The portfolio management methodology discussed in the present paper combines the principles of Quality Engineering, Value Investing and Forecasting was able to beat the average market return with a huge margin. The portfolio management methodology allowed long and short positions on stocks and options. The portfolio management process outlined here made the portfolio focused and as a result the expenses associated with the transaction costs were lower.

The main objective of this research is to propose a holistic approach for optimal, efficient and accurate portfolio management. In contemporary stock market conditions, three questions are crucial: What to buy or sell? How much to buy and sell? When to buy or sell? So the need of holistic approach has appeared; to forecast the stock price and the stock trends then optimize the portfolio. Besides that, the ability of volatility models to forecast future fluctuations of different asset classes is of interest to financial market participants. These arguments motivate our empirical future examination of the forecast quality of the S&P 100 implied volatility. Real case analysis and the forecasting results will reveal the limitations and the advantages of the proposed theoretical integrative-framework.

Stock market analysis, also known as technical analysis, is the process of deriving patterns from price movement. In the literature, different methods have been applied in order to predict stock market returns. These methods can be grouped in four major categories: technical analysis methods, fundamental analysis methods, traditional time series forecasting, and machine learning methods ("Neuro-Logit", Artificial Neural Network based Logistic Regression Analysis).