In this project, we implement portfolio theory to construct our portfolio, applying the theory to real practice. There are 3 parts in this project, including portfolio optimization, Capital Asset Pricing Model (CAPM) analysis and Factor Model analysis. We implement portfolio theory in the portfolio optimization part. In the second part, we use the CAPM to analyze and improve our portfolio. In the third part we extend our CAPM to factor models to get a deeper analysis of our portfolio.

This project has three parts. The first part is to use the efficient frontier and find the tangency portfolio to form our optimal portfolio. We built our portfolio using the Interactive Brokers software and rebalanced every week for 4 holding periods to see the relationship between our projected returns and actual market returns. In the second part we considered the Capital Asset Pricing Model (CAPM) and ran linear regressions on the stocks we chose in the first part of the project. This process is based on our idea of finding the systematic risk in each stock to improve our stock choosing ability. In the last part we introduce the concept of factor models and add more factors into our original CAPM model. Via a back-testing method, we test the reasonability of our factors and give advice to further improve our portfolio optimization project.

A Multifactor model is used to explain asset return and risk and its explanatory power depends on common factors that the model uses. Researchers strive to find reasonable factors to enhance multifactor model¡¦s efficiency. However, there are still some unknown factors to be discovered. Miller (2006) presents a general concept and structure of hybrid factor model. The study follows the idea of Miller (2006) and aims to build a complete flow of constructing hybrid factor model that is based on fundamental factor model and statistical factor models. We also apply the hybrid factor model to the Taiwan stock market. We assume that a fundamental factor model is already developed and therefore this study focuses on building the second stage, statistical factor model. Principal Component Analysis is used to form statistical factor and spectral decomposition is used to prepare data for principal component analysis. Those methods are applied to stocks on the Taiwan Stock Exchange in the period of January 1, 2000 to December 31, 2009. This study presents a complete construction flow of hybrid factor models and further confirms that a hybrid factor model is able to find missing factors in a developing market such as Taiwan¡¦s stock market. The study also discovers that the missing factors might be market factor and extensive electronic industry factor.

This study examined the relationship between personality dimensions and supervisory ratings of job performance, in a sales context in Sweden. A sample of 34 telesales workers, employed at two major telecom companies, completed the NEO PI-3 (McCrae & Costa, 2010). As hypothesized, it was found that Extroversion, Conscientiousness, and Neuroticism correlated moderately with job performance. In line with past research, this suggests that individuals, who display high levels of Extroversion and Conscientiousness, as well as low levels of Neuroticism, perform better in sales related occupations. Unlike hypothesized, no correlation was found between job performance and Agreeableness and Openness to Experience. Additional computations indicated the importance of specific sub dimensions of Extroversion and Conscientiousness in respect to job performance. Practical implications in respect to recruitment and directions of future research are discussed.

This study aims to find an effective linear combination of factors in different economic cycle periods and then construct two factor timing multi-factor alpha models, one each for the expansion and contraction periods. Then, we wish to examine a further two effects, namely calendar effect and cross effect. The calendar periods are divided into the first half year and the second half year. The cross effect is the combination of the economic cycle and the calendar effect. In addition, this study puts different loadings in core and satellite descriptors, which means we wish to examine which descriptors are more important when we rebalance our portfolio weekly. The empirical results show that the Value factor is effective in expansion and the first half year, and the Size and Earnings Quality factors are effective in contraction and the second half year. Moreover, the Price Momentum and Trading Activity factors are effective most of the time. We find that the optimal weight for core descriptors is 0.3 and for satellite descriptors is 0.7. Finally, the information ratios of our models are superior to the Standard alpha model by Hsu et al. (2011) and the Market Trend-based alpha model by Wang (2011). Taking the AMCross as an example, when the tracking error is below 3%, the IR is 1.40, the active return is 3.09%, the tracking error is 2.20%, the turnover rate is 207% and the transaction costs are 1.2%.

Quantitative analysis is one branch of portfolio management. The advantages of quantitative analysis are fast and objective. It has developed significantly in recent years because of the improvements in computer technology. This thesis applies the structure of a multi-factor model (MFM) to undertake quantitative analysis. Singapore has one of the most prosperous financial markets in Southeast Asia. The Singapore Stock Exchange (SGX) and Financial Times and the London Stock Exchange (FTSE) are now in cooperation, which has added vitality to this market. It has great influence in global financial markets, and this is why we select its security market to be our target in MFM. The model refers the multi-factor processes of Jeng and Tsai (2011) . For backtesting, we adopt an enhanced strategy as testimony. We transmit information from the MFM to the enhanced strategy. Then we create the stock weightings to constitute the enhanced portfolio. This model includes 68 significant descriptors, 14 composite factors and 7 industry factors. The Singapore MFM shows 43% adjusted R-Square in the sample period. The enhanced portfolio we suggested has an information ratio of 76.80% with a tracking error of 4.02% and 1.53% for monthly turnover rate.

This purpose behind this study is to develop a model for forecasting the performance of the Taiwanese economy based on monthly time series data. We first extract the useful factors through factor analysis. Next, we rank the factor scores according to the rules of the trend and interpret the scores as signals to buy or sell appropriately. Our main result is that the Sharpe ratio of out-of-sample back-testing from January 2007 to December 2010 is 0.48, indicating an ability to forecast financial crises. In addition, a Sharpe ratio of 0.95 during the 2008 financial crisis suggests that our model may have been effective in predicting this crisis. Moreover, the macroeconomic factor model can provide better forecasting skills during financial crises. To conclude, this research may be of importance in explaining the relationship between macroeconomic variables and the business cycle, as well as in providing investors with better forecasting signals of the stock market in Taiwan.

Problembakgrund: Studier för sambandet mellan volatilitet och avkastning har för det aggregerade marknadsperspektivet varit odelat enliga i att detta är negativt. Detsamma gäller inte sambandet vid studier på aktier för enskilda företag där ett antal har kunnat observera ett positivt samband. Detta skulle betyda att det är fördelaktigt när en akties volatilitet ökar, vilket går emot tidigare teorier som säger att sjunkande aktiekurser leder till en ökande volatilitet. I en teori har reala optioner presenterats som en förklaring genom dess konvexitet som leder till ett samtidigt ökande värde när volatilitet ökar. Problemformulering: Existerar ett positivt samband mellan volatilitet och avkastning för enskilda aktier noterade på den svenska aktiemarknaden? Syfte: Studiens huvudsyfte ligger i att avgöra om det går att observera ett positivt samband mellan volatilitet och avkastning på företagsnivå. Sambandet kontrolleras för de variabler som indikerar på en relativt stor tillgång reala optioner för att avgöra om ett företags flexibilitet gör att avkastning och volatilitet ökar samtidigt genom de reala optionernas värdeökning i enlighet med den teori presenterad av Grullon, Lyandres och Zhdanov. Ett delsyfte är därefter att undersöka huruvida vanliga prisjämviktsmodellers förklaringsgrad kan förbättras för att utreda om reala optioner har en så betydande effekt för svenska aktiers avkastning att investerare bör ta dessa i beaktande. Teori: Studien avhandlar de två teorier som tidigare presenterats som huvudförklaringar för det asymmetriska sambandet mellan volatilitet och avkastning, hävstångseffekten och volatilitetsfeedback-effekten. Dessutom presenteras den teori som genom ett företags flexibilitet eventuellt förklarar ett symmetriskt samband och de nyckeltal som indikerar på en relativ tillgång reala optioner. För att kunna undersöka detta samband använder vi CAPM, Fama-French tre- och Carhart fyrfaktormodell, samt en vidare modifierad modell som beaktar reala optioner. Metod: För att besvara vår problemformulering har vi valt att genomföra denna kvantitativa studie med en deduktivt ansats. Ett totalurval bestående av 1131 företag på aktiemarknaden mellan åren 1992 – 2011 ligger som grund för de statistiska testen.  Empiri/analys: Resultaten visar på att det inte föreligger ett positivt samband mellan volatilitet och avkastning för enskilda aktier noterade i Sverige, det samband vi finner är signifikant negativt. De undersökta prisjämviktsmodellerna visar på en något ökande förklaringsgrad för de variabler som indikerar reala optioner men utan signifikanta resultat. Dessa resultat skiljer sig från referensstudien på den amerikanska marknaden av Grullon et al. som kunnat visa på ett positivt samband. Slutsats: Ett existerande symmetriskt samband går inte att helt utesluta, resultaten visar däremot på att de teorier som driver ett negativt samband är dominerande på den svenska marknaden. Detta kan bero på exempelvis skillnader i företagsklimat eller juridiska trösklar mellan länder som hämmar ett företags möjligheter till att vara flexibla och att denna effekt därför blir begränsad.

The thesis consists of four papers that address likelihood-based unit root tests for panel data with cross-sectional dependence arising from common factors. In the first three papers, we derive Lagrange multiplier (LM)-type tests for common and idiosyncratic unit roots in the exact factor models based on the likelihood function of the differenced data. Also derived are the asymptotic distributions of these test statistics. The finite sample properties of these tests are compared by simulation with other commonly used unit root tests. The results show that our LM-type tests have better size and local power properties. In the fourth paper, we estimate the spaces spanned by the common factors and the spaces spanned by the idiosyncratic components of the static factor model by using the quasi-maximum likelihood (ML) method and compare it with the widely used method of principal components (PC). Next, by simulation, we compare the size and power properties of established tests for idiosyncratic unit roots, using both the ML and PC methods. Simulation results show that the idiosyncratic unit root tests based on the likelihood-based residuals generally have better size and higher size-adjusted power, especially when the cross-sectional dimension is small and the time series dimension is large.

This thesis investigates a newly developed term coined by the author called generalized identification, which is the tendency that people who identify high with one group tend to identify high with other groups as well, and how personality variables from the Five-Factor model may relate to this tendency. A common component of identification towards 10 preselected groups was calculated (N = 148), with a principal component analysis. The result reveal that the generalized identification account for 41 % of the total variance. A stepwise multiple regression analysis further showed that Openness to Experience and Agreeableness from the Five-Factor model explained 26 % of the variance in the generalized identification. However, due to low reliability when measuring personality traits, the relationship between personality and generalized identification could not be interpreted in a satisfying way, and it needs to be further explored before drawing firm conclusions.