Investing in stock market requires in-depth knowledge of finance and stock market dynamics. Stock Portfolio Selection and management involve complex financial analysis and decision making policies. An Individual investor seeking to invest in stock portfolio is need of a support system which can guide him to create a portfolio of stocks based on sound financial analysis. In this paper the authors designed a Financial Decision Support System (DSS) for creating and managing a portfolio of stock which is based on Artificial Intelligence (AI) and Machine learning (ML) and combining the traditional approach of mathematical models. We believe this a unique approach to perform stock portfolio, the results of this study are quite encouraging as the stock portfolios created by the DSS are based on strong financial health indices which in turn are giving Return on Investment (ROI) in the range of more than 11% in the short term and more than 61% in the long term, therefore beating the market index by a factor of 15%. This system has the potential to help millions of Individual Investors who can make their financial decisions on stocks and may eventually contribute to a more efficient financial system.

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Stock markets are highly volatile by nature and difficult to predict due to the non-linear and complex nature of the market. A system that can forecast and predict the stock prices is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine learning is widely being used in the financial domain including prediction of stock prices. Based on the extensive literature review in this domain, traditional methods of using Machine Learning techniques including Artificial Neural Networks (ANN) for stock price prediction have taken in to account only the Technical Features. The current machine learning models do not take in to account the Intrinsic or fundamental features of the stock; the results of such prediction models are not accurate and at best could predict an intraday price of stocks with high levels of Variance. Literature review in the domain of stock predictions has shown that future stock prices are seldom dependent on the past performance and technical indicators and they invariably depend on the fundamental value and macro-economic factors.In this paper, we propose development of anArtificial Intelligence based decision support system (DSS) for guiding individual investors to buy and sell stocks. The Financial decision support shall be based on mathematical modeling of the various financial parameters to predict stock prices on a long term basis with a reasonable degree of accuracy and eliminate the behavioral biases of human decisions.The ANNs in this study were trained using open source financial data of select stocks listed on the BSE/NSE. The results of this study are quite encouraging as the stock prices can be predicted at least one month in advance and are closer to the real-time market prices. This DSS has the potential to help millions of Individual Investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.

In economics, a country's current account is one of the two components of its balance of payments, the other being the capital account (sometimes called the financial account). The current account consists of the balance of trade, net primary income or factor income (earnings on foreign investments minus payments made to foreign investors) and net cash transfers that have taken place over a given period of time. A country's balance of trade is the net or difference between the country's exports of goods and services and its imports of goods and services, ignoring all financial transfers, investments and other components, over a given period of time. A country is said to have a trade surplus if its exports exceed its imports and a trade deficit if its imports exceed its exports. Positive net sales abroad generally contribute to a current account surplus; negative net sales abroad generally contribute to a current account deficit (CAD). In any country's economy the Current Account Deficit (CAD) is the one of the key indicators of Macro Economic stability. In this paper we analyse India's CAD situation and how changes in the CAD affects the rupee stability. We build a model that explains the effects of CAD on the value of the domestic currency (Rupee). Indian economy and currency (In-terms of $) is taken up as a case study for building the model and discussed for the effects and conclusions based on hypothetical data. Since most of the payments done by India are in US Dollars, the model takes in to account the Supply and Demand functions for the Dollar and its effect on the local currency fluctuations. The model also explains how the currency fluctuations in-turn contributes to widen the CAD and finally leading to currency crisis.