Smart Portfolio Builder for Investors (MUSHEER)

Abstract

The product should be like in an ideal situation and what limitations we will face due to practical limitations. Developing a product that needs to be embedded into the lives of people and the aim is to provide them service in the long run requires that the product is acceptable to the customers, they can trust it and it performs in line with the expectations of our clients. Musheer.com is a proposed name for the web-based platform, giving users a feeling of security, the theme is also set in a formal fashion with the presentation showcasing ‘government like’ long term frame of reference and a sense of welfare for all, not competition. The importance of interaction in terms of investment constituents and transparency in the investment process for the investors is a huge factor when it comes to ensuring customer satisfaction. People want to know if we’re serious about their money and goals just as much as they are. This means that the frequency of review for all participants becomes a critical success factor. Musheer is an online platform for the investors to look into the changing trends of stock market and make their investments in bonds and stocks in the successful companies. The companies are categorized based on the risk and reward they offer compared to the risk clients can afford. A questionnaire is used to gather information about the clients and calculate a risk score. The biggest challenge faced by us was to collect the stock market data of past years since no proper public data was available. Other challenges faced were to select an appropriate model training the data on that model. To test the authenticity of questionnaire proposed solution was also a tough task. We collected the past data from S&P Global IQ, a stock market website. To select the suitable data training model, we chose different models and tested them on the data. After comparing the results we selected the most efficient model. The questionnaire contains eight questions. The answer to these questions is fed to a formula which calculates the risk score. The risk score of the companies as well as of clients is calculated and the companies are suggested to them accordingly. Along with the companies which are suggested to the customers to invest in, we also show them the graphs and the future predictions of their money if they invest in the companies suggested to them. The good thing about our model is that we use the data predicted for the next few weeks and then select the companies. This helps our model to suggest companies to the customer not on the basis of past data but on the basis of future predictions.