With an ever-increasing and expanding economy, the Indian stock market is seen as a very lucrative investment option for many investors. One of the most important factors that any investor would look into before investing in a particular stock is the financial health and stability of the company. Investment in present-day has become a new way of making money, but not all know how and where to invest. How much to invest and for how long to stay in for one particular stock is again a question and changes based on different perceptions. Tools available to analyze the best stock from an exchange trading market are enormous, but how efficient is the tool is totally a different ball game. Many investors have developed several ways of finding their efficient stock for investment and have been successful in different ways and parameters. Warren Buffer, the best-known investor in modern-day maintained his portfolios even in the time of global crisis as he has his own ways of finding the best investment options and knowing how long to be invested in the same stock and for how long. The paper aims to present a combination of stocks from the BSE100 that are highly stable with the least probability of default. Based on different parameters, the stocks are evaluated and curtailed down to investment grading stocks. The paper uses an Artificial Neural Network (ANNs) along with the help of Principal Component Analysis (PCA). The Principal Component Analyzer was used to obtain a portfolio of effective stocks from BSE 100 with the help of the Warren Buffet approach.
Cite this article:
Sherin Varghese, Sandeep Kumar Thakur, Medha Dhingra. Finding Efficient Stocks in BSE100: Implementation of Buffet Approach. Asian Journal of Management. 2020; 11(1):05-10. doi: 10.5958/2321-5763.2020.00002.5
Sherin Varghese, Sandeep Kumar Thakur, Medha Dhingra. Finding Efficient Stocks in BSE100: Implementation of Buffet Approach. Asian Journal of Management. 2020; 11(1):05-10. doi: 10.5958/2321-5763.2020.00002.5 Available on: https://ajmjournal.com/AbstractView.aspx?PID=2020-11-1-2