Is Bankruptcy Risk a Systematic Risk? Evidence from Pakistan Stock Exchange

This study empirically investigates the relationship between default risk and cross-section of stock returns in the Pakistan Stock Exchange (PSX). Stock price data from all listed and delisted companies use to calculate monthly returns from 2001-2016. Ohlson's O-score is employed to measure exposure of firm to systematic deviation within bankruptcy risk. Besides, asset-pricing models like the Capital Asset Pricing Model (CAPM) and Fama French (FF) models are employed. Portfolios are sorted in deciles by default probability. This result finds that stocks of firms significantly exposed to not diversified Default Risk yield higher returns. Besides that, the FF models explain cross-sectional stock returns since factors incorporate information on financial distress and default. After that, the book-to-market equity factor is not significant in elucidating returns of distressed firms because of market inefficiency. Results have practical implications for portfolio managers and investors of an emerging economy in developing diversified portfolios during periods of uncertainty and market volatility.JEL Classifications: G12, G15, G33How to Cite:Chhapra, I. U., Zehra, I., Kashif, M., & Rehan, R. (2020). Is Bankruptcy Risk a Systematic Risk? Evidence from Pakistan Stock Exchange. Etikonomi: Jurnal Ekonomi, 19(1), 51 – 62. https://doi.org/10.15408/etk.v19i1.11248

Pengaruh faktor masa dan abiotik terhadap kelakuan keluar masuk sarang lebah kelulut, Tetrigona apicalis (Smith, 1857) (Hymenoptera: Apidae)

Lebah kelulut merupakan spesies pendebunga yang penting di kawasan tropika dan subtropika. Dalam kajian ini, pengaruh faktor masa dan abiotik terhadap kelakuan keluar dan masuk sarang oleh lebah kelulut Tetrigona apicalis telah dikaji. Faktor masa dan abiotik yang dikaji termasuklah waktu per hari, suhu, kelembapan relatif, keamatan cahaya dan kelajuan angin. Kajian ini telah dijalankan di pusat koleksi Meliponini di Institut Genom Malaysia, Bangi, Selangor, Malaysia. Jumlah lebah kelulut T. apicalis yang masuk dan keluar dari sarang telah dikira melalui pemerhatian secara langsung selama 5 minit bagi setiap jam dari 09:00 hingga 17:00 dari bulan Mei hingga Oktober 2019. Hasil kajian menunjukkan bahawa masa (f=3.965, P=0.002, P<0.05) secara signifikannya mempengaruhi kekerapan masuk dan keluar lebah kelulut T. apicalis dari sarang. Bagi faktor abiotik, kelembapan relatif secara signifikannya mempengaruhi kelakuan masuk (f=16.664, P= 0.001, P<0.05) dan keluar (f=7.939, P=0.006, P<0.05) lebah kelulut T. apicalis dari sarang. Ujian regresi berganda menunjukkan terdapat interaksi yang rendah tetapi signifikan di antara kesemua faktor abiotik terhadap kelakuan keluar dan masuk sarang lebah kelulut T. apicalis (r2=0.13, f=3.10, P=0.02, P<0.05). Kajian ini boleh digunakan untuk mendidik penternak lebah kelulut untuk memastikan pengurusan yang lebih baik bagi mengekalkan perkhidmatan pendebungaan yang mampan sepanjang tahun

Are Stock Prices a Random Walk? An Empirical Evidence of Asian Stock Markets

Investigating if the market is efficient is an old issue as market efficiency is imperative for channeling investments to best-valued projects and its importance endures. There is contradictory evidence in the literature provided by empirical researches. The primary purpose of this research has been to find out whether share prices are a random walk process by applying multiple unit root tests, Runs Test and newly developed State Space Model. The empirical findings of the study provide sufficient evidence that the stock prices of KSE 100 Index, S &amp; P BSE 500 Index, and CSE All Share Index is not a random walk process and are thus weak form inefficient hypothesis. In this study, the concept of the random walk is examined considering only the stock markets while bypassing the other asset markets. This research supply exciting facts about independent samples from Pakistan, India, and Bangladesh and complement the existing literature on emerging markets.DOI: 10.15408/etk.v17i2.7102</p

Transcriptomic analysis of Dubas bug (Ommatissus lybicus Bergevin) infestation to Date Palm

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Incisor malocclusion using cut-out method and convolutional neural network

Malocclusion is a condition of misaligned teeth or irregular occlusion of the upper and lower jaws. This condition leads to poor performance of vital functions such as chewing. A common procedure in orthodontic treatment for malocclusion is a conventional diagnostic procedure where a dental health professional takes dental x-rays to examine the teeth to diagnose malocclusion. However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. However, several issues concerning the dental images as input of the classification model may affect the accuracy of the classification. In addition, unstructured images with varying sizes and the problem of a machine learning algorithm that does not focus on the region of interest (ROI) for incisor features bring challenges in delivering the treatment. This study has developed a malocclusion classification model using the cut-out method and Convolutional Neural Network (CNN). The cut-out method restructures the input images by standardising the sizes and highlighting the incisor sections of the images which assisted the CNN in accurately classifying the malocclusion. From the results, the implementation of the cut-out method generates higher accuracy across all classes of malocclusion compared to the non-implementation of the cut-out method

Functional Approach towards Approximation Problem

Approximation algorithms are widely used for problems related to computational geometry, complex optimization problems, discrete min-max problems and NP-hard and space hard problems. Due to the complex nature of such problems, imperative languages are perhaps not the best-suited solution when it comes to their actual implementation. Functional languages like Haskell could be a good candidate for the aforementioned mentioned issues. Haskell is used in industries as well as in commercial applications, e.g., concurrent applications, statistics, symbolic math and financial analysis. Several approximation algorithms have been proposed for different problems that naturally arise in the DNA clone classifications. In this thesis, we have performed an initial and explorative study on applying functional languages for approximation algorithms. Specifically, we have implemented a well known approximate clustering algorithm both in Haskell and in Java and we discuss the suitability of applying functional languages for the implementation of approximation algorithms, in particular for graph theoretical approximate clustering problems with applications in DNA clone classification. We also further explore the characteristics of Haskell that makes it suitable for solving certain classes of problems that are hard to implement using imperative languages.Muhammad Imran Shafi: 29A Sodergatan 19547 Marsta, 0737171514, Muhammad Akram C/O Saad Bin Azhar Folkparksvagen 20/10 Ronneby, 076289911

Functional Approach towards Approximation Problem

Approximation algorithms are widely used for problems related to computational geometry, complex optimization problems, discrete min-max problems and NP-hard and space hard problems. Due to the complex nature of such problems, imperative languages are perhaps not the best-suited solution when it comes to their actual implementation. Functional languages like Haskell could be a good candidate for the aforementioned mentioned issues. Haskell is used in industries as well as in commercial applications, e.g., concurrent applications, statistics, symbolic math and financial analysis. Several approximation algorithms have been proposed for different problems that naturally arise in the DNA clone classifications. In this thesis, we have performed an initial and explorative study on applying functional languages for approximation algorithms. Specifically, we have implemented a well known approximate clustering algorithm both in Haskell and in Java and we discuss the suitability of applying functional languages for the implementation of approximation algorithms, in particular for graph theoretical approximate clustering problems with applications in DNA clone classification. We also further explore the characteristics of Haskell that makes it suitable for solving certain classes of problems that are hard to implement using imperative languages.Muhammad Imran Shafi: 29A Sodergatan 19547 Marsta, 0737171514, Muhammad Akram C/O Saad Bin Azhar Folkparksvagen 20/10 Ronneby, 076289911

Transcriptomic analysis of Dubas bug (Ommatissus lybicus Bergevin) infestation to Date Palm

publishedVersio