PERFORMANCE ANALYSIS FOR THE TWO-MINUTE PORTFOLIO IN BOTH CANADIAN AND U.S. STOCK MARKET

The “Two-Minute Portfolio” was first introduced by Rob Carrick in 1999 for the Globe and Mail’s Finance Section. By using his strategy with equal weighting in each market sector, Rob claims that individual conservative long-term investors would spend little time in the portfolio selection and still outperform the market (TSX). Over time, the “Two-Minute Portfolio” evolves its strategy to improve the performance. Based on the four main characteristics of the Two-Minute Portfolio: Equal-weight strategy, Large-Cap (blue-chip) companies, Dividend-paying constraint, and Annual rebalancing schedule, we construct the Two-Minute Portfolios in both TSX and S&P 500 markets. We tested the “Two-Minute Portfolio” strategy for its long-term mean return and risk-adjusted return. We found that the Two-Minute Portfolios do not provide statistically significant excess returns. However, in terms of the risk-adjusted measurement, Two-Minutes Portfolios may perform better than benchmarks. We further found that the added Dividend-Paying constraint does not provide significant improvement to the portfolio

Conditional Generation of Medical Images via Disentangled Adversarial Inference

Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs) use a conditioning factor to generate images and have shown great success in recent years. Intuitively, the information in an image can be divided into two parts: 1) content which is presented through the conditioning vector and 2) style which is the undiscovered information missing from the conditioning vector. Current practices in using cGANs for medical image generation, only use a single variable for image generation (i.e., content) and therefore, do not provide much flexibility nor control over the generated image. In this work we propose a methodology to learn from the image itself, disentangled representations of style and content, and use this information to impose control over the generation process. In this framework, style is learned in a fully unsupervised manner, while content is learned through both supervised learning (using the conditioning vector) and unsupervised learning (with the inference mechanism). We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL); second, we introduce a self-supervised regularization method to further separate information in the content and style variables. We show that in general, two latent variable models achieve better performance and give more control over the generated image. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.Comment: Published in Medical Image Analysi

SecureAD: A secure video anomaly detection framework on convolutional neural network in edge computing environment

National Research Foundation (NRF) Singapore under Strategic Capability Research Centres Funding Intiatives; Ministry of Education, Singapore under its Academic Research Funding Tier