The next part is about empirical strategy. The empirical strategy you will tell, what are statistical methods tools and techniques the others use to prove that whatever they're trying to establish actually happens in practice. So, this part is highly technical. Let me warn you, unless you are trained in econometrics, unless you're trained and have some background in mathematics and statistics, it may be difficult to interpret these things. For example, you may suddenly find a sentence that we use regression discontinuity technique. Now what is this regression discontinuity? So, if you'll try understanding it may take a lot time. Which may not be required purely from out training point of view. So this empirical strategy section can be safely ignored from a purely training point of view. Of course, it's great to learn but not required from a trading point of view. Now coming back, the next section is important. It is about results. Here the author show what has been the performance of the strategy. This is very, very important. Now, here's the difference between an academic exercise and a data mining exercise. Now what happens in a data mining exercise, the purpose of each test, there in a pure data mining exercise is to show that something actually works. So others, they're people try like ten tests and pick up the one that works. And say that this works, if you've seen a typical technical analysis kind of show where people say that some particular pattern works. Head & Shoulders, for example, works. If you dont know Head & Shoulders, nice. You can think that it's a shampoo brand and forget it. But then, how do they prove? They pick some instance where it works. So they say that, six months back on a particular stock, Head & Shoulders and it worked. And that's the money it made. The strategy made. That's why it's an amazing strategy. Now, again the Head & Shoulders pattern has emerged and hence you are likely to make money if you follow this particular strategy. This is a typical data mining kind of an approach. The approach that academic papers take is not this. The author attempts to reject a particular hypothesis, continuously tries to show that a particular trading strategy does not work. And if he fails to show that it does not work then he says, there is likelihood that this may work. He will never, ever conclude with certainty that the particular strategy will always work. After a careful attempt to reject the possibility that the strategy does not work then, he concludes that probably this works. There's a very different approach. Now coming back to Head & Shoulders. Now, if you give it to careful researcher with the Head & Shoulders, do you know what they would do? He will take it off 20 years or whatever 30, 40, 50 years data is available for. He will look at all the instances where the study has This pattern has emerged. He look at all, this is very important, he will not pick and choose some time this work, some 20 years back this strategy, this shape and module is work, you cannot do that. What a careful researcher will do will look at all instances where this ship a must and then out of these instances, what has been the result, how many times you made money, how many times you lost money? How many times, nothing has happen. Suppose this strategy has work, let say on average, given a new some 5% data. Now another author compares this to what would have happened if you just randomly trade it without the strategy being there. Suppose you, literally you call some monkeys and ask them to throw darts on a stock, what would have been your return? You can assume market return as a, that you'll get from a past due tradings strategy that you get from an index as a benchmark. And they compare whether the strategy of buying whenever this particular shape emerges outperforms this kind of past due investing. The approach here is to attempt to reject something and despite your best effort, if you cannot reject then, you sort of say that maybe this is working. So that's the approach these people will take. So as I've said, this Head & Shoulders, they compare it with market return. And mind you, most of the time with these kind of strategies, you will find that those strategies don't work in practice. Just think of it this way. So if we could just look at some shared by made money, everyone would have made money, right? That sound that easy. So, you should be very very careful while looking at this self selected examples. Whenever you hear of some tip or a trading strategy, you should always keep this in mind, the pros that you're following, is he trying to accept something or reject something? So from that point if you're reading this result section is very important. What this result section will do? Is that, initially, it will show you the main result? The authors describe the main result. And then, they show you a series of tests which are performed where the authors attempt to reject this particular hypothesis that they have? And they failed to reject it. That is why they claimed that there is a paper. There is an idea which needs to be, which can be exploited. They also attempt to show that, what if it's some luck because this is work? They perform tests to sort of disprove that. And these are called robustness tests. Now, what are these robustness tests? Let me give you some examples. It's quite possible that out of a 20 year period, you have a one or two year period which was extraordinary, let's say financial crisis. You have a strategy where you short banks or any financial institution. And you choose a sample period between, let's say, 2005 and 2015 or 2012. And you find that the strategy has worked on an average and it is given new annualized, say, 20% discount, amazing. But then, it's quite possible that all your returns are concentrated between 2007 and 2009 or 2008. What does this mean? That means that, this strategy has worked only during financial crisis. Now, if financial crisis forever then, it's fine. But during normal times, it may not work. More importantly, when the economy booms, this may go the other way. That's more important. Suppose you have a strategy of going short on value stocks or distressed stocks, when the economy is in a bad shape. Or when it is entering a crisis, entering a recession, this strategy may do very well. But when the recovery starts, these are the very stocks which fell 90%, 95% during a crisis may recover three times, four times in no time. Remember those who have shorted, who shorted Citi at very low levels. They would've lost their shares when the stock recovered. So, an average result over 20 years will not tell you all these things. So the subsequent test which others perform, they try to control for all these possible explanations. So in tactical terms, they have time fixed effects, which basically control for impact of time. In other words, they are trying to show that by employing these, they are trying to show that these results are not driven by a particular predict, particular year, particular month. Then they also show that these are not driven by few stocks, and it's quite possible that your portfolio consists of hundred stocks, but two or three of them suddenly moved abnormally because of some reason. And your entire portfolio makes positive return because of them. Now this is not a replicable situation, because such two or three stocks may not exist next time. Please understand, our purpose is not just understanding paper, our purpose is to be whether we can utilize the strategy elsewhere. So they use this kind of fix effects like at form level to roll out, that this is not driven by a particular form or a particular group of forms. What they also do is that they sub the sample. As a further robustness they divide it into some smaller time intervals, and they show that the strategy works in each time interval. More importantly each time interval, the economic situation may be very different. They also show that this is not the impact of some alternative explanation. Something else going on which caused this. So, all these tests are designed to rule out these kind of ordinary explanations, which could have caused these results. And hence, made these results not applicable in another situation. So, from that point of view at least the first paper that you're going to read try to see at least the purpose of these tables. You may not understand exactly what the underlying econometric is, but at least before these results section, each subsection in that section no other in the first, four lines all that clearly mentions why this particular test is done. If yo understand that, and the fact that the paper is published, has gone through PLU process, you can be reasonably sure that very, very low hanging kind of objections, that would have been taken care of. Does this mean that the paper is perfect? Does this mean that there is no other alternative explanation possible? Does this mean that the strategy always works? No. I told you right at the beginning. We are not giving you lollipops here. Low hanging fruit you just pick up, and you start making money from tomorrow. That's not what is going to happen. But then, I can tell you with a lot of confidence that the reserve that these papers have, is unmatched. This is far, far more reliable and applicable than a result of a pure data mining exercise. So once you go through this reserve section, so how much attention you are to pay? Let's recap a bit, I told you there is an abstract, there is an introduction, then you have institutional background, then you have data, then you have algorithm, trading algorithm, then you have empirical strategy, then you have results and finally there is conclusion. Now, before we go to conclusion in some papers. You will also have clearing section. Now, for my clearing point of view, you can just ignore it completely. Now it's a very important part, I'm not trying to belittle that section. Basically here the authors try to show that how their paper contributes to the existing theory of, in economics, corporate finance, accounting. Whichever area that paper deals with. But purely from a trading point of view, it's not required. Now, is it totally useless to understand theory? No, not at all. But the point is, this section is extremely dense, highly mathematical. You'll have theorems, you'll have proofs. So if you have a liking for these things, please go ahead. A positive side effect of understanding this section is that, this will help you to improvise. Understanding theory will help you to improvise, but then if you don't understand, it's fine you can still implement the strategy as it is. So now coming back to the last part of conclusion. So conclusion, here again we summarize the results and make sure they are quantifiable to the literature, or the improvements can be done. That is also some of the people give us clues to what further can be done. You may just start from there. And if it's a recent paper, you can be assured that nobody would have done that also. So you can, from that point of view, this conclusion is important, you just typically want one or more pages, sometimes less than that. And with that, the paper ends. Then you have citations and then you have tables. Now, when you read. One thing that I forgot to tell you. When you read a data section. As you read the data section, also see that 11 table. First two, three tables will summarize data, data sources, variables definitions and all that. So read them in parallel. Also when you read the result section, try to read, because the way the papers are organized is first 30 pages as I have told you first 30 pages of text, 25 30 pages of text, citation three four pages, then tables. So text corresponding to table and the table are separated, they are not put together. So when you read a particular section on results, on a particular result, I strongly encourage you to also look at tables simultaneously. Then the understanding will be better. When the author says this strategy leads to 3% return annualized, just look at the table and verify for yourself you find that 3% somewhere. That'll help you understanding better. So, you should read them together. So, once this table part, then you will have pictures. Pictures basically convey, they help improve understanding, and that's how a paper is typically structured. So now what we'll do, now that you understand how a paper looks like, I encourage you to look at this. If you see the slide it'll show you, it's showing you where this Piotroski paper is available, I strongly encourage you to download this paper, maybe print it out or use your device. And distance. Verify for yourself where did you find these sections. Or any other trading strategies paper. If you find anything which you cannot understand you can write to us. We will answer your questions. And if you find any new section because there is no law. This is not a legal format. This is not a format determined by law. You may have, depending on the context of the paper, you may have other sections as well. Or there may be a page which may not have any sections. As I've told you, theory for example, most papers don't have theories. Some papers may have it. Some papers may combine two or more sections. For example, hypothesis and empirical strategy may be combined. That's possible. Now that you understand the structure of a paper, I can assure you that you will not get frightened by the technicalities involved in a paper. Before proceeding further on this video, I strongly encourage you to download this Petroski paper, or any other paper, and just try to see yourself. As I've said before, there could be change in the structure. But more important for you is to understand where this trading strategy section, or the algorithm section is and start trying to replicate. Another thing that, a prerequisite for this kind of course is that you must be familiar with some kind of statistical package to analyze it. It could be as straightforward as an Excel. Or if you know programming that's even better. Not essential again, you can do lot of your work in Excel itself. But I strongly encourage that you brush up your basics in any of these packages, so that you can download data and start testing them. That's very important. Remember, the key is to have this mindset that you are making an attempt to reject the hypothesis and not to come out with something. Ultimately you are going to put your hard earned money, yours or your investors, into these strategies. So it's no fun in getting high results on paper and when you actually go and practice, finding something else. And that's possible if the testing is done with this attitude to find some results. You'll always try to reject it. And I'm confident that on an average you will do well. So with this, we will conclude this section on how to read a paper. Now, how do you go forward from here? In the next part, I will pick up Pertroski's paper and take you to through the abstract, line by line. Now the purpose of this, of course I can't take you through the entire paper, line by line. That itself will be 12 hours or 15 hours. And as I've told you that's not needed. But I have said that I'll take you through, line by line, so that you understand how to read the abstract and what does this paper do? And then from there I'm going to jump straight away into the algorithm section, and tell you exactly how the Petroski score works. Once you know scoring, what I'll do is I'll explain how do you actually trade. I'll also explain what are the kind of results that Petroski has obtained. And then I will also show you, by taking up real examples of a particular company from India, that how do you get these scores? And you can, only in other countries what will happen is the way these items are described in balance sheet, the ordering may be different. But the broad idea should work everywhere. So that's the plan going forward. So I urge you strongly, I encourage you to just go through this Petroski paper, at least broadly, before you start the next part on abstract and also the scoring of strategies.
