Here we are going through some useful discussion on SECTOR ROTATION by OXY
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Shall be posting the 3 best and worst performing sectors out of the following.
1) Aluminum
2) Auto
3) Auto Ancillary
4) PSU Banks
5) Pvt sector Banks
6) Cables
7) Cements
8) Civil construction
9) Computer Hardware
10) Real estate
11) Engineering
12) Fertilizers
13) FMCG
14) Hotels
15) Media
16) Pharma
17) Retail
18) Software
19) Sugar
20) Textiles
21) Oil Exploration
22) Power
23) Refineries
24) Shipping
25) Steel
26) Telecom
Calculation for the same is done based on equally weighted geometric return values. Each sector consists of between 5 and 16 stocks and a median of 1 stocks, with exception of aluminum is weighted based on only 2 stocks. The base date for the same is 1 Jan 2008 and the base value is 100.
For the day ended : 13 June 2008
The index formula is 100*(Value on current day/Value on nth day). A value of 100.28 means that there's been a .28% appreciation in the index since the day it began.
Best Performing since 1.1.2008:
1) Pharma - 100.2849
2) Software - 95.2413
3) Aluminum - 89.0643
Worst Performing since 1.1.2008:
1) Real Estate - 46.9464
2) Cables - 49.1626
3) Textiles - 49.9353
Suppose there are two stocks in an index - A and B. The index is calculated as follows:
(Rate of change of A since nth day + Rate of change of B since nth day)/2
This gives an equal weight of .5 to each stock.This is the basis of my calculation.
Stock selection is done based on three criteria:
1) Descending order of market cap with a value of at least Rs.1000 crores (in some cases, where unavoidable, Rs.500 crores)
2) Differentiation of that stock's business with other stocks in the same sector (For example: Media and entertainment stocks include television, radio, cinema screening, film production, film distribution etc. Care is made to include at least one of each type)
3) Liquidity of that stock
Each stock is equally weighted in order to avoid any bias to one stock's movement. Since there are no small caps, the possibility of an outrageous move within a very short span is as good as ruled out.
The main trick may be after identifying sector & constituent companies/scrip IN that sector having high probability is to be longed & in weak sector the weakest are to be shorted (all in Fut / Option).
This can be stock/Index.
This Opposite positions can act as insurance.
.................................................
For the day ended 20 June 2008
Best performing since 1.1.2008
1) Pharma 92.45
2) Software 82.22
3) Aluminum 81.2624
Worst Performing Since 1.1.2008
1) Real Estate 41.5569
2) Cables 47.4067
3) Civil Construction 48.1443
Re-created my sectoral database, increased the median number of stocks per sector and added new sectoral indices to the list.
The long term investor doesn't need to go by technicals He will focus more on the Fundamental outlook of a Stock & sector (like despite the current battering of infra & cap goods sector stocks they'll remain good long terms bets to him/her & hence a buying opportunity In fact Buffet advises to buy & hold stocks which would not be affected if the stk mkts were to close down tomorrow!)
It's in fact the short term trader who needs to look at short term sectoral strength/outperformance. The only category that can be excluded perhaps is the Nifty trader, but even such a trader could use a collective view of the various sectors as a short-term sentiment indicator.
At the same time, there's no sacrilege in a long term investor taking a graphical view of how a sector has performed compared to the mkt &/or other sectors over say the last 5 yrs.
And i also don't see why a swing or even an intraday trader shouldn't seek outperformance (based on one or more parameters like sectoral strength).
Oxy, just wondering if the calculation can be done on a day-to-day basis i.e. (Close today/Close yesterday) * 100 This would eliminate the need for choosing a base year Stocks can be included/replaced at any time (may be 3-6 mths after listing when the daily range is more in tune with other constituents)
It can be done Kalyan. Substituting cum(ROC(C,1,%) instead of 100*(C/ValueWhen(1, value on nth day,C) - 1) will do the trick. Tried this and got the same result for ranking, only the values varied. Another problem with cumulative ranking is, if a stock is delisted for a particular time, then the index will be deflated to that extent during that time.
One can find out if a sector is ob/os the same way one'd figure out if the mkt is ob/os (using a mkt proxy like Nifty or CNX500 &/or mkt data comprising all its constituent stocks); In fact once one has the base material - the sectoral index & it's constituents - one can slice & dice it any way one wants, to extract whatever info one seeks with the advantage of being able to view a sector as a whole rather than extrapolating what is conveyed individually by 1 or 2 stocks (i.e. you get to work with a larger sample without needing to repeat the rather tedious sampling process every time. This process is abstracted & encapsulated in the form of the Sectoral Index created once).
Finally, everything need not be useful to everyone but i think it's better to keep an open mind
...................................
Originally Posted by Dev Mookerji
As we are not fund managers, and talking about retailers, u must be agrred to, that very few can take more than 2/4 (different scrips not lots) future positions at a time.
Now My point is by simply exploring the system one can watch the stocks making monthly/quarterly/yearly low/high, and subsequently we can judge the weaker and stronger stocks to trade accordingly in one or two or best few stocks giving stronger signal in any direction to trade with in own system .
I'nt it a good idea to select from those list of stocks, which are lesser in number to select with, instead rigorous, sector analysis and then again to analysing stocks from each sector, decided to pick stocks to trade from each sector?
Let the Fundamentalist/Economists/Fund managers do the things, not we the short time trader.
ANS:It's precisely because retailers cannot afford to diversify much that they need to be more rigorous in their stock selection. Sectoral analysis can be an additional tool (and not the ONLY tool) in this process. (Once you narrow down to a few sectors your 'list of stocks' actually reduce to a 'lesser number' - so i don't see how this method increases the workload)
Where's the big harm in retailers adopting some of the best practices of Fund managers (at least to the extent possible with our tools & information access)? And it's not that big Fund Managers have all the advantages. Nimble traders can outperform Funds through quick churning of their small portfolios. Sectoral rotation can be part of a structured method of achieving this.
Why can't the trading goal extend beyond successful trades to greater ROCE (even for retailers)? (Moreover, outperformance on ones winning trades would act as an insurance for the inevitable losses that will happen from time to time)
IMHO, there's more to a robust trading methodology than just Buy-Sell signals. First & foremost (& like all things durable) it needs to have a strong foundation, a solid framework. And (like most other functional 'systems' including economic systems, businesses) there's a Macro & Micro aspect to it. Sectoral Analysis can be used to to fortify the macro framework.
Taking a couple of 'live' examples from this board as illustration:-
Sectoral Analysis can be incorporated in the SMART system, either as a primary component of the stock selection process or as a secondary filter (This would be an addition at the macro level; the micro aspects of the system - entry, exit, trade mgmt etc.- can remain unchanged)
Likewise, a pattern that Asishda has been pointing to of late, in various threads - essentially a Climax bar/Churn bar pair - can be a valuable* addition at the micro level (trading insight & tactics).
surely the views of a highly experienced trader deserves some attention.
( what i call 'valuable' in public. CV may well come up with a Data Mining & Analysis report dismissing as 'nonsense' my gullible beliefs based only on random observations, circumstantial evidence & a statistically insignificant dataset)
..................................................................................................
Shall be posting the 3 best and worst performing sectors out of the following.
1) Aluminum
2) Auto
3) Auto Ancillary
4) PSU Banks
5) Pvt sector Banks
6) Cables
7) Cements
8) Civil construction
9) Computer Hardware
10) Real estate
11) Engineering
12) Fertilizers
13) FMCG
14) Hotels
15) Media
16) Pharma
17) Retail
18) Software
19) Sugar
20) Textiles
21) Oil Exploration
22) Power
23) Refineries
24) Shipping
25) Steel
26) Telecom
Calculation for the same is done based on equally weighted geometric return values. Each sector consists of between 5 and 16 stocks and a median of 1 stocks, with exception of aluminum is weighted based on only 2 stocks. The base date for the same is 1 Jan 2008 and the base value is 100.
For the day ended : 13 June 2008
The index formula is 100*(Value on current day/Value on nth day). A value of 100.28 means that there's been a .28% appreciation in the index since the day it began.
Best Performing since 1.1.2008:
1) Pharma - 100.2849
2) Software - 95.2413
3) Aluminum - 89.0643
Worst Performing since 1.1.2008:
1) Real Estate - 46.9464
2) Cables - 49.1626
3) Textiles - 49.9353
Suppose there are two stocks in an index - A and B. The index is calculated as follows:
(Rate of change of A since nth day + Rate of change of B since nth day)/2
This gives an equal weight of .5 to each stock.This is the basis of my calculation.
Stock selection is done based on three criteria:
1) Descending order of market cap with a value of at least Rs.1000 crores (in some cases, where unavoidable, Rs.500 crores)
2) Differentiation of that stock's business with other stocks in the same sector (For example: Media and entertainment stocks include television, radio, cinema screening, film production, film distribution etc. Care is made to include at least one of each type)
3) Liquidity of that stock
Each stock is equally weighted in order to avoid any bias to one stock's movement. Since there are no small caps, the possibility of an outrageous move within a very short span is as good as ruled out.
The main trick may be after identifying sector & constituent companies/scrip IN that sector having high probability is to be longed & in weak sector the weakest are to be shorted (all in Fut / Option).
This can be stock/Index.
This Opposite positions can act as insurance.
.................................................
For the day ended 20 June 2008
Best performing since 1.1.2008
1) Pharma 92.45
2) Software 82.22
3) Aluminum 81.2624
Worst Performing Since 1.1.2008
1) Real Estate 41.5569
2) Cables 47.4067
3) Civil Construction 48.1443
Re-created my sectoral database, increased the median number of stocks per sector and added new sectoral indices to the list.
The long term investor doesn't need to go by technicals He will focus more on the Fundamental outlook of a Stock & sector (like despite the current battering of infra & cap goods sector stocks they'll remain good long terms bets to him/her & hence a buying opportunity In fact Buffet advises to buy & hold stocks which would not be affected if the stk mkts were to close down tomorrow!)
It's in fact the short term trader who needs to look at short term sectoral strength/outperformance. The only category that can be excluded perhaps is the Nifty trader, but even such a trader could use a collective view of the various sectors as a short-term sentiment indicator.
At the same time, there's no sacrilege in a long term investor taking a graphical view of how a sector has performed compared to the mkt &/or other sectors over say the last 5 yrs.
And i also don't see why a swing or even an intraday trader shouldn't seek outperformance (based on one or more parameters like sectoral strength).
Oxy, just wondering if the calculation can be done on a day-to-day basis i.e. (Close today/Close yesterday) * 100 This would eliminate the need for choosing a base year Stocks can be included/replaced at any time (may be 3-6 mths after listing when the daily range is more in tune with other constituents)
It can be done Kalyan. Substituting cum(ROC(C,1,%) instead of 100*(C/ValueWhen(1, value on nth day,C) - 1) will do the trick. Tried this and got the same result for ranking, only the values varied. Another problem with cumulative ranking is, if a stock is delisted for a particular time, then the index will be deflated to that extent during that time.
One can find out if a sector is ob/os the same way one'd figure out if the mkt is ob/os (using a mkt proxy like Nifty or CNX500 &/or mkt data comprising all its constituent stocks); In fact once one has the base material - the sectoral index & it's constituents - one can slice & dice it any way one wants, to extract whatever info one seeks with the advantage of being able to view a sector as a whole rather than extrapolating what is conveyed individually by 1 or 2 stocks (i.e. you get to work with a larger sample without needing to repeat the rather tedious sampling process every time. This process is abstracted & encapsulated in the form of the Sectoral Index created once).
Finally, everything need not be useful to everyone but i think it's better to keep an open mind
...................................
Originally Posted by Dev Mookerji
As we are not fund managers, and talking about retailers, u must be agrred to, that very few can take more than 2/4 (different scrips not lots) future positions at a time.
Now My point is by simply exploring the system one can watch the stocks making monthly/quarterly/yearly low/high, and subsequently we can judge the weaker and stronger stocks to trade accordingly in one or two or best few stocks giving stronger signal in any direction to trade with in own system .
I'nt it a good idea to select from those list of stocks, which are lesser in number to select with, instead rigorous, sector analysis and then again to analysing stocks from each sector, decided to pick stocks to trade from each sector?
Let the Fundamentalist/Economists/Fund managers do the things, not we the short time trader.
ANS:It's precisely because retailers cannot afford to diversify much that they need to be more rigorous in their stock selection. Sectoral analysis can be an additional tool (and not the ONLY tool) in this process. (Once you narrow down to a few sectors your 'list of stocks' actually reduce to a 'lesser number' - so i don't see how this method increases the workload)
Where's the big harm in retailers adopting some of the best practices of Fund managers (at least to the extent possible with our tools & information access)? And it's not that big Fund Managers have all the advantages. Nimble traders can outperform Funds through quick churning of their small portfolios. Sectoral rotation can be part of a structured method of achieving this.
Why can't the trading goal extend beyond successful trades to greater ROCE (even for retailers)? (Moreover, outperformance on ones winning trades would act as an insurance for the inevitable losses that will happen from time to time)
IMHO, there's more to a robust trading methodology than just Buy-Sell signals. First & foremost (& like all things durable) it needs to have a strong foundation, a solid framework. And (like most other functional 'systems' including economic systems, businesses) there's a Macro & Micro aspect to it. Sectoral Analysis can be used to to fortify the macro framework.
Taking a couple of 'live' examples from this board as illustration:-
Sectoral Analysis can be incorporated in the SMART system, either as a primary component of the stock selection process or as a secondary filter (This would be an addition at the macro level; the micro aspects of the system - entry, exit, trade mgmt etc.- can remain unchanged)
Likewise, a pattern that Asishda has been pointing to of late, in various threads - essentially a Climax bar/Churn bar pair - can be a valuable* addition at the micro level (trading insight & tactics).
surely the views of a highly experienced trader deserves some attention.
( what i call 'valuable' in public. CV may well come up with a Data Mining & Analysis report dismissing as 'nonsense' my gullible beliefs based only on random observations, circumstantial evidence & a statistically insignificant dataset)