This thesis investigates and compares the performance and characteristics of defensive and cyclical sectors on the Swedish stock market during 2003-2020 and the financial crisis in2007-2008, taking monthly price developments from nine sectors. The purpose is to examine the differences in sector performances based on the estimations of systematic risk. Using the relationship between risk and return, we aim to find the most beneficial investment strategy for investors with a long-term investment horizon and provide knowledge to investors who may want to change investment schemes during stock market crises to protect their portfolios from risk. To determine the sectors' classifications, the beta coefficient from CAPM is used. Moreover, alpha and Sharpe ratios are used as performance measures with the aim to find evidence of differences in performance between the classifications. The results show that beta is inconstant over time, and sectors behave differently depending on their dependence to business conditions, demonstrated by different patterns in beta for the two different classifications when comparing the crisis to the full period. The empirical evidence indicates that a defensive investment strategy is beneficial when considering the relationship between risk and return.

This study examines how investors can take advantage of Black Swan events by applying an investment strategy that involves investing in stocks that have performed badly during Black Swan events. The stocks are chosen from and compared to the Dow Jones Industrial Average Index. The purpose is to find out if the investment strategy has had a higher return than the benchmark index DJIA. The results show that the investment strategy outperforms the DJIA by 111% between the years 2000 to 2020, however, the results show no statistical significance. Beta is used as risk measurement to explain the correlation between the portfolios and the benchmark index by calculating CAPM. Standard deviation is used to calculate the Sharpe ratio and thereby assess a risk-adjusted result.

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Hållbarhet är en trend på aktie- och fondmarknaden som ökat markant under de senaste åren.Det råder fortfarande en viss osäkerhet huruvida prestationen mellan hållbara ochtraditionella fonder skiljer sig. Tidigare forskning har visat på att hållbara fonder bättre klararav perioder präglade av osäkerhet och risk.Syftet med denna kvantitativa studie är därför att jämföra prestationen för svenska hållbarafonder med svenska traditionella under olika marknadsförhållanden. Hela studieperioden ärmellan år 2017-2023 och urvalet är totalt 13 hållbara fonder och 24 traditionella fonder.Resultatet presenteras med hjälp av deskriptiv statistik och en regressionsanalys.Studien gav ett varierande resultat vid olika perioder. Den första perioden visade inte någonstörre skillnad mellan fonderna, medan det under de två senare perioderna påvisades atthållbara fonder presterat bättre än traditionella. Sammanfattningsvis indikerar studien attunder perioder av osäkerhet och risk tenderar hållbara fonder att prestera bättre äntraditionella. Studien identifierar en positiv men inte särskilt stark korrelation mellanhållbarhetsbetyg och riskjusterad avkastning för fonderna. Detta antyder att fondernasprestation inte helt kan förklaras av deras hållbarhetsmått och vidare studier krävs för attförstå de bakomliggande faktorerna som kan påverka fonders prestation. / Sustainability has become a growing trend on the stock- and fund market throughout therecent years, although there is still some uncertainty on how the performance differs betweensustainable funds/stocks and more traditional funds/stocks. Previous research have shownthat sustainable funds show greater accomplishment in coping with periods of uncertainty andrisk.The purpose of this qualitative study is thereby to compare the performance of Swedishsustainable funds, with the performance of Swedish traditional funds. The study’s periodspans between the years 2017 and 2023. The selection contains a total of 13 sustainable fundsand 24 traditional funds. The result is presented with the help of descriptive statistics andregression analysis.The result of this study produced varying results across different periods, with the initialperiod showing no significant difference while the latter two periods indicated betterperformance by sustainable funds. Shortly summed up, the study shows that in periods ofuncertainty and risk, the sustainable funds appear to perform better than the traditional funds.The study identifies a positive, but not a particularly strong correlation between sustainabilityrating and risk-adjusted return for the funds. This means that the performance cannot alone beexplained by their sustainability rating, and more studies need to be done to understand theunderlying factors that may have an impact on fund performance.

This study examines the hedging effectiveness of the Silver Bullet Fund created by AuAg Funds. The Silver Bullet Fund will be examined alongside a financial proxy and an industry proxy, to assess whether to hedge or not during turbulent times.     The study examines the hedging performance of the Silver Bullet fund by utilizing a static model to capture the behavior of a crisis at a specific point in time, alongside a dynamic model to capture the behavior of crises over time. Further research suggests that several other econometric models can be used for analysis with the same purpose. However, the evidence in this study suggests that a hedged portfolio outperforms an unhedged portfolio during crisis for the S&P500 Index, while for the STOXX 600 Europe Automobiles & Parts Index should not be hedged during the crisis under the static assumption. Moreover, there are also occasions where the standardized returns for the three variables lies outside of the confidence intervals. The study also finds that under the dynamic model, the financial proxy Hedge Ratios during all three crises, compared to the industry proxy, sees the highest value of the Hedge Ratios, however, presenting low hedging effectiveness.

We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNNs) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pretraining, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics.

Performance measurement is an integral part of investment analysis and risk management. Investment performance comprises two primary elements, namely; risk and return. The measurement of return is more straightforward compared with the measurement of risk: the latter is stochastic and thus requires more complex computation. Risk and return should, however, not be considered in isolation by investors as these elements are interlinked according to modern portfolio theory (MPT). The assembly of risk and return into a risk-adjusted number is an essential responsibility of performance measurement as it is meaningless to compare funds with dissimilar expected returns and risks by focusing solely on total return values. Since the advent of MPT performance evaluation has been conducted within the risk-return or mean-variance framework. Traditional, liner performance measures, such as the Sharpe ratio, do, however, have their drawbacks despite their widespread use and copious interpretations. The first problem explores the characterisation of hedge fund returns which lead to standard methods of assessing the risks and rewards of these funds being misleading and inappropriate. Volatility measures such as the Sharpe ratio, which are based on mean-variance theory, are generally unsuitable for dealing with asymmetric return distributions. The distribution of hedge fund returns deviates significantly from normality consequentially rendering volatility measures ill-suited for hedge fund returns due to not incorporating higher order moments of the returns distribution. Investors, nevertheless, rely on traditional performance measures to evaluate the risk-adjusted performance of (these) investments. Also, these traditional risk-adjusted performance measures were developed specifically for traditional investments (i.e. non-dynamic and or linear investments). Hedge funds also embrace a variety of strategies, styles and securities, all of which emphasises the necessity for risk management measures and techniques designed specifically for these dynamic funds. The second problem recognises that traditional risk-adjusted performance measures are not complete as they do not implicitly include or measure all components of risk. These traditional performance measures can therefore be considered one dimensional as each measure includes only a particular component or type of risk and leaves other risk components or dimensions untouched. Dynamic, sophisticated investments – such as those pursued by hedge funds – are often characterised by multi-risk dimensionality. The different risk types to which hedge funds are exposed substantiates the fact that volatility does not capture all inherent hedge fund risk factors. Also, no single existing measure captures the entire spectrum of risks. Therefore, traditional risk measurement methods must be modified, or performance measures that consider the components (factors) of risk left untouched (unconsidered) by the traditional performance measures should be considered alongside traditional performance appraisal measures. Moreover, the 2007-9 global financial crisis also set off an essential debate of whether risks are being measured appropriately and, in-turn, the re-evaluation of risk analysis methods and techniques. The need to continuously augment existing and devise new techniques to measure financial risk are paramount given the continuous development and ever-increasing sophistication of financial markets and the hedge fund industry. This thesis explores the named problems facing modern financial risk management in a hedge fund portfolio context through three objectives. The aim of this thesis is to critically evaluate whether the novel performance measures included provide investors with additional information, to traditional performance measures, when making hedge fund investment decisions. The Sharpe ratio is taken as the primary representative of traditional performance measures given its widespread use and also for being the hedge fund industry’s performance metric of choice. The objectives have been accomplished through the modification, altered use or alternative application of existing risk assessment techniques and through the development of new techniques, when traditional or older techniques proved to be inadequate. / PhD (Risk Management), North-West University, Potchefstroom Campus, 2014

Performance measurement is an integral part of investment analysis and risk management. Investment performance comprises two primary elements, namely; risk and return. The measurement of return is more straightforward compared with the measurement of risk: the latter is stochastic and thus requires more complex computation. Risk and return should, however, not be considered in isolation by investors as these elements are interlinked according to modern portfolio theory (MPT). The assembly of risk and return into a risk-adjusted number is an essential responsibility of performance measurement as it is meaningless to compare funds with dissimilar expected returns and risks by focusing solely on total return values. Since the advent of MPT performance evaluation has been conducted within the risk-return or mean-variance framework. Traditional, liner performance measures, such as the Sharpe ratio, do, however, have their drawbacks despite their widespread use and copious interpretations. The first problem explores the characterisation of hedge fund returns which lead to standard methods of assessing the risks and rewards of these funds being misleading and inappropriate. Volatility measures such as the Sharpe ratio, which are based on mean-variance theory, are generally unsuitable for dealing with asymmetric return distributions. The distribution of hedge fund returns deviates significantly from normality consequentially rendering volatility measures ill-suited for hedge fund returns due to not incorporating higher order moments of the returns distribution. Investors, nevertheless, rely on traditional performance measures to evaluate the risk-adjusted performance of (these) investments. Also, these traditional risk-adjusted performance measures were developed specifically for traditional investments (i.e. non-dynamic and or linear investments). Hedge funds also embrace a variety of strategies, styles and securities, all of which emphasises the necessity for risk management measures and techniques designed specifically for these dynamic funds. The second problem recognises that traditional risk-adjusted performance measures are not complete as they do not implicitly include or measure all components of risk. These traditional performance measures can therefore be considered one dimensional as each measure includes only a particular component or type of risk and leaves other risk components or dimensions untouched. Dynamic, sophisticated investments – such as those pursued by hedge funds – are often characterised by multi-risk dimensionality. The different risk types to which hedge funds are exposed substantiates the fact that volatility does not capture all inherent hedge fund risk factors. Also, no single existing measure captures the entire spectrum of risks. Therefore, traditional risk measurement methods must be modified, or performance measures that consider the components (factors) of risk left untouched (unconsidered) by the traditional performance measures should be considered alongside traditional performance appraisal measures. Moreover, the 2007-9 global financial crisis also set off an essential debate of whether risks are being measured appropriately and, in-turn, the re-evaluation of risk analysis methods and techniques. The need to continuously augment existing and devise new techniques to measure financial risk are paramount given the continuous development and ever-increasing sophistication of financial markets and the hedge fund industry. This thesis explores the named problems facing modern financial risk management in a hedge fund portfolio context through three objectives. The aim of this thesis is to critically evaluate whether the novel performance measures included provide investors with additional information, to traditional performance measures, when making hedge fund investment decisions. The Sharpe ratio is taken as the primary representative of traditional performance measures given its widespread use and also for being the hedge fund industry’s performance metric of choice. The objectives have been accomplished through the modification, altered use or alternative application of existing risk assessment techniques and through the development of new techniques, when traditional or older techniques proved to be inadequate. / PhD (Risk Management), North-West University, Potchefstroom Campus, 2014

本研究以近15年台灣股票市場所有上市、上櫃、下市、下櫃股票為樣本，利用每季公布之財務報表的資料，市值、現金流量與股價比率、本益比、資產報酬率、負債比率、報酬率之標準差等指標作為篩選股票依據。 首先，先用財務報表的資料建構出Smart Beta Factor，結合主成分分析將各股評分，作為股票篩選之指標。第一步驟先把市值較低、成交金額過低的股票刪除，並依照不同指標篩選出五倍符合投資組合之股票數，接著運用主成分分析評分後的指標將各公司排序，選出分數高的作為投資組合，以達到分散風險的目標。 本文所討論之Smart Beta Factors有Size、Quality、Value、Momentum、Volatility，並將各Smart beta factor結合主成分分析，計算分數以選出優良股票，並以等權重方式進行資產配置，希望能建構出最有利的投資組合，使得獲利穩定成長。 / In this study, using nearly 15 years quarterly financial statement of stock market in Taiwan as samples. Not only use the financial statement to construct the smart beta factor, also use the principle components analysis to calculate the scores of all the stocks, then choose the stock by the scores. First, delete the stocks of low market value and the stocks of low turnover rate. Second, selected five times the number of the investment portfolio by different indicators, then elect the number of investment portfolio stocks by the highest scores calculated by principal component analysis. To achieve the goal of risk diversification. The smart beta factors discussed in the paper are Size, Quality, Value, Momentum, Volatility, also the multiple factor. To combine the method of principal component analysis, calculate the score to select the stocks, in order to contract the portfolio which has the best performance, and can make stable growth of profits.

Over the past years, the total assets under management among hedge funds and equity mutual fundshave increased significantly. The question from an investor point of view iswhich investment vehicle can provide the greatest return adjusted for risk. The purpose of this study involves an analysis on the historical net asset values todetermine and evaluate what one can except from actively managed hedge andequity mutual funds. It supports the determination of the most profitable asset, adjusted for risk, as part of a diversified portfolio. The performance is measured net of fees and costs with the inclusion of potential performance fees individual hedge funds may apply. Hedge funds practice different investment approaches depending on what strategy is applied and hence, return levels can vary dramatically. The study is designed to answer questions by comparing net returns and risk-adjusted returns for respective investments and the different hedge fund strategies. With a deductive research approach, the analysis is conducted by applying existing models and theories as the Fama-French three-factor model through time-series regressions measuring excess returns (alpha), risk-adjusted performance measures as Sharpe ratio, M-squared and the Sortino ratio. The results show that hedge funds outperform equity mutual funds in all examined aspects and produce positive monthly net alphas,on average. Equity mutual funds are unable to provide investors with positive excess returns and subsequently fail the purpose of an actively managed fund by providing returns lower than the return of the market. The results are increasingly strengthened with both time-series regressions and performance measures showing homogenous results and reaching the equal conclusions. From the conclusions that hedge funds provide the most profitable investment compared to equity mutual funds, the hedge fund strategy CTA/managed futures strategies perform best in both net and risk-adjusted terms.