Thursday, March 29, 2012

Some comparison


Some comparison

Recently I got hold of some old lectures of Walter Schloss, a renowed value investor who was only noticed by Wall Street when Warren Buffett mentioned his name in "The superinvestors of Graham and Doddsville". One of his letters talks about overvaluation in Blue-chip stocks sometime around May 1956. He talks about four companies namely, General Electric (GE 19.80 ↓-1.05%)Dow Chemical (DOW 33.83 ↓-0.85%), Minnesota Mining (3M (MMM 88.01 ↓-0.50%)) and Minneapolis Honeywell (HON 59.96 ↓-1.30%). Here is a snippet from the article:

Dow chemical, based on its current price, is selling at 49 times is earnings for the past 10 years. Similarly, Minnesota Mining is presently selling at 61 times its last 10 years earnings, 51 times its last 5 years earnings and 32 times its 1955 earnings.

One of the four companies (General Electric) is distributing nearly all its current earnings and still only yields 3.1%.

How this can be compared with our current valuations? See some of my previous post on overvaluation in FMCG and Pharma here. I am repeating the table here with some additions:

ShareMarket Cap (INR Cr)TTM P/EP/E of 10 Year Average EPS
3M India386041.2387.5
ABB  (19.97 ↓-1.14%)1652558.461.8
Asian Paints (500820 3188.45 ↑2.81%)2695030.696.1
BHEL1169002767.8
Bosch (500530 7600.10 ↓-1.33%)1887025.3751.5
Colgate (CL 95.74 ↓-0.32%)1127524.866.5
Crompton Greaves (EQCROMPGREAV 133.95 ↓-2.62%)1950022.778.2
Cummins (CMI 116.85 ↓-0.96%) India1445032.865.9
Dabur1850935.6982.25
Exide (XIDE 3.03 ↓-0.98%)1276025.8587
Godrej Consumer Products98602983
HDFC Bank (EQHDFCBANK 510.10 ↓-0.65%)1000003491.9
Infosys (INFY 55.55 ↓-0.64%)15965025.9555.7
Marico (531642 169.30 ↑0.50%)755032.670
Nestle (NSRGY 62.00 ↓-0.56%)3063043.794.4
Titan1340046.75164.35

Even companies like Bajaj Electrical, Hawkins (HWKN 37.77 ↑0.13%) cooker and TTK Prestige are trading at 60-70 times last 10 years earnings. Nestle, which nearly pays all its earnings as dividends, yields just 1.55% at the current price. Remember that the price-wise correction wasn't very severe in US. But over the next 24 years, i.e. till 1980, the index just managed to double from 500 to around 1000, a return of less then 3% compounded annually. Investor would have got dividends though to satisfy himself. May God Bless Indian Investor at this time.

It appears to be a financial axiom that whenever there is money to invest, it is invested; and if the owner cannot find a good security yielding a fair return, he will invariably buy a poor one. But a prudent and intelligent investor should be able to avoid this temptation. - Benjamin Graham

Sometimes number two (or even five) is better than number one

Sometimes number two (or even five) is better than number one
This is a long post with some examples. This post is to highlight the fact that it is not always number one in any particular industry, either in terms of sales or profits, who is the best. There might be special situations when some company down the rank in terms of sales and profits doing better than the leaders in its industry.

I will explain this with two examples. The first one is in organized sanitary-ware industry. There are many players in this industry with leaders like HSIL (Hindustan Sanitaryware and Industries), Kajaria Ceramics, Somany Ceramics and some unlisted players like Parryware, Jaquar etc... But out of these, there is one company that stands apart is Cera Sanitaryware. The company is small in terms of size but if you compare the balance sheet of the company with that of the leaders in the industry, it really is a much better company. Here is a comparison of some of the parameters that really matters to an investor:

ParameterHSILSomanyKajariaCera
Total Income (FY10 Consolidated)818.85545.29736.35193.83
Net Profit(FY10 Consolidated)43.6520.3935.8519.61
ROCE10.8218.6919.8228.01
Average ROCE13.1413.4113.7822.33
OPM17.9310.5415.818.84
Average OPM17.3510.9516.1317.49
Net Profit Margin7.143.84.8910.11
Fixed Asset Turnover0.781.841.341.94
Interest Cover2.643.712.3312.83
Debt Equity Ratio11.871.390.31

One parameter that has a lot of impact is ROCE. If the ROCE is high, the company does not need to invest a lot of capital. This increases fixed asset turnover, reduces debt/equity ratio, increases interest cover and improves net profit margin. So the company does not need to focus on each and every ratio. Just improving ROCE itself has a lot of impact on the company's balance sheet. Interest cover of 4 or less is considered risky so all the leaders are riskier to invest in at this time than Cera.

Now let us talk about the second company in a completely different sector, paper. The industry has many leaders like Tamilnadu Newsprint, JK Paper, Ballarpur, Andhra Pradesh Paper Mills, Seshasayee Paper etc... But of all the companies, one that stands apart is South India Paper Mills(SI Paper). Here is a comparison of the company with that of leaders:

ParameterTamilnadu NewsprintJK PaperBallarpurAP Paper MillsSI Paper
Total Income(FY10 Consolidated)1079.761260.863818.49657.36128.29
Net Profit(FY10 Consolidated)126.0691.03197.0354.1913.77
ROCE8.9518.318.09.2523.85
Average ROCE13.2711.169.7665.7522.11
OPM26.419.7717.9122.5319.18
Average OPM24.2417.4522.3915.8615.58
Net Profit Margin11.747.175.168.310.75
Fixed Asset Turnover0.470.890.50.611.35
Interest Cover4.23.542.183.5110.86
Debt Equity Ratio1.691.171.60.980.267

Some of the figures may not be very accurate since consolidated figures are hard to find. But the ones given above do give an approximate picture. Except for the last parameter, for all other parameters, the company is better if the parameter is higher. You can see that in both the examples, the smaller companies are having a much better control over their balance sheet compared to their leaders.

This analysis is not true for all the industries. Asian Paints is the leader in paint industry and still has better balance sheet than its competitors like Berger and Kansai Nerolac. Till very recently, Infosys was doing much better than the leader TCS but now TCS has overtaken Infosys in terms of profitability etc... 
An institution with securities of its own to sell cannot be looked to for entirely impartial guidance. - Benjamin Graham

Source : http://www.indiavalueinvest.in 

The impact of competition on profits


The impact of competition on profits

Let me give you an example of what impact the competition has on company's profits. Following are the profits of two companies:

YearProfit 1   Profit 2
199298.48   21.5
1993127.27   33
1994189.96   40.5
1995239.22   53.2
1996412.7   54.2
1997580.25   74.3
1998837.44   86.2
19991069.94   98.5
20001310.09   118.6
20011540.95   173.15
20021701.46   201.52
CAGR(1992-2002)      32.96%   25.08%
Now look at the results of these same companies after 2002:

Year  Profit 1  Profit 2
20021701.46   201.52
20031771.79   263.08
20041208.4   251.92
20051408.1   309.57
20061890.53     315.1
20071914.88   413.81
20082117.18   534.08
CAGR(1992-2002)     3.71%   17.64%

The first company is none other than HUL and the second one is Nestle. Both of them started on the same footing in 1992 where HUL's profits were almost 4.5 times that of Nestle. Since there were no competition, HUL ran up very fast to achieve a ratio of almost 11(1310.09/118.6) in 2000, more than twice what it was in 1992. Then P&G and Colgate realized the potential of India becoming huge market, increased their penetration and threw HUL on the back foot. Look at the sluggish growth of HUL between 2002-2008. The FMCG market has grown phenomenally well over the last 6 years but HUL couldn't capture most of it since the competition went ahead of it. We can conclude that the profits of HUL between 1995-2002 were inflated because of no real competition from anybody. When you invest in a blue-chip, make sure it is facing tough competition otherwise your investment will go sour.

How great companies outperform index over time?

How great companies outperform index over time?



Many a times so called analysts mark companies in FMCG and Pharma sectors as defensive. But if we look at the returns generated by these companies over long term, they are many a times much better than the returns generated from index. Today, I will describe two good companies from FMCG sector which have shown the same kind of characteristics over the last 10 years. The first is Nestle and the other one is Glaxosmithkline Consumer Healthcare. If you look at the returns generated by these companies over the last ten years, they can be summarized as shown in the following table:
* Average price
Company  Price 2002-03               Price 2007     Price 2011
Sensex  3000           18000    19000
Nestle  500              1100    3500
Glaxo Consumer       250           550    2200

You can see that during the bull market till 2007, both the companies underperfomed the Sensex by a hugh margin but after 3 more years, they are now outperforming the index. The Sensex generated returns of more than 40% compounded annually between 2002-03 and 2007 and many of the stocks like L&T, Reliance and BHEL went up by more than 25 to 50 times. The returns generated from both these stocks were of the order of 15-20% at best during those times. But the situation has changed over the last three years, all the stocks that generated great returns earlier are still trading 30-40% below their 2007 peak while these companies multiplied their returns and generated more than 40% returns compounded annually during the last three and a half years while the Sensex hasn't moved much. It is just in hindsight that somebody would have bought L&T and Reliance in 2003, sold them in 2007 and bought Nestle and Glaxo from that money. But buying good companies at great prices never turns out to be a bad deal.

Source : http://www.indiavalueinvest.in/

Money is made when you fool Mr Market

Money is made when you fool Mr Market
As I have noted many a times earlier, Mr Market is right most of the time, but not all the times. An investor makes a lot of money when he bets that the market is wrong and later it proves to be wrong. Let me take two examples here:

1. Blue Star in 2001. The company's average share price in 2001 was around INR 35. In the next 7 years, i.e. between 2002-2008, the company gave away dividends worth INR 96.5. Any retail investor who bought 1000 shares of Blue Star @ INR 35, would have got INR 96.5K as dividends in the next 7 years and today those INR 35K would be worth 750K (The company announced 5:1 split in 2006). In 2001 itself, company was giving away INR 5.5 as dividends, i.e. yield at INR 35 would have been 16%. Mr Market avoids companies with such a high dividendyield since it fears the company will cut the dividend or will go bust. Mr Market turned out to be wrong in this case.

2. CRISIL in 2001. The company's average share price in 2001 was around INR 100. In the next 7 years, i.e. between 2002-2008, the company gave away dividends worth INR 124. Any investor who bought 100 Shares of CRISIL got INR 12.4K as dividends in the next 7 years and his INR 10K would be worth 235K today.

Can I see these kind of opportunities today in Indian Stock market? Have a look at the top dividend yields page on Moneycontrol.

Next BlueStar or CRISIL?

As discussed in my previous post , I went through the list of high dividend paying companies. The criterion was to have a minimum yield of 5%. There were 336 companies matching the criteria. To find the next BlueStar or CRISIL, I eliminated companies with following critiera:
1. The company is very small. If it has revenue less than INR 100 Crore and/or profits less than INR 10 Crore. This removed 19 companies.
2. The company has a lot of debt, i.e. more than half its networth. This removed 161 companies.
3. The company is not paying dividend consistently. This include companies which paid special dividend to make yield more than 5%.
4. Companies whose business model can't be trusted to remain sustainable, i.e. Aftek, Gulf Oil Corp etc. This eliminated 8 companies.
5. The companies whose earnings will be negative this year. This eliminated 16 companies.
6. The company is a bank. This eliminated 10 companies.

I was left with a list of 71 companies to keep an eye on. Some of them include Royal Orchid Hotels, Mangalam Cement, Ador Welding, VST Industries, HCL Infosystems, Lakshmi Machine Works, GNFC, Plastiblends, etc. The companies in these list are from sectors ranging from Hotels to Cement, from Engineering to Tobacco, from IT to Fertilizers. Take your pick.
Source : http://www.indiavalueinvest.in

How Government Policies affect companies?

How Government Policies affect companies?
There is already a well known example of Oil Marketing Companies in the Indian Stock Market. But these companies are majority government owned and so the impact of government policies on these companies can be understood. But even companies not owned by Government can have a major impact of policy changes. Let's take an example of cooker makers. There are two main organized cooker makers in India, Hawkins and TTK Prestige. The following shows their profits and share prices during the last thirteen years.


HawkinsPrestige
Year   ProfitsShare Price        Profits Share Price
1997     4.7  55.5        8.3945.5
1998     NA 46        5.0825.75
1999     4.01       37.3         9.3345.5
2000     3.614628.1        3.6430.75
2001     1.86830.1        1.5516.8
2002     -2.0624.25                  0.714.5
2003     -6.9118.15        -11.476.65
2004     0.79816.45            0.2113.35
2005     3.1151.4        3.8146.4
2006     4.02771.05        7.11150.75
2007     7.49483.1        11.77122.7
2008     11.261153.3        20.67116.05
2009     19.116162.2        22.3890.8


The two durations of government policy changes are marked with bold. What was that change? The change was very simple. The Central Excise Duty on cookers was increased from 8% to 16% from 1st April 2000. The results of these companies started deteriorating from that year itself as the profits went down by 50% or more. The companies started making losses in 2002-2003 and the government woke up. The duties were again revised from 16% to 8% from 1st April 2003. The companies started making profits from the first year itself and see the results of the last year. 

The share prices too declined along with profits during 2000-2003 by around 40% to 70%. Hawkins skipped dividend only in 2003 while TTK skipped for three years of 2002-2004. The investors who bore the pain of heavy losses and no income of dividend have been rewarded handsomely as the prices are up by more than 35-50 times (not percent) today.

Source : http://www.indiavalueinvest.in

Wednesday, March 28, 2012

NSE2RICH

Where is https://www.nse2rich.com website gone ?

Does anybody know.

Tuesday, March 27, 2012

Reasons why another recession is imminent


Reasons why another recession is imminent



Just like you ,i love reading what people say,this time i stopped by a post where author Daryl Montgomery defines 8 reasons why another recession is imminent.
Some of them i think are really good and indicate strongly in support of this theory,
  1. Falling of U.S. orders for durable goods
  2. Industrial output in China fell 2.8%
  3. The ECRI (Economic Cycle Research Institute) weekly leading indicators index has fallen as low as minus 10.5
  4. Negative Consumer Metrics Institute’s Growth Index
  5. U.S. trade deficit widened in May and was the largest in 18 months
  6. Falling  U.S. consumer confidence
  7. Unemployment stats
  8. The economic cheerleader-in-chief, Fed Chair Ben Bernanke, gave a gloomy report on the U.S. economy last week in his bi-annual testimony before congress.
but still there are some points which i think favors bull market such as rise in earnings of companies,European problem is a bit under control and also important fact that China is still on the command.There is great discussion going on there. http://seekingalpha.com/article/216981-8-more-reasons-why-a-double-dip-is-coming
Though we cannot reject these reasons why author still believes that there might be a recession coming but i think things are more under control as compared to past.


Intraday Charting Software India


Intraday Charting Software India


It was really difficult to find a software for intraday or day trading for Indian markets BSE and NSE.Though there were some software’s which were providing the historical data charts but not the day trading ones like 1 minute,5 minute etc.
IGuideStocks : Built in Java this software looks a clean one and have some good features.Have a look at technical indicators it supports. Here is a list of all supported indicators:
SMA -> Simple Moving Average
MMA -> Market Moving Average
WMA -> Weighted Moving Average
EMA -> Exponential moving Average
Bollinger bands
RSI
Stockastics
Willam %R
Volume Oscillator
Money Flow Index
On Balance Volume
Chaikin Oscillator
Average True Range
Aroop Oscillator
Chaikin Money Flow
Willam AD
Commodity Channel Index
iGuideStocks support intraday charts for all NSE listed stocks. User can switch on intraday mode any time on any stock by clicking on intraday icon on the graph. The intraday graphs are auto refreshed. User can apply any indicator on intraday graphs. User can come out of intraday mode by clicking any other stock on the left explorer.
It has built in stock screener which works on the 15 strategies to choose from.Along with it users can customise colors and differentiate between different patterns.
Review
Well it is a good start as we would say for it but it lacks some thing like more faster data feed,slow movement(not that slow i would say )It could be better if they would have gone for C++ or other language but any way it is good one.Lastly we would say that it is a good buy for just $14 a month.They are also giving a demo version for trying.We will be adding tutorial posts on how to use this software soon.




First have an java installed in your system
Then Download App Free here
http://www.iguidestocks.com/project/global/igs/download.jsp
make sure to read the installation guide on their website






EMA – Exponential Moving Average


EMA – Exponential Moving Average

 This is a bit like simple moving average but is different in terms of weighing the data.In SMA all the periodical data gets the equal weight while in EMA the latest data gets more weight age . So it reacts faster to the recent price changes.Traders generally opt for 12 and 26 days EMA for short term,and 50 and 200 for long term.Short time period EMA are useful in creating the MACD and
percentage price oscillator.

Formula and calculation of EMA

The formula for an exponential moving average is:
EMA(current) = ( (Price(current) – EMA(prev) ) x Multiplier) + EMA(prev)
or
EMA = Last Day Weight x Last Day Price + Weight of Previous Exponential Moving Average x Previous Exponential Moving Average
For a percentage-based EMA, “Multiplier” is equal to the EMA’s specified percentage. For a period-based EMA, “Multiplier” is equal to 2 / (1 + N) where N is the specified number of periods.
For example, a 10-period EMA’s Multiplier is calculated like this:
(2 / (Time periods + 1) ) = (2 / (10 + 1) ) = 0.1818 (18.18%)
or
Weightcurrent = 2 / (Number of Days in Moving Average + 1)
Since the sum of all of the weights must equal 100%, the weights of the preceding 10 days must equal:
WeightMA = 100% – Weightcurrent
For this example, the weight of the preceding 10 days is 100% – 18.18% = 81.82%.
There are many variations of the exponential moving average. Many of these variations base their calculations of the EMA on the volatility of the market.
http://thismatter.com/money/technical-analysis/moving-averages.htm
http://en.wikipedia.org/wiki/Moving_average
For calculation of Exponential Moving Average In Excel
For excel calculation
We are using here the sheet provided by stockcharts for explaining it in a fast manner
First we have put the data on excel sheet from period 1 to 20 then we need to calculate the Smoothing constant we are taking here the period for 10 days so it will be calculated as
Smoothing constant or k =2/(1+10)
Now put this k in a cell
After the  Smoothing constant calculation, we move on to take the SMA of 10 days.It is easy as
simply put the formula of average from period 1 to period 10 and then using the + sign to
expand it for last periods.
Now comes EMA calculation
We use this formula
it will be used for first cell only
=sum(Period 1 cellname: period 10 cellname)/EMA period   – Which is 10 in this example
for next cells it will change a bit and it will be now
=sum(Period 1 cellname: period 10 cellname)/Smoothing constant cell name  – here it is 0.181818182
For plotting on graph chart select the EMA range and go on the insert chart then click on the chart type and set ok

Download the sheet for more help
http://stockcharts.com/education/indicatoranalysis/EMAExample.xls
Now should i move to SMA or EMA. Which one is good for analysis point of view.As it all depends on the type of trader you are. The simple moving average obviously has a lag, but the exponential moving average may be prone to quicker breaks.Which means SMA shows a slower indications while EMA have more break failures.Some investors prefer simple moving averages over long time periods to identify long-term trend changes and for smaller EMA.Things depends on the way the stock price is moving that is the volatility.Each investor or trader should experiment with different moving average lengths and types to determine the best fit and trade-off between sensitivity and signal reliability.



How To Install Odin Share Trading Software


How To Install Odin Share Trading Software

Many investors are quite confused between when they have to reinstall the odin software which is used for share trading.If they are looking for support it takes much of the time calling representatives of the company then he will take on the computer on remote and then install it.
Here we show you how to simply do it in steps.
Remember it is for single user odin
First of all go to the webpage where the software is available to download.
We are taking here the Globe capital market’s odin as example
we go to this page http://safemailexpress.com/ (Ask for your download page from the company
Then Download these files
Single Code Diet Software
Diet Odin – New Setup – 9.1.0.5
Daily Update Files
Script Files (Necessary for trading in commodity and equity) IF you are only equity trader go for (first four)
NSECM
NSEFO
BSECM
BSEFO
MCX
NCDEX
MCXSX
# Diet users please download Diet Odin – Update Patch -1 for upgrade.
# Diet users please download Diet Odin – Update Patch -2
Registry File for Internet Diet-Code Users
Now Installation
Just Double click the New Setup – 9.1.0.5 file agree and just keep on clicking next,there is no need to change any settings.
Now do same for the patch files Update Patch -1,Patch 2
Now Click On registry file
Now Copy the script files that is NSECM,NSEFO,BSECM,BSEFO,MCX,
NCDEX,MCXSX to this path
C:\ODIN\Diet\MASTERS remember it should not be archieved but it should be in file format.Mean you should copy files only not the folder look at this pics it should look like this

Now it is ready to work
You can login



KDJ Indicator


KDJ Indicator

KDJ indicator
It has been developed from stochastics but it includes one more extra line as J line.
The J line represents the divergence of the %D value from the %K .The value of J can go beyond [0, 100] for %K and %D lines on the chart.When the J is over 100 or under 0 it is one of the most bullish or bearish signs out there.  The J seems to be a lagging indicator, but it seems accurate for large swings.
The K line is the fastest and the j line is the slowest of the two lines.The investor needs to watch as the D line and the price of the issue begin to change and move into either the overbought (over the 80 line) or the oversold (under the 20 line) positions. The investor needs to consider selling the stock when the indicator moves above the 80 level. Conversely, the investor needs to consider buying an issue that is below the 20 line and is starting to move up with increased volume.
How to calculate (Formula)
%K = 100[(C – L5close)/(H5 – L5)]
C = the most recent closing price
L5 = the low of the five previous trading sessions
H5 = the highest price traded during the same 5 day period.
The formula for the more important %D line looks like this:
%D = 100 X (H3/L3)
Now calculate %J = 3 x D  – 2 x K
The above formula is for knowledge purpose as now a days the software’s do the all of calculation part so you don’t have to worry
much about it.Lets take a look from google chart for google company stock.

Wikipedia KDj



Coppock Curve Indicator Analysis


Coppock Curve Indicator Analysis

Some people say as it is one of the pre-requisite conditions for a new bull market.Its history is back in 1962 when it was first published in Barron’s by Edwin Coppock.
It is not a short term or intra day tool but it is really good for determining or forecasting the long-term price momentum.Many analysts treat it as a reliable indicator
of finding the difference between the bear market rallies and true bottoms in the stock market.
Well this indicator as we already told you is for long term atleast a month and is calculated as a 10-month weighted moving average of the sum of the 14-month rate of change and the 11-month rate of change for the index
1) use monthly data
2) use closing prices
3) calculation A: 14 month rate of change
4) calculation B: 11 month rate of change
5) summation of: (calculation A + calculation B)
6) take the 10 month weighted moving of the value in line 5
(WAverage((RateofChange(C,14) + RateofChange(C,11)),10))
well here is the formula used by meta stock software
http://trader.online.pl/MSZ/e-st-Coppock_Curve-Signal_Formulas.html
We don’t think there is any tool which is providing this indicator free online so it is better to look for a software which you can download metastock is one which has included this indicator.
Learn how to calculate it using software http://www.safehaven.com/article-1766.htm


How to calculate And Plot Chart Moving Average In Excel


How to calculate And Plot Chart Moving Average In Excel

 A moving average is a statistic used to analyze subsets of a large data set. It is often used to analyze stock quotes, stock returns, economic data, such as gross domestic product and consumer price indices. It is time series data on the average of a subset of data to calculate points.The Moving Average analysis tool projects values in the forecast period the average value of the variable based on a number of previous periods. Moving averages provide information about trends, the arithmetic average of all historical data would mask.
Using Microsoft Excel to calculate moving averages for large databases. The data can be easily calculated and organized in a few minutes, allowing the user more time to concentrate on the real analysis instead of creating a series.
Charting the Moving Average
1. On the Tools menu, click Data Analysis.
2. In the Data Analysis dialog box, click Moving Average, and then click OK.
3. The Moving Average dialog box opens.
In the Input Range box, enter a single row or column of data.
In the Interval box, enter the number of values that you want to include in the moving average. In this example, enter 3, the default interval.
Note The interval is the number of data points used to calculate the moving average. The larger the interval, the smoother the moving average line; the smaller the interval, the more the moving average is affected by individual data point fluctuations.
In the Output Range box, enter the cell address where you want the results to start.
Select the Chart Output check box to see a graph comparing the actual and forecasted inventory levels.
Click OK.


Video tutorials










How to do it without Data Analysis Tool Help
First of all for it you have to start a new workbook in Microsoft Excel.
After it enter the date and data point in two columns.
Now you have to think about what type of moving average you want to calculate on the quarterly, six month or yearly basis.
For it you have to carefully select the cells as for six months basis
for example if you have cells B1 through B12 populated with data, you would click on cell C6 since B6 contains the last value of semi annual one.
You have to then Type =AVERAGE(data range),in the formula bar where data range is specified as the range of the first period.
In this example, the formula would appear as =AVERAGE(B1:B6).
Now move your mouse on the lower right hand corner of the cell with the formula until you see a “+”. Left click once and drag the formula down to the last data point.
Sources:
http://office.microsoft.com/en-us/excel/HA010864801033.aspx


Type Of Technical Analysis Charts


Type Of Technical Analysis Charts

The basic tool in technical analysis is movement of prices,measured by charts.Due to it many times a person who is involved in technical analysis is called as “chartist”.
The basic three main type of charts used in it are :
1)Line chart
2)Bar chart
3)Point and figure chart
Line charts

These are simple graphs drawn by plotting the closing price of the stock on a given day and connecting the points thus plotted over a period of time.This charting pattern is the most basic type of charting used in finance and it is generally created by connecting a series of past prices together with a line.The charts are easily drawn and widely used.The price is marked on the Y – axis and the period of time on the X- axis.Line charts are helpful in identifying price patterns.

Bar charts :

They are also called as OHLC charts.It plots the span between the high and low prices of a trading period .In order to draw a bar chart, the the data on a day’s high,low and closing prices is necessary.The visual representation of price activity over a given period of time is used to spot trends and patterns. To plot a stock’s price movement,the high and low reached on a said day is marked and conected by a vertical line.The closing price is indicated by a small horizontal tick line on this line.

Point and figure chart



It is not used very commonly but it differs from the others in concept and construction.While the line and bar charts are plotted at specific time intervals,the PFC charts not have a time dimension.A PFC concern only with the measures of prices.In prices also it doesn’t measure all the movements.PFC records only those changes in prices which are larger than a specific amount called points.This charting pattern has been designed for long-term investment,point and figure (P&F) charts have been described as one of the simplest systems for better determining solid entry and exit points in stock market
Others are
Day trading charts or daily charts
It is a line graph that displays the intraday movements of a given security. Daily data is  made up of intraday data that has been compressed to show each day as a single data point, or period.It displays all of the price movement for the period and are typically used by day traders to implement short-term strategies.Traders mainly concentrate on charts made up of daily and intraday data to forecast short-term price movements.
Renko charts : Based on only price movement this charting pattern doesn’t consider time and volume.It is constructed by placing a brick in the next column once the price surpasses the top or bottom of the previous brick by a predefined amount. White bricks are used when the direction of the trend is up, while black bricks are used when the trend is down.It’s also important to note that Renko charts may not change for several time periods. Prices have to rise or fall “significantly” in order for bricks to be added.
Kagi charts look similar to swing charts and do not have a time axis.An entry signal is triggered when the vertical line changes from thin to thick and is not reversed until the thick line changes back to thin. The direction of the chart changes when the price rises or falls by a certain percentage For eg if there is a upward movement of  5 % chart will begins to move upward changing its previous position and vice versa.


Candlestick Chart :
It is a mix of  line-chart and a bar-chart, in that each bar represents the range of price movement over a given time interval.Each candlestick includes the open, high, low, and close, of the timeframe, and also shows the direction and the range of the timeframe. It is most often used intechnical analysis of equity and currency price patterns.





Double Top technical analysis


Double Top technical analysis

This is a major reversal pattern in technical analysis which occurs after an extended uptrend.In simple understanding this pattern looks like 2 consecutive peaks which are almost equal with a moderate trough in between.
It is considered as a indication of buyers are loosing interest in it and upward trend is weakening.Upon completion of this pattern, the trend is considered to be reversed and the security is expected to move lower. stockcharts.com have specified some key points which should be considered before reaching any conclusion.First is the prior trend in case of the double top, a significant uptrend of several months should be in place. First peak should mark highest point in current trend.Its trough or the declines.Second peak now the second peak would be with low volume and meets resistance from the previous high.Now when there is decline from second peak the decline in volume and accelerated descent should be watched.Then there are support break and support turned resistance.And lastly the price target.The pattern is completed when thesecurity falls below or breaks down the support level that had backstopped each move the security made, thus marking the beginnings of a downward trend. 
Volume is also a key point to watch out as there should be increase in volume when the security falls below the support level.

Video tutorial



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McCLELLAN OSCILLATOR – With Excel Spreadsheet

McCLELLAN OSCILLATOR – With Excel Spreadsheet

The McClellan Oscillator, developed by Sherman and Marian McClellan in the late 1960′s, it is a breadth-based indicator which calculates the difference between two exponential moving averages by using the advances and declines from the same day period.
(19 Day EMA of Advances – Declines) – (39 Day EMA of Advances – Declines)
McClellan Oscillator:
Today’s 10% Index – Today’s 5% Index = Today’s McClellan Oscillator
McClellan Summation Index (Old Method):
Yesterday’s Summation Index + Today’s McClellan Oscillator = Today’s Summation Index
While according to Investopedia “McClellan is a good short-term indicator, anticipating positive and negative changes in the advance/decline  stats for better market timing.”
when it bottoms in oversold territory in the area of -100 and below.
When the McClellan Oscillator moves below the Zero Line a SELL Signal is rendered, and a BUY Signal results when it moves above zero.These are not hard and fast rules.As we all know for correct results the data input should also be correct so it should be checked.
Buy signals are indicated when the oscillator advances from oversold levels to positive levels
Sell signals are indicated by declines from overbought to negative territory
Download Sample  Excel Spreadsheet
This sheet will help
www.mcoscillator.com/data/osc_data/OSC-DATA.xls
Must read
http://www.mcoscillator.com/learning_center/kb/mcclellan_oscillator/Calculating_the_McClellan_Oscillator/

Demark Technical Indicator


Demark Technical Indicator

Demarker Technical Indicator is used for comparing the most recent price level to the previous period’s price level to measure the demand of that asset.This is a good indicator to identify the risk involved and the levels at which investor wish to place a transaction
It is based on the comparison of the period maximum with the previous period maximum. If the current period (bar) maximum is higher, the respective difference between the two will be registered. If the current maximum is lower or equaling the maximumof the previous period, the naught value will be registered. This oscillator is bounded between -100 and +100 and, unlike many other oscillators, it does not use smoothed data.
The differences received for N periods are then summarized used as the numerator of the DeMarker. Then the numerator is divided by the same value plus the sum of differences between the price minima of the previous and the current bars. If the price minimum of the current period is higher than that of the previous, the nought value is registered. When the indicator is below 30, the upward price reversal should be expected. When the indicator rises above 70, the downward price reversal should be expected. Using longer periods when calculating the indicator helps to catch the long term market tendency. Indicators based on short periods let you plan thetransaction time so that it falls in with the major trend.
Generally, values above 60 are indicative of lower volatility and risk, while a reading below 40 is a sign that risk is increasing.
Prediction Algorithm:
1. Calculate DeMax (i): If HIGH(i) > HIGH(i – 1), then DeMax(i) = HIGH(i) – HIGH(i – 1), else DeMax(i) = 0
2. Calculate DeMin (i) If LOW(i) < LOW(i - 1), then DeMin(i) = LOW(i - 1) - LOW(i), else DeMin(i) = 0
3. Calculate DeMarker indicator : DMark (i) = SMA (DeMax, N) / (SMA (DeMax, N) + SMA (DeMin, N))
4. If DMark<30, predict BUY ElseIf DMark>70, Predict SELL
Where: HIGH (i) – current maximum price;
LOW (i) – current minimum price;
HIGH (i – 1) – previous maximum price;
LOW (i – 1) – previous minimum price;
SMA – simple moving average;
N – number of periods used in the calculation
Source
http://ta.mql4.com/indicators/oscillators/DeMarker



Free Fund Manager Desktop Software (Nse /Bse)


Free Fund Manager Desktop Software (Nse /Bse)

This software looks really simple,it can be used a s desktop application and for tracking portfolio along with the latest news from Edelweiss.

Really a great software which is simple fast and really good and free thanks to edelweiss for such a great software for Indian Nse and Bse market and that too free.You can create yourportfolio and allocate your transactions to various sub-portfolios according to your financial goals. Now, do all this even if you are not connected to the internet. Get research and strategies from the Edelweiss Research Desk that are relevant to your investment profile and pattern. If you want to keep your onlineportfolio in sync with your Desktop Application, click on Preferences and follow instructions. Click on Refresh to update your portfolio with the latest stockprices and mutual fund NAVs to help you track portfolio performance.
You can check
Portfolio Views
Market Views
Strategy Views
Stocks in Profit
Sectorial Views
Strategies
Stocks in Loss
P/E


Ratios

Edelweiss Views

Quarterly Results
You can do research on your investment and more
Size :3.8 mb
Download


Agent-Based Market Simulation Software For Stock Market Price Forecasting


Agent-Based Market Simulation Software For Stock Market Price Forecasting

Rating: ★★★★☆
Adaptive Modeler 1.2.5 Evaluation Edition
Well this is something new and different from the usual stock analysis software’s.It is a forecasting tool which is used to  simulate complex systems such as stock markets better than traditional mathematical finance. Adaptive Modeler is a tool for creating agent-based market simulation models for price forecasting of real world market-traded securities such as stocks, ETFs or forex currencies.Adaptive Modeler is primarily designed for active trading of stocks or stock indices (i.e. using futures or ETFs) with sufficient volatility and small spreads.

Is it accurate ?
We can’t say it is as accurate tool as it is well know fact that financial markets have been studied using analytical mathematics based on a generalization of market participants and other simplifications and idealizations. However, the behavior of financial markets as observed in reality can not be fully described by such mathematical models. In reality, market prices are established by a large diversity of investors with different decision making methods and different investment goals (such as risk preference and time horizon). The complex dynamics of these heterogeneousinvestors and the resulting price formation process require a simulation model of multiple heterogeneous agents and a virtual market.
Adaptive Modeler does not contain built-in market data feeds nor does it contain interfaces for automatic order placement with online brokers. Market data is imported from ASCII (CSV) files and output data such as forecasts and trading signals can be exported to CSV files for further processing by other applications.

Adaptive Modeler 1.2.5 Evaluation Edition
Download Now
Our review
It seems to be a good piece of software, much simpler to use.You can take the feeds from yahoo and then just edit is according to its format.You have to adjust the time frame ,it needs minimum 250  quote to work around.Help file makes it easy to understand it.You can use the trial version of this software which is free and it does not expire (no limited trial period).It is for generalinvestors and you don’t need any programming skills.We miss the feature of  online data feeding for this software ,but still it is very good.Adaptive Modeler currently only reads quotes from ASCII (CSV) files. No built-in support for online data feeds is provided.Though you can use conversion tools for exporting data feeds to ASCII files in real-time with an additional tool:
For using Yahoo feeds for it follow these steps :
Download the excel datasheet from yahoo for the date range which you want to want to try,then go to excel open it ,go to data >> choose sort >> choose date >> Oldest to newest ok.

Features (Official):

Market data retrieval

  • support for quote intervals ranging from 1 millisecond to multiple days
  • support for variable intervals (i.e. for constant range bars or tick data)
  • flexible and intelligent (CSV) ASCII file reader that automatically accepts a wide range of format variations such as those used by most charting and technical analysis software packages
  • use of open, high, low, close, bid, ask and volume data fields
  • automatic detection of quote interval, market trading hours, number of decimal digits, etc.
  • automatic detection of date and time formats (in most cases)
  • automatic detection/handling of missing quotes and changes in market trading hours
  • accurate calculation of actual market trading time during a period for accurate compounding of returns, volatilities, etc.

User configurable model parameters

  • population size
  • initial agent wealth distribution method (equal, Pareto, Maxwell-Boltzmann)
  • initial agent position distribution method (equal, Gaussian)
  • stepsize of agent position values
  • transaction costs for agents
  • minimum price increment
  • market trading hours
  • forecast source (Virtual Market Price or Best Agents Price)
  • Best Agents group size
  • breeding frequency
  • minimum breeding age
  • parent selection group size
  • mutation probability
  • random seed value
  • maximum genome size and depth
  • minimum and maximum initial genome depth
  • functions and terminals to use for creating trading rules
  • optional uniqueness requirement for creation of new trading rules

Model creation and evolution

  • saving and loading of model configurations
  • pausing and resuming
  • step mode
  • Mersenne twister pseudo random number generator
  • multi-threading (model evolution continues during most user operations)

Available output data (data series)

  • return calculations of security, Trading Simulator and individual agents such as cumulative (excess) return and compounded (excess) return
  • bar-to-bar returns, log returns, absolute returns, etc. of security and forecasts for quantitative analysis
  • return distributions (of security or forecasts) with kurtosis
  • weighted/historical volatility of security, Trading Simulator and individual agents
  • autocorrelation of returns, volatility, volume and other series
  • Hurst exponent of security
  • Virtual Market price and Best Agents Price
  • bid, ask and spread on Real Market and Virtual Market
  • trading volume and number of trades on Virtual Market
  • number of buy/sell orders in orderbook before/after market clearing
  • agent defaults and margin calls
  • forecast, forecasted price change, forecast error, mean absolute error, (root) mean squared error, right/wrong forecasted price changes, Forecast Directional Accuracy, Forecast Directional Significance, Forecast Directional Area Under Curve (AUC)
  • filtered volatility (volatility during right forecasted bars and during wrong forecasted bars)
  • historical averages and distribution data series for agent values such as age, wealth, position, (excess) return, volatility, beta, trade duration, number of offspring, genome size, genome depth
  • genetic operators statistics such as average nodes crossed, average nodes mutated, number of mutations
  • Trading Simulator data series such as wealth, position, trades, cumulative (excess) return, compounded (excess) return, weighted/historical volatility, beta, alpha, (relative) Value at Risk, Sharpe ratio, Sortino ratio, risk-adjusted return, maximum drawdown, MAR ratio
  • Historical and Monte Carlo Simulations of Trading Simulator returns based on user specified parameters such as investment horizon, sample period / expected drift and (filtered) volatility, expected forecast accuracy, wealth, VaR confidence level, etc.
  • and others

Trading Simulator

  • user configurable parameters (allow short positions, broker commissions, spread, slippage, etc.)
  • forecast accuracy filter
  • performance overview including cumulative (excess) return, compounded (excess) return, beta, historical volatility, (Relative) Value at Risk, Maximum Drawdown, Sharpe ratio, Sortino ratio, Alpha, Risk-adjusted return, MAR ratio
  • user configurable performance calculation settings including calculation period, compounding period, risk free rate, VaR confidence level
  • sub period returns and statistics

Charts

  • bar charts and line charts with up to 8 series per chart (real-time)
  • histogram charts (real-time)
  • dragging and dropping of data series into charts
  • horizontal dragging of charts to browse through history
  • transparant data overlay and crosshair for showing current or mouse-over values
  • linking of charts for synchronized browsing and crosshairs
  • lineair/logarithmic scaling (automatic)
  • moving averages

Population window

  • scatter plots of 2 agent values
  • colored scatter plots of 3 agent values
  • agent density plots
  • correlation and regression (of agent values)
  • 3 different axis modes (auto, standard deviation intervals and custom)
  • 3 different gridline modes (round numbers, standard deviation intervals and bin edges)

Market Depth window

  • visualizes virtual market pricing mechanism by showing depth of orderbook before and after clearing
  • shows matching volume
  • cumulative or non-cumulative volumes
  • price range shown can be adjusted by user

Agent window

  • shows agent details such as age, wealth, position, (excess) returns, trade duration, volatility, beta, generation, genome size, genome depth, etc.
  • shows trading rule
  • allows fast browsing through all agents

User Interface

  • customizable user interface (tabbed windows, moving windows, maximizing windows, renaming, maximizing charts, changing colors of chart gridlines and axes, white or black backgrounds)
  • creating multiple window instances possible (for Charts, Population and Agent Windows)
  • saving and loading of Styles (workspace layout)
  • choice between displaying US or European dates
  • automatic scaling of GUI elements to system font and dpi settings to support various screensizes
  • context-sensitive help (dialog boxes, data series tree, gene selection)
  • optional user interface tooltips
  • Startup window with recently used models, examples and tip of the day
  • Getting Started Tutorial

Data exporting

  • automatic (real-time) exporting of any data series values to a CSV file
  • manual exporting of historical values of any data series to a CSV file

Batch processing and automation

  • automatically create models for all quote files in a folder (using a given model configuration and Style)
  • automatically create multiple runs (models) for a security (using a given model configuration and Style)
  • automatically export results of multiple models to a single export file (CSV)
  • automatically update existing models from the command line
  • automatic naming, saving and closing of models
  • saving and loading of batch settings
  • batch creation through application user interface or command line

Logger

  • keeps track of missing quotes, unexpected quote times and other non-critical irregularities in received quotes



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