Dr. Hassan Murad School of Management (HSM)

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A Class of Generalized Estimators of Population Mean Under Ranked Set Sampling Schemes
(UMT, Lahore, 2022) Asad Ali
An efficient estimate of the population mean plays an important role in the field of sample surveys, especially when population is heterogenous. The main focus of this dissertation is to enhance efficiency of mean estimators for the heterogenous populations. In Chapter 1, a comprehensive discussion about the Simple Random Sampling (SRS), Stratified Random Sampling (StRS), Ranked Set Sampling (RSS), Stratified Ranked Set Sampling (StRSS),Extreme Ranked Set Sampling (ERSS), Paired Ranked Set sampling (PRSS), Median Ranked Set Sampling (MRSS), Double Ranked Set Sampling (DRSS), Stratified Extreme Ranked Set Sampling (StERSS), Stratified Median Ranked Set Sampling (StMRSS), Stratified Double Ranked Set Sampling (StDRSS), Paired Double Ranked Set Sampling (PDRSS), Stratified Paired Double Ranked Set Sampling (StPDRSS), Extreme-cum-Median Ranked Set Sampling (EMRSS) has been given. In Chapter 2, the literature on ranking schemes of RSS and estimators with one or two auxiliary variables have been discussed theoretically. Research gap and objectives of this dissertation have also been identified in this chapter. In Chapter 3, notations, symbols and different mathematical relations have been given. Mathematical forms of existing estimators have also been discussed in this chapter.
A Clustering Method for Portfolio Optimization The Case of Pakistan Stock Exchange
(UMT.Lahore, 2020) Rafia Ghulam Ali
Extantevidence shows that clustering analysis can improve portfolio selection and performance. This study aims to improve portfolio performance using K-means clustering technique in Pakistan Stock Exchange (PSX). For this purpose, using daily return for the period of January 2010 to December 2018, the study develops three stock clusters based on Firm Size, Return on Equity and Return on Assets. The findings reveal that the clustering technique out performs naïve portfolio strategies in traditional Markowitz mean-variance framework.
A Comparative Analysis of Machine Learning Algorithms for Price Prediction of Educational Supplies
(UMT.Lahore, 2024-07-26) Muhammad Usman Ameer
A Comparative Analysis of Machine Learning Algorithms for Price Prediction of Educational Supplies
(UMT, Lahore, 2025) Muhammad Usman Ameer
This study conducts a comprehensive comparison of Linear Regression, Decision Tree, and Random Forest algorithms to predict educational supply prices, evaluated using key performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the R² score. Among these, the Random Forest algorithm demonstrated a slight advantage in predictive accuracy. However, the results suggest that all models possess significant potential for enhancement. Future research should delve into the integration of additional features, such as market trends and economic indicators, and explore the adoption of more sophisticated algorithms, including hybrid models, to further refine predictive performance. These findings offer critical insights and guidance for improving financial planning processes within educational institutions.
A Comparative Performance Analysis of Islamic & Conventional Banking
(UMT.Lahore, 2017) Altaf Ahmad
The assessment of financial performance of banks is of particular importance in all societies and economic systems, given the scarcity of economic and financial resources compared to the large needs. Banks face a peculiar situation where most of the financial resources are borrowed from the depositors or shareholders. Therefore, one of the most important challenges facing bank managers is how to efficiently use the financial resources available to them. Financial indicators are the most common analytical tools used to assess the overall and partial performance of all organizations. The objective of this research is to critically evaluate the performance indicators which are used in previous studies and propose a comprehensive indicator to check the performance of selected Islamic and Conventional banks of Pakistan over the period of 2011-2016. The comprehensive indicator of performance is comprised of indicator of profitability, customer satisfaction and cost & revenue efficiency. The efficiencies are measured by using Stochastic Frontier Analysis, while overall indicator has been constructed by using Principle Factor Analysis. This study provides ranking of selected banks based on the new performance indicator and insights to the possible determinants in conventional and Islamic banking system. This study then compares the determinants of performance such as Bank size, Operating efficiency, Management efficiency, Employee efficiency, and Funding cost between conventional and Islamic banks which are comparable in size. To compare the determinants of performance, regression analysis is applied. Feasible Generalized Least Square (FGLS) approach is later used to estimate the determinants of performance. Findings show that Meezan bank is ranked at the top in revenue efficiency and Askari bank ranks first in cost efficiency. In overall performance comparison, Meezan bank is at the highest position. This study identifies Operating efficiency, Management efficiency, Employee efficiency, and Funding cost as important determinants of the performance of Pakistani banking sector.
A comparative study of pashto and english linguistic taboos
(UMT Lahore, 2009) Muhammad Kamal Khan
The present thesis is a comparative study of an ethno-linguistic phenomenon known as 'taboo' in Pashto and English cultures. It is a custom which consists in avoiding mentioning certain words and expressions which is a common practice in more than one society of the world. The stuify has analyzed the nature ofthe approach ofPashto speakers towards some of the well known taboo-words and expressions and has also compared them with English speakers. It is based on data collected through questionnaires, interviews and personal observations. The findings of qualitative data collected through interviews and observations from Pashto speakers were used in support of the quantitative data. Quantitative data were analyzed through the application of statistical software-SPSS. The t-tests revealed that Pashto language is more prone to linguistic taboos than English. Less or no difference was found on the basis of area (rural/urban) in the approach of people of both cultures. Further exploration of the data reveals that almost similar to English, female speakers of Pashto observe their linguistic taboos more strictly than male speakers. The data also shows that the observance of taboos is higher in older age-group than the younger one in Pashto. Due to many reasons the younger generation is less terrified by the linguistic taboos. The present study also confirms the ideas of the previous researchers that these taboos are mostly violated in extreme anger as when one is highly emotional and out of control. On the basis of the pedagogical implications of the study, a dictionary of taboo words in Pashto is recommended.
A Comparative Study of Service Quality and Student Satisfaction of Public and Private sector Business Schools of Lahore
(UMT.Lahore, 2016) ASAD MOHI-UD-DIN
Major intention of this research thesis was to study the influence of service quality on satisfaction of students in business schools of Lahore, Pakistan. Second aim of this research was to check the difference between satisfaction level of students among public sector university student and private universities students. To fulfill these objectives this research use quantitative research strategy, in which data was collected through self-administrative questionnaire. After going through the cleaning process, data was analyzed by using statistical techniques, i.e. regression, mean comparison, etc. Research model was found significant as the significance value is lesser than .05 and it has ability to predict the students satisfaction. R2 is 0.51 which shows that 51 % of the variation in student satisfaction is being explained by this model. Academic and non-academic aspects of service quality in higher education were identified as important quality indicators. Academic aspects like teacher’s expertise and their interest in their subject also influence the satisfaction of student. Interestingly the results of this research are bit clashing with the theoretical perspective. Non academic aspect of the service quality got the lowest score, which means students are least concerned with the administrative staff’s support. Access to resource persona and the resources got the highest score. Non-Academic Aspect of service quality got the lowest score that is .476 it indicates that students are least concerned with administrative staff support. Followed by the academic aspect of the service quality that have positive relationship student satisfaction and it is explaining almost 94%. That show students are concerned with what they are being taught in classes.
A Comparative Study of Spline Models
(UMT.Lahore, 2020) Fizzah Haq
Economic problems that faced in a country cause great changes particularly in the economy progression. Also they are influenced by various internal or external situations. Economic or political problems generally cause rapid and sudden increases in inflation and commodity prices. Gold has grown into an alternative investment mode for Pakistani investors. Price rise is defined as inflation, which is the increase in the cost of living as the price of goods and services increases. Inflation rate is the annual year percent change in price level. This report used the wholesale price index (WPI) as an inflation analog. It is commonly used by numerous analysts, states, banks and sectors, because it aims at price movements most comprehensively. The data of gold price and whole sale price index that used in this study was obtained from Census and Statistics Department State Bank of Pakistan from duration of 1960 to 2018, the sampling period consisted of 59 measurements per annum. The aim of this study is to compare two spline models regression spline and penalized spline and also to achieve a precise and accurate estimate for the price of gold. The prediction has been made for Pakistan's bases of inflation rates over the last six decades.Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluation criteria to test the reliability of both analytical techniques. R-studio 3.5.2 is used for this imperial analysis. Best result hasbeen achieved using the cubic penalized spline according to MSE and MAPEperformance criterions. F-test is used to compare the two spline models and conclude that both techniques are nor statistically significant neither functionally relevant, both models are significantly different. The R^2 significance of the penalized spline model for gold price data is higher than the R^2 of regression spline model.Thus, it is conclude that specially in case of prediction the penalized spline model is a more effective and appropriate statistical method for gold price data and also for such types of economic data in Pakistan.
A comparison of Deep and Classical approaches in the outcome prediction of Business Process Monitoring
(UMT, Lahore, 2020) Muhammad Usman Khan
Prescient cycle checking targets determining the conduct, execution, and results of business measures at runtime. It recognizes issues before they happen and re-apportion assets before they are squandered. Albeit Direct learning (DL) has yielded discoveries, most existing methodologies expand on classical machine learning (ML) procedures, especially with regards to result arranged prescient cycle checking. This situation mirrors an absence of comprehension about which occasion log properties encourage the utilization of DL methods. To address this hole, the creators thought about the exhibition of DL (i.e., straightforward feedforward profound neural organizations and long transient memory organizations) and ML strategies (i.e., arbitrary backwoods and backing vector machines) in view of five freely accessible occasion logs. It could be seen that DL by and large beats traditional ML strategies. Besides, three explicit suggestions could be induced from further perceptions: First, the outperformance of DL procedures is especially solid for logs with a high variation to-case proportion (i.e., numerous non-standard cases).
A COMPARISON OF DIFFERENT WEATHER FORCASTING MODELS
(UMT.Lahore, 2017) Zaheer Abbas
In this thesis, we studied the performance of different statistical models and compare their forecast accuracy. In particular, we used multiple linear regression (MLR), seasonal autoregressive fractional integrated moving average (SARFIMA), and artificial neural network (ANN).
A Comparison of Stock Market volatility
(UMT,Lahore, 2016-04) Faizan Naqvi
Purpose: The main purpose of this research is to investigate the differences in volatility of emerging vs developed stock markets to safeguard the interest of small and foreign investors. This study examine the relationship between daily information flow and conditional volatility in developed and emerging economies and to find out whether traded volume is a good proxy for daily information flow. Design/methodology/approach: To achieve the objectives of this research standard method used to analyze the volatility, i.e., TARCH, EGARCH and ARMA model. This research used secondary data of 18 Emerging and 22 developed stock market indices. Daily closing prices and daily traded volume is used as the proxies for conditional volatility & information flow respectively. This study covers the time period from 1990 to 2015. Whilte’s general test and Breusch-Godfrey test is used for Heteroscedasticity and Auto-correlation correspondingly. TARCH and EGARCH both models are estimated under General Error Distribution. Findings: The study concludes that magnitude and direction of shocks are equally important in developed markets but in case of emerging markets magnitude of shock play vital role. Volatility persistence is quite high in both sets of markets but more high in developed stock markets. Asymmetry is uniformly present in all markets considered; leverage effect of bad news has stronger effect in developed markets. Results show positive relationship between volume and volatility also when volume is decomposed into its components, added to variance equation volatility persistence decreases. Research limitations: Risk adjusted returns offered by both emerging and developed economies would also be considered along with volatility analysis to analyze as to which markets are paying more dividends or returns accordingly to risk associated with that particular stock. Practical implications: This research is very useful from investor’s point of view as the risk analysis is done across variety of emerging and developed markets, investor could make a judgment for investment according to his risk appetite. This research also examines the relationship between volume-volatility so it would help the technical and financial analysts to better understand the volatility of different stock market indices while doing investment decisions. ii Originality/value: As far as we know there is no evidence on the volatility comparison sample time span considered to incorporate the major shifts in the economies. Volumevolatility relationship and the decomposition of volume into its components then adding into variance equation resulting in reduced GARCH effect adjoin value. Therefore, this study adds new knowledge to the literature of volatility analysis.
A METHODOLOGY FOR GLAUCOMA DISEASE DETECTION USING DEEP LEARNING TECHNIQUES
(UMT, Lahore, 2020) FATIMA GHANI
The main source of the glaucoma is irreversible impairment of vision. In literature we reviewed many methods to machine learning used on fundus pictures by different researchers. Any current machine learning solutions include C4.5, the Naïve Bayes Classifier, and Random Wood. Many methods cannot more reliably diagnose glaucoma disorder. We developed an architecture focused on the methodology of Deep Learning ( DL) which is a Convolution Neural Network (CNN) for the classification of Glaucoma diseases. We used numerous deep learning neural networks such as the Inception-V3 and the Vgg16 model for Glaucoma classification and identification purposes. We have obtained 508 fundus photos belonging to 25 groups from the JSIEC, Shantou City, Guangdong Province , China, Joint Shantou Foreign Eye Centre. Since uploading the photos, we've applied the increase to the provided dataset and rendered the 1563 training and testing data collection pictures. The downloaded dataset is not labelled, so we wanted a named picture dataset for our research in deep learning. But we have labelled both photos with the class name of the disease after the augmentation. We also used two deep neural network models Inception V-3 and Vgg16 in this paper which are supervised learning methods for classification arrangements. Such structures require operating processes that need to learn to use previous knowledge , make judgments about it and fix it if any errors arise. Taking into consideration the success findings collected, it is shown that the pre-trained Inception V-3 model has the best classification efficiency with 90.01% accuracy for two other models suggested (90.01% accuracy for InceptionV3 and 83.46% accuracy for Vgg16).
A Methodology for Power Forecasting in Pakistan Using Different Machine Learning Techniques
(UMT, Lahore, 2020) Zoya Zahid
Over the last decade, the energy sector has experienced a major modernization cycle. Its network is undergoing accelerated upgrades. The instability of production, demand, and markets is far less stable than ever before. Also, the corporate concept is profoundly questioned. Many decision- making processes in this competitive and complex setting depend on probabilistic predictions to measure unpredictable futures. In recent years, the interest in probabilistic energy forecasting analysis has rapidly begun, even though many articles in the energy forecasting literature focus on points or single-valuation forecasting. In Pakistan, the bulk of early studies require various kinds of econometric modeling. However, the simulation of time series appears to deliver stronger results given the projected economic and demographic parameters usually deviate from the achievements. We used machine learning methods, such as ARIMA and Long-Short - Term Memory (LSTM), to calculate Pakistan's future primary energy demand from 2019 to 2030. In this study, we used the methods used in machine learning. We have accessed the dataset of the electricity sector for forecasting purposes from the hydrocarbon development institute of Pakistan (HDIP). The dataset of HDIP is from 1999 to 2019 with different attributes like Electricity Installed Capacity (Hydel Thermal (WAPDA), Thermal (K-Electric), Thermal (IPPs), Nuclear), Energy Consumption by Sector (Domestic, Commercial), Resource Production (Oil, Gas, Coal, Electricity), and Resource Consumption (Oil, Gas, Coal, Electricity). We have forecast the energy demand of each attribute till 2030 with ARIMA technique, and LSTM. Predicting overall primary energy demand using machine learning appears to be more accurate than summing up the individual forecasts. Tests have shown that specific energy sources exceed annual growth levels
A NEW HYBRID EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL CHART USING MIXTURE RATIO ESTIMATOR OF MEAN
(UMT.Lahore, 2020) Hafiz Zain Pervaiz
The Control charts are the most important tool of Statistical Process Control (SPC) tool kit. It is commonly used to differentiate between the “assignable and un-assignable causes.”The purpose of the effective process monitoring system is to detect the presence of an “assignable cause.” The control charts are of different types. Some are “memory control charts” and other is “memory-less control charts.”Shewhart are memory-less control charts and are being used to detect a large size shift whereas the memory type charts are used for dealing with small size shifts. The use of statistical quality control charts in different fields of life revealed that the most of the control charts are structured to cater information about the quality characteristic/ studied variable. If we are able to acquire some information about some other variable(s) which is correlated with our variable of interest, we can enhance the efficiency of the control chart by the efficient charting statistic. The additional information is known as auxiliary information and the variable providing the additional information is referred as auxiliary variable. This additional/auxiliary information is used at different points in survey sampling to estimate the unknown parameters. Whenever the auxiliary information is used in the parameter estimation process, the precision of the estimating the parameter is improved. In this thesis, we proposed A New Hybrid Exponentially Weighted Moving Average HEWMA control chart. The proposed control chart is based ona mixture ratio estimator of mean using a single auxiliary variable and a single auxiliary attribute (Moeen et al., 2012). We call it as Z- HEWMA control chart.The proposed control chart performance is evaluated using out-of-control-Average Run Length (ARL1). The control limits of the proposed chart is based on estimator, its mean square errors. A simulated data is used to compare the proposed Z-HEWMA, traditional/simple EWMA chart and CUSUM control chart. From this study the fact is revealed that Z-HEWMA control chart shows more efficient results as compared to traditional/simple EWMA and CUSUM control charts.The Z-HEWMA chart can be used for efficient monitoring of the production process in manufacturing industries where auxiliary information about a numerical variable and an attribute is available.
A Statistical Study to Explore the Factors Associated with Hepatitis B and Hepatitis C in Punjab
(UMT.Lahore, 2022) Javeriah Rafiq
A prevalence study on hepatitis B and C infections was conducted to acquire estimates at the province level in Punjab as well as to examine epidemiological dynamics and underlying risk factors.
A study of Most Important Determinant of Job Performance
(UMT.Lahore, 2013-02) Zeeshan Hassan
Job Performance is a widely spread phenomenon in organizational context. There are many studies that have conducted on measuring job performance that widening the literature circle of human resource management. This study mainly focused on exploration of supervisor’s perceived job performance in the field of construction. There are mainly 19 different supervisor’s skills found from literature those have impact on perceived job performance. Current study primarily seeks the most relevant job skills of construction supervisor among the literature skills. For this purpose interview of construction managers conducted who are working under Pakistan Engineering Council registered companies. A pilot study was conducted to establish internal consistency of the tailor made questionnaire. Population for the current study was construction supervisors working under Pakistan Engineering Council construction companies. Questionnaires were sent to 300 supervisors in 40 different construction organizations and after scrutiny data received from 25 organizations was found correct. According to theoretical model designed for this particular study 19 literature job skills was condensed through interview into 9 job skills of construction supervisors. Factor analysis as statistical technique used for finding out the important factors. The results obtained show that communication skill among team member is most important skill for construction supervisor. Whereas decision making skill remain important at second, creativity and political skills are important for construction supervisor at number three and four respectively. It is found that there are other ways of measuring job performance i.e. role based job performance which is one of the main limitation of the study. It is recommended to perform the same study on role based job performance to highlight the most important job role of construction supervisors.
A study of relationship between Knowledge Management Enablers, Process and Organization Performance
(UMT, Lahore, 2012) Ahmer Naveed
The concept of knowledge management hasbeenthe buzzwordfromlast two decades in the academic literature. Manyleading practitioners and researchers have written about the enabling factors and processes of Knowledge management. There is need to understand in such a dynamic business environmentthe relationship between knowledge management enablers, processes and their effect on organizational performance. In this thesis relationship between knowledge management enablers, processes and organizational performance is investigated in the context of banking sector in Pakistan. Because due to difference in conditions such as demographics, the effects of relationship of different components of knowledge management may be altered and might not be as significant as it is encountered in some other parts of the world. Knowledge management enablers are the spirit of Knowledge management. Knowledge management enablers are the persuading factors or mechanism through which knowledge foster inside the company. While knowledge processes are meant for assembling, sharing and extending of knowledge within the company. It was found that culture of learning; trust and formalized structure have a significance relation with knowledge process. Similarly it was also found that externalization; combination and internalization conversion modes of knowledge process have significant relation with organization creativity. However collaborative culture and centralized structure hypothesis were not supported in this particular population. Also socialization conversion mode of knowledge process was found to behaving a negative relation with organization creativity in the banking environment of Pakistan.
A Study on Economic determinants Affecting Child Labour
(UMT.Lahore, 2018-06) Arshad Hameed
This study provided the main influence factors on child labour by using World Bank data of 112 countries. The strategic way of low and above average of child labour techniques were used to check the more significant behavior of the study child labour. The significance relationship between the child labour and FDI, Per Capita GDP, Inflation, percentage of employee from industrial and Literacy rate. All the data were analyzed by using SPSS statistics version 23 and then analyzed for the significant outcomes. Child Labour and predictors was analyzed by t-test, Leven test, and binary logistic regression to check the significant effect. The relationship GDP per capita, Inflation, Literacy rate has significant relationship with child labour. While the FDI and industrial have insignificant effect. The t-test was used to check the equality between two groups of child labour below average and above Average.
A Study on Economic determinants Affecting Child Labour- A Global Prospective
(UMT.Lahore, 2018-06) Arshad Hameed
This study provided the main influence factors on child labour by using World Bank data of 112 countries. The strategic way of low and above average of child labour techniques were used to check the more significant behavior of the study child labour. The significance relationship between the child labour and FDI, Per Capita GDP, Inflation, percentage of employee from industrial and Literacy rate. All the data were analyzed by using SPSS statistics version 23 and then analyzed for the significant outcomes. Child Labour and predictors was analyzed by t-test, Leven test, and binary logistic regression to check the significant effect. The relationship GDP per capita, Inflation, Literacy rate has significant relationship with child labour. While the FDI and industrial have insignificant effect. The t-test was used to check the equality between two groups of child labour below average and above Average.
A Viable Takaful Model for Live Stock Industry in Pakistan
(UMT.Lahore, 2021-11-29) MUHAMMAD AMMAR ASHRAF
Pakistan is an agricultural country where most of the country’s economy is based on agriculture. Due to the huge potential in the market farmers tend to minimize their risk by taking insurance policies for their livestock and Takaful industry does not have any Shariah alternative product for this sector. The purpose of the study is to suggest new product for Takaful industry that insures the safety and prosperity of livestock. This research has qualitative approach, utilizing unstructured interview technique with eleven participants. From interviews we gathered the views and opinions of participants about the best suitable and viable Takaful model for livestock in Pakistan. To have a comprehensive understanding of these issues, interviews were conducted with Shariah experts of Takaful industry including Shariah compliance Department, Shariah Advisors and Product Development Department. This study differs from prior studies on livestock. Findings of the interviews from these experts are that among all existing Takaful models, Waqf-Wakala model is the most suitable for livestock industry in Pakistan. Regulatory bodies needs to create a workable environment that motivates positive steps towards livestock Takaful. Currently livestock industry is providing most percentage of progression in the economy of Pakistan. And whenever something is this vital for a country, it must be kept safe. Hence ensuring the livestock is very important for its safety. Through Takaful we make sure that the livestock animals that we eliminate all risk factors such as catching an illness or virus or get injured etc. insurance will cover all such risks and additionally provide good nutrition and health to these animals as well. The livestock animal will be looked after and be kept in very good conditions in order to combat all the risk factors involved and the animal is safe and healthy.

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