based on the results, their relevance an advantages are discussed. / Nessa dissertação é mostrada a aplicabilidade da Teoria Moderna de Portfolio de Harry M. Markowitz, aliada a Pesquisa Operacional, na diversificação de ações em uma carteira de investimento, minimizando risco total do portfólio com um dado retorno esperado. Então, utiliza–se a Programação Linear para modelar a variância da carteira e o Método Simplex para determinar a carteira ótima. Em uma segunda etapa utiliza–se a Programação Quadrática para modelar a variância da carteira e implementa–se o modelo no software MATLAB. Diante desses resultados, discutem–se quais as vantagens e relevâncias desses resultados.

nÃo hà / Quando a conjuntura econÃmica de um paÃs propicia baixa taxa de juros de mercado, a rentabilidade de aplicaÃÃes ditas seguras, como em renda fixa, deixa de ser negÃcio atrativo para investidores, que optam por submeter-se a um risco maior em busca de maiores rendimentos. Em tais cenÃrios, investidores arriscam-se no mercado acionÃrio, no qual ganhos maiores podem ser auferidos, apesar do risco superior ao da renda fixa. A Teoria Moderna do PortfÃlio mostra que esse risco pode ser reduzido pela diversificaÃÃo de ativos. Esta pesquisa tem por objetivo verificar se um modelo quantitativo baseado na Teoria Moderna do PortfÃlio à capaz ajudar na diversificaÃÃo de um portfÃlio, reduzindo risco a nÃveis inferiores aos da carteira de mercado, enquanto proporciona rendimentos superiores aos de s de mercado. Os testes utilizaram sÃries histÃricas de 36 ativos negociados na BOVESPA entre 1999 e 2012, e foram conduzidos em janelas de amostras de 12, 36, 60 e 120 observaÃÃes. Os resultados mostram que a ampliaÃÃo do horizonte de investimento permite a obtenÃÃo de desempenho superior do portfÃlio selecionado pela otimizaÃÃo baseada na mÃnima variÃncia, comparativamente à aplicaÃÃo livre de risco (CDI) e ao Ãndice Bovespa.

Ever since it was first introduced in an article in the Journal of Finance 1952, Harry Markowitz’ mean - variance model for portfolio selection has become one of the best known models in finance. The model was one of the first in the world to deal with portfolio optimization mathematically and have directly or indirectly inspired the rest of the world to develop new portfolio optimization methods. Although the model is one of the greatest contributions to modern portfolio theory, critics claim that it may have practical difficulties. Partly because the Markowitz model is based on various assumptions which do not necessarily coincide with the reality. The assumptions which are based on the financial markets and investor behavior contain the simplification that there are no transaction costs associated with financial trading. However, in reality, all financial products are subject to transaction costs such as brokerage fees and taxes. To determine whether this simplification leads to inaccurate results or not, we derive an extension of the mean-variance optimization model which includes brokerage fees occurred under the construction of an investment portfolio. We then compare our extension of the Markowitz model, including transaction costs, with the standard model. The results indicate that brokerage fees have a negligible effect on the standard model if the investor's budget is relatively large. Hence the assumption that no brokerage fees occur when trading financial securities seems to be an acceptable simplification if the budget is relatively high. Finally, we suggest that brokerage fees are negligible if the creation of the portfolio and hence the transactions only occurs once. However if an investor is active and rebalances his portfolio often, the brokerage fees could be of great importance. / Harry Markowitz portföljoptimeringsmodell har sedan den publicerades år 1952 i en artikel i the journal of Finance, blivit en av de mest använda modellerna inom finansvärlden. Modellen var en av dem första i världen att hantera portföljoptimering matematiskt och har direkt eller indirekt inspirerat omvärlden att utveckla nya portföljoptimeringsmetoder. Men trots att Markowitz modell är ett av de största bidragen till dagens portföljoptimeringsteori har kritiker hävdat att den kan ha praktiska svårigheter. Detta delvis på grund av att modellen bygger på olika antaganden som inte nödvändigtvis stämmer överens med verkligheten. Antagandena, som är baserad på den finansiella marknaden och individers investeringsbeteende, leder till förenklingen att transaktionskostnader inte förekommer i samband med finansiell handel. Men i verkligheten förekommer transaktions-kostnader som courtageavgifter och skatter nästintill alltid vid handel av finansiella produkter som t.ex. värdepapper. För att avgöra om modellen påvisar felaktiga resultat på grund av bortfallet av courtageavgifter härleds en utvidgning av Markowitz modell som inkluderar courtageavgifter. Utvidgningen av Markowitz modell jämförs sedan med originalmodellen. Resultaten tyder på att courtageavgifter har en försumbar effekt på originalmodellen om investeraren har en stor investeringsbudget. Slutsatsen är därför att, förenklingen att inga courtageavgifter förekommer är en acceptabel förenkling om investeringsbudgeten är stor. Det föreslås slutligen att courtageavgiften är försumbar om transaktionen av aktier endast sker en gång. Men om en investerare är aktiv och ombalanserar sin portfölj flitigt, kan courtageavgifterna vara av stor betydelse.

The stock market is a non-linear field, but many of the best-known portfolio optimization algorithms are based on linear models. In recent years, the rapid development of machine learning has produced flexible models capable of complex pattern recognition. In this paper, we propose two different methods of portfolio optimization; one based on the development of a multivariate time-dependent neural network,thelongshort-termmemory(LSTM),capable of finding lon gshort-term price trends. The other is the linear Markowitz model, where we add an exponential moving average to the input price data to capture underlying trends. The input data to our neural network are daily prices, volumes and market indicators such as the volatility index (VIX).The output variables are the prices predicted for each asset the following day, which are then further processed to produce metrics such as expected returns, volatilities and prediction error to design a portfolio allocation that optimizes a custom utility function like the Sharpe Ratio. The LSTM model produced a portfolio with a return and risk that was close to the actual market conditions for the date in question, but with a high error value, indicating that our LSTM model is insufficient as a sole forecasting tool. However,the ability to predict upward and downward trends was somewhat better than expected and therefore we conclude that multiple neural network can be used as indicators, each responsible for some specific aspect of what is to be analysed, to draw a conclusion from the result. The findings also suggest that the input data should be more thoroughly considered, as the prediction accuracy is enhanced by the choice of variables and the external information used for training. / Aktiemarknaden är en icke-linjär marknad, men många av de mest kända portföljoptimerings algoritmerna är baserad på linjära modeller. Under de senaste åren har den snabba utvecklingen inom maskininlärning skapat flexibla modeller som kan extrahera information ur komplexa mönster. I det här examensarbetet föreslår vi två sätt att optimera en portfölj, ett där ett neuralt nätverk utvecklas med avseende på multivariata tidsserier och ett annat där vi använder den linjära Markowitz modellen, där vi även lägger ett exponentiellt rörligt medelvärde på prisdatan. Ingångsdatan till vårt neurala nätverk är de dagliga slutpriserna, volymerna och marknadsindikatorer som t.ex. volatilitetsindexet VIX. Utgångsvariablerna kommer vara de predikterade priserna för nästa dag, som sedan bearbetas ytterligare för att producera mätvärden såsom förväntad avkastning, volatilitet och Sharpe ratio. LSTM-modellen producerar en portfölj med avkastning och risk som ligger närmre de verkliga marknadsförhållandena, men däremot gav resultatet ett högt felvärde och det visar att vår LSTM-modell är otillräckligt för att använda som ensamt predikteringssverktyg. Med det sagt så gav det ändå en bättre prediktion när det gäller trender än vad vi antog den skulle göra. Vår slutsats är därför att man bör använda flera neurala nätverk som indikatorer, där var och en är ansvarig för någon specifikt aspekt man vill analysera, och baserat på dessa dra en slutsats. Vårt resultat tyder också på att inmatningsdatan bör övervägas mera noggrant, eftersom predikteringsnoggrannheten.

Optimal portfolios have historically been computed using standard deviation as a risk measure.However, extreme market events have become the rule rather than the exception. To capturetail risk, investors have started to look for alternative risk measures such as Value-at-Risk andConditional Value-at-Risk. This research analyzes the financial model referred to as Markowitz 2.0 and provides historical context and perspective to the model and makes a mathematicalformulation. Moreover, practical implementation is presented and an optimizer that capturesthe risk of non-extreme events is constructed, which meets the needs of more customized investment decisions, based on investment preferences. Optimal portfolios are generated and anefficient frontier is made. The results obtained are then compared with those obtained throughthe mean-variance optimization framework. As concluded from the data, the optimal portfoliowith the optimal weights generated performs better regarding expected portfolio return relativeto the risk level for the investment.

In 2023, the topic of ongoing inflation is being discussed almost daily as it has become inevitable. The global economy is facing significant uncertainty and downward pressure as several leading developed nations adopted expansionary fiscal policies and quantitative easing monetary policies during the pandemic. Those action has lead to an unprecedented level of inflation today. The purpose of this report is to investigate different portfolio strategies and evaluate how various asset classes perform under varying inflationary conditions. Using regression analysis, the study assesses the performance of different assets during high and low inflation regimes. Additionally, two different portfolio strategies are implemented and compared against the 60/40 portfolio strategy, which is considered a benchmark approach among investors. The first strategy involves a modified version of the Markowitz optimization method, which determines the optimal weights of the portfolio during high and low inflationary environments. The second strategy entails identifying a signal and then dynamically adjusting the portfolio's weights based on the signal's value. The findings indicate that during high inflation periods, oil, gold, energy, basic materials, and technology sectors exhibit strong performance. Furthermore, the results reveal that the first strategy is more effective than the second strategy and the 60/40 benchmark. An interesting topic for further investigation is exploring the impact of short selling on portfolio allocation and strategy, which was not addressed in this report. / Under år 2023 är ämnet om pågående inflation nästan oundvikligt. Den globala ekonomin har stått inför betydande osäkerhet och nedåtgående tryck då flera ledande utvecklade nationer antagit expansiva finanspolitiska åtgärder och kvantitativa lättnadsmonetära åtgärder under pandemin. Dessa åtgärder har lett till en enastående nivå av inflation idag. Syftet med denna rapport är att undersöka olika portföljstrategier och hur olika tillgångsslag presterar under olika inflationsregimer. Med hjälp av regressionsanalys undersöks hur olika tillgångar presterar under hög respektive låg inflation. Därefter genomförs två olika portföljstrategier som sedan jämförs mot en 60/40 portföljstrategi, som anses vara en standardstrategi bland investerare. Den första strategin som genomförs är en modifierad version av Markowitz optimeringsmetod. Metoden används för att identifiera de optimala vikterna av portföljen under hög respektive låg inflationsmiljö. Den andra strategin som undersöks innebär att identifiera en signal och sedan dynamiskt justera portföljens vikter baserat på signalens värde. Resultaten visar att olja, guld, energi-, basmaterial- samt teknologisektorn presterar bra under hög inflation. Resultaten påvisar även att den första strategin är den mest effektiva i jämförelse med den andra strategin och 60/40 portföljstrategin. En aspekt som inte inkluderades i denna rapport är att undersöka hur blankning påverkar portföljallokeringen och strategin. Detta kan vara ett intressant ämne för vidare forskning.\\\\

Portfolio allocation theories have been studied and used ever since the mid 20th century. Nevertheless, many investors still rely on personal expertise and information gathered from the market when building their investment portfolios. The purpose of this master’s thesis is to examine how personal preferences and expertise perform compared to mathematical portfolio alloca- tion theories and how the risk between these di↵erent strategies di↵er. Using two portfolio allocation theories, the Black-Litterman model and mod- ern portfolio theory (Markowitz), a portfolio managed by the investment firm Placerum Kapitalf ̈orvaltning in Ume ̊a will be compared and challenged to investigate which strategy gives the best risk adjusted return. Using scenario modelling, the portfolios can be compared using both historical data and future forecasted scenarios to analyze the past, present and future of the allocation theories and Placerum’s investment strategy. The first allocation theory, the Black-Litterman model, combines historical information from the market with views and preferences of the investor to select the optimal allocations derived from return and volatility. The second allocation theory, the modern portfolio theory (Markowitz), only uses histori- cal data to derive correlations and returns which are then used to select the optimal allocations. By analysing several risk measures applied on the portfolios historical and forecasted data as well as comparing the performance of the portfolios, it is shown that the investment strategy used at Placerum succeeds with its intentions to achieve relatively high return while reducing the risk. However, the portfolios given using the two allocation theories results in higher potential returns but at the cost of taking on a higher risk. Comparing the two studied allocation theories, it is shown that when using the Black-Litterman model with the assumptions and views defined in this project, modern allocation theory actually beats it in terms of potential return as well as in terms of risk adjusted return, even though its underlying theory is much simpler.

From the beginning of 20th century many studies proved randomness in price evolution of investment instruments. Therefore models respecting this randomness must be used in portfolio management. This thesis' aim is to provide basic theory regarding some of the stochastic methods and show their practical use in real situations.

This master thesis is focused on portfolio diversification. In the Czech Republic, the majority of the population still deposits their free funds to current accounts, but the yield is not sufficient to cover the devaluation caused by inflation. In addition, investments in securities enable these funds to be better valued (naturally with a higher risk). The aggregate of all investments is called the investment portfolio. Harry Markowitz is the founder of modern portfolio theory. The aim of the thesis is to compile an optimal portfolio from chosen financial assets. The theoretical part of the thesis describes the terms such as the financial market, its nature and function and the basic elements of the investment strategy - profitability, risk and liquidity. On top of that, this part describes problems of portfolio theory with a focus on the Markowitz model of optimization. In total 15 stocks-issuing companies are selected from various industries. These companies are traded both on the Czech and American stock markets. The practical part is focused on creating optimal portfolio of selected financial assets. For different attitudes of the investor to risk and its selected strategy the optimal portfolio according to Markowitz is compiled. The weights of individual securities are determined as well as the yield and risk of the portfolios created and an effective boundary is demarcated.