Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.

The purpose of the paper was an attempt to trigger the connection between interest rates (Karachi Inter-Bank offer rate) of different maturities and the stock index. KSE-100 index of Pakistan was selected, and its monthly returns were tested with Karachi Inter-Bank offer rate rates as the independent variable. For the proposed connection, the model was developed, and certain statistical tests were applied. Monthly stock prices of KSE-100 and Karachi Inter-Bank offer rates of 5 different maturities were taken. Descriptive and correlation regression analysis was used to explain the relationship that was significant but inverse. The stock market in Pakistan is highly influenced by political situations prevailing in the country. The stock market is considered an interpreter of the economy. Other than interest rates influence the stock market, one basic factor was selected for study purpose, and conclusions were drawn. This study would help demonstrate the investors' investment decisions who invest their resources in the stock market by borrowing at a prescribed rate.

We analyze the impact of corporate political connections on the cost of equity of non-financial firms listed at the Pakistan Stock Exchange. We extract data from the DataStream and Election Commission of Pakistan for the years 2001 to 2018. The Generalized Method of Moments is used for data analysis. This research finds that firms use political connections to enjoy a lower cost of equity capital. Further, firms with strong ties to political power obtain more benefits on financing cost as compared to non-connected firms. Besides, we also find that firms affiliated with a large business group enjoy a lower cost of equity than non-affiliated connected firms. The findings may be helpful for regulators to formulate suitable policies concerning the use of corporate political strategies and to assist unconnected and non-affiliated firms to access finance easily.

Non-Banking Financial Institutions (NBFIs) have lower profitability as compared to banking institutions, which is one of the important reasons for sluggish growth and NBFI's contribution to economic development. Previous studies investigated the factors contributing to the financial performance of conventional and Islamic banks, but scant literature is available on the determinants of performance of non-banking financial institutions, especially in comparison to banking institutions. The present study provides detailed insight into internal determinants for the performance of Non-banking financial institutions in Pakistan. Secondary data was extracted from financial statement analysis of SBP and annual reports of selected conventional banks, Islamic bank, modaraba, and leasing companies for the period of 2007 to 2016. Size (Sz), Capital Adequacy (CA), Credit Risk (CR), Efficiency (Eff), and Liquidity Risk (LR) have been taken as independent variables, and financial performance of FIs was measured through dependent variables of Return on Assets (ROA) and Return on Equity (ROE). Ratio analysis was done, and statistical techniques of correlation, regression were applied to identify the relationship among variables. Analysis was done for all sample institutions as a whole and separately for each type as well. It brought an immense variety in the analysis and result, which shows the different impact of independent variables on the dependent variable for different types of institutions. The factors contributing towards slow and negative growth of non-banking financial institutions have not been analyzed as much as it should be done. It is the novelty of this work is to recognize problematic areas of non-banking financial institutions of the country.