Browsing by Author "Muhammad Rizwan"

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Edge Irregularity Strength of Certain Families of Toeplitz Graph
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Rizwan; FA15-RMT-005; LHR TP 7009
There are several types of labeling, in graph theory, such as irregular labeling, magic labeling, anti-magic labeling, and cordial labeling. In this thesis we intend to find the irregular labeling or irregular assignment of some family of the Toeplitz graph. Graph theory is a very powerful tool of pure and applied mathematics. It concerns with different fields of science and has wide applications. This thesis is divided into four chapters. In the very first chapter which is about the introduction we define some of the initial definitions of graph theory such as, vertices, edges, graph, degree of graph, complete graph, walk, path, cycle, subgraph, isomorphic graph, connected graph, bipartite graph, matrix of graph, adjacency matrix, Toeplitz matrix and Toeplitz graph. In second chapter we define different types of labeling such as, vertex labeling, edge labeling, total labeling, irregular labeling, vertex irregular labeling, edge irregular labeling, and the irregularity strength of some families of Toeplitz graph. In this work this aspects is also a key component and will be discussed in detail through this chapter. In third chapter of this dissertation, we find vertex irregular labeling and edge irregular labeling. Finally we finds the edge irregularity strength of the following families of Toeplitz � �𝑛 〈4,8〉,𝑇𝑛 〈5,10〉 𝑎𝑛𝑑 𝑇𝑛 〈6,12〉. graph such that � �𝑛 〈3,6〉, In the last chapter, we conclude our thesis and used suitable references. Though there are the other research scholars who have made research papers on this topic, yet this work is unique in its significance and scope because no one has worked on it.
The influence of Stock Price Crash Risk on the cost of capital
(Library Information Services COMSATS University Islamabad Lahore Campus, 2020-02-03) Muhammad Rizwan; FA18-RMS-002; LHR TP 6381; Dr. Muhammad Usman
This study examines the effect of Sock Price Crash Risk (SPCR) on the cost of capital in Chinese listed firms in the Shenzhen stock exchange and the shanghai Stock Exchange. We take sample as 290 firms one the base of the highest value of assets of each firm. The cost of capital consists of two factors; the cost of equity (COE) and the cost of debt (COD). The SPCR is measured by using two statistics, one is NCSKEW means the negative coefficient of skewness of the firm-specific weekly returns and the second is DUVOL that means Down to-Up Volatility used to measure the crash likelihood weekly return of firm-specific and use the Modified PEG ratio model of Eston approach to measuring the cost of equity. We use the regression model with analyses of panel data. in the practice, we find SPCR is a significantly positive relationship with the cost of equity and cost of debt. Also, the sample was divided into the State-Owned enterprise (SOEs) and Non-State-Owned enterprise (NSOEs) for comparison. The results effect of SPCR on the cost of equity stronger in SOEs than NSOEs and the results effect of SPCR on the cost of debt stronger in NSOEs than the SOEs. In order to reduce the cost of equity capital of listed companies and improve the financing efficiency of China's capital market, regulatory authorities should perfect and strengthen the formulation of relevant laws and regulations on enterprise information disclosure. Enterprises should strengthen the construction of internal control, and make joint efforts to create a good information disclosure environment and prevent the risk of a stock price crash.
Towards Better USD/PKR Exchange Rate Forecasting Using Public Sentiment Analysis
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Rizwan; CIIT/SP23-RCS-022/LHR; Dr. Abid Sohail; LHR TP 9995
Exchange rate forecasting plays a crucial role in financial planning, investment decisions, and economic policy formulation. Traditional forecasting models primarily rely on historical financial data and macroeconomic indicators; however, these models often fail to capture the influence of public perception and market sentiment on currency movements. This study aims to improve the forecasting accuracy of the USD/PKR exchange rate by incorporating public sentiment analysis derived from online sources such as social media platforms, news articles, and financial forums. The research collects historical exchange rate data along with large-scale textual data reflecting public opinions related to economic conditions and currency fluctuations. Natural Language Processing (NLP) techniques are applied to preprocess the textual data and perform sentiment analysis, categorizing the information into positive, negative, and neutral sentiments. These sentiment indicators are then integrated with traditional time-series forecasting models and machine learning techniques to predict future movements of the USD/PKR exchange rate.