[MUSIC] Learning Outcomes. After watching this video, you'll be able to understand the background information needed for Mohan Ram's G-Score strategy. [MUSIC] Let us quickly revise what we have learned so far. We saw how to calculate the book to market ratio. Now, do you remember what this book to market ratio is? Book to market ratio is nothing but book value of equity divided by the market value of equity. Now if you arrange the firms in ascending order, according to their book to market values, the firms with the high book to market values are called value firms. And the firms with the low book to market value are called growth firms. Another name for growth firms is glamour firms. Now historically, value firms have earned more returns than the growth firms. To put this in simple words, no book to market firms on an average earn negative excess returns. And high book to market firms on an average earn positive excess returns. This is called the book to market effect. How did we use this book to market effect to our advantage? If you remember the strategy. We arrange the forms according to their book to market values. And divide them into quintiles. We took the high book to market quintile and called the forms in this quintile as value stocks. According to the book to market effect, the portfolio of all the value stocks in the high book to market quintile should give you superior records, but wait. Not all the stocks in the portfolio give you superior records. So is there any way in which we can pick only those stocks that give superior returns out of the high book to market quintile and reject the bad performance. That is is there any way which we can separate the stocks with superior returns and stocks with bad returns. That this is what the Piotroski Score Strategy does. Research has shown that, using financial statement analysis, we can separate firms into winners and losers, how can we do this? We can do this on the basis on information from financial statements that is not correctly informed in the stock prices. Please note, another name for financial statement analysis is fundamental analysis, during the course I will be using these two words interchangeably. So, what Piotroski does is he makes use of this fact and applies financial treatment analysis to the stocks in the high quarter market quintile. To separate the expected winners from the expected losers. Please not, I'm using the word expected here. Why expected? Even after applying financial segment analysis, we cannot exactly predict if a stock in the portfolio is a winner or a loser. This is the uncertainty and the risk we face in our strategy. I know, I know what is going on in your mind right now. You are thinking whether you can extend the F score strategy to other quintiles. Say, the low book to market quintile. Can similar things be done in the case of logbook to market forms also? You might be thinking, on an average portfolio of logbook to market forms let me get excess returns. So can you use financial statement analysis and separate the expected winners from expected losers, just as we did in the case of high book to market forms? Well, people have already done that. They found that escorts strategy does not work that great, that's bad. When it comes to phones in the netbook to market quintile. The question that you're facing right now is, why does the Piotroski F score strategy does not work low book to market forms. While it works in the case of high book to market forms. The answer is very simple. The analysts usually ignore high book to market stocks, that is value stocks, and do not do any research on these stocks. So there is less chance of all the financial information getting impounded in this stock price. So by applying fundamental analysis, we can separate the expected winners and the expected losers. Let us know, look at low book to market forms. Unlike the high book to market forms, low book to market forms are growth stocks and they attract the attention of analysts In fact, many of these stocks may be overvalued compared to their fundamentals because of the hype or the excitement surrounding their licensed stock market performance. Now if you remember, growth stocks are the ones that experience strong past performance and value stocks are the ones that typically underperform in prior periods. Please note that these growth forms will help many sources of disclosure other than financial statements. So when traditional fundamental analysis might not be that useful in case of growth forms, there might be useful information in the other disclosures. Also, rapid growth in many local market forms potentially makes current fundamentals more important than other non-financial measures. This means we need to modify the fundamental analysis to suit deep growth funds. And this is what Professor Mohandaran did. And this is what we will be learning in our next strategy. So the name of the strategy you are going to learn now is ta da! Mohanram's G score strategy. Professor Mohanram uses financial statement information, along with other disclosures, to create signals that augment the traditional fundamental analysis. Then he aggregates all the signals into a single score. Similar to the F score, he calls the score the G score. A, B, C, D, E, F, G. Can you see the connection? F and G, now we are ready for the next section. In the next section we will see how we can modify the fundamental analysis to suit growth, and this will create the necessary foundation required for us.