Inside-Out: Perception of Key Finance Professionals about Theory and Practice of Islamic Banking

Islamic banking has shown tremendous growth in the first decade of 21st century. By the end of 2008, global volume of assets under Islamic banking reached US$951 billion. In Pakistan, Islamic banking has displayed a tremendous annual average growth of 76% during the last seven years and accounts for 6% of the market share (SBP-2010). However, keeping in view the religious ideologies of the significant majority of Pakistanis, Islamic banking industry has expanded at less than its expected potential. This study documents the perception of key players in finance industry about Islamic banking, to highlight the underlying issues directly responsible for slower-than-potential-expansion of this industry. Findings suggest, although theoretically, that the industry perceives Islamic banking correctly, however professionals do not feel content with its practice

autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain from tens to thousands of approximate implementations for a single arithmetic operation it is intractable to find an optimal combination of approximate circuits in the library even for an application consisting of a few operations. An open problem is "how to effectively combine circuits from these libraries to construct complex approximate accelerators". This paper proposes a novel methodology for searching, selecting and combining the most suitable approximate circuits from a set of available libraries to generate an approximate accelerator for a given application. To enable fast design space generation and exploration, the methodology utilizes machine learning techniques to create computational models estimating the overall quality of processing and hardware cost without performing full synthesis at the accelerator level. Using the methodology, we construct hundreds of approximate accelerators (for a Sobel edge detector) showing different but relevant tradeoffs between the quality of processing and hardware cost and identify a corresponding Pareto-frontier. Furthermore, when searching for approximate implementations of a generic Gaussian filter consisting of 17 arithmetic operations, the proposed approach allows us to identify approximately $10^3$ highly important implementations from $10^{23}$ possible solutions in a few hours, while the exhaustive search would take four months on a high-end processor.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: ``Are SNNs secure?'' Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.Comment: Accepted for publication at the 2020 International Joint Conference on Neural Networks (IJCNN

Risk and Returns of Shar??ah Compliant Stocks on the Karachi Stock Exchange: A CAPM and SCAPM Approach

This study documents the asset pricing mechanism of Shar??ah compliant securities listed on the the Karachi Stock Exchange. We select the CAPM market model to test for the impact in variations of stock returns on a sample of Shar??ah-compliant companies on ten years monthly data (2001-10). We first test the basic CAPM (Capital Asset Pricing Model) and its modified form known as the Shar??ah-compliant asset pricing model (SCAPM). We also analyse return differences due to size (market capitalization), book to market (B/M) value, price-earning ratio (PER), and cash-flow yield (CFY). Our results find a strong impact of the market index on stock returns (adj-R2 70%) and confirm the anomalies of size, B/M, CFY, and PER, while SCAPM is slightly better in explaining variations in cross-sectional stock returns

Impact of Real Sector Variables on Shari’a Compliant Stock Returns

Shari’a compliant stocks are a recent development under Islamic finance, whereby stocks are screened through Shari’a compliance filters. This study is conducted to understand and document the important Real Sector macroeconomic factors contributing in determining stock prices of Shari’a compliant companies in Pakistan. Our sample includes all 97 non-financial companies screened by Al-Meezan Investment Management Ltd, based on financial results of 2009. We have included Six Macroeconomic variables in addition to market index in our study for ten years period (2001-10). Results identified Zero real sector variable in pricing, however, with the inclusion of market index in analysis, the single important variable in pricing of Shari’a compliant securities is market Beta. Evidence favors CAPM for pricing of securities in local market as market index captures the risk of macroeconomic variables