* To hire 25,000 employees in FY09; factors 19-21% revenue growth
* Sitting on $2bn cash; 15 big deals in the pipeline
* 11-13% hike in offshore salaries; 4-5% hike in onsite salaries - 2.3% impact in Q1
* Lost Rs 2,000cr revenue and Rs 1,000 crore profit due to rupee appreciation in FY08
* Hedging: $760mn; $7mn mark-to-market loss due to hedging
* Reducing onsite exposure by 1%
* Assumes flat margins for Q1FY09 ending June 30, 2008
* Pricing assumed to be stable given current conditions; increased by 6% YoY
* Closed four deals in Q4; one large deal around $200-300mn
* Clients: 40; Net hiring: 2,586; Total hiring in FY08: 33,177; Total employees: 91,187
* Dividend: Rs 7.25 per share + a special dividend of Rs 20 per share for FY08
As it guided the markets to a $5 billion revenue in financial year 2008-09, the markets reacted positively to Indian IT services provider Infosys Technologies, which posted a "market expected" result for the quarter ended March 31, 2008.
The results were no surprise since the rupee had depreciated against the dollar by around 1%. Over 60% of Infosys' revenues still come from the US. The management, however, admitted that the slowdown in the US was a concern.
The slowdown in its revenue growth, it said, was a bigger concern that the rupee appreciation against the dollar. It also sees a "delay in decision-making" when it comes to deals, but "clients are looking to increase their offshore budgets". It added, though, that while it was looking at medium- to long-term growth, the next two quarters would bring in more clarity.
Guidance for financial year 2008-09
* Income expected between Rs 19,894-20,214 crore; YoY growth of 19.2%-21.1%
* EPS to be around Rs 92.32- 93.92; YoY growth of 16.3%-18.3%
Infosys became the 10th Indian company (excluding banks and financial institutions) to become $1 billion net profit company. The other companies are ONGC, Reliance Industries, Indian Oil, NTPC, Sail, Hindustan Zinc, Tata Steel, Bharti Airtel and TCS (based on nine-months annualised).
Infosys is sitting on a pile of cash. Its cash, bank balance and deposits with financial institutions and corporate bodies increased from Rs 6,129 crore to Rs 8,396 crore. It has bank balance of Rs 6,950 crore (Rs 5,834 crore) while deposits with FIs and corporate body is at Rs 1,446 crore (Rs 295 crore).
The share of salaries and wages to total expenditure has gone up from 66.59% in Q3FY08 to 67.55% in Q4FY08. The share of S&G in total expenditure declined from 19.24% to 18.99%.
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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