The main financial markets of every country are stock exchange and consider as an imperative cause for the corporations to increase capital. The novelty of this study to explore machine learning techniques when applied to financial stock market data, and to understand how machine learning algorithms can be applied and compare the result with time series analysis to real lifetime series data and helpful for any investor. Investors are constantly reviewing past pricing history and using it to influence their future investment decisions. The another novelty of this study, using news sentiments, the values will be processed into lists displaying and representing the stock and predicting the future rates to describe the market, and to compare investments, which will help to avoid uncertainty amongst the investors regarding the stock index. Using artificial neural network technique for prediction for KSE 100 index data on closing day. In this regard, six months’ data cycle trained the data and apply the statistical interference using a ARMA (p, q) model to calculate numerical result. The novelty of this study to find the relation between them either they are strongly correlated or not, using machine learning techniques and ARMA (p, q) process to forecast the behavior KSE 100 index cycles. The adequacy of model describes via least values Akaike information criterion (AIC), Bayesian Schwarz information criterion (SIC) and Hannan Quinn information criterion (HIC). Durbin- Watson (DW) test is also applied. DW values (< 2) shows that all cycles are strongly correlated. Most of the KSE-100 index cycles expresses that the appropriate model is ARMA (2,1). Cycle’s 2nd,3rd,4th and 5th shows that ARMA (3,1) is best fitted. Cycle 8th is shows ARMA (1,1) best fit and cycle 12th shows that the most appropriate model is ARMA (4,1). Diagnostic checking tests like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Theil’s U-Statistics are used to predict KSE-100 index cycles. Theil’s U-Statistics demonstrate that each cycle is strongly correlated to previous one.

Abstract. This paper presents the effect of 'schedule compression' on software project management effort using COCOMO II (Constructive Cost Model II), considering projects which require more than 25 percent of compression in their schedule. At present, COCOMO II provides a cost driver for applying the effect of schedule compression or expansion on project effort. Its maximum allowed compression is 25 percent due to its exponential effect on effort. This research study is based on 15 industry projects and consists of two parts. In first part, the Compression Ratio (CR) is calculated using actual and estimated project schedules. CR is the schedule compression percentage that was applied in actual which is compared with rated schedule compression percentage to find schedule estimation accuracy. In the second part, a new rating level is derived to cover projects which provide schedule compression higher than 25 percent.

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