Socially Responsible Investing (SRI) has experienced substantial growth over the last decade, although there is still a lack of consensus on whether this form of investing leads to competitive investment returns. This paper considers the case for SRI by examining the relationship between a range of Environmental Social Governance (ESG) rating factors and financial performance of US listed companies. Previous research in this area has largely been at the portfolio level and focussed on return as a performance measure. This study makes an important contribution to the literature by utilising stock level data to consider the relationship between ESG ratings and not only stock return but also wider measures of financial performance, namely valuation and operating performance. Using a multifactor framework, this study provides evidence of a significant positive relationship between a range of ESG rating criteria and market to book value and return on assets measures, whilst a positive although inconsistent relationship between ratings and stock return is apparent. I argue that the relationship with valuation and operating performance is more clearly identified due to these measures being based on annual data, hence being consistent with rating data and also more stable than stock return (which is impacted by shorter term factors). In comparison to Brammer et al. (2006), the analysis shows that higher stakeholder ratings on the whole are more positively related to stock return. The results are broadly consistent with findings of Galema et al. (2008), although additionally highlight the significance of higher ratings for both valuation and operating performance and that employee conditions are more relevant than other stakeholder rating criteria.

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Thesis (M.S.)--Eastern Illinois University. / Includes bibliographical references (leaves 85-87).

Inaug.-Diss.--Basel. / Vita. Published also as v. 9 of Schriften des Schweizerischen Wirtschaftsarchivs. Bibliography: p. 114-118.

Two central themes in asset pricing theory are how averse households are to taking on risks, and how willing they are to substitute consumption over time in response to the incentives provided by asset returns. These issues are central to understanding both asset returns and consumption patterns. Most work in this field operates on the basic observation that not all households invest in the stock market. Studies that account for market segmentation assume that all stockholders hold a financial index (S&P, NYSE) and use one of these indexes as a proxy for household-specific portfolio. According to the latest data from the 2004 Survey of Consumer Finances, however, the median US stockholders who own stocks directly hold only 3 stock securities. Another data observation from the SCF (and other sources) is that stockholders with different wealth levels have different returns on their stocks. These data observations call into question the validity of financial index as a proper proxy for household-specific portfolio. This research starts from the two basic observations that most stockholders hold only a few individual stocks and stockholders with different wealth levels have a different rate of return on their stocks. If a large fraction of households do not hold a financial index, then how does that affect our inference about households' willingness to substitute consumption over time for the incentives provided by asset returns and to accept risks? Furthermore, what does it teach us about what a good model of assets prices looks like? And why do households hold only a few individual stocks? My research addresses these issues. Specifically, in the first chapter I study the heterogeneity in households' portfolio choice and performance and find that the tradeoffs between average payoffs and risk alone cannot explain heterogeneity in portfolio returns. In the second section, I address a long-standing question in macroeconomics and finance- the value of the risk aversion for households with different wealth levels. In the third chapter, I study the effect of political affiliation on portfolio choice. / text

In a previous MITACS project in collaboration with Addenda Capital, two basic liability matching strategies have been investigated: cash flow matching and moment matching. These strategies performed well under a wide variety of tests including historical backtesting. A potential shortcoming for both of these methods is that the optimization process is done only once at the beginning of the investment horizon and uses deterministic moment matching constraints to immunize the portfolio against interest rate movements. Though the portfolio subsequently need to be frquently rebalanced, this static optimization does not take into account the relatively high rebalancing costs it involves. The main objective of this present project is to further enhance the moment matching method by implementing and testing a stochastic dynamic optimization and by comparing its efficiency with the static one. Our dynamic optimization problem is to minimize the portfolio cost and its expected rebalancing costs one month ahead over a set of interest rate scenarios by the use of stochastic moment matching constraints. Our backtesting results show some improvements with the 6 moments matching strategy as the dynamic optimization slightly shrinks the difference in asset-liability gap between scenarios compared with the static optimization. However, after analyzing the realized periodic rebalancing costs each month (a constant bid-ask spread has been assigned to each asset's position change in the optimal portfolio), the immunization improvements are mitigated by substantialy higher costs. We also noticed, in the case of the duration/convexity matching strategy, that the dynamic optimization is not that much more efficient than the static method. Thus, these results confirm that the 6 moments matching technique is still more efficient with both the static and stochastic dynamic optimization. Our extensive dynamic analysis of transaction costs through backtesting showed that from an efficiency to cost ratio and an efficiency to simplicity ratio, the static 6 moments matching method seems so far to be a more practical solution for liability matching.

In this study, mathematical programming models are developed which aim to determine simultaneously the optimum combination of investment decisions, financing methods and tax strategy for capital budgeting, taking into account tax-induced interactions between cash flows. The tax treatment of finance leases and the corporate group tax relief provisions are included. Shareholder risk considerations are taken into account in deriving an appropriate discount rate, using an iterative procedure based on the Capital Asset Pricing Model. The commercial mathematical programming software XPRESS-MP is utilised to achieve operational use of the models in complex tax situations. A mathematical analysis of certain patterns of accelerated tax depreciation recently available in the UK for capital expenditure is presented. This analysis shows that, where there is a time lag between asset purchase and the incidence of tax relief, an optimal cost of capital may be derived at which the incremental value of the accelerated allowances is at a maximum. In the case of declining balance depreciation for plant and machinery, it is shown that this optimal cost of capital is independent of the proportion of the asset cost that may be allowed against tax in the first year.

Thesis (B.A.Hons. 1980) from the Department of Politics, University of Adelaide.