* Revenue Rs 2,195.56 crore; YoY up 32.2%; QoQ up 8.1%
* PAT Rs 433.63 crore; YoY up 28.6%; QoQ up 6%
* EPS Rs 6.48; QoQ up 5.9%
* EBITDA margin for the quarter: 21.46%
* Added 3,424 employees; total: 49,199
* Attrition fell to 13.1% from 13.9 % QoQ
* 32 customers added
* Outlook positive: Increases fiscal year 2008 revenue guidance to $2.1 billion from $2.08 billion
Riding on the back of large outsourcing deals and high-level offshore utilisation, Hyderabad-based IT services provider Satyam Computer Services posted a net profit of Rs 433.63 crore for the quarter ended December 31, 2007 when compared with Rs 337.23 crore during the corresponding quarter - a growth of 28.58%.
Revenues stood at Rs 2,195.56 crore, an increase of 32.17%, from Rs 1,661.12 crore during the same period last year. Its earnings per share (EPS) was Rs 6.48 -- a YoY increase of 26.1% and a sequential increase of 5.9%. The EBITDA (operating profit) margin for the quarter under review stood at 21.46%. Satyam recorded a sequential revenue growth of 10.5% and 50% on a year-on-year basis in dollar terms (US GAAP) during Q3.
"The highlight of the quarter was the continued improvement in all operating parameters. Increased productivity due to higher utilisation, increased billing rates and offshore shift led to improvement in margins to 165 basis points," said chief financial officer V Srinivas. "As such, we are increasing our fiscal year 2008 revenue guidance to $2.1 billion, from $2.08 billion (from 42% growth to 45.2%)," founder and chairman B Ramalinga Raju said.
"We mitigated the currency appreciation through judicious rate increases -- 2.4% for onsite work and 2.3% for offshore projects. These were our most significant increases ever. Additionally, we raised the percentage of our offshore work from 50.4% to 52.1%, which enhanced our operating margin," he added. The volume of work Satyam performed for clients also jumped by 9.4%.
Stating that the company was assessing the slowdown in business in the US, its major market that contributes 60% to its overall business, Raju said the company was closely watching the economic environment, which could have a bearing on its customers. "We, however, will be better prepared for some ground realities during the next financial year," he said.
During the quarter, Satyam added 32 new clients, eight of which are Fortune 500 companies taking its clientele base to 181 Fortune 500 companies. "We have made good progress during the quarter by bagging four large deals in different verticals, each valued at $50 million. We are in pursuit of 21 such big-ticket deals, some of which are from Europe and Asia Pacific," Ram Mynampati, president (commercial and healthcare businesses), Satyam, said. The company hired 3,424 associates in the third quarter with its attrition declining to 13.1% on a trailing twelve months basis.
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.
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