Can sector specific REIT strategies outperform a diversified benchmark?

Purpose: We investigate the performance of different portfolios of REITs which specialise by property type compared to the performance of a diversified free-float market capitalisation weighted benchmark index to determine whether superior risk-adjusted returns can be achieved.. Design/methodology/approach: Firstly we examine the performance of portfolios constructed using the criteria of Equal Weight, Minimum Variance, Maximum Sharpe and Risk Parity rather than free-float market capitalisation. Secondly we apply an automated trading strategy of Trend Following to see if this filter will improve risk-adjusted returns. Findings: The two step process of forming combinations of REIT sectors with the subsequent addition of a trend following overlay can offer clear benefits relative to a passive benchmark investment. Research limitations/implications: Although three of the four strategies were shown to outperform the benchmark index on a risk-adjusted basis, one issue was that the efficient portfolios tended to have large weightings to relatively few sectors. We also found that maximum drawdowns (losses) of the strategies tended to be rather high, as was the benchmark Practical implications: The methods outlined in this paper can be applied to construct superior risk-adjusted REIT portfolios globally. Originality/value: Although studies have been undertaken separately on REIT specialisation, and Trend Following in equity and commodity markets this paper is the first to combines the two topics, and therefore has particular value for real estate fund managers globally

eSource in Clinical Research

<p>This topic of eSource came to my mind from my experience in Clinical Research/ Pharmaceutical Research while conducting clinical trials at different sites. Whenever a site conducting trials then this site will be in need for a data documentation system to document and store the data that is being created and establishing from the clinical trials that are being conducted. This data documentation system is called Source. However, the Source could be electronic Source as in eSource or not electronic as in Paper Source. I have used both the eSource and the Paper Source. This Symposium presentation will show the advantages of the eSource in the Clinical Research.</p&gt

Cholesterolic Granuloma of the Ovary: A Case Report

<p>Cholesterol granuloma is the consequence of a chronic inflammatory reaction due to the accumulation of cholesterol crystals in the tissues. The symptoms, clinical examination and preoperative imaging of cholesterol granuloma may be misdiagnosed as ovarian cancer. Since the final diagnosis of a pelvic mass depends on histopathological findings, cholesterol granuloma should be borne in mind as a differential diagnosis of pelvic mass.</p&gt

Precision Colon Cancer Screening: Leveraging Data Analytics and Machine Learning for Early Detection

<p>Colon cancer is a significant global health concern, accounting for millions of new diagnoses and fatalities annually. Early detection of colon cancer is pivotal in improving patient outcomes and reducing mortality rates. In this paper, we present a novel approach to early colon cancer detection by leveraging data analysis techniques and machine learning algorithms. Our research aims to address the critical need for more accurate and efficient diagnostic methods for colon cancer, particularly in its early stages.</p><p>Traditional screening methods, such as colonoscopy and stool-based tests, have proven effective but are often limited by factors such as invasiveness, patient compliance, and cost. data analysis and machine learning offer a promising alternative by enabling non-invasive, cost-effective, and highly accurate early detection. This paper outlines our comprehensive study, from data collection and preprocessing to the development and evaluation of a robust early detection system.</p><p>Our methodology involves acquiring colonoscopy datas from various sources and applying data preprocessing techniques, including segmentation and feature extraction. We also employ a range of machine learning algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), to classify these datas into cancerous and non-cancerous categories. Furthermore, we explore the fusion of data analysis and machine learning to harness the strengths of both approaches.</p><p>The results of our experiments demonstrate the effectiveness of our proposed method in early colon cancer detection. Through rigorous evaluation using appropriate metrics, we illustrate the high accuracy, sensitivity, and specificity of our system, providing evidence of its potential clinical utility. </p><p>In our discussion, we interpret the implications of our findings, emphasizing the clinical relevance of this approach for early diagnosis and improved patient outcomes. We also discuss the limitations and ethical considerations associated with this technology, along with directions for future research and development.</p><p>Our research represents a significant contribution to the field of colon cancer detection, providing a promising avenue for improving early diagnosis and ultimately saving lives. As colon cancer continues to be a leading cause of cancer-related mortality worldwide, the integration of data analysis and machine learning may play a pivotal role in reducing its impact on public health.</p&gt