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Thursday, January 31, 2008

Results Update - 31/01/2008 - Part 2

IOC Q3 net up 17% to Rs 2,091cr

Indian Oil Corporation today announced a 16.70% rise in net profit at Rs 2,090.69 crore for the quarter ended December 31, 2007 when compared with Rs 1,791.37 crore in Q3FY07.

According to a release issued to the Bombay Stock Exchange, total income increased to Rs 65,404.84 crore for the quarter ended December 31, 2007 from Rs 56,438.16 crore for the quarter ended December 31, 2006.

Unitech Q3 net up 39% at Rs 526cr

Real estate firm Unitech today reported a 39% increase in consolidated net profit at Rs 525.78 crore for the third quarter ended December 31, 2007 when compared with Rs 377.84 crore in Q3FY07.

Total income of the group during the quarter under review was Rs 1,165.11 crore, up 19% from the corresponding period last fiscal, the company said in a release today.

ACC FY07 net up 15% at Rs 1,427cr

ACC today announced a 15.14% increase in net profit at Rs 1,427.34 crore for the year ended December 31, 2007 when compared with Rs 1,239.60 crore for the year ended December 31, 2006.

According to a release issued to the BSE, total income increased to Rs 7,189.43 crore in FY07 from Rs 5,984.56 crore in FY06.

The company, on a standalone basis, reported a net profit of Rs 1,438.59 crore in FY07 as against Rs 1,231.84 crore in FY06. Total income increased to Rs 7,135.97 crore from Rs 5,945.13 crore in FY06.

The board today approved a final dividend of Rs 10 per share for FY07.

Along with the interim dividend of Rs 10 per share paid earlier, the total dividend for FY07 is Rs 20, the release added.

Uttam Galva Steels Q3 net up 29%

Uttam Galva Steels has recorded a net profit of Rs 29.15 crore in the quarter ended December 31, 2007, an increase of 29% over the same period last year.

Net sales for the quarter touched Rs 582.69 crore, up 12%.

For the nine months (April-December 2007), net profit stood at Rs 93.39 crore, up 14%.

Ankit Miglani, director (commercial), Uttam Galva Steels, said focus on value-addition combined with operational efficiencies contributed to a healthy bottomline.

“With the completion of our expansion plans in their final stages, we will be better equipped not only to fulfill diverse customer needs in the domestic market but also further consolidate our presence in the global markets in the quarters ahead,” he said.

The company has crossed exports of two million tonne of value added steel. In the last one year, Uttam Galva has increased its exposure to the export markets from 120-135 countries. The company has entered into an agreement with Ispat Industries to buy five lakh tonne of hot rolled (HR) coils per annum making it the sole domestic supplier of HR coils to Uttam.

Posted by Srivatsan at 9:10 AM

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Understanding Short Term Trading

Before I begin, this blog is not for intraday traders. My definition of short term implies duration of around 2 to 3 months.

Short Term stock picking is no rocket science, but rather a visual interpretation of technical charts. A basic moving average on a time frame chart will show the direction of the securities movement.

Moving averages is a mathematical results calculated by averaging a number of past data points. Moving averages (MA) in it's basic form is calculated by taking the arithmetic mean of a given set of values on a rolling window of timeframe. Once the value of MA has been calculated, they are plotted onto a chart and then connected to create a moving average line. Typical moving averages used for short term trading are 50 MA and 100 MA.

Types of Moving Averages

1) Simple Moving Average (SMA)

SMA is calculated by taking the arithmetic mean of a given set of values on a rolling window of timeframe. The usefulness of the SMA is limited because each point in the data series is weighted the same, regardless of where it occurs in the sequence. Critics argue that the most recent data is more significant than the older data and should have a greater influence on the final result.

2) Exponential Moving Average (EMA)

EMA overcomes the limits of SMA, where more weight is given to the recent prices in an attempt to make it more responsive to new information. When calculating the first point of the EMA, we may notice that there is no value available to use as the previous EMA. This small problem can be solved by starting the calculation with a simple moving average and continuing on with calculating the EMA.

The primary functions of a moving average is to identify trends and reversals, measure the strength of an asset's momentum and determine potential areas where an asset will find support or resistance. Moving averages are lagging indicator, which means they do not predict new trend, but confirm trends once they have been established.

A stock is deemed to be in an uptrend when the price is above a moving average and the average is sloping upward. Conversely, a trader will use a price below a downward sloping average to confirm a downtrend. Many traders will only consider holding a long position in an asset when the price is trading above a moving average.

In general, short-term momentum can be gauged by looking at moving averages that focus on time periods of 50 days or less. Looking at moving averages that are created with a period of 50 to 100 days is generally regarded as a good measure of medium-term momentum. Finally, any moving average that uses 100 days or more in the calculation can be used as a measure of long-term momentum.

Support, resistence and stoploss can be infered by referring the closet MA below or above the market price. The other factor that is used in short term momentum is the trading volume. The moving averages along with the trading volume can provide a better insight to short term movement.

Markets are moved by their largest participants - I believe this is the single most important principle in short-term trading. Accordingly, I track the presence of large traders by determining how much volume is in the market and how that compares to average. Because volume correlates very highly with volatility, the market's relative volume helps you determine the amount of movement likely at any given time frame--and it helps you handicap the odds of trending vs. remaining slow and range bound.