Rule Britannia!: British Stock Market Returns, 1825-1870

This article presents a new series of monthly equity returns for the British stock market for the period 1825-1870. In addition to calculating capital appreciation and dividend yields, the article also estimates the effect of survivorship bias on returns. Three notable findings emerge from this study. First, stock market returns in the 1825-1870 period are broadly similar for Britain and the United States, although the British market is less risky. Second, real returns in the 1825-1870 period are higher than in subsequent epochs of British history. Third, unlike the modern era, dividends are the most important component of returns.

Islamic mutual funds' financial performance and international investment style: evidence from 20 countries

We pursue the first large-scale investigation of a strongly growing mutual fund type: Islamic funds. Based on an unexplored, survivorship bias-adjusted data set, we analyse the financial performance and investment style of 265 Islamic equity funds from 20 countries. As Islamic funds often have diverse investment regions, we develop a (conditional) three-level Carhart model to simultaneously control for exposure to different national, regional and global equity markets and investment styles. Consistent with recent evidence for conventional funds, we find Islamic funds to display superior learning in more developed Islamic financial markets. While Islamic funds from these markets are competitive to international equity benchmarks, funds from especially Western nations with less Islamic assets tend to significantly underperform. Islamic funds’ investment style is somewhat tilted towards growth stocks. Funds from predominantly Muslim economies also show a clear small cap preference. These results are consistent over time and robust to time varying market exposures and capital market restrictions.

Assessing the performance of private equity investments

This study presents an alternative investment projection model to estimate the future values of Private Equity (PE) investments. The performance of PE investments is assessed by analyzing the risk-return relationship relative to simulated Public Market (PM) investments that mimic the cash flow patterns of PE investments. The model allows for a quantified analysis of the underlying inputs that outline the PE performance and risks, and accounts for survivorship bias. These inputs include the fund manager’s decisions regarding the selection, leverage, size, duration and timing of investment and divestments.

The certification role of venture capitalists, top underwriters and big-n auditors in ipos

Esta tese avalia o impacto dos principais atores recorrentes durante o processo de IPO, em particular, o venture capitalist, o underwriter, e o auditor, sobre as condições de comercialização das ações da empresa, capturado pelo bid-ask spread, a fração de investidores institucionais que investem na empresa, a dispersão de capital, entre outros. Além disso, este estudo também analisa alguns benefícios que os fundos de Venture Capital (VCs) fornecem às empresas que eles investem. Ele investiga o papel dos VCs em dificultar o gerenciamento de resultados em IPOs e quantifica o papel desempenhado por eles no desempenho operacional das empresas após sua oferta inicial de ações. No primeiro capítulo, os resultados indicam que as empresas inflam seus resultados principalmente nos períodos pré-IPO e do IPO. Quando nós controlamos para os quatro períodos diferentes do IPO, observamos que IPOs de empresas investidas por VCs apresentam significativamente menos gerenciamento de resultados no IPO e em períodos seguintes à orfeta inicial das ações, exatamente quando as empresas tendem a inflar mais seus lucros. Este resultado é robusto a diferentes métodos estatísticos e diferentes metodologias usadas para avaliar o gerenciamento de resultados. Além disso, ao dividir a amostra entre IPOs de empresas investidas e não investidas por VCs, observa-se que ambos os grupos apresentam gerenciamento de resultados. Ambas as subamostras apresentam níveis de gerenciamento de resultados de forma mais intensa em diferentes fases ao redor do IPO. Finalmente, observamos também que top underwriters apresentam menores níveis de gerenciamento de resultados na subamostra das empresas investidas por VCs. No segundo capítulo, verificou-se que a escolha do auditor, dos VCs, e underwriter pode indicar escolhas de longo prazo da empresa. Nós apresentamos evidências que as características do underwriter, auditor, e VC têm um impacto sobre as características das empresas e seu desempenho no mercado. Além disso, estes efeitos são persistentes por quase uma década. As empresas que têm um top underwriter e um auditor big-N no momento do IPO têm características de mercado que permanecem ao longo dos próximos 8 anos. Essas características são representadas por um número maior de analistas seguindo a empresa, uma grande dispersão da propriedade através de investidores institucionais, e maior liquidez através um bid-ask spread menor. Elas também são menos propensas a saírem do mercado, bem como mais propensas à emissão de uma orferta secundária. Finalmente, empresas investidas por VCs são positivamente afetadas, quando consideramos todas as medidas de liquidez de mercado, desde a abertura de capital até quase uma década depois. Tais efeitos não são devido ao viés de sobrevivência. Estes resultados não dependem da bolha dot-com, ou seja, os nossos resultados são qualitativamente similares, uma vez que excluímos o período da bolha de 1999-2000. No último capítulo foi evidenciado que empresas investidas por VCs incorrem em um nível mais elevado de saldo em tesouraria do que as empresas não investidas. Este efeito é persistente por pelo menos 8 anos após o IPO. Mostramos também que empresas investidas por VCs estão associadas a um nível menor de alavancagem e cobertura de juros ao longo dos primeiros oito anos após o IPO. Finalmente, não temos evidências estatisticamente significantes entre VCs e a razão dividendo lucro. Estes resultados também são robustos a diversos métodos estatísticos e diferentes metodologias.

The impact of the Eurozone sovereign debt crisis on bond fund performance persistence: Evidence from a small market

We evaluate the impact of the Eurozone sovereign debt crisis on the performance and performance persistence of a survivorship bias-free sample of bond funds from a small market, identified as one of the most affected by this event, during the 2001–2012 period. Besides avoiding data mining, we also introduce a methodological innovation in assessing bond fund performance persistence. Our results show that bond funds underperform significantly both during crisis and non-crisis periods. Besides, we find strong evidence of performance persistence, for both short- and longer-term horizons, during non-crisis periods but not during the debt crisis. In this way, the persistence phenomenon in small markets seems to occur only during non-crisis periods and this is valuable information for bond fund investors to exploit.

The conditional performance of euro bond funds: evidence from Portugal during the debt crisis

This paper evaluates the performance of a survivorship bias-free data set of Portuguese funds investing in Euro-denominated bonds by using conditional models that consider the public information available to investors when the returns are generated. We find that bond funds underperform the market significantly and by an economically relevant magnitude. This underperformance cannot be explained by the expenses they charge. Our findings support the use of conditional performance evaluation models, since we find strong evidence of both time-varying risk and performance, dependent on the slope of the term structure and the inverse relative wealth variables. We also show that survivorship bias has a significant impact on performance estimates. Furthermore, during the European debt crisis, bond fund managers performed significantly better than in non-crisis periods and were able to achieve neutral performance. This improved performance throughout the crisis seems to be related to changes in funds’ investment styles.

Correcting for bias when estimating the cost of hospital-acquired infection : an analysis of lower respiratory tract infections in non-surgical patients

Hospital acquired infections (HAI) are costly but many are avoidable. Evaluating prevention programmes requires data on their costs and benefits. Estimating the actual costs of HAI (a measure of the cost savings due to prevention) is difficult as HAI changes cost by extending patient length of stay, yet, length of stay is a major risk factor for HAI. This endogeneity bias can confound attempts to measure accurately the cost of HAI. We propose a two-stage instrumental variables estimation strategy that explicitly controls for the endogeneity between risk of HAI and length of stay. We find that a 10% reduction in ex ante risk of HAI results in an expected savings of £693 ($US 984).

Error, Bias, and Long-Branch Attraction in Data for Two Chloroplast Photosystem Genes in Seed Plants

Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brady

Attention and memory bias for body image and health related information using an Emotional Stroop task in a non-clinical sample

It has been proposed that body image disturbance is a form of cognitive bias wherein schemas for self-relevant information guide the selective processing of appearancerelated information in the environment. This threatening information receives disproportionately more attention and memory, as measured by an Emotional Stroop and incidental recall task. The aim of this thesis was to expand the literature on cognitive processing biases in non-clinical males and females by incorporating a number of significant methodological refinements. To achieve this aim, three phases of research were conducted. The initial two phases of research provided preliminary data to inform the development of the main study. Phase One was a qualitative exploration of body image concerns amongst males and females recruited through the general community and from a university. Seventeen participants (eight male; nine female) provided information on their body image and what factors they saw as positively and negatively impacting on their self evaluations. The importance of self esteem, mood, health and fitness, and recognition of the social ideal were identified as key themes. These themes were incorporated as psycho-social measures and Stroop word stimuli in subsequent phases of the research. Phase Two involved the selection and testing of stimuli to be used in the Emotional Stroop task. Six experimental categories of words were developed that reflected a broad range of health and body image concerns for males and females. These categories were high and low calorie food words, positive and negative appearance words, negative emotion words, and physical activity words. Phase Three addressed the central aim of the project by examining cognitive biases for body image information in empirically defined sub-groups. A National sample of males (N = 55) and females (N = 144), recruited from the general community and universities, completed an Emotional Stroop task, incidental memory test, and a collection of psycho-social questionnaires. Sub-groups of body image disturbance were sought using a cluster analysis, which identified three sub-groups in males (Normal, Dissatisfied, and Athletic) and four sub-groups in females (Normal, Health Conscious, Dissatisfied, and Symptomatic). No differences were noted between the groups in selective attention, although time taken to colour name the words was associated with some of the psycho-social variables. Memory biases found across the whole sample for negative emotion, low calorie food, and negative appearance words were interpreted as reflecting the current focus on health and stigma against being unattractive. Collectively these results have expanded our understanding of processing biases in the general community by demonstrating that the processing biases are found within non-clinical samples and that not all processing biases are associated with negative functionality

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.