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00:00If you're into investing and looking for a powerful, back-tested strategy that combines
00:03the best of momentum and value investing, you're in for a treat today. In today's video, I'm going
00:08to be talking about one of the strategies called the trending value strategy that was featured in
00:11Shankar Nath's YouTube channel. For people who are not aware of Shankar, he's one of my favorite
00:16YouTubers who provides really good and quality content on investing and wealth building. So,
00:20please do check him out and his videos. Shankar had proposed this strategy in one of his videos
00:24and it also said that he managed to get a 96.9% return, which grabbed a lot of attention.
00:31In this video, I will break this entire strategy down step-by-step on how it works.
00:35Number two, I will walk you through a Python-based screener that I built that can help you automate
00:39the entire process of stock selection for you. And number three, I will also share what happened
00:43to my investment on a small model portfolio that I built based on this strategy and whether it ended
00:47up profitable or I lost my money. So, stay tuned. The content sharing in this video is based on my
00:52personal experience and is meant purely for education and information purposes. It is not
00:55financial investment, tax or legal advice and should not be considered a recommendation to buy,
00:57sell or hold any financial instruments. Please remember that investing and trading involves
01:00significant risk of loss and past performance is not indicative of future results. Always do
01:04your own research and consult with a qualified financial tax and legal professional before making
01:06any financial decision. The strategy was originally proposed by James O'Shaughnessy in his book,
01:11What Works on Wall Street. Investors usually fall into two categories, the momentum investors and the
01:15value investors. Momentum investors are the ones who chase stocks that are already showing signs of an upward
01:20market momentum and it is catching the attention of a lot of investors and have started to load up
01:23their portfolios with it. Whereas the value investors are the ones who look for fundamentally
01:27strong and yet undervalued stocks that the market hasn't fully recognized yet. So, the idea is to get
01:32into the trend earlier than the others so that you can right the upside. So, up until James had proposed
01:37this strategy, the momentum investing and the value investing approach were used independently.
01:41However, he was the first who brought these two elements together and proposed that a very good
01:45synergy exists between the two and its combined effect can produce better returns for investors.
01:50So, he did a detailed study and some of his findings were startling. For example, if somebody had
01:55invested in the full S&P 500 stocks, the returns would have been 11.2%. If the same amount was invested
02:00in momentum stocks, the return would have jumped to 14.5%. And if the same amount was invested in the
02:05undervalued stocks, the returns would have moved to 17.3%. And finally, he also tested and confirmed that if
02:11the trending value approach was used and invested on those stocks, the overall returns would have
02:15jumped to 21.2%, which is 10% more than what the S&P 500 would have got. Shankarnath's video is loosely
02:23based on the work done by CapitalMines. So, CapitalMines had done a fantastic job taking Shaughnessy's work
02:29and then testing that with Indian data for a period from 2007. And then what they found was really
02:34startling. From 2007, while the Nifty would have given you a compounded rate of about 9.9%, this
02:41particular strategy would have given you 20.6%, which is almost again the same findings, which is 10% more
02:45than what the benchmark would have given you. And if you looked at the market cap distribution, I think
02:49quite obviously, most of the stocks either belong to the small cap or the micro cap segment. And also, for
02:54obvious reasons, the strategy had a bigger drawdown, minus 72, as against the Nifty 50, which had about
03:01minus 60. Now, let's take a look at the overall strategy itself and how this is built. The stock
03:06universe, the original strategy from James did not mention any specific stock universes, but I think
03:10Shankarnath in his video explained that he had considered all stocks with a market cap more than
03:14500 crores as a starting point. And the strategy is all about creating a value composite score. The value
03:19composite score is calculated using six of these parameters, starting with the PE ratio, which as you
03:24know, is nothing but the current market price divided by the earnings per share. So, the lower the
03:28PE ratio is better. Number two is the PB ratio, which is nothing but market cap by assets minus
03:32liabilities. And again, in this case, also lower the PB ratio, the better. The third is the price
03:36to cash flow from operations. And again, the lower, the better price to sales, which is nothing but the
03:43market cap divided by the total revenue. Again, the value is lower, the better EV by EBITDA. Again,
03:48the lower is better. Finally, the dividend yield in this case, higher, the better because we want
03:53stocks that gives us the maximum dividend yield. So, to arrive at 25 stocks that we want for
03:59investment, there is a four-step process. First is we select the stock universe. In this case,
04:04we've already selected this, which is every stock that has a market cap of more than 500. Finding
04:09the design value is pretty straightforward. Let's assume the stock universe gave us about a thousand
04:15companies or a thousand stocks, and then we extract the PE ratio for each of those thousand stocks.
04:20Since the lower is better, we sorted ascending so that the lower values are on the top.
04:24And then in the sorted list, because we have thousand stocks in it, the design is calculated
04:30by thousand, dividing it by ten, which is hundred. So, the first hundred is given a design score of one.
04:34The second hundred is given a design score of two. Likewise, you would give score for each hundred and
04:39the final last set of hundred would have a design value of 10. And that's how it is basically calculated.
04:44So, in step number two, you do that for every one of these six items, and then you find the composite
04:49score by adding all the design values together. So, you get the final consolidated design value
04:54across all thousand stocks. And we move on to step number three, which is the momentum filter.
04:59So, we've completed the value filter, out of which we have selected the first design alone. In the
05:02example that I just told, out of the thousand stocks, we have only taken the top hundred, which
05:06is the design one, right? So, we've already filtered for value. Now, we need to filter for momentum.
05:09So, the way we do it is, you find the last six months return that hundred of these stocks have given,
05:14and then you started descending, which means that you're basically having the stocks that gave you the
05:17maximum returns on the top. And out of the hundred, you take only the first 25 stocks. And this is your
05:23final stocks that you're planning to be investing. If it's a bit confusing, don't worry. Let me just
05:27explain the code. I think it will become much clearer here. So, this is the Python implementation
05:30of the screener that I built. When you run the screener, it'll do all of the things that we just
05:35talked about and output the 25 stocks that you need. So, we've created separate screens for each of
05:40the data values. In this case, it is basically scraping the P value, PB value, and the dividend yield.
05:45And in this one, it is basically the price to free cash flow. And in this case, EV to EBITDA.
05:50And in this case, price to sales ratio. And in this case, it is basically taking the last six
05:54months return, which basically is the momentum filter, right? And then, once you extract those,
06:00you need to apply the decile function to it. Once this is all created, you create a consolidated rank,
06:05just the way we had discussed. And then, once you have the final data set, you compare it with the
06:09six months return for each of those stocks. And then, you sort the entire thing by descending,
06:13so that you get the stocks that has the maximum momentum, which is the maximum six months return
06:16on the top. Out of which, you just remove the top 25, and then provide the final data set.
06:22So, when you run this program, you get two data sets downloaded as an Excel file. One is called
06:28the final.xls, which contains the last six months return sorted value. You could consider the top 25
06:34rows from this sheet, and this is the final sheet. And for people who want to look at a more granular
06:37level data sets, there is another file called the merge.xls. This contains the entire data set,
06:42including the decile values created and how the whole calculation work. All of that is available
06:47in this particular detail sheet. And this is how the final Excel sheet that is downloaded will look
06:51like. You'll have all the stocks that were in the first decile, and you'd have all the details that we
06:56just talked about, including the individual decile values for those six parameters. You'd have the
07:00consolidated decile as well. And then, the six months return is sorted descending, so that you get
07:04these stocks which have the maximum momentum on the top. So, all you need to do is just take the top 25
07:10and consider for your investment. And that's how the strategy is designed.
07:13One word of caution. This is not an investment advice. I'm basically doing this for an education
07:18purposes only. I've given you the Python code, so please feel free to test it the way you want it.
07:24It is just an utility that will probably going to make your life easier doing this calculation for
07:27you. However, please exercise caution. And again, this is not an investment advice.
07:32I think one common question people ask is about the rebalancing strategy. Now that someone has invested in it,
07:36how do we rebalance it going forward? Shankar's advice is basically, you can choose an interval,
07:42either a quarterly or once in six months. And then, depending upon if it's a quarterly,
07:48every quarter you run this screen, and then you get the top 25 stocks. And then, if you have stocks
07:53already invested that has fallen out of this list, you go ahead and sell those stocks, replace them
07:56with the new stocks that have come in the top 25 ranking, and then you invest on those ones for the
08:01same weight. And that's how you rebalance every quarter or every six months.
08:04I told you that I would share my model portfolio that I created based on this strategy. I couldn't
08:09have chosen a bad time to do this. I did this on 15th of October, I remember, and then it was only
08:15a very small amount for one lakh. I just wanted to test this and see how this worked. These are all
08:19the 25 stocks that I picked back in October. Right now, you know how the market is going,
08:24so I can't expect anything better. I'm currently under the water here, about 8,000 rupees. But I've
08:29definitely not lost hope because the strategy is really good. The fundamentals of the strategy is
08:32really good. I liked it. So I'm going to continue to hold on to this. Given it's been three months
08:37now, it's now time for rebalancing. Just this night, I've run the list again. And then tomorrow
08:42morning, I'd basically be removing the ones that have gone out of the list, and then buying the ones
08:46that have come in newly, and then continue to maintain, you know, this particular portfolio going
08:50forward. From time to time, I'll also share how this portfolio is doing maybe once in a quarter,
08:53as and when I'm rebalancing. I'll provide you an update on how this is going. But I've still not lost hope.
08:56I'm still going to continue doing it. This is just me. It's not investment advice again. Hope you like
09:01the overall strategy. Please do let me know your feedback. You know, I'd love to hear from you.
09:05If you'd like to see more such videos, please like the video and share it with your friends. I'll see
09:10you again in another video. Until then, thank you. Bye. If you genuinely found this video useful,
09:14please consider subscribing and liking the video. And I will see you soon in another video. And until then,
09:18take care, and happy trading.
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