Forecasting Stock Market Returns: An Empirical Investigation for United Kingdom

Stock markets play a vital role in the economic development as a transmission mechanism through which savings are mobilized and adequately circulated across various economic sectors with a view to realize comprehensive growth. The current paper aims at identifying those factors that predict the stock market returns. For this purpose, a multivariate panel regression approach is employed. The empirical econometric model of the study is developed at two levels- firm level and macroeconomic level indicators. The annual panel data is constructed for 50 non-financial firms that are listed at London Stock Exchange during the period 2008-2017. We have employed robust Least Square estimation method.The findings showed that among financial performance factors, only net profit margin has significant predicting power for stock market returns. It presented signaling effect of net profit margin that attracts more investments. Moreover, we found that the selected set of macroeconomic factors have significant predicting power for stock market returns. Our paper contributes in the field of corporate finance as point of reference in the literature for the factors that predicts the stock market returns in the context of United Kingdom. In addition, it will eventually attract the attentions of academics, managers, policymakers, and investors. Keywords: Financial performance, Macroeconomic conditions, Stock market returns, Panel regression and Least Squares. JEL Codes: D22, G15 and F52. DOI: 10.7176/EJBM/12-1-03 Publication date: January 31st 202

Determinants of Capital Structure in Pakistan

In financial management, capital structure is a systematic method for financing the operating activities through equity, debt or combination of the both. It is also referred to as a degree of debt in the capital arrangement of a business. However, it is a significant and an important decision for corporate firms. The business operations are significantly dependent on managing the cost of capital which is determined through capital structure of an organization. Hence, the objective of designing capital structure strategy is for reducing the borrowing cost and maximizing returns from acquired capital which has been acquired from various resources.The main purpose of our study is to empirically investigate the determinants of capital structure in the context of Pakistan. The balanced panel data set of our study is constructed using annual reports for 30 non-financial firms listed at Pakistan Stock Exchange for the period 2008 to 2017. We utilized Ordinary Least Squares estimation technique to estimate the econometric model. The empirical findings present that profitability and tangibility are key determinants for capital structure of firms in Pakistan. Moreover, tangibility has positive association with leverage. It shows that creditors are attracted by firms having high tangible assets. It is due to sureness for reclamation of their loans. On the other hand, profitability showed negative association with leverage. It implies that more profitable firms do not take external debts due to availability of cash reserves that they created from profits. Further, our study suggests the relevance of theories namely trade-off static theory and pecking order theory for identifying the determinants of capital structure in Pakistan. Keywords: Capital structure, Firm-specific factors, Ordinary Least Squares (OLS). JEL Codes: D22, F65

Stock Market Returns Under Macroeconomic Conditions: An Empirical Evidence from BRIC Economies

Financial markets continuously incorporate information about macroeconomic events and monetary policy in order to make profitable investment decisions, and these are reflected in stock returns. In fact, financial markets have no perfect information, they do behave in forward looking manner, in that they monitor for new information that could affect the profitable investment decisions.  The main objective of our paper is to explore the sensitivity of stock market returns to macroeconomic environments in Brazil, Russia, India, and China. In order to achieve the objective, the authors utilized data for major macroeconomic factors namely exchange rate, inflation rate, interest rate and oil price for the sample period starting from May 2007 to April 2017. We utilized OLS estimation technique to estimate the empirical models of our study. The findings of show no significant relationship between respective exchange rate, inflation rate, interest rate and oil price on market returns of either BRIC economy. However, the regression analysis reveals insignificant positive relationship of exchange rate, inflation rate and interest rate with stock market returns while oil prices has insignificant negative relationship. This suggests influence of other domestic and international macroeconomic factors on stock market returns. Furthermore, in the collective panel regression model of BRIC economies, we found that inflation rate has significant influence on stock market returns of BRIC economies. The findings of study help investors to know how specific stock performs to take profitable investment decision. It also assists policy makers to enhance and monitor monetary policy and helps managers in risk management. Keywords: Macroeconomic indicators, Stock market returns, OLS JEL classification: E31, E44, G1 DOI: 10.7176/JESD/10-1-0

Model-based approaches for the detection of biologically active genomic regions from next generation sequencing data

Next Generation Sequencing (NGS) technologies are quickly gaining popularity in biomedical research. A popular application of NGS is to detect potential gene regulatory elements that are captured or enriched by certain experimental procedures, for example, Chromatin Immunoprecipitation (ChIP-seq), DNase hypersensitive site mapping (DNase-seq), and Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE-seq), among others. While ChIP-seq can be use to identify protein-DNA interaction sites, both DNase-seq and FAIRE-seq can be used to identify open chromatin regions, which are more likely to contain elements involved in gene expression regulation. We collectively refer to these types of sequencing data as DAE-seq, where DAE stands for DNA After Enrichment. DAE-seq data can provide important insight into gene regulation, which is crucial to understanding the molecular mechanism of phenotypic outcomes, such as complex diseases. Here we address several practical issues facing biomedical researchers in the analysis of DAE-seq data through the development of several new and relevant statistical methods. We first introduce a three-component mixture regression model to discover ``enriched regions, i.e., the genomic regions with more DAE-seq signal than expected in relation to background regions. We demonstrate its practical utility and accuracy in detecting regions of active regulatory elements across a wide range of commonly used DAE-seq datasets and experimental conditions. We then develop a novel Autoregressive Hidden Markov Model (AR-HMM) to account for often-ignored spatial dependence in DAE-seq data, and demonstrate that accounting for such dependence leads to increased performance in identifying biologically active genomic regions in both simulated and real datasets. We also introduce an efficient and novel variable selection procedure in the context of Hidden Markov Models when the means of the emission distributions of each state are modelled with covariates. We study the asymptotic properties of the proposed variable selection procedure and apply this approach to simulated and real DAE-seq data. Lastly, we introduce a new method for the joint analysis of total and allele-specific read counts from DAE-seq data and RNA-seq data. In all, we develop several statistical procedures for the analysis of DAE-seq data that are highly relevant to biomedical researchers and have broader applicability to other problems in statistics.Doctor of Philosoph

A statistical model to assess (allele-specific) associations between gene expression and epigenetic features using sequencing data

Sequencing techniques have been widely used to assess gene expression (i.e., RNA-seq) or the presence of epigenetic features (e.g., DNase-seq to identify open chromatin regions). In contrast to traditional microarray platforms, sequencing data are typically summarized in the form of discrete counts, and they are able to delineate allele-specific signals, which are not available from microarrays. The presence of epigenetic features are often associated with gene expression, both of which have been shown to be affected by DNA polymorphisms. However, joint models with the flexibility to assess interactions between gene expression, epigenetic features and DNA polymorphisms are currently lacking. In this paper, we develop a statistical model to assess the associations between gene expression and epigenetic features using sequencing data, while explicitly modeling the effects of DNA polymorphisms in either an allele-specific or nonallele-specific manner. We show that in doing so we provide the flexibility to detect associations between gene expression and epigenetic features, as well as conditional associations given DNA polymorphisms. We evaluate the performance of our method using simulations and apply our method to study the association between gene expression and the presence of DNase I Hypersensitive sites (DHSs) in HapMap individuals. Our model can be generalized to exploring the relationships between DNA polymorphisms and any two types of sequencing experiments, a useful feature as the variety of sequencing experiments continue to expand

To evaluate the effect of consuming soy products on the rate of fall in serum oestragen level in post TAHBSO women on oestrogen implant.

This was a prospective comparative study which was undertaken from January, 2008 to May, 2012. A total of 35 women, who had undergone TAHBSO for benign gynaecological conditions, were each inserted with a 50 mg oestradiol implant in the sub-rectus space intra-operatively. Serum oestradiol levels were measured on a two-monthly basis until the level fell below 50 pmol/litre and the patients have developed post-menopausal symptoms. A second 50 mg oestradiol implant was then inserted as out-patients in either of the lumbar region of the abdomen using a special trocar. These patients were then advised to consume at least one glass of soya products every day. Serum oestradiol levels were again measured on a two-monthly basis until the level fell below 50 pmol/litre and the patients have developed post-menopausal symptoms. The means of each two monthly intervals were compared using the comparisons of means to determine whether there was any difference in the rate of fall of the oestrogen levels with or without soy product intake

Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

Electronic Health Records (EHRs) are commonly used to investigate relationships between patient health information and outcomes. Deep learning methods are emerging as powerful tools to learn such relationships, given the characteristic high dimension and large sample size of EHR datasets. The Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU patients, where researchers investigated the relationships between clinical measurements, and in-hospital mortality. However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs). Although a rich literature exists regarding the treatment of missing data in traditional statistical models, it is unclear how this extends to deep learning architectures. To address these issues, we propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random (MNAR) patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori. We show that the use of our method leads to more realistic imputed values relative to the state-of-the-art, as well as significant differences in fitted downstream models for mortality.Comment: 37 pages, 3 figures, 3 tables, under review (Journal of the American Statistical Association

Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data

Controller Design Of Unicycle Mobile Robot

ABSTRACT: The ability of unicycle mobile robot to stand and move around using one wheel has attracted a lot of researchers to conduct studies about the system, particularly in the design of the system mechanisms and the control strategies. This paper reports the investigation done on the design of the controller of the unicycle mobile robot system to maintain its stability in both longitudinal and lateral directions. The controller proposed is a Linear Quadratic Controller (LQR) type which is based on the linearized model of the system. A thorough simulation studies have been carried out to find out the performance of the LQR controller. The best controller gain, K acquired through the simulation is selected to be implemented and tested in the experimental hardware. Finally, the results obtained from the experimental study are compared to the simulation results to study the controller efficacy. The analysis reveals that the proposed controller design is able to stabilize the unicycle mobile robot. ABSTRAK: Kemampuan robot satu roda untuk berdiri dan bergerak di sekitar telah menarik minat ramai penyelidik untuk mengkaji sistem robot terutamanya didalam bidang rangka mekanikal dan strategi kawalan robot. Kertas kajian ini melaporkan hasil penyelidikan ke atas strategi kawalan robot bagi memastikan sistem robot satu roda dapat distabilkan dari arah sisi dan hadapan. Strategi kawalan yang dicadang, menggunakan teknik kawalan kuadratik sejajar (Linear Quadratic Control) yang berdasarkan model robot yang telah dipermudahkan. Kajian simulasi secara terperinci telah dijalankan bagi mengkaji prestasi strategi kawalan yang dicadangkan. Dari kajian simulasi sistem robot, pemilihan faktor konstan, K yang sesuai di dalam strategi kawalan telah dibuat, agar dapat dilaksanakan ke atas sistem robot yang dibangunkan. Keputusan dari kajian simulasi dan tindak balas oleh sistem robot yang dibangunkan akhirnya dibandingkan bagi melihat kesesuaian faktor kostan, K yang dipilih. Analisa menunjukkan dengan menggunakan strategi kawalan yang dicadangkan dapat menstabilkan robot satu roda. KEYWORDS: unicycle mobile robot; nonholonomic system; LQ