Essays on gender and investment decisions : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Albany, New Zealand

The puzzle of whether gender differences exist in behavioral biases and investment preferences of highly skilled and experienced professionals remains unsolved. Subsequently, this thesis consists of three related essays on investment decisions by gender of professionals in the field of finance. The first essay shows that prospect theory value influences insider trading decisions, and the impact is stronger among female executives’ trades. Female insiders tend to carry more biased trades and suffer significantly higher resultant losses, as compared to their male counterparts. Female insiders who buy (sell) when their company's prospect theory value is above (below) other firms’ prospect theory values, lose 47 basis points over the next month. While the findings contradict the overconfidence hypothesis that predicts poor trading decisions by male insiders, the results are consistent with the male insiders’ superior information access hypothesis, suggesting that informational disadvantage serves as a possible channel of higher behavioral biases in female insiders’ trading. The second essay demonstrates that the gender of mutual fund managers affects the liquidity of a portfolio. Female managers prefer higher portfolio liquidity than their male counterparts. Funds managed by single female managers are 8-25% more liquid than single male managed funds. Contrary to the excessive trading hypothesis that expects a higher liquidity preference by overconfident male fund managers, the findings support the inclination of female fund managers for the price efficiency hypothesis. Funds experience increased liquidity when they transition to a female manager. The third essay documents that the collective self-construal of female fund managers explains their tendency to invest less actively as compared to their male counterparts. Funds with a higher proportion of female managers in the management team closely track the multifactor benchmark. For the funds managed by more female managers than males, the economic benefits of diversification are 1.86% lower than other funds. Consistent with the literature, female fund managers herd more, take less risk, and are less overconfident than males. These investment behaviors are likely to be the possible explanations of the less active investing strategy of female fund managers

Big Data in HEP: A comprehensive use case study

Experimental Particle Physics has been at the forefront of analyzing the worlds largest datasets for decades. The HEP community was the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems collectively called Big Data technologies have emerged to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and promise a fresh look at analysis of very large datasets and could potentially reduce the time-to-physics with increased interactivity. In this talk, we present an active LHC Run 2 analysis, searching for dark matter with the CMS detector, as a testbed for Big Data technologies. We directly compare the traditional NTuple-based analysis with an equivalent analysis using Apache Spark on the Hadoop ecosystem and beyond. In both cases, we start the analysis with the official experiment data formats and produce publication physics plots. We will discuss advantages and disadvantages of each approach and give an outlook on further studies needed.Comment: Proceedings for 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016

Comparative Evaluation of Lamina Cribrosa Anatomical Parameters with Retinal Nerve Fiber Layer Thickness Defects In Primary Open-angle Glaucoma Cases And Controls

OBJECTIVES To assess the lamina cribrosa (LC) anterior lamina cribrosa depth (ALCD), lamina cribrosa thickness (LCT) and retinal nerve fiber layer thickness (RNFLT) in primary open-angle glaucoma (POAG) cases and age-matched controls and to compare these anatomical variables among POAG cases and age-matched controls. METHODOLOGY The case-control study was researched at Al-Ain Eye Institute, Karachi, in four month’s duration (November 2018 till February 2019). Expert eye specialist recruited 57 POAG cases and 46 age-matched healthy controls. Observation of intraocular pressure (IOP) and open angle was done using Goldmann tonometry and Slit-lamp biomicroscopy with stereoscopic ophthalmoscopy respectively. Visual field parameters of mean deviation (MD) and pattern standard deviation (PSD) measured using Humphrey Field Analyzer. Highly sensitive spectral domain ocular coherence tomography with enhanced depth imaging (EDI-OCT) was used to determine ALCD, LCT and RNFLT. RESULTS Statistically significant results were produced by RNFLT defects when it is compared in groups of mild with moderate cases of POAG (P-value 0.037). ALCD and LCT did display an association with RNFLT defects but did not produced statistically significant results. CONCLUSION Assessments of ALCD and LCT can provide important prognostic evidence about RNFLT and can assist in future planning of mild and moderate cases suffering from POAG

Frequency and Risk Factors of Depression among Medical Students: A Cross-Sectional Study in Karachi

OBJECTIVES The study aimed to determine the frequency of depression among medical students and to identify the different risk factors associated with depression. METHODOLOGY A cross-sectional study was conducted among medical students at a private medical college in Karachi. The study was initiated after approval was taken from the ethical committee. Consent was taken before the data collection after explaining the details of the study. Students were selected for this study as per inclusion criteria. They were provided with the PHQ-9 questionnaire in which they were inquired about the factors for depression. The total students with depression positive were presented by their frequencies with a 95% confidence interval. RESULTSThree hundred seventy medical students participated, and 207 (56%) tested positive for depression. Notably, depression was more prevalent among final-year students, with 80% affected. Additionally, the severity of depression gradually increased with advancing medical years, reaching the highest level in the final year, where 61 students (80%) reported significant depression. The most frequent causes of depression were living away from home and facing the challenges of a demanding curriculum. CONCLUSION The study findings revealed a higher likelihood of depression among medical students, particularly in their final year. This vulnerability was exacerbated by the stress associated with extensive coursework and peer pressure to achieve excellent exam grades

Improving Performance And Programmer Productivity For I/o-intensive High Performance Computing Applications

Due to the explosive growth in the size of scientific data sets, data-intensive computing is an emerging trend in computational science. HPC applications are generating and processing large amount of data ranging from terabytes (TB) to petabytes (PB). This new trend of growth in data for HPC applications has imposed challenges as to what is an appropriate parallel programming framework to efficiently process large data sets. In this work, we study the applicability of two programming models (MPI/MPI-IO and MapReduce) to a variety of I/O-intensive HPC applications ranging from simulations to analytics. We identify several performance and programmer productivity related limitations of these existing programming models, if used for I/O-intensive applications. We propose new frameworks which will improve both performance and programmer productivity for the emerging I/O-intensive applications. Message Passing Interface (MPI) is widely used for writing HPC applications. MPI/MPI- IO allows a fine-grained control of assigning data and task distribution. At the programming frameworks level, various optimizations have been proposed to improve the performance of MPI/MPI-IO function calls. These performance optimizations are provided as various function options to the programmers. In order to write an efficient code, they are required to know the exact usage of the optimization functions, hence programmer productivity is limited. We propose an abstraction called Reduced Function Set Abstraction (RFSA) for MPI-IO to reduce the number of I/O functions and provide methods to automate the selection of appropriate I/O function for writing HPC simulation applications. The purpose of RFSA is to hide the performance optimization functions from the application developer, and relieve the application developer from deciding on a specific function. The proposed set of functions relies on a selection algorithm to decide among the most common optimizations provided by MPI-IO. Additionally, many application scientists are looking to integrate data-intensive computing into computational-intensive High Performance Computing facilities, particularly for data analytics. We have observed several scientific applications which must migrate their data from an HPC storage system to a data-intensive one. There is a gap between the data semantics of HPC storage and data-intensive system, hence, once migrated, the data must be further refined and reorganized. This reorganization must be performed before existing data-intensive tools such as MapReduce can be effectively used to analyze data. This reorganization requires at least two complete scans through the data set and then at least one MapReduce program to prepare the data before analyzing it. Running multiple MapReduce phases causes significant overhead for the application, in the form of excessive I/O operations. For every MapReduce application that must be run in order to complete the desired data analysis, a distributed read and write operation on the file system must be performed. Our contribution is to extend Map-Reduce to eliminate the multiple scans and also reduce the number of pre-processing MapReduce programs. We have added additional expressiveness to the MapReduce language in our novel framework called MapReduce with Access Patterns (MRAP), which allows users to specify the logical semantics of their data such that 1) the data can be analyzed without running multiple data pre-processing MapReduce programs, and 2) the data can be simultaneously reorganized as it is migrated to the data-intensive file system. We also provide a scheduling mechanism to further improve the performance of these applications. The main contributions of this thesis are, 1) We implement a selection algorithm for I/O functions like read/write, merge a set of functions for data types and file views and optimize the atomicity function by automating the locking mechanism in RFSA. By running different parallel I/O benchmarks on both medium-scale clusters and NERSC supercomputers, we show an improved programmer productivity (35.7% on average). This approach incurs an overhead of 2-5% for one particular optimization, and shows performance improvement of 17% when a combination of different optimizations is required by an application. 2) We provide an augmented Map-Reduce system (MRAP), which consist of an API and corresponding optimizations i.e. data restructuring and scheduling. We have demonstrated up to 33% throughput improvement in one real application (read-mapping in bioinformatics), and up to 70% in an I/O kernel of another application (halo catalogs analytics). Our scheduling scheme shows performance improvement of 18% for an I/O kernel of another application (QCD analytics)

Smart Read/Write For Mpi-Io

We present a case for automating the selection of MPI-IO performance optimizations, with an ultimate goal to relieve the application programmer from these details, thereby improving their productivity. Programmers productivity has always been overlooked as compared to the performance optimizations in high performance computing community. In this paper we present RFSA, a Reduced Function Set Abstraction based on an existing parallel programming interface (MPI-IO) for I/O. MPI-IO provides high performance I/O function calls to the scientists/engineers writing parallel programs; who are required to use the most appropriate optimization of a specific function, hence limits the programmer productivity. Therefore, we propose a set of reduced functions with an automatic selection algorithm to decide what specific MPI-IO function to use. We implement a selection algorithm for I/O functions like read, write, etc. RFSA replaces 6 different flavors of read and write functions by one read and write function. By running different parallel I/O benchmarks on both medium-scale clusters and NERSC supercomputers, we show that RFSA functions impose minimal performance penalties. © 2009 IEEE