Performance of ethical equity investing in the UK: active, passive and criteria

Ethical equity investing account for roughly 22% of all assets under management in the UK – this puts it in a position where it can no longer be neglected. This thesis evaluates the performance of ethical equity investing in the UK. We look at three key issues: performance of ethical funds versus conventional funds; performance of ethical indices versus conventional indices; and finally, performance of certain ethical criterion versus other such criterion. Previous studies have looked at these issues but they have used a Mean-Variance (MV) and/or asset pricing model based methodologies; both these approaches suffer from serious drawbacks and hence we choose to employ a more robust Marginal Conditional Stochastic Dominance (MCSD) methodology. This is the first study in the area of ethical investing to use an MCSD approach to evaluate performance. In line with previous studies, we find that neither ethical nor conventional funds dominate each other. However, we find in contrast with previous studies that on average both ethical & conventional funds dominate the market; the said outperformance is resilient to the effect of fees. We also find in contrast with previous studies that the US & Global ethical indices are dominated by conventional ones. Thus in the US & Global context a passive ethical index investor has to pay a price for being ethical. In the UK & EU context, they pay no such price. We believe that the contrast in our findings with those of previous studies arises out of our use of a superior MCSD methodology as compared to the MV and/or model based methods used by them. And finally, we find that UK ethical funds which employ a comprehensive ethical strategy (i.e. subscribe to all ethical criteria) and/or invest locally (i.e. only in UK listed firms) outperform the market. Since the US & Global ethical indices also fare poorly, it appears that UK ethical investors would be better off investing in funds & indices with a local focus

Stemming the tide: Does climate risk affect M&A performance?

We examine the effect of climate change risks (CCR) on firms' decision of engaging in mergers and acquisitions (M&A) and M&A performance. In this study we use the responses by firms on ‘climate change-related risks and opportunities’ of the CDP survey and 1,372 deals of listed US firms during 2010-2020. Consistent with risk vulnerability theory, our evidence indicates that firms with higher CCR have a lower probability of engaging in M&As. After controlling for possible endogeneity, our results also indicate that if acquirers with higher climate change risks choose to engage in M&A, it significantly reduces the announcement returns. These findings suggest that extant measures of climate change risks should be rethought when evaluating M&A efficiency. More broadly, our paper provides causal evidence that managers need to integrate CCR into their formal risk management systems to avoid unsuccessful M&As

Does it pay to be ethical? Evidence from the FTSE4Good

The empirical mean-variance evidence comparing the performance of socially responsible investments (SRI) and conventional investments suggests that there is no significant difference between the two. This paper re-examines the problem in the context of Marginal Conditional Stochastic Dominance (MCSD), which can accommodate any return distribution or concave utility function. Our results provide strong evidence that there is a financial price to be paid for socially responsible investing. Indices composed of socially responsible firms are MCSD dominated by trademarked indices composed of conventional firms as well as by indices carefully matched by size and industry with the firms in the SRI indices. Zero cost portfolios created by shorting the SRI index and using the proceeds to invest in the conventional index generate higher average returns, lower variance and higher skewness than either of the two indices standing alone. They also MCSD dominate the SRI and conventional indices standing alone

Importance of the fund management company in the performance of socially responsible mutual funds

We compare the performance of a sample of UK based SRI funds with similar conventional funds using a matched pair analysis based on size, age, investment universe and fund management company (FMC). We find that both the SRI and conventional funds outperform the market index about 50% of the time, even after fees. Sub sample tests show that the SRI funds in our sample perform better in the pre and post financial crisis periods while underperforming during the financial crisis period. Importantly, we find that the FMC plays a major role in the outperformance of both SRI and conventional funds

Deep Proteomics of Mouse Skeletal Muscle Enables Quantitation of Protein Isoforms, Metabolic Pathways, and Transcription Factors

Skeletal muscle constitutes 40% of individual body mass and plays vital roles in locomotion and whole-body metabolism. Proteomics of skeletal muscle is challenging because of highly abundant contractile proteins that interfere with detection of regulatory proteins. Using a state-of-the art MS workflow and a strategy to map identifications from the C2C12 cell line model to tissues, we identified a total of 10,218 proteins, including skeletal muscle specific transcription factors like myod1 and myogenin and circadian clock proteins. We obtain absolute abundances for proteins expressed in a muscle cell line and skeletal muscle, which should serve as a valuable resource. Quantitation of protein isoforms of glucose uptake signaling pathways and in glucose and lipid metabolic pathways provides a detailed metabolic map of the cell line compared with tissue. This revealed unexpectedly complex regulation of AMP-activated protein kinase and insulin signaling in muscle tissue at the level of enzyme isoforms

Development of RFID Based Smart Sensor Prototype for Wireless Industrial Monitoring and Control

The Purpose of this paper is to present one of the various wireless technologies currently available for industrial monitoring and control. Applications of wireless data transmission are universal. In industrial automation, the benefits of adopting wireless technologies in eliminating the needs for cables in hard to reach areas within the plant, increasing data availability and quality and monitoring and controlling remote assets, that otherwise were inaccessible. Radio frequency identification (RFID) technology is commonly used for object or animal identification and tracking. This article explores the feasibility of its use in a rapid solution to wireless real time monitoring of industry. A prototype system for wireless industrial monitoring and control was developed using a commercially available 12.5 GHZ RFID passive tags. Various parameters are sensed by respective sensors (Slaves), which are then monitored by low power, high performance, 8bit AVR microcontroller. Monitored signals are then sent to the RFID tag or transponder unit, hence the smart feature of the sensor. A receiving unit (Interrogator) emits an electromagnetic field which when detected by passive RFID tag causes it to transfer sensor information (data stored in memory) to the interrogator. Interrogator detects these parameters and sends them to the data collection PC (Master Unit). The architecture of the developed wireless sensor prototype allows for additional RFID tags (Slave Units) to be integrated into it without changes to the sensor designs. Design also provides means to update operating and monitoring parameters as well as sensors/RF link specific firmware modules ‘over - the - air’

Texas Guide for Controlling Pests on Citrus.

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An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

Index tracking with utility enhanced weighting

Passive index investing involves investing in a fund that replicates a market index. Enhanced indexation uses the returns of an index as a reference point and aims at outperforming this index. The motivation behind enhanced indexing is that the indices and portfolios available to academics and practitioners for asset pricing and benchmarking are generally inefficient and, thus, susceptible to enhancement. In this paper we propose a novel technique based on the concept of cumulative utility area ratios and the Analytic Hierarchy Process (AHP) to construct enhanced indices from the DJIA and S&P500. Four main conclusions are forthcoming. First, the technique, called the utility enhanced tracking technique (UETT), is computationally parsimonious and applicable for all return distributions. Second, if desired, cardinality constraints are simple and computationally parsimonious. Third, the technique requires only infrequent rebalancing, monthly at the most. Finally, the UETT portfolios generate consistently higher out-of-sample utility profiles and after-cost returns for the fully enhanced portfolios as well as for the enhanced portfolios adjusted for cardinality constraints. These results are robust to varying market conditions and a range of utility functions