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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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.

Purpose: Aims of this paper is to evaluate a comparative study between China and Russia funds in respect of the risks and returns. We also want to examine what has affected the funds in their respective domestic stock market.                                                             Method: The study is based on qualitative methodology to complement the quantitative survey by first gathering of secondary data from Morningstar, and fund manager´s stories on fund and banking companies' websites.  Primary data is conducted by the interview with fund manager. The sample consists of all land funds for China and Russia has found more than 10 years on the stock market. Results and Conclusion: The survey shows that China funds will generate better in decline than Russia Funds in both return and risk-adjusted Sharpe ratio. Because the China funds had better risk diversification and its holdings spread across different industry area while Russia funds is more directed towards oil and gas industry. The upturn managed Russia Funds better to recovery than China Funds in terms of return and risk-adjusted Sharpe ratio, which was due to China funds were cautiously optimistic, with the government's stimulus package, while Russia Funds earned at the price of oil in the world increased and a greater willingness to take risks of the global financial system. During the 10 years period, Russia funds better growth compared to China Funds in the total seen by far. For Russia have large oil resources and raw materials including exporting to the fast growing Asian. In China, due to good growth in the consumption good and growing middle class in the country, but also increased projects in financial and infrastructure.

The study examines how the investment strategy Magic Formula (Greenblatt, 2006) has performed on the Swedish stock market. It is also investigated how the performance is affected when the strategy is combined with momentum. Since the expected pension for future generations is expected to decline it is important to have private savings with as high return as possible. Therefore, it is relevant to investigate if simple investment strategies can be used to achieve higher return. The purpose with this study is to find out if the investment strategies Magic Formula and Magic Formula combined with momentum has had a higher risk-adjusted return than the benchmark index OMX30. The results show that both Magic Formula and Magic Formula combined with momentum yielded a higher risk-adjusted return than the benchmark index. The results also showed that Magic Formula yielded an even better risk-adjusted return when it was combined with momentum. We wish that the result that was found in this study will give inspiration to private investors in order to achieve a higher return in their savings and a more satisfactory pension in the future