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Sensex | Portfolio Yoga - Part 2

A easy way to make money in markets

Is making money in markets easy? Well, its both Yes and No. Yes, if you are positioned rightly, making money is as easy as pie. And No, that does not mean that money can be made without a process driven approach and definitely not by trying the luck in markets when the whole herd of sheep is headed that way.

The simplest way to build wealth is by buying when cheap and selling when expensive. Fundamental Analysts go a long way to analyze what is cheap and what is expensive and while many do get it right, its not something that can be attempted by every other investor who may neither have the time nor the expertise in reading through and understanding balance sheets, cash flows, management guidance among others.

A easier way would be to buy the broader markets when markets as a whole are cheap and selling when they start turning expensive. I have in the past written about how one can use Index PE to determine where the markets are placed at the current juncture and use that info to decide what is the ideal strategy.

So, before we go any further, lets look at the monthly chart of Nifty trailing PE (Standalone)

Nifty

As can be seen in the chart above, we are well below what can be said as over-valued though the caveat is that the price earnings is based on past earnings and if future earnings are bad, we may see the PE rise even without there being much movement in the Index.

On the other hand, we do have some cushion due to the fact that we use Standalone results to calculate the earnings instead of Standalone which is at a higher keel. But since the data we have is Standalone, we shall stick to that for the time being.

While its true that a picture can say a thousand words, I believe that its better to stick to numbers to be sure of what the chart conveys in reality.

While the above chart if of Nifty, I have used the Sensex PE to calculate returns based on where we entered. The reason for using Sensex data was that it provided me with a much larger sample size compared to Nifty.

What I have tried to do is calculate the Compounded Annual Growth Rates based on where the PE was present at that time. So, if the PE was at 20.5, its returns would be recorded in the 21 frequency (representing all data from 20 to 20.99)

Sen

As you can see , the best time to buy would be when the PE ratio is between 10 to 19 and the worst time would be when PE is above 24. Save for the two green picks at 26 PE for 7 years and 10 years, almost all of the rest is the worst returns for the period. Even the outlier is more due to happenstance than something which is worth pondering and investigating further (just for info, the sample size of PE between 25 and 26 is 2 – months of July and August 2000 being the data points). Even in that outlier, do notice that the 1 year return was a negative 33.5%, something that is not easily digestible even by die hard bulls, let alone normal investors.

As on date, Sensex PE is at 18.26 (Source: http://www.vectorgrader.com/indicators/india-sensex-price-earnings) or is at 19.2 (Source: http://www.equitymaster.com/india-markets/bse-replica.asp?order=eps%20desc) .Either way, we are at the top of the band and unless we see strong earnings growth in the coming quarter results, any strong gains from hereon will only push the PE of Sensex into area where the probability is high that returns will be below par.

While I still believe that the markets are a Buy on dips, I would wait for a larger correction to jump in (add to existing positions, that is) than jump in at the first sign of correction. 

Investing By Day of the Month

Idea borrowed from here

While the original post was done on Dow, I have done the same on Sensex. Number of data points is 7606 since the history of BSE Sensex is much smaller than Dow which has been in existence for more than a Century.

Here is the result,

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Here is how it looks if plotted on daily basis

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and here is the aggregate performance

Avg

PostScript: Changed the Average Monthly Performance Chart since previous one used wrong data 🙁

Performance of Markets in the months prior to National Elections (India)

With Elections due in May (unless early elections are called for), I look at how the markets have behaved prior to previous elections. Have used BSE Sensex returns for the computation. Election month performance is the performance of the market during the month of the election (results may or may not have been out in the same month).

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Full Moon – New Moon affect on Indian Markets

On the web, there is a lot of material on how Full Moon and New Moon affect a variety of events on earth and the stock market is said to be no exception either.

Here are some links you may want to glance through,

http://emf.sagepub.com/content/12/1/107.abstract

http://www.theidiotandthemoon.com/moontrading.html

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1366663

Above and many others which can be easily searched for seem to suggest a linkage between Lunar Cycle and Market Returns. But as a trader, the question we need to get an answer for is

Does the linkage have predictive ability? 

If we can see a Predictive ability in terms of Pre and Post Lunar day, we can then use that information to modify the style of trading to take advantage of the same.

For this test, I used BSE Sensex (since the history is the largest available among Indian Stock Indices).

Period of testing was from 1980 to 2013 (November)

Not being a statistical does limit the amount of tests I can run.

The first thing I wanted to do was compare and contrast (correlation in other words) the average returns of 5 days Prior to the D day to the 5 days Post the D Day.

Reason for choosing 5 (and not 4 or 6): A month consists of 30 days which is equal to 2 Lunar Cycles. In a 30 day month, we generally have markets open for around 20 days (Saturday / Sunday being holidays). 20 divided by 2 = 10 and hence I have used 5 for post information and 5 for Pre information.

While the time frame is long enough to dismiss Sample Size Bias, there is still a problem in terms of Lunar Days which fell on either Sunday / Saturday or a Holiday. Since the whole excercise was done in AmiBroker, it has omitted days when there was no data and hence the test may not be complete in all respects.

But since this can affect both New Moon and Full Moon, I believe that the probability of they cancelling each other is high and hence do not believe that they will affect my conclusions in a great manner.

Now for the raw results:

The Correlation between return (average) of 5 days prior to Full Moon compared to 5 days post Full Moon = 0.05

The Correlation between return (average) of 5 days prior to New Moon compared to 5 days post Full Moon = 0.01

The correlation being so close to Zero essentially suggests that returns are random in nature and there is no way to guess as to if markets will rise in the next 5 days if they have fallen 5 days prior to the D day.

Below is the other data points I thought may give some indication of the returns as well as the probabilities;

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The only thing that caught my eye was that regardless of whether the previous 5 days were positive or negative, the next 5 were positive. But this can be easily explained by the fact that the markets have moved tremendously over the same period. The best way to remove that bias would have be to Zero Center the same, but that is for some day in the future 🙂

 

 

 

 

BSE Sensex vs. S&P 500 (US) – Year by Year returns comparison

The chart below displays the returns of BSE Sensex vs. S&P 500 of US since 1981 till date (November 7). What is interesting is that since 2003, we have outperformed S&P 500 on every positive occasion and under-performed S&P 500 whenever the year ended in negative territory. I have used the Open and Close to determine the returns. 2013 seems to be the first occasion when S&P is strongly performing on the positive side compared to the Sensex since the start of the secular world wide bull market of 2003.

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