Today's Settlement Prices :: Gold 15445 :: Silver 21809 :: Copper 173.90 :: Lead 54.05 :: Nickel 510.40 :: Zinc 57.40 :: Crude Oil 2252 :: Natural Gas 217.20 :: Crude Palm Oil 290.50 :: Heating Oil 64.80 :: Soya Oil 452.70 & INR 51.04
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Saturday, February 28, 2009
27-Feb-09 Mcx Settlement Prices
Friday, February 27, 2009
Rural India portrays rosy picture in services sector: Survey
Contrary to the popular and traditional perception that villages
facilitate mainly agricultural activities, rural India also has a
major contribution in the services sector, housing 60 per cent of the
total enterprises in the country, a study has said.
"An estimated 1.65 crore service sector enterprises were in operation
in India during 2006-07. Of these enterprises, 60 per cent (0.99
crore) were in rural India and 40 per cent (0.66 crore) in urban
area," the National Sample Survey Organisation (NSSO) report released
on Friday said.
More than half of the estimated 3.35 crore people working in service
sector enterprises belong to rural India. The services sector is one
of the prime engagers of workforce in the economy.
The report, first in a series of two studies planned to be brought out
based on the survey, reveals that 85 per cent of all enterprises fall
under own account enterprises (OAEs) category that are run without any
hired worker on a fairly regular basis.
The remaining 15 per cent were found to be under Establishments --
those running with at least one hired worker on a fairly regular
basis.
Transport, storage and communications engage 25 per cent of the
workers, followed by financial intermediation engaging 17 per cent
workers and hotels and restaurants 15 per cent, the findings revealed.
The service sector enterprises in the survey include all service
sector enterprises (excluding trade), other than government and public
sector undertakings and those covered under annual survey of
industries.
The services covered were hotels and restaurants, storage and
warehousing, transport and communication, financial intermediation,
real estate, renting and other business activities, education, health
and social work, among others.
About 90 per cent of enterprises were propriety enterprises and 7 per
cent were cooperative societies and self help groups. About 59 per
cent enterprises were not registered with any agency.
Further, 74 per cent enterprises had fixed premises of operation and
32 per cent were located within household premises.
The NSSO revealed that 86 per cent of all enterprises did not receive
any assistance from any government or non-government agencies while
loan was the most dominant form of assistance in case of 14 per cent
of enterprises receiving assistance.
Major problems faced by 57 per cent enterprises were "competition from
larger units" and "shortage of capital". As far as work was concerned,
two per cent enterprises had undertaken at least some work on contract
basis, while another 32 per cent had undertaken some economic
activity.
"About 81 per cent of working owners or managing partners of
proprietary and partnership enterprises were literate, with some
formal education," the report said, adding even in rural areas,
literacy with some formal education was as high as 77 per cent.
The survey was conducted on 1,90,282 sample enterprises, of which 438
enterprises belonged to list frame and 1,89,844 belonged to sample
villages/urban blocks of area frame.
Financial sector enterprises and other service sector enterprises
comprising relatively large units were put under "list frame" while
the remaining units within the coverage were categorised in "area
frame".
The field work of the survey was carried out during July 2006 to June
2007.
The survey excluded Leh (Ladakh), Kargil, Poonch and Rajauri districts
of Jammu & Kashmir, interior villages of Nagaland and inaccessible
villages of Andaman and Nicobar islands.
Reuters - India's Oct-Dec GDP up 5.3 pct from a year earlier
This article was sent to you from Pradyutghosh@gmail.com, who uses Reuters Mobile Site to get news and information on the go. To access Reuters on your mobile phone, go to:
http://in.mobile.reuters.com
India's Oct-Dec GDP up 5.3 pct from a year earlier
Friday, Feb 27, 2009 5:37AM UTC
NEW DELHI (Reuters) - India's economy grew a slower than expected 5.3 percent in the December quarter from a year earlier, slowing sharply from the previous quarter's 7.6 percent as the global economic crisis cut demand and exports.
The annual growth for India's fiscal third quarter was lower than a median forecast of 6.2 percent in a Reuters poll of economists and also lower than a upwardly revised 8.9 percent annual expansion in the same quarter a year ago.
The manufacturing sector fell 0.2 percent in the October-December quarter from a year earlier, while the farm sector contracted an annual 2.2 percent, government data showed on Friday.
India has estimated the economy to grow 7.1 percent in 2008/09, slowing from the 9.0 percent in the previous year.
Tata Nano - World's Cheapest Car! Coming Soon...
Markets decline after the figures of India's GDP slips to 5.3% in Q3
The Indian economy grew by 5.3 per cent in the third quarter, the slowest quarterly growth this fiscal, pulled down by contraction in manufacturing and farm production even as some services showed robust expansion.
The farm sector, believed to be de-coupled from the global financial meltdown, also succumbed to the pressure of the slowing economy and fell by 2.2 per cent in October-December, 2008-09 against the growth of 6.9 per cent a year ago.
In the third quarter, industrial production, led by manufacturing, contracted in the two months of October and December.
For the whole quarter, manufacturing declined by 0.2 per cent against a substantial expansion of 8.6 per cent a year back.
Bucking the trend, community, social and personal services grew by a strong 17.3 per cent against 5.5 per cent in the year-ago period, part of which may be contributed by revised the salary structure of government employees.
For the first nine months of this fiscal, the economy grew by 6.9 per cent against nine per cent in the same period of 2007-08.
For the whole of 2008-09, the Indian economy is projected to grow by 7.1 per cent. To achieve that, the economy must grow quite substantially by over seven per cent next quarter.
Diversified funds hold on to cash
Diversified equity schemes seem to prefer cash over equity. According
to ICRA online data, some schemes have close to 60% of their total
assets under management (AUM) in cash.
Industry experts say that most funds are sitting on cash anywhere
between 10-15% of AUM, as the market continues to move in a narrow
range. ``Our analysis shows that the cash portion of the portfolio has
been growing steadily in the last few months,'' says a mutual fund
analyst.
Why are funds sitting on cash, when they can buy shares cheaply and
maximise returns for their investors? ``A fund could be sitting on
high cash levels for a variety of reasons, including waiting for the
correct entry point to negative or range-bound market view. In case of
NFOs, the fund may also be in the deployment mode,'' explains Sameer
Kamdar, ceo, Asset Management, ASK Investment Holdings. ``People don't
think the market will move up sharply soon. In such a situation, they
prefer to stay on cash,'' says Waquar Naquvi, ceo, Taurus Mutual Fund.
``Most fund houses are sitting on cash up to 15-30%.''
Another reason why some of the schemes may prefer to sit on cash is
because of the nature of their investment. ``When you are running a
small or a mid-cap scheme or a scheme looking for new opportunities,
you will have to keep some cash aside. Especially in a market like
this, the cash could come in very handy,'' says an MF manager, who
doesn't want to be named. ``Also, some funds try to show better
performance by sitting on cash, as most funds are in the negative
territory.''
However, Naqvi points out that extremely high cash element in the
portfolio won't work over a long period of time. ``It should be a
short term strategy. If you keep extremely high percentage of cash,
then you won't qualify as an equity MF for the tax purpose. And the
investors would suffer,'' he says. An equity fund should have to
invest at least 65% of its portfolio in stocks to qualify for the long
term tax-free capital gains status.
So, what exactly is the ideal percentage of cash in a portfolio? Some
fund managers believe 5% cash is ideal, but they point out that ideal
percentage works in an ideal market. ``There is no ideal percentage of
cash one should have in the portfolio. It all depends on the style and
the view of the fund manager,'' says Kamdar. ``MNCs may have such
figures, but Indian companies don't stick such rules,'' points out
Naqvi.
Source: http://timesofindia.indiatimes.com/Business/India-Business/Diversified-funds-hold-on-to-cash/articleshow/4198045.cms
US Budget Fine Print -- Reasons For Taxing The Rich
Fine Print From US budget
"While middle-class families have been playing by the rules, living up to their responsibilities as neighbors and citizens, those at the commanding heights of our economy have not. They have taken risks and piled on debts that while seemingly profitable in the short-term, have now proven to be dangerous not only for their individual firms but for the economy as a whole. With loosened oversight and weak enforcement from Washington, too many cut corners as they racked up record profits and paid themselves millions of dollars in compensation and bonuses. There's nothing wrong with making money, but there is something wrong when we allow the playing field to be tilted so far in the favor of so few."
In 2009 and 2010, it doesn't see any income from this provision, although we can't tell on first quick read if that's because the taxes wouldn't kick in until later or if it's an acknowledgment that there won't be much in the way of profits in the private equity and hedge fund industries for the next couple of years.
In 2011, the budget says this change would decrease the deficit by $2.74 billion. That would accelerate to $4.35 billion in 2012. For the period from 2010 to 2019 in total, the budget foresees a total decrease to the deficit of $23.89 billion from this change.On Rupees & Won:
The Rally That Wasn't Having counted five waves down in many markets, I made some general comments about the different types of rallies that markets makes off of lows. That most markets did in fact pause their declines for the past two days following that five-wave move bolsters our belief that we are likely fairly close to being on-the-mark with our analysis of the current positions of these markets within the wave counts. And yet, all that these markets could muster following the five waves down was to pause and look around and mount feeble rallies with shallow retracements. By all accounts, based on this price action and wave structure, most markets must still be in their fifth waves to the downside.
On Rupees & Won:
We haven't looked at the Indian rupee in a few weeks, so it's appropriate to get caught up with this market. I'm showing the Korean won versus the dollar as well, as these charts and wave counts are very similar. The move up in both the rupee and the won look like fifth waves in progress. Our prior wave count in the rupee considered that the fourth wave was ongoing, but recent action demands placing (4) at the December low, which was a perfect retracement to the previous 4 low in November. Both the rupee and the won are now right at the levels of their November highs. The charts show the dollar's appreciation versus these currencies. Noting that these charts are on log scale, the slide in both the rupee and the won is accelerating in a climactic fifth wave. We'll keep an eye on these two markets going forward, but the near-term prognosis for the health of these currencies is not good, since this fifth wave does not appear to be nearly complete by any possible wave count.
More blood to spill -- JPMorgan to cut 12,000 jobs as it folds in Washington Mutual operations, sees savings of $2B
JPMorgan to cut 12,000 jobs as it folds in Washington Mutual operations, sees savings of $2B
NEW YORK (AP) -- JPMorgan Chase & Co. said Thursday it will eliminate about 12,000 jobs as it folds in the operations of Washington Mutual Inc.
According to slides on the company's Web site from an investor day presentation, the New York-based bank expects about $2 billion in net savings to be achieved through the acquisition, the majority of which will be realized by the end of this year. This includes about $1.35 billion related to the job cuts, the bank said.
Shares soared $2.12, or 9.8 percent, to $23.85 in morning trading.
JPMorgan acquired the assets of Seattle-based WaMu, the largest bank ever to fail in U.S. history, at the end of September. The purchase added massively to JPMorgan's consumer banking business and helped the company book a $1.1 billion gain in the fourth quarter.
However, analysts and investors have been worried that corroding loans, particularly soured mortgages, inherited from WaMu could mar JPMorgan's results going forward.
Assuming a 36 percent peak-to-trough decline in home prices, the bank expects remaining lifetime losses on WaMu's home lending portfolio to be $32 billion to $38 billion. The bank said it has not yet experienced losses beyond initial expectations. However, if delinquencies and losses did increase more than expected, the bank would need to add to loan loss reserves.
The bank sees $1 billion to $1.4 billion in quarterly losses from noncredit impaired home equity loans this year. Home equity losses are expected to level off in 2010, but will likely remain high, JPMorgan said.
Meanwhile, quarterly losses among subprime mortgage loans could be as high as $375 million to $475 million over the next several quarters, JPMorgan said.
Retail Financial Services Chief Executive Charlie Scharf said there are early signs of stabilization in the troubled California housing market. Discounts on the appraised value of properties have declined compared with year-ago figures, and sales are being completed at a faster rate. Florida, however, has yet to exhibit any positive trends. Losses in the New York market are also expected to rise.
In its credit card segment, JPMorgan expects losses from the WaMu portfolio to approach 15 percent in the first quarter. The bank expects its total credit card loss rate to edge up to 7 percent.
Among its commercial business, JPMorgan said its construction and development portfolio is the greatest area of concern, with losses expected to rise through 2010.
The bank also anticipates waning demand for commercial loans as businesses borrow less for expansion projects amid the worsening economy.
On a positive note, JPMorgan said it has been able to stabilize WaMu deposits. Since taking over operations on Sept. 25 through Feb. 13, WaMu deposits have increased by $500 million. This follows the withdrawal of $15 billion in deposits during the two weeks in September after the bankruptcy filing of Lehman Brothers Holdings Inc., which led to the bank's failure.
Earlier this week, JPMorgan announced plans to slash its quarterly dividend to 5 cents per share from 38 cents in an effort to preserve capital.
Chief Executive Jamie Dimon said the cut was a precautionary move to ensure that the company has financial flexibility should economic conditions worsen. The move will save the company about $5 billion per year.
Dimon said he is not predicting, but is ready for: A recession lasting two years, a U.S. unemployment rate above 10 percent, and a 40 percent peak-to-trough decline in home prices.
Dimon expects the bank to be profitable throughout 2009, and said the bank is on track to report first-quarter earnings roughly in line with analyst expectations.
Analysts surveyed by Thomson Reuters, on average, forecast earnings of 33 cents per share for the first quarter.
JPMorgan has yet to post a quarterly loss during the financial meltdown that began in 2007, when mortgage defaults started spiking. The bank in January reported a modest fourth-quarter profit of $702 million -- thanks mostly to its purchase of Washington Mutual.
JPMorgan on Monday said it expects first-quarter markdowns of about $2 billion in its investment bank -- less than the $2.9 billion marked down in the fourth quarter. The New York-based bank also anticipates write-downs of approximately $400 million in its private equity business.
JPMorgan, like San Francisco-based rival Wells Fargo & Co., has received $25 billion in government aid. Weaker competitors Citigroup Inc. and Bank of America Corp. have each gotten $45 billion in government support.
Fwd: Bankrupt Canadian telecom equipment maker Nortel to cut 3,200 more jobs
Bankrupt Canadian telecom equipment maker Nortel to cut 3,200 more jobs
NEW YORK (AP) -- Bankrupt telecom equipment company Nortel Networks Corp. plans to cut its work force by 3,200 jobs worldwide.
The Canada-based telecom equipment maker said Wednesday the new round of job cuts will be made over the next several months. The reduction is on top of 1,800 job cuts already announced.
Nortel filed for creditor protection Jan. 14 in Canada and the United States.
President and Chief Executive Mike Zafirovski said tough decisions are being made to restructure the company and work towards a successful emergence from creditor protection.
Nortel currently employs about 30,000 people around the world, including 5,800 at Canadian operations in Ottawa and Toronto.
The company also said its board has approved management's recommendation to eliminate bonuses for 2008. Nortel Networks Corp. says it is seeking Canadian court approval to end its equity-based compensation plans.
Facing a sharp drop in orders from phone companies, Nortel used the bankruptcy filings to buy time to explore restructuring options like selling off assets.
During the 1990s telecom and Internet boom, Nortel had more than 95,000 employees. At one point in 2000 it accounted for one-third of the market value on the entire Toronto Stock Exchange.
After the dot-com bust, Nortel had problems of its own: an accounting crisis that sparked shareholder lawsuits, regulatory investigations and the firing of key executives, including CEO Frank Dunn.
In time of recession - Govt clears higher DA for central staff of 22%
Central government employees and pensioners will get an additional 6 per cent dearness allowance from next month, putting a burden of over Rs 6,000 crore (Rs 60 billion) on the exchequer.
Employees and pensioners will get the additional DA with retrospective effect from January one this year, as per a decision cleared by the Cabinet Committee on Economic Affairs (CCEA) in Delhi.
The revised DA would be 22 per cent, payable from next month's salary, against 16 per cent at present, Home Minister P Chidambaram told reporters in Delhi.
The combined burden of the increased DA to employees and the retired will be Rs 6,020 crore (Rs 60.20 billion) from January 2009 to February 2010, he said.
However, the exchequer will take a hit of Rs 5,159 crore (Rs 51.59 billion) for the full year, Chidambaram said.
The decision to increase DA was taken because of the rise in the consumer price index for industrial workers. DA is revised twice a year, from January one and July 1, payable in salaries of March one and September 1, respectively.
The additional financial implication on account of the increase in DA to central government employees (excluding pensioners) would be Rs 4,100 crore (Rs 41 billion) for 14 months from January one this year.
The rise in allowance, called dearness relief, to pensioners will cost the exchequer Rs 1,920 crore (Rs 19.20 billion) during the period.
However, for a full year, the implication would be Rs 3,514 crore (Rs 35.14 billion) because of revised DA for employees and Rs 1,645 crore (Rs 16.45 billion) for pensioners.
Central government employees and pensioners got a hike in salaries from September after the government approved the Sixth Pay Commission's report with some modifications.
http://www.rediff.com/money/2009/feb/26govt-clears-higher-da-for-central-staff.htm
26-Feb-09 Mcx Settlement Prices
Today's Settlement Prices :: Gold 15248 :: Silver 21538 :: Copper 174 :: Lead 51.85 :: Nickel 502.60 :: Zinc 56.25 :: Crude Oil 2232 :: Natural Gas 206.90 :: Crude Palm Oil 286.30 :: Heating Oil 64.75 :: Soya Oil 452.35 & INR 50.47
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Thursday, February 26, 2009
Diamond Cables To Issue Bonus Shares
Diamond Cables Ltd has informed BSE that a meeting of the Board of Directors of the Company will be held on March 06, 2009, inter alia, to consider the issue of the Bonus Shares.
US Current financial crisis vs. Japan's 1990's crisis
US Current Financial Crisis versus Japan's 1990s Crisis
While explaining his Financial Stability Plan, president Obama pointed to the dangerous consequences of not acting in a timely manner giving what happened in Japan in the 1990s, "where they did not act boldly and swiftly enough, and as a consequence they suffered what was called the "lost decade" where essentially for the entire '90s they did not see any significant economic growth." Treasury Secretary Tim Geithner also pointed that the current Plan is guided by the lessons of financial crisis throughout history mentioning that Japan crisis in the 90s lasted longer and caused greater damage because government applied the brakes too early, and the US current Administration cannot make that mistake.
The comparison between the current crisis and Japan's 90s crisis is fairly true in terms of the origins as both were initiated by wide spreading of reckless lending on the assumption that real estate prices would continue to go up, while it sharply reversed down triggering the financial market turmoil. Also the Japanese authorities took a long time until 1998 to begin cleaning up and recapitalizing its banking sector's assets.
However, a major difference in the initiation of the crises is that in Japan, corporate borrowing soared before the burst against the collateral of rising shares and commercial property prices, while in the US crisis, residential debt that much of the complicated mixture of debt products originated from has been the cause of the problem. Cleaning the balance sheet of the corporate sector is much easier than cleaning the balance sheet of the household sector, that's beside the difficulty of justifying using taxpayers money to bail out homeowners who bought mortgages they could not afford. Actually, not only the origin of debt is different, but also the size of debt. In Japan, the size of debt during the crisis reached 52% of the GDP (10% of the GDP for household), while it reached 294% of the GDP in US, almost 100% of the GDP belongs to the household sector. Additionally, the size of banks' holdings of troubled assets (in absolute terms), reached to $910 billion in Japan, or 35% of the GDP, while it amounted in US to $5.7 trillion or 40% of the GDP (according to Goldman Sacks estimates). The US banks forced to write down securitized products promptly due to mark-to-market accounting, while Japanese banks in 90s were allowed to take time to dispose their nonperforming loans since their lending assets were largely not tradable. The IMF recently estimated the size of write downs in US by $2.2 trillion.
Finally, as much as the nature of the economy is different (though macroeconomic indicators are not) between US and Japan in terms of debt and saving ratios, the scope of the crisis creates different implications. Japan's 90's crisis was an internal one, and through exports Japan could pass the economic recession as global demand remained solid. The current crisis started in US but spread globally, demand for good and services and capital availability thinned worldwide, which needs demand to be motivated from inside US. The US started earlier in its efforts to overcome the crisis. The last $787 billion Financial Stability Plan takes most of the above facts into account, it allocates large sums to boost demand through government expenditures, and it allocates other part of the fund to clean the balance sheet of the banking sector. It also attempts to improve the affordability of mortgages by providing access to low-cost refinancing and provide incentives for loan modification for mortgages already owned or guaranteed by Freddie Mac or Fannie Mae. The majority of subprime mortgages were not of this type, thus, the most in need mortgage debtors do not qualify.
Why u must invest in gold now?
For centuries gold has been the ultimate cushion.
A way to safeguard your investments against the dangers of stocks price falls, fluctuating rate changes, inflation, rising/falling real estate prices, natural calamities, wars and more.
If ever there was a more testing time for gold to prove its mettle, it is now: during a global recession.
The million dollar question today is: will gold retain its sheen in the year 2009, the year of reckoning? Nearly everyone has been breaking their heads trying to figure out the safest investment avenue this year.
In India the stock markets are low, interest rates have come down and elections are round the corner. Is gold the obvious investment destination in such a scenario?
What moves gold prices
Let us first try and understand the factors affecting gold prices before we decide whether we should invest in it.
Tightening of gold supply
Gold mining is decreasing and the demand for gold is increasing. Gold supply has decreased by almost 40 per cent as the cost of mining, legal formalities and geographical problems have increased which has led to a fall in gold mining. Economics have taught us that lesser the supply, greater the demand and in turn greater the increase in price.
Inflation and interest rates
Gold has always been considered a good hedge against inflation. Rising inflation rates typically appreciates gold prices. It has an inverse relationship with interest rates. As gold is pegged to the US dollar, US interest rates affect gold prices. Whenever interest rates fall, gold prices increase. Lowering interest rates increases gold prices as gold becomes a better investment option vis-Ã -vis debt products that earn lower interest. Gold loses its shine in a rising interest rate scenario.
Currency fluctuation
As gold is pegged to the US dollar, it has an inverse relationship with the dollar. Right now with US being in great financial turmoil, the dollar has weakened against many other currencies. Dollar is expected to weaken further and prices of gold are expected to rise further. Dollar is a de-facto currency of exchange around the world. But now with US on the brink of depression, gold is substituted as a safe haven for investments. Though dollar seems to be getting stronger, it may be a temporary effect and very soon it can head southwards once again, in turn making gold an attractive and safe investment.
Geo-political concerns
Whenever there is geo-political strife, investors around the world rush to prevent erosion of their investments and gold as a safe haven attracts one and all. For example after 9/11 terror strike in the United States the demand for gold had increased. With the recent events like tension between India-Pakistan, Israeli strikes over Gaza, the ongoing war in Iraq, the tension between US and Iran coupled with recession have investors scrambling for gold.
Central bank demand
With the dollar losing its value, central banks of most of the developed countries have started to increase their share of gold. This explains the increasing market demand for gold.
Weakness in financial markets
General rule of thumb in the market is that gold is always attractive when all other investments are unattractive. Why is this? As gold is negatively co-related to stocks, bonds, and real estate, gold is considered to be a safe haven and hence during any crises, investors would like to sell off what they would term as risky investments and be invest the funds in gold.
Why you should invest in gold
You might be wondering why gold is termed as safe haven. Gold is the most liquid asset in the world. If you wish to sell a gold bar or gold coin of 99.9 purity or 99.5 purity anywhere in the world, you will get its due worth without the risk attached to other investments like exchange rate risk.
While investing in gold the first question you must ask yourself is whether you should buy at these levels or wait for a while for the prices to come down even further?
As for the future of gold in 2009 and further to come 2009 may see gold touching $1,000 or more an ounce and then again settling in the range of $800 to $900 as banks from around the world would sell their gold stocks once it ouches $1,000. But gold will stay bullish till the time the current financial crisis prevails.
Financial crisis or no financial crisis, investing 5 percent of your portfolio in gold is always advisable. Gold is a good form of investment for diversification. Remember one thing, just like any other investments; space out your investments in gold. Do not buy at one go; be it gold bars, gold coins, or units on gold exchange traded funds (ETFs). Invest regularly and diligently.
Jewellery or gold bars?
Here are a few options that an investor in gold can consider.
Jewellery
As an investment avenue, gold jewellery is a BIG NO. Investing in jewellery is not investment as jewellery is generally not made from 24 carat gold; it is generally made from 22 carat or 18 carat gold since 24-carat gold is brittle and cannot be set to beautiful designs of jewellery as being brittle it can break easily.
The main drawback here is that you have to pay for the making charges. If and when you want to liquidate the money invested in jewellery, your making charges are cut and you end up getting less than what you had invested.
Gold bars or coins
YES.
Investment in gold should be either in gold coins or bars. Remember to buy government-certified gold coins or bars and preferably the purity level should be 99.9 as they are easy to sell. Also, be sure of the place from where you are buying the coins. But preferably from banks.
Gold exchange traded funds
This is the latest buzz. It is like buying gold but in indirect form. You invest money in a gold fund, which in turn invests your money in the physical form of gold. These funds are open during the new fund offer and for buying or selling them they are listed on the stock exchanges.
When you want to redeem the units, you can go to the fund house and get them converted to either physical gold bars or cash. This form of investment is good for people who have storage problem like no bank lockers and also for spacing out your investment in gold. This way you can take advantage of gold price volatility.
Also, wealth tax does not come into picture if you invest through ETFs.
There are other forms of investments in gold like derivatives, spread betting, certificates and others. But for retail investors gold ETFs or physical gold bars and coins are the best forms of investment.
The only drawback of investing in gold is that it is a sterile investment: it neither pays any dividend nor interest.
Danske Bank: This Looks Like A Meltdown
Market update
Overnight the sell-off in Central and Eastern European markets accelerated further after Moody
.s announced that it may downgrade Swedish and Austrian banks with exposure to Central and Eastern Europe. This move will further exacerbate concerns over the state of CEE economies and markets. The region
We think it unlikely that local authorities will be able to muster sufficient resources to curb the sell-off in CEE currencies in the present environment. To us this looks like a market meltdown on the same scale as occurred during the Asian crisis of 1997. No currency in the region will be unaffected by this.
Doubtless the markets have decided that the CEE region is the subprime area of Europe and now everybody is running for the door. In addition, it is not just regional currencies that are under pressure. Stock markets are also being sold off with yields and rates beginning to rise significantly. Furthermore, Credit Default Swaps have risen dramatically across the region during the last couple of weeks.
Macro economic implications Continuing aggressive
Foreign direct investment in the region is also likely to plummet.
The reversal of both portfolio flows and FDI will undoubtedly have a serious negative impact on regional investments. Furthermore, with many households and corporations highly exposed to FX loans (especially in Swiss francs) debt servicing costs are rising dramatically. This is likely to have a substantial negative effect on private consumption, especially in Poland, Hungary and Romania.
In our view GDP growth is like to be negative in
all CEE countries this year. In those countries .least. affected by the crisis (i.e. Poland, the Czech Republic, Slovakia and Slovenia) GDP is like to drop at least 2-5%, while those countries worst affected (i.e. the Baltic States, Bulgaria, Romania and Ukraine) are likely to face double digit declines in GDP. In other words, in terms of expected output lost in the region this is as bad as or even worse than the Asian crisis of 1997-98.LIC ups stake in ICICI Bank to 9.38% - Business Line
LIC ups stake in ICICI Bank to 9.38% - Business Line
MUMBAI: Life Insurance Corporation of India has hiked its stake to 9.38 per cent in the country's largest private sector lender, ICICI Bank, after purchasing shares worth Rs 145.62 crore through open market transaction.
In a disclosure on the Bombay Stock Exchange, ICICI Bank said LIC has purchased over 2.27 crore shares for Rs 145.62 crore, representing 2.04 per cent stake. Before the purchase, LIC held 7.34 per cent stake, while now it has over 10.44 crore shares repr esenting 9.38 per cent stake in the bank.
Shares of ICICI Bank were trading at Rs 232.90, down 3.59 per cent on the BSE on Tuesday. - PTI
Recipe for Disaster: The Formula That Killed Wall Street
Recipe for Disaster: The Formula That Killed Wall Street
By Felix Salmon
A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li's work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.
For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.
His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.
Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li's formula hadn't expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.
David X. Li, it's safe to say, won't be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li's Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.
How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.
A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there's always some risk—the higher the interest rate the bond must carry.
Bond investors are very comfortable with the concept of probability. If there's a 1 percent chance of default but they get an extra two percentage points in interest, they're ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.
Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There's no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There's certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there's no easy way to assign a single probability to the chance of default.
Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.
The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don't affect the mortgage pool much as a whole: Everybody else is still making their payments on time.
But not all calamities are individual, and tranching still hadn't solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there's a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there's a higher probability they will default, too. That's called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.
Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.
Yet during the '90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you're talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.
To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let's call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.
But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney's parents get divorced, what are the chances that Alice's parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.
If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.
But it's a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.
In the world of mortgages, it's harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation's macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?
Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master's degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master's in actuarial science and a PhD in statistics, both from Ontario's University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.
Li's trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street's ever more complex investment structures.
In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled "On Default Correlation: A Copula Function Approach." (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.
If you're an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn't constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li's paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.
When the price of a credit default swap goes up, that indicates that default risk has risen. Li's breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It's hard to build a historical model to predict Alice's or Britney's behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice's and Britney's default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).
It was a brilliant simplification of an intractable problem. And Li didn't just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.
The effect on the securitization market was electric. Armed with Li's formula, Wall Street's quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li's copula approach meant that ratings agencies like Moody's—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.
As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn't matter. All you needed was Li's copula function.
The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.
At the heart of it all was Li's formula. When you talk to market participants, they use words like beautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.
"The corporate CDO world relied almost exclusively on this copula-based correlation model," says Darrell Duffie, a Stanford University finance professor who served on Moody's Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world's financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. "Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus," wrote derivatives guru Janet Tavakoli in 2006.
The damage was foreseeable and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that "the correlations between financial quantities are notoriously unstable." Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn't alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn't perfect. Li's approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford's Duffie and ask him to come in and talk to them about exactly what Li's copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.
In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn't understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.
In finance, you can never reduce risk outright; you can only try to set up a market in which people who don't want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn't have any risk at all, when in fact they just didn't have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.
Li's copula function was used to price hundreds of billions of dollars' worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.
Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.
"Everyone was pinning their hopes on house prices continuing to rise," says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. "When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn't rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO."
Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?
They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.
"The relationship between two assets can never be captured by a single scalar quantity," Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It's impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.
No one knew all of this better than David X. Li: "Very few people understand the essence of the model," he told The Wall Street Journal way back in fall 2005.
"Li can't be blamed," says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.
Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. "People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked," he says. "Co-association between securities is not measurable using correlation," because past history can never prepare you for that one day when everything goes south. "Anything that relies on correlation is charlatanism."
Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn't talk without permission from the PR department. In response to a subsequent request, CICC's press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.
In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.
As Li himself said of his own model: "The most dangerous part is when people believe everything coming out of it."
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