We propose 'Tapestry', a novel approach to pooled testing with application to COVID-19 testing with quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. While other pooling techniques require a second confirmatory assay, Tapestry obtains individual sample-level results in a single round of testing, at clinically acceptable false positive or false negative rates. We also propose designs for pooling matrices that facilitate good prediction of the infected samples while remaining practically viable. When testing n samples out of which k n are infected, our method needs only O(k log n) tests when using random binary pooling matrices, with high probability. However, we also use deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems to balance between good reconstruction properties and matrix sparsity for ease of pooling. A lower bound on the number of tests with these matrices for satisfying a sufficient condition for guaranteed recovery is k √ n. In practice, we have observed the need for fewer tests with such matrices than with random pooling matrices. This makes Tapestry capable of very large savings at low prevalence rates, while simultaneously remaining viable even at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We describe how to combine combinatorial group testing and compressed sensing algorithmic ideas together to create a new kind of algorithm that is very effective in deconvoluting pooled tests. We validate Tapestry in simulations and wet lab experiments with oligomers in quantitative RT-PCR assays. An accompanying Android application Byom Smart Testing makes the Tapestry protocol straightforward to implement in testing centres, and is made available for free download. Lastly, we describe use-case scenarios for deployment.

Covid‐19 has impacted the financial markets dramatically. The risk and return expectations of investors have changed, leading them to reallocate their portfolios. This paper aims to analyse the impact of Covid‐19 on the portfolio allocation decisions of individual investors. The study examines the perceptions of investors about various investment avenues before and during the period of extreme uncertainty caused by the COVID‐19 pandemic. The data were collected from individual investors residing in Delhi and Mumbai. AHP is used to rank the investment preferences of the respondents. The results show that due to the present financial crisis pertaining to COVID‐19, investors have started reallocating their portfolios. Since the returns on risky assets are not as expected, investors are moving towards a conservative portfolio. However, the case of transition from risky to risk‐free assets is not the same in the case of all investors.

PurposeThis paper attempts to identify the biases in decision-making of individual investors. The paper aims to develop and validate a higher-order behavioral biases scale.Design/methodology/approachScale development is done by identifying the relevant items of the scale through existing literature and then, adding new items for some biases. In phase 1, using a structured questionnaire, data was collected from 274 investors who invest in financial markets. The major dimensions of the scale have been pruned by using exploratory factor analysis administered on data collected in phase 1. Higher-order CFA is used to analyze the data and to validate the scale on another set of data (collected in phase 2) containing 576 investors.FindingsThe study reveals that the scale for measuring behavioral biases has many dimensions. It has two second-order factors and 13 zero-order constructs. Two second-order constructs have been modeled on the basis of cause of errors in investment decision-making, that is, biases caused due to cognition, biases caused due to emotions.Originality/valueBehavioral biases are yet to receive a due attention, especially, in the Indian context. The present research is focusing on providing an empirically tested scale to test the behavioral biases. Some of the biases, which have been analyzed using secondary data in previous studies, have been tested with the help of statements in this study.

The study intends to examine the cause-and-effect relationship between Covid-19 and the factors affecting investment behavior in a South Asian economy. The investment behavior is considered as an MCDM problem. To address this problem, the study employs MCDM approach i.e., a blend of both DEMATEL and Grey theory due to its potential to deal with subjective judgments of investors. The results indicate that Covid-19 is the leading cause behind financial stress, psychophysiological health outcomes, investors' perception about the market, and investors' strategy. Among sub-factors, portfolio allocation is the most influenced sub-factor. Alteration in portfolio is a major challenge for emerging countries which have become attractive destinations for global investors. Overall, the significant contribution of the paper is to establish the interlinkages among the factors affecting investment behavior, given the uncertainty triggered by the pandemic. Although the literature provides evidence on this problem during normal situations, analysis of investment behavior during severe crisis is still lacking. The research will be immensely useful to different stakeholders such as government, policymakers, financial advisors, and investors in making their strategic or operational decisions.