Mahesh Pareek

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The Black Swan

Posted by maheshpareek on August 27, 2009

The Black Swan: Quotes & Warnings that the Imbeciles Chose to Ignore

Nassim Nicholas Taleb: The Black Swan: The Impact of the Highly Improbable (April 2007)

For the last 12 years, I have been telling anyone who would listen to me that we are taking huge risks and massive exposure to rare events. I isolated some areas in which people make bogus claims –epistemologically unsound. The Black Swan is a philosophy book (epistemology, philosophy of history & philosophy of science), but I used banks as a particularly worrisome case of epistemic arrogance –and the use of “science” to measure the risk of rare events, making society dependent on very spurious measurements. To me a banking crisis –worse than what we have ever seen — was unavoidable and NOT A BLACK SWAN, just as a drunk and incompetent pilot would eventually crash the plane. And I kept receiving insults for 12 years!

Quotes From the Black Swan (written b. 2003-2006) that the IMBECILES did not want to hear

Globalization creates interlocking fragility, while reducing volatility and giving the appearance of stability. In other words it creates devastating Black Swans. We have never lived before under the threat of a global collapse. Financial Institutions have been merging into a smaller number of very large banks. Almost all banks are interrelated. So the financial ecology is swelling into gigantic, incestuous, bureaucratic banks – when one fails, they all fall. The increased concentration among banks seems to have the effect of making financial crises less likely, but when they happen they are more global in scale and hit us very hard. We have moved from a diversified ecology of small banks, with varied lending policies, to a more homogeneous framework of firms that all resemble one another. True, we now have fewer failures, but when they occur ….I shiver at the thought.

Banks hire dull people and train them to be even more dull. If they look conservative, it’s only because their loans go bust on rare, very rare occasions. But (…)bankers are not conservative at all. They are just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug.
The government-sponsored institution Fannie Mae, when I look at its risks, seems to be sitting on a barrel of dynamite, vulnerable to the slightest hiccup. But not to worry: their large staff of scientists deemed these events “unlikely”.

There is no way to gauge the effectiveness of their lending activity by observing it over a day, a week, a month, or . . . even a century!
(…) the real- estate collapse of the early 1990s in which the now defunct savings and loan industry required a taxpayer-funded bailout of more than half a trillion dollars. The Federal Reserve bank protected them at our expense: when “conservative” bankers make profits, they get the benefits; when they are hurt, we pay the costs.
Once again, recall the story of banks hiding explosive risks in their portfolios. It is not a good idea to trust corporations with matters such as rare events because the performance of these executives is not observable on a short-term basis, and they will game the system by showing good performance so they can get their yearly bonus. The Achilles’ heel of capitalism is that if you make corporations compete, it is sometimes the one that is most exposed to the negative Black Swan that will appear to be the most fit for survival.
As if we did not have enough problems, banks are now more vulnerable to the Black Swan and the ludic fallacy than ever before with “scientists” among their staff taking care of exposures. The giant firm J. P. Morgan put the entire world at risk by introducing in the nineties RiskMetrics, a phony method aiming at managing people’s risks, causing the generalized use of the ludic fallacy, and bringing Dr. Johns into power in place of the skeptical Fat Tonys. (A related method called “Value-at-Risk,” which relies on the quantitative measurement of risk, has been spreading.)

Please, don’t drive a school bus blindfolded.

Owing to [...] misunderstanding of the causal chains between policy and actions, we can easily trigger Black Swans thanks to aggressive ignorance—like a child playing with a chemistry kit.

Ten principles for a Black Swan-proof worldBy Nassim Nicholas Taleb
Published: April 7 2009 20:02 | Last updated: April 7 2009 20:02

1. What is fragile should break early while it is still small. Nothing should ever become too big to fail. Evolution in    economic life helps those with the maximum amount of hidden risks – and hence the most fragile – become the biggest.
2. No socialisation of losses and privatisation of gains. Whatever may need to be bailed out should be nationalised; whatever does not need a bail-out should be free, small and risk-bearing. We have managed to combine the worst of capitalism and socialism. In France in the 1980s, the socialists took over the banks. In the US in the 2000s, the banks took over the government. This is surreal.
3. People who were driving a school bus blindfolded (and crashed it) should never be given a new bus. The economics establishment (universities, regulators, central bankers, government officials, various organisations staffed with economists) lost its legitimacy with the failure of the system. It is irresponsible and foolish to put our trust in the ability of such experts to get us out of this mess. Instead, find the smart people whose hands are clean.
4. Do not let someone making an “incentive” bonus manage a nuclear plant – or your financial risks. Odds are he would cut every corner on safety to show “profits” while claiming to be“conservative”. Bonuses do not accommodate the hidden risks of blow-ups. It is the asymmetry of the bonus system that got us here. No incentives without disincentives: capitalism is about rewards and punishments, not just rewards.
5. Counter-balance complexity with simplicity. Complexity from globalisation and highly networked economic life needs to be countered by simplicity in financial products. The complex economy is already a form of leverage: the leverage of efficiency. Such systems survive thanks to slack and redundancy; adding debt produces wild and dangerous gyrations and leaves no room for error. Capitalism cannot avoid fads and bubbles: equity bubbles (as in 2000) have proved to be mild; debt bubbles are vicious.
6. Do not give children sticks of dynamite, even if they come with a warning . Complex derivatives need to be banned because nobody understands them and few are rational enough to know it. Citizens must be protected from themselves, from bankers selling them “hedging” products, and from gullible regulators who listen to economic theorists.
7. Only Ponzi schemes should depend on confidence. Governments should never need to “restore confidence”. Cascading rumours are a product of complex systems. Governments cannot stop the rumours. Simply, we need to be in a position to shrug off rumours, be robust in the face of them.
8. Do not give an addict more drugs if he has withdrawal pains. Using leverage to cure the problems of too much leverage is not homeopathy, it is denial. The debt crisis is not a temporary problem, it is a structural one. We need rehab.
9. Citizens should not depend on financial assets or fallible “expert” advice for their retirement. Economic life should be definancialised. We should learn not to use markets as storehouses of value: they do not harbour the certainties that normal citizens require. Citizens should experience anxiety about their own businesses (which they control), not their investments (which they do not control).
10. Make an omelette with the broken eggs. Finally, this crisis cannot be fixed with makeshift repairs, no more than a boat with a rotten hull can be fixed with ad-hoc patches. We need to rebuild the hull with new (stronger) materials; we will have to remake the system before it does so itself. Let us move voluntarily into Capitalism 2.0 by helping what needs to be broken break on its own, converting debt into equity, marginalising the economics and business school establishments, shutting down the “Nobel” in economics, banning leveraged buyouts, putting bankers where they belong, clawing back the bonuses of those who got us here, and teaching people to navigate a world with fewer certainties.

Then we will see an economic life closer to our biological environment: smaller companies, richer ecology, no leverage. A world in which entrepreneurs, not bankers, take the risks and companies are born and die every day without making the news.

In other words, a place more resistant to black swans.

The writer is a veteran trader, a distinguished professor at New York University’s Polytechnic

The World According to Nassim Taleb
Nassim Taleb combines broad theoretical knowledge with practical experience. Although he is fiercely outspoken, he delivers his challenges to conventional wisdom with a gentle delivery that carries a trace of a French accent. He is one of the world’s leading quantitative traders, who has held senior options trading positions at Union Bank of Switzerland, Bankers Trust, Banque Indosuez and CS First Boston. His most recent job in the dealer community was head of global options at CIBC Wood Gundy. In 1991 he suddenly left Wall Street to spend two years as a local on the floor of the Chicago exchanges. Taleb reads classical literature, speaks seven languages and holds a Wharton MBA. He is a PhD candidate at the University Paris-Dauphiné, where he will soon defend his dissertation on option pricing. He is also the author of the book Dynamic Hedging: Managing Vanilla and Exotic Options (Wiley, 1996). Taleb was interviewed in November by editor Joe Kolman.
Derivatives Strategy: What problems do you have with financial engineering?
Nassim Taleb: I disagree with such an approach in financial risk management. Some people looked at the literature and saw differential equations and said, “Gee, it’s like engineering.”
Engineering relies on models because you can capture the relationships in the physical world very well. Models in the social sciences serve a different purpose. They make strong assumptions. Economists have known for a long time that math in their profession has a different meaning. It’s just a tool, a way to express yourself.
DS: So real engineering could lead to a bridge that you could reliably drive cars across. But modeling in financial engineering isn’t certain enough to run a portfolio …
NT: Exactly. In finance, you are not as confident about the parameters. The more you expand your model by adding parameters, the more you become trapped in an inextricable apparatus of relationships. It is called overfitting.
DS: What do you think of value-at-risk?
NT: VAR has made us replace about 2,500 years of market experience with a co-variance matrix that is still in its infancy. We made a tabula rasa of years of market lore that was picked up from trader to trader and crammed everything into a co-variance matrix. Why? So a management consultant or an unemployed electrical engineer can understand financial market risks.
To me, VAR is charlatanism because it tries to estimate something that is not scientifically possible to estimate, namely the risks of rare events. It gives people misleading precision that could lead to the buildup of positions by hedgers. It lulls people to sleep. All that because there are financial stakes involved.
To know the VAR you need the probabilities of events. To get the probabilities right you need to forecast volatility and correlations. I spent close to a decade and a half trying to guess volatility, the volatility of volatility, and correlations, and I sometimes shiver at the mere remembrance of my past miscalculations. Wounds from correlation matrices are still sore.
DS: Proponents of VAR will argue that it has its shortcomings but it’s better than what you had before.
NT: That’s completely wrong. It’s not better than what you had because you are relying on something with false confidence and running larger positions than you would have otherwise. You’re worse off relying on misleading information than on not having any information at all. If you give a pilot an altimeter that is sometimes defective he will crash the plane. Give him nothing and he will look out the window. Technology is only safe if it is flawless.
A lot of people reduce their anxiety when they see numbers. They want a triple-digit delta or gamma, for example, not taking into account that it is foolish to be precise with deltas when you don’t even know the parameters.
Before VAR, we looked at positions and understood them using what I call a nonparametric method. After VAR, all we see is numbers, numbers that depend on strong assumptions. I’d much rather see the details of the position itself rather than some numbers that are supposed to reflect its risks.
Clearing firms understood that very well. Ironically, the stock market crash coincided with the discovery of this so-called parametric system used to run the risks of option traders. In the old days the clearing firms looked at how many calls you were short and how many you were long, and if you sold a lot of calls they would get nervous and call you up and ask you to liquidate some of them. After they went to parametric monitoring of option positions using second-rate statistical methods, the options traders started building up massive short put positions that, along with portfolio insurance, helped to accelerate the crash. Now they’re coming back to square one with their nonparametric methods, particularly with the puts.
DS: Do you think the whole idea of trying to use statistics to model a particular distribution is fraudulent? Or is it possible to come up with something approximating the truth?
NT: The problem we have with statistics is that although we know something about distributions, we know very little about processes. A process is a distribution that has time in it, and things change with time. People look at fat tails and say, “We can simulate distributions with fat tails.” But the reason distributions have fat tails may be because these distributions don’t have stable properties over time.
DS: VAR proponents will also admit that VAR doesn’t work as well on something with an asymmetric payoff.
NT: Yes, but any dynamic trading strategy by a leveraged investor that has a stop loss in it has an asymmetric payoff and needs to be treated like an option. If I trade deutsche mark or bond futures with a stop loss, the frequency of my losses will be greater than the frequency of my profits but the magnitude of my losses will be smaller to compensate. It will look like a payoff of an option, and that’s not captured by VAR. The VAR assumes than traders are stuffed animals between two reports.
DS: Are you saying VAR can’t be used to measure risks on a trading desk?
NT: The risks of common events perhaps, those that do not matter, but not the risks of rare events. Moreover traders will find the smallest crack in the VAR models and try to find a way to take the largest position they can while showing the smallest amount of risk. Traders have incentives to go for the maximum bang because of the free option they’re granted.
DS: What free option is that?
NT: Most institutional traders don’t pay for their losses. If you make a dollar you get paid 10 cents. If you lose a dollar you pay zero. That obviously looks like an option payoff.
So let’s say your trader trades two bonds of slightly different maturities. They’re very close but because they have deceptively close maturities the position will not produce a big VAR number. Sometimes they are treated as the same bond. The position, however, could easily bankrupt the company because of the sheer size that was built on it. An institution just last month lost bundles because a trader built up massive positions in Bunds against German swaps; their system, otherwise sophisticated, did not differentiate between them.
DS: What’s going to happen if everybody in the financial system starts using VAR?
NT: VAR players are all dynamic hedgers and need to revise their portfolios at different levels. As such they can make very uncorrelated markets become very correlated. Those who refuse to learn from the portfolio insurance debacle do not belong in risk management.
In 1993 hedge funds were long seemingly independent markets. The first margin call in the bonds led them to liquidate their positions in the Italian, French and German bond markets. Markets therefore became correlated.
VAR is a school for sitting ducks. Find me a dynamic hedger who is a reluctant liquidator and I will front-run him to near-bankruptcy.
DS: So one problem with VAR models is that they don’t account for the fact that the market corrects for the models that trades are based on?
NT: Bingo. Even more: Our perception of what’s going on in the real world can hurt us simply because we have to realize that we are the major players ourselves and we act according to our perceptions. In physics it’s called the Heisenberg uncertainty principle. In the social sciences its even more pronounced.
When people ask me what alternative to VAR I have to offer, my answer is smaller leverage, less naïve diversification, less reliance on dynamic hedging.
DS: Are all correlations suspect?
NT: You can find a relationship between any two items if you look hard enough. It may be entirely spurious and have no predictive power, but you will find one. To give you an idea, you’ll always find what we call data miners who will show you that there is a 100 percent correlation between his great aunt’s blood pressure and the back-month Nikkei volatility. When you’re a trader you get a lot of calls from people who found relationships that can produce a 10 Sharpe ratio. That means it’s almost impossible to lose money on the trade. Sure enough, when you start trading you realize that the relationship was not there. Trading has fewer biases than statistics.
DS: What are the most common mistakes you see traders and risk managers making?
NT: As a trader, my job is to understand biases and trade on them. There are all kinds of biases. The most common is the small sample bias. Let’s say you have 1:1,000 odds you will come home every day with a dollar and once in a while you lose $1,000. Many traders show very steady incomes but they could be fooling themselves because they don’t have a long enough period to chart their performance. Their Sharpe ratio will not be indicative.
In option trading there is a similar bias. Short premium option traders, typically those who sell out-of-the-money options, are more likely to make money on a daily basis and then blow up. Likewise the yield hogs, those traders who would take any risk for a few basis points. You can fool yourself with your Sharpe ratios, and you can fool all of the financial engineers, but you can’t fool an old Chicago trader who went bankrupt twice.
Another bias is what I call the size bias. If you have 20,000 traders in the market, sure enough you’ll have someone who’s been up every day for the past few years and will show you a beautiful P&L. If you put enough monkeys on typewriters, one of the monkeys will write the Iliad in ancient Greek. But would you bet any money that he’s going to write the Odyssey next? You know that because of the sheer size of the sample, you’re likely to find a lucky monkey once in a while. But the same applies to traders.
A third bias is the survival bias. Everybody will tell you that stock investing is a great idea because it’s been back-tested by some serious guru and if you had bought one share of some stock during the Revolution you would now own the GNP of some banana republic. But you forget that your back-testing is only on stocks that are alive today and does not cover stocks in imperial Russia that a rational investor would have bought at the beginning of the century. Many continental stocks were recycled into wallpaper. When you look at markets you are only looking at the remnants, the parts that have survived. Or take real estate. People always say it goes up. But that works only if you always bought in places that became fancy.
DS: So essentially, you’d like to replace statistical valuation that’s at the center of most derivatives trading with valuation that’s more based on experience.
NT: You learn a lot about valuation from trading with other traders, by seeing what others give you and what they take away from you. What they give is generally worth less, and what they take is worth more. It’s sort of like cars that are lemons. When you buy a lemon, only the seller knows it’s a lemon. You need to drive it for a while to know its a lemon. It’s the same with options. You don’t know an option is a lemon, but you have to assume if someone is selling it you, you have a high probability of it being a lemon. Very often you won’t know the option’s value until you actually manage it for a while. Some options hedge very well and some don’t.
DS: Can you give me an example?
NT: Sure, take upside calls on the S&P. Retail investors tend to sell a lot of higher-strike calls in equity markets and buy a lot of lower-strike puts. You look at the distributions and you assume you’re being compensated with the volatility differential, buying higher-strike calls and selling lower-strike puts. But once you start running it, you will notice that some undetectable behavior makes you lose money on the trade. And your back-testing cannot fully detect that.
It’s more intricate than it seems. It’s not just the volatility of the upside or the downside, it’s the volatility around a particular strike that has a large open interest. We call these “sticky strikes.” The markets tend to compress in variation around these strikes. Good traders can sense that.
Also when you buy a stock warrant on an illiquid stock, you need to take into account that your own dynamic hedging, added to that of other dynamic hedgers, will reduce the volatility and stabilize the stock.
DS: You left a job as the senior options at the Union Bank of Switzerland to go to Chicago and become a floor trader. Why did you leave Wall Street? What did you think you were missing by trading from a screen in New York?
NT: I left Wall Street for the first time in 1991. I was obsessed with price formation. I couldn’t understand from the screen how prices were determined. It took me six months to be able to read prices in the pit. Locals basically read information from the order flow and squeezed the weak party. There’s always a pack of five or six dominating locals who abruptly change the prices, who bid a lot higher than the previous offer and have the guts to do it, and the rest of them follow.
DS: How did that knowledge change the way you trade when you went back to trading from a screen?
NT: It is the most enriching experience for a trader. I learned more about market dynamics in my second six months than from years on a desk. I learned that traders’ income is not the bid-offer spread, but the micro-squeezes that take place. Markets move from squeezes to squeezes. Traders make money on stop losses and other free options. It made me interested with information economics.
DS: You’re not ready to give up on all quantitative techniques. You were trained as an econometrician. You don’t make wild speculative bets and I assume you try to hire traders who have some kind of quantitative skills.
NT: I have the following problem. Anytime I take a street-smart kid with a strong Brooklyn accent and train him or her in quant methods, I develop a wonderful quant trader who knows how to squeeze the sitting ducks. When you take extremely quantitative trainees, particularly from the physical sciences, and try to make them arbitrage traders, they freak out and become pure gamblers. They can’t see the edge, and they become the sitting ducks. The world has too much texture, more than they can squeeze into the framework they’re used to. I see a huge incidence of pure speculative gambling on the part of these people who are hired on the strength of their knowledge of quantitative methods.
DS: How about risk managers? What do you look for in risk managers that you hire?
NT: I try to probe their minds to see what makes them tick. And I start quizzing them quite unfairly about market history. I ask them about what happened to the correlation between bonds and mortgages on the day when the stock market crashed. I quiz them about the gold rally in the early 1980s. I test to see if they intuitively understand squeezes. If they don’t show any interest in data, in any true market history, I stop the interview and send them home. To me it is extremely dangerous to have in such positions people who only trust equations. You can’t get the edge if you learn just from your own mistakes. You need to learn from other people’s mistakes as well and these are public information.
DS: Where do you think research in the financial markets is heading? What’s valuable and what’s not?
NT: Financial economics has been extremely successful at melding the math with economic insight. This is providing traders with better understanding of derivatives pricing. My motto is that the markets follow the path that hurts the highest number of dynamic hedgers. It was exciting to read a mathematical proof of it by Grossman and Zhou in the latest Journal of Finance. We are having less success with the frenetic financial engineering efforts, which have a lot of mathematical acrobatics but a hollow ring.

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What’s Involved In Getting a Ph.D. In Finance?

Posted by maheshpareek on April 6, 2009

This seems to be take your readers to work week — in my last few pieces, I’ve discussed the teaching and research sides of a finance professor’s job and where data for research projects comes from. So, this time, I thought I’d take a step back and talk about what is involved in getting a Ph.D. in finance.

The biggest misconception is that the Ph.D. is something akin to a “super MBA”. In reality, the MBA and Ph.D. programs are almost totally different animals. The MBA is geared towards practitioners. In contrast, the Ph.D. is primarily intended as training to be a researcher. So, the natures of the programs (and therefore their approaches) are distinctly different from each other.

Some doctoral programs require an MBA before entry, but quite a few don’t (like the Unknown Alma Mater). In fact, the best preparation for getting a finance Ph.D. isn’t an MBA – an MS in Finance, an MS in Econ, or a masters in math, engineering, or physics (there’s a LOT of math involved at this level) would probably prepare you better.

In terms of admittance standards, you’ll need a GMAT score in the mid-to-upper 700s to be even considered for admittance at top schools, and even lower ranked schools will probably look for a GMAT well above 600. As a benchmark, I went to a solid “2nd tier” program, and my classmates all had GMATs of between 700 and 760. Of the two component parts of the GMAT, the math score is the more important than the verbal one, and most schools look for someone at the 90th percentile (for lower ranked schools) or better. At top schools, almost everyone has GMAT math scores in the top 1 or 2%.

Probably the best way to describe a Ph.D. level curriculum is to start at the end and work backwards. The final step in getting a Ph.D. is to write a dissertation, which is an original piece of research. In finance, dissertations usually run between 65 pages (the shortest I’ve seen) and 150 pages. The dissertation is supervised by one person (called the chair of the dissertation committee), and must also be approved by a committee of 3-4 other faculty members. There’s a love-hate relationship between Ph.D. students and their dissertation chairs, because a good chair constantly asks the student for “more” – more analysis, better writing, more literature review, etc… Because of this, the dissertation is probably the most exhaustingly thorough piece of research most Ph.D.s will do in their lifes.

Working backwards, to be able to do original research in a dissertation, you must be familiar with what’s already been done on the research question you’re asking. The main way a student gets this familiarity is through “seminars”, which are the backbone of a doctoral program. Seminars are much more self-directed that a typical textbook/instructional class. In a seminar, you may read anywhere between 25 and 100 journal articles during the course of the semester (one of my professors covered almost 120 articles in a 10 week quarter, which was a brutal pace). For example, in a typical 3 hour seminar week, you typically cover anywhere from 3-8 articles. If the article is a theory piece, it will have a good deal of math (calculus, partial differential equations, real analysis, or linear algebra), and if it’s an empirical piece, there will be a good deal of statistics (more math).

In an undergrad or MBA course, the professor usually presents the material. However, in a doctoral seminar, the papers get presented by the students. Each week, one or more students walks the rest of the group through the article (or articles) to be covered, and the professor asks questions (usually from the sidelines). In many of the seminars, you’re also expected to produce a small research piece as part of the seminar. In addition to giving you hands-on experience doing research, one of these small seminar research projects often becomes the basis for an eventual dissertation.

There are a number of different ways to organize the seminar sequence, but typical seminar topics might include Finance Theory, Corporate Finance, Investments, Derivatives, Markets and Institutions, and Empirical Methods.

Most students don’t have the skill set necessary to handle the seminars right off the bat, so the first year (or in my program, about the first 1 1/2 years) is devoted to “foundational” classes. In the case of the program I attended, this involved classes in microeconomic theory (much of finance is nothing more than applied microeconomics), statistics, linear algebra, real analysis, and econometrics.

Since I worked you through the sequence backwards, here’s how it looks going forward (with approximate time ranges). Again, this is based on my program, and others might differ:

* Foundational classes – 1-1/2 years
* Seminars – 1-2 years
* Dissertation – variable, but usually 1-2 years.

Adding it up, the typical time to complete the doctorate is about 4-5 years. The fastest I’ve seen it done at my school was a little over 3 years, and the slowest is about 8 years.

Note: If you’re new to Financial Rounds , welcome. I hope you look around a bit — if you want to find out more about the blog, check out the Frequently Asked Questions (FAQ) page http://financialrounds.blogspot.com/2006/08/financial-rounds-faq.html. And if your want to subscribe to our RSS feed, there are links on the sidebar.

Source http://financialrounds.blogspot.com/2006/07/whats-involved-in-getting-phd-in.html

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BSE NSE Stock up and down

Posted by maheshpareek on March 28, 2009

मैनेजिंग डायेरक्टर, जियोजित फाइनेंशियल सर्विसेज

बैंकों और वित्तीय संस्थानों के दिवालिया होने और उन्हें राहत पैकेज मुहैया कराने के इन दिनों में जिस अर्थशास्त्री कींस की सबसे ज्यादा बात हो रही है उन्होंने एक बार कहा था, ‘आपके वित्तीय रूप से सक्षम रहने के मुकाबले कहीं ज्यादा दिन तक बाजार असंतुलित रह सकते हैं।’ यह सिद्धांत यूं तो मंदडि़यों और तेजडि़यों, दोनों पर लागू होता है, लेकिन इसके तहत जो संदेशा छिपा है, वह स्पष्ट है जिसके बारे में किसी को कोई संदेह नहीं हो सकता। इसके मुताबिक बुद्धिमान निवेशक अगर बाजार के उतार-चढ़ाव से दूर रहने में सफल होता है तो उसका कामयाब होना तय है।

क्या एसेट श्रेणी के तौर पर इक्विटी में निवेश करना चाहिए, इस बात का फैसला शेयर बाजार में उस वक्त जारी गतिविधियों पर निर्भर करना चाहिए। छोटे निवेशक को किसी शेयर विशेष में निवेश करने या उससे बाहर निकलने के लिए सही वक्त का अंदाजा लगाने की कोशिश नहीं करनी चाहिए, लेकिन यह भी कहा जा सकता है कि हर निवेशक इक्विटी बाजारों में दाखिल होने या फिर बाहर निकलने का अंदाजा लगा सकता है।

ऐतिहासिक रूप से शेयर बाजार उस वक्त चरम पर पहुंचते हैं जब ब्याज दरें काफी ऊंचाई पर हों या फिर जरूरत से ज्यादा विश्वास के बूते बाजार संबंधी क्रेडिट के लिए ज्यादा मांग की वजह से उस स्तर पर पहुंचने की संभावनाएं रखती हो। इक्विटी बाजारों के बारे में ज्यादा जानकारी न रखने वाला मेरा एक मित्र है, जो ब्याज दरों के चढ़ने पर इक्विटी में अपना सारा निवेश बेचकर सावधि जमा और इनकम फंड जैसे पारंपरिक निवेश उत्पादों की राह पकड़ता है। इस निवेशक ने इस दशक की शुरुआत में फिक्स्ड इनकम निवेश से इक्विटी का रास्ता पकड़ना शुरू किया था, जब ब्याज दरें काफी निचले स्तरों पर थीं।

इसी निवेशक ने 2008 मध्य में इक्विटी से एफडी की ओर मुड़ना शुरू किया हालांकि वह जनवरी 2008 के दौरान बाजार की ऊंचाइयों का फायदा उठाने में नाकाम रहा। आज फिर यह रक्षात्मक अनुशासित निवेशक मुस्करा रहा है और दलील दे रहा है कि उसे अर्थव्यवस्था की कोई खास जानकारी नहीं है। जिन चीजों ने उसे सही वक्त पर सही फैसले करने के लिए प्रोत्साहित किया है, वह है सामान्य ज्ञान और भावनाओं पर सख्त नियंत्रण।

बाजार में गुजारे 25 साल में मैंने कई बार ब्याज दरों और बाजार के चढ़ने या उतरने के बीच इस रिश्ते पर गौर किया है। मुझे इस निवेशक के पक्ष में खड़ा होने में कोई हिचकिचाहट नहीं है जिसने मार्केट की चाल समझने के लिए बाजार से जुड़ी जानकारी कम और सामान्य ज्ञान का ज्यादा इस्तेमाल किया। और वह भी ऐसे वक्त जब हर निवेशक पर बाजार ‘जानकारी’ की अभूतपूर्व आपूर्ति की बमबारी हो रही थी। अगर ब्याज से होने वाली आमदनी उत्पाद के साथ जुड़े कम जोखिम से ज्यादा है तो निवेशक की जोखिम सहने की क्षमता के आधार इक्विटी से उसकी ओर जाने के अवसर होते हैं।

इसके उलट, मेरा एक खूब पढ़ा-लिखा मित्र भी है जो 10 साल पहले 150 रुपए के स्तर पर होने के वक्त एक बेहतरीन शेयर को पहचानने में कामयाब रहा। 2007 में जब वह 1,500 रुपए तक चढ़ा तो उसने बिकवाली कर मुनाफा वसूली करने की सलाह मिलने के बावजूद भी इंतजार करने का फैसला किया। 2008 में मंदी ने बाजार को घेरा तो यह शेयर नीचे आने लगा और मंदी के बाजार में एक साल से ज्यादा वक्त तक इंतजार करने के बाद इस दोस्त ने थक-हारकर यह शेयर 200 रुपए में बेचा और दावा किया कि वह कम से कम अपनी पूंजी बचाने में कामयाब रहा। यह गलती कई लोग करते हैं। ऐसे निवेशक ज्यादातर बार बढि़या मुनाफे पर बाहर निकलने में नाकाम रहते हैं और शेयर विशेष से भावनात्मक रूप से बंध जाते हैं।

अनुशासित रवैया अपनाकर बाजार चक्र की जानकारी बटोरना काफी आसान है लेकिन अविश्वसनीय जानकारी की जरूरत से ज्यादा सप्लाई की वजह से एक शेयर विशेष चुनना निवेशक के लिए काफी मुश्किल हो जाता है। कई शातिर सट्टेबाज बाजार में गैरकानूनी रूप से फायदा उठाने के लिए जानकारी का गलत फायदा उठाते हैं जिनके जाल में निवेशक आसानी से फंस जाते हैं।

छोटी या मझोली कंपनियों में निवेश को लेकर काफी सावधानी बरतनी चाहिए। ऐसी कंपनियों की बाजार में उपलब्ध जानकारी आमतौर पर गलत या भ्रामक होती हैं। छोटे निवेशकों को इस ग्रुप की केवल उन्हीं कंपनियों में निवेश करना चाहिए जिनके बारे में उन्हें पुख्ता जानकारी है। बहुत से निवेशक लक्ष्यों और अनुशासन के साथ इक्विटी बाजार में प्रवेश तो करते हैं लेकिन बाद में अक्सर वे सट्टेबाजों की तरह ट्रेडिंग करने लगते हैं। एक बात याद रखें कि इक्विटी बाजार में तभी सफलता मिलती है जब आप लालच को छोड़कर सही जानकारी और समय पर निवेश करते हैं।
Source Economictimes

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कैसे जानें बाजार में तेजी शुरू हुई या नहीं?

Posted by maheshpareek on February 27, 2009

बाजार में तेजड़ियों की दिवाली दोबारा कब शुरू होगी ? और उनके जैसे छोटे निवेशक इस बात का पता कैसे लगाएंगे कि दलाल स्ट्रीट पर तेजी के प्रतीक बुल ने दौड़ना शुरू कर दिया है या नहीं। सवाल काफी आसान लगता है लेकिन कई सवाल भी खड़े करता है। वह यह कि बाजार में स्थिति पलटने के शुरुआती संकेत कौन से होते हैं। इस अहम सवाल का जवाब तलाशने में आपकी मदद करने के लिए यह लेख आपको उन तमाम बिंदुओं से रूबरू कराएगा जिनके जरिए आप यह पता लगा सकते हैं कि बाजार में नई तेजी के बीज कहां बोए जा रहे हैं।
रैली की प्रकृति

निवेशक होने के नाते आपकी पहली प्राथमिकता प्रक्रिया के दौरान ही बाजार की रैली की प्रकृति के बारे में जानने की होनी चाहिए। आपको इस बात की जानकारी जुटानी चाहिए कि बाजार की रैली व्यापक है या फिर सेक्टर केंद्रित। आईसीआईसीआई डायरेक्ट के अनूप बागची ने कहा , ‘ आपको यह बात याद रखनी चाहिए कि सेक्टर आधारित रैलियां तेजड़ियों की पकड़ में नहीं बदलतीं , जैसा कि हमने 1992 और 2001 में देखा। दोनों बार बाजार की तेजी क्रमश : पुरानी अर्थव्यवस्था और टेक सेक्टरों पर केंद्रित थी। ‘

निवेशकों को सलाह दी जाती है कि वह रैली के व्यापक चलन पर निगाह डालें ताकि यह पहचान सकें कि सेंसेक्स में उछाल का मतलब वास्तव में यह है भी कि तेजडि़यों की भूमिका बाजार में फिर अहम बन रही है। बागची के मुताबिक कई ऐसे सेक्टर होते हैं जो प्रदर्शन के मामले हर दूसरे क्षेत्र को पीछे छोड़ देते हैं लेकिन इस बेहतरीन प्रदर्शन को बाजार की तेजी नहीं कहा जा सकता और न ही यह लंबे वक्त के लिए जारी रहती है। सेक्टर केंद्रित रैलियां , सामान्य तौर पर सेक्टर रोटेशन के बड़े अंश से पहचानी जा सकती हैं।

वोलैटिलिटी इंडेक्स

इसके बाद वोलैटिलिटी इंडेक्स ( वीआईएक्स ) होता है जिसे फियर इंडेक्स के नाम से भी जाना जाता है। शुरुआत करने वाले निवेशकों को यह जानकारी होनी चाहिए कि यह इंडेक्स बाजार की उथल – पुथल का हाल बताता है। यह इस बात की जानकारी देता है कि अमुक इंडेक्स आने वाले 30 दिनों में कितनी उठापटक दर्ज कर सकता है। मिसाल के तौर पर वीआईएक्स , नेशनल स्टॉक एक्सचेंज ( एनएसई ) अगर 40 के स्तर पर है तो इससे यह संकेत मिलता है कि इक्विटी बाजार अगले एक महीने के दौरान 40 फीसदी तक उछाल या गिरावट दर्ज कर सकते हैं।

स्थिर बाजार में वीआईएक्स सामान्य तौर पर 10 से नीचे रहता है। अगर वीआईएक्स ऊंचे स्तर पर है तो इससे यह संकेत मिलता है कि निवेशकों में खौफ बढ़ गया है। बाजार में तेजी लौटने के पहले संकेत के तौर पर आप इस शानदार टूल का इस्तेमाल कर सकते हैं। अगर वोलैटिलिटी 20 से नीचे चली जाएगी तो आपका निवेश काफी हद तक सुरक्षित हो जाएगा।

एमकैप – जीडीपी अनुपात

मार्केट कैपिटलाइजेशन – सकल घरेलू उत्पाद ( जीडीपी ) अनुपात यह पता लगाने का लोकप्रिय अनुमान है कि बाजार बॉटम आउट ( अत्यंत निचला स्तर छू लिया है या नहीं ) हो गए हैं या नहीं। सिद्धांत रूप से यह माना जाता है कि जब एमकैप – जीडीपी अनुपात एक से ऊपर चला जाता है तो इक्विटी बाजार में वैल्यूएशन आकर्षक हो जाता है।
डेली मूविंग एवरेज
बाजार में तेजी लौट आई या नहीं , इसके निष्कर्ष पर पहुंचने से पहले आप एक और पैमाने पर गौर कर सकते हैं और वह है डेली मूविंग एवरेज ( डीएमए ) । तेजी के बाजार में इंडेक्स अपनी 200 सिम्पल मूविंग एवरेज से ऊपर होगा और स्टॉक की वैल्यू ( कम से कम निफ्टी के प्रमुख शेयर ) 200 डीएमए से ऊपर होगी।

जियोजित फाइनेंशियल सर्विसेज में हेड ऑफ रिसर्च एलेक्स मैथ्यू ने कहा , ‘ जब तक सेंसेक्स / निफ्टी / शेयर उसकी 50 , 100 , 200 डीएमए से नीचे होते हैं तो बाजार को मंदडि़यों के कब्जे में बताया जाता है। अगर बाजार में सुस्ती की वजह से इंडेक्स 50 डीएमए से नीचे चला जाएगा तो वह वापसी करना शुरू करेगा जिसके चलते निफ्टी / सेंसेक्स / स्टॉक को उसकी 50 , 100 या 200 डीएमए तक ले जाएगा। ‘

इस स्थिति को तफ्सील से समझाने के लिए मैथ्यू ने एक उदाहरण दिया जिसमें हाजिर निफ्टी 2934 , निफ्टी 50 डीएमए 2864 , 100 डीएमए 3100 और 200 डीएमए 3822 पर है। उन्होंने कहा , ‘ मंदी के बाजार में क्योंकि निफ्टी अपने 50 डीएमए से ऊपर है इसलिए वह 3100 की 100 डीएमए या कई बार 3822 की 200 डीएमए को टेस्ट कर सकता है। सेंसेक्स में इस तरह के उछाल को मंदी के बाजार की रैलियां कहा जाता है। ‘ उनके मुताबिक ज्यादा कारोबार और कम इम्पैक्ट कॉस्ट बाजार में तेजी लौटने की मुख्य विशेषताएं हैं।

दूसरे सुराग

उपरोक्त पैमानों के अलावा ऐसे कुछ और भी सुराग हैं जिन पर निगाह रखकर आप बाजार की तेजी की शिनाख्त कर सकते हैं। विश्लेषकों का कहना है कि आम तौर पर इक्विटी बाजारों में रफ्तार उस वक्त आती है जब महंगाई दर कम हो , ब्याज दरें निचले स्तर पर हों , प्राइस टू बुक वैल्यू अनुपात , नरम मौद्रिक नीति , तंत्र में अधिक नकदी , बायबैक , नए आईपीओ का अभाव तथा कमजोर हाथ बनाम मजबूत हाथ ( अत्यधिक निराशावाद से घिरे छोटे निवेशक मजबूत संस्थागत निवेशकों को भारी बिकवाली करें ) की स्थिति हो। प्राइस टू बुक वैल्यू अनुपात के मामले में पुराने आंकड़ों पर नजर डालने की सलाह दी जाती है कि मल्टीपल्स में कैसे विस्तार आया या कॉन्टैक्ट हुए।

हालांकि कुछ ऐसी चीजें हैं जिन्हें दिमाग में रखने की जरूरत है। मोतीलाल ओसवाल सिक्योरिटी में फंड मैनेजर मनीष सोंथालिया ने कहा , ‘ पहला , बाजार अर्थव्यवस्था से कहीं पहले बॉटम आउट हो जाते हैं और इसका आकलन कंपनियों के तिमाही नतीजों के आधार पर किया जा सकता है। आम तौर पर अर्थव्यवस्था की स्थिति पलटने से दो तिमाही पहले बाजार में इसका अक्स दिखने लगता है।

दूसरा , बाजार में तेजी , मंदी और सुस्ती की अधिकता से पैदा होती है या ठीक इसके उलट भी होता है। यह एक चक्र है क्योंकि वक्त बदलता रहता है और कुछ भी अनंत काल तक नहीं रहता। तीसरा , बाजार में सुस्ती 18 से 24 महीने तक जारी रहती है। हम बाजार की मौजूदा मंदी के 14 वें महीने में हैं और मार्केट में तेजी लौटने में बस कुछ ही वक्त बाकी है। ‘
कैसे उठाएं फायदा
तमाम बातें एक तरफ हैं। बाजार में तेजी के बीज पड़ने के वक्त आपकी रणनीति ज्यादा से ज्यादा फायदा बनाने की होनी चाहिए। आप ऐसा मिड कैप और स्मॉल कैप शेयरों के बजाय बुनियादी रूप से मजबूत लार्ज कैप शेयर खरीदकर कर सकते हैं। ऐतिहासिक चलन इस बात का सबूत है कि लार्ज कैप शेयरों ने हमेशा से तेजी के बाजार की अगुवाई की है। स्मॉल कैप और मिड कैप शेयर , तेजी आने पर सबसे धीमी गति से रफ्तार पकड़ने वाले स्टॉक में शामिल होते हैं। सोंथालिया ने कहा , ‘ विडंबना यह है कि छोटे निवेशक ठीक इसके उलट कदम उठाते हैं। ‘

उनके मुताबिक सेक्टर के आधार पर बैंकिंग और ऑटो सेक्टर सबसे पहले चढ़ते हैं। ऐसा इसलिए है क्योंकि अर्थव्यवस्था पर इनका सीधा असर होता है। अगर इन कंपनियों के शेयरों की कीमतें चढ़नी शुरू होती है और ऊंचे स्तरों पर बरकरार रहती हैं तो यह निश्चित संकेत माना जा सकता है कि अर्थव्यवस्था एक बार फिर गति पकड़ रही है और बाजार में तेजी शुरू हो चुकी है।

सोंथालिया ने कहा , ‘ यह बिक्री की संख्या और इन सेक्टरों की कंपनियों की ओर से घोषित किए जाने वाले मुनाफे से और पुख्ता होता है। अगर आप जल्द कदम उठाते हैं और चक्र की शुरुआत में इन सेक्टरों के शेयर खरीदते हैं तो यह आपको भारी मुनाफा दे सकता है। ‘ उन्होंने टाटा मोटर्स ( उस वक्त टेल्को ) का उदाहरण दिया जिसका शेयर 2002 में 60 रुपए से बढ़कर बीते पांच साल में बढ़कर 900 रुपए के पार पहुंच गया था। हालांकि , एक बार फिर इसमें भारी गिरावट आई है।

विश्लेषकों का मानना है कि इस बात के संकेत हमारे सामने हैं जो इस बात की तस्दीक करते हैं कि बाजार की मौजूदा सुस्ती जल्द ही खत्म हो सकती है। निवेश करने के लिए रकम तैयार है और अमेरिका में कुछ स्थिरता आने के संकेत मिलते ही भारतीय बाजार उड़ान भरना शुरू करेंगे। जैसा कि वॉल स्ट्रीट पर पुरानी कहावत है। कोई भी बड़ी हस्ती तेजी के चरम पर और मंदी के निम्नतम स्तर पर घंटी नहीं बजाना चाहती। आपको अपने फैसले खुद करने होते हैं और निवेश के अपने स्टाइल को लेकर प्रतिबद्ध रहना होता है।
मंदी के बाजार के संकेत
- एनएसई वोलैटिलिटी इंडेक्स 40 फीसदी के ऊपर रहे

- कारोबार कम हो

- कई शेयर 52 सप्ताह के निम्न स्तर पर या उससे नीचे कारोबार कर रहे हों

- बाजार में लगातार उछाल की उम्मीद न हो
Source Economictimes.com

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Rajiv Gandhi’s cheese makers fall on hard times

Posted by maheshpareek on February 13, 2009

When the late PM Rajiv Gandhi and his wife Sonia toured Kalimpong in Darjeeling district in 1975, they bought a block of cheese from Larks’
Provision, which sold products produced by the Swiss Welfare Dairy (SWD). The dairy was a non-profit centre set up in 1945. ”Rajiv Gandhi liked the cheese so much that he ordered 30 kg,” says Pran Nath Sood, proprietor of Larks’ Provision.

For decades, the dairy was known for its delectable offerings. It became so famous, that this ’boutique’ dairy’s delicacies were dispatched on demand to embassies in New Delhi, shops in Puducherry, Kolkata, Mumbai, Bangalore and even Nepal.

Today, though SWD is no longer around, its legacy lives on – just about – in people like Soma Tamang, 65, who makes Kalimpong’s famous milk-candy. Boutique dairies, incidentally, are a niche industry whose products are a tad more expensive and of superior quality than everyday products. For example, Larks’ Provision exclusively sells ‘Cheddar’ cheese, which is harder than normal cheese. It originated in Cheddar in Somerset, UK. Sood says milk and a processing powder called rennit is used, which is made in Holland and Switzerland.

Tamang has been rolling lollypops, brown-sugary treats on small sticks and selling them for 30 years. But competition and lack of modern technology is eating into the business. This, despite a 25-stick packet costing just Rs 25.

Obviously, the profit margins are small. Tamang earns between Rs 1,000 to Rs 1,500 a week selling lollypops and cottage cheese. But it’s a losing battle. ”The price of raw materials like milk and sugar has increased. I was even asked to pay a tax of Rs 5,000; we don’t own a factory,” she says. Lack of proper marketing, packaging and lollypops’ exceedingly short shelf-life has turned it into a struggling ’boutique’ business.

Tamang is one among more than 50 families in and around Kalimpong, which make and sell lollypops and cottage cheese. It was a skill handed down by Father Andreas Butty, a Swiss priest, who started an orphanage in Pedong near Kalimpong in 1938, later setting up the dairy to provide jobs for locals. The dairy shifted to Kalimpong in 1947.

In the true spirit of a non-profit dairy, it employed nearly 60 people even though only 30 were needed, says Peter Rai, ex- dairy manager. Fr Butty also bought two acres of land. He left India in 1986. Rai took over the farm, renaming it Celina Dairy after his wife. But personal reasons and the 1986 Gorkhaland agitation closed it. ”It’s due to Fr Butty selfless service that today, nearly 50 families are able to earn their livelihood,” says Rai. But that’s now threatened.

Darjeeling district magistrate Surendra Gupta says, ”A survey has been conducted about the needs of these people and we’re trying to fit them into various schemes.” But it is not clear how, if ever, the authorities plan to implement their slated good intentions. No project has been set up so far for these people.

Gupta says he has proposed to the director of West Bengal Cottage Industry, H Mohan, that the local products receive a Geographical Indication certificate. But all of that is yet to happen.
Source TNN http://timesofindia.indiatimes.com/Bush_should_be_given_Bharat_Ratna_Abhishek_Singhvi/articleshow/4123522.cms?TOI_latestnews

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CANADIAN CITIZENSHIP IMMIGRATION TO CANADA

Posted by maheshpareek on January 20, 2009

Got Rs 45 lakh? Buy Canadian citizenship
2 May 2007, 0039 hrs IST, Prabhakar Sinha, TNN

NEW DELHI: Those who have reportedly been paying sums like Rs 30 lakh to become kabootars (illegal migrants) may have missed a trick. For as little as Rs 45 lakh, an entire family can become Canadian citizens completely legally. In fact, for a somewhat larger amount, a family could become citizens of the US, UK, Australian or New Zealand too.

The majority of Indians hankering to settle abroad may not know this, but many Indian businessmen have been utilising schemes floated by these countries to gain citizenship in return for investing a few hundred thousand dollars there. What’s more, some foreign banks are even willing to part-finance your investment.

Governments in the US, Canada, UK, New Zealand and Australia, among others, are offering citizenship to anybody (and his/her family) who is willing to invest a certain minimum sum in their country. The sums vary in each case, as do the strings attached.

In the UK, citizenship comes with an investment of £750,000 (about $1.5 million or a little over Rs 6 crore). In the US, $500,000 (Rs 2 crore) will get you and your family citizenship, for New Zealand a million New Zealand dollars (Rs 3 crore).

In Australia, there is no minimum investment stipulated. However, a commitment to start a business within four years will do the trick.

In the case of Canada, the stipulated minimum investment that gets you automatic citizenship is 400,000 Canadian dollars or about Rs 1.4 crore. However, a major Canadian bank, Desjardins — with assets of $130 billion and ranked 92nd internationally — has come up with its own scheme under which it will finance around 70% of the amount if the investor makes a down payment of 30%. That means just Rs 45 lakh will do to get you Canadian citizenship.
Citizenship-for-investment schemes have been around for a couple of years in most cases, but Indians were unable to take advantage of them as they were not allowed to invest more than $25,000 per year abroad till December last year. With the RBI increasing the limit from $25,000 to $50,000 and then to $100,000 last month, ‘buying’ foreign citizenship has become possible. A family of five, for instance, could take out $500,000 in one go without violating RBI guidelines.

Desjardins’ offer is recognition of this fact. Explaining the scheme, Marc Audet, vice president of Desjardins, told TOI that the bank offers a scheme under which one needs to deposit only C$120,000 with the bank. The bank will finance the remaining C$280,000 to file the application for permanent residency (which entitles you to citizenship after two years) in Canada.

The C$400,000 are invested in interest-free Canadian government bonds. After five years, when the government returns the money, the bank will keep all of it.

In effect, the C$120,000 you pay upfront becomes the interest earned by the bank on the C$280,000 that it lends you to file the application.

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All stocks are cheap. So which all should you buy

Posted by maheshpareek on December 22, 2008

All stocks are cheap. So which all should I buy?
The intrinsic value of any asset, be it stocks, bonds or real estate is nothing but the discounted value of cash flows that can be taken out of the asset from now until eternity. This method of valuation is popularly known as the DCF method. Hence, the DCF analysis should be performed on stocks that the investor considers cheap and consider investing in those that give the maximum upside with respect to their market prices. However, we would like to add that future cash flows are a function of factors like the company’s balance sheet, its management and most importantly, its competitive advantage. Hence, these factors need to be carefully evaluated while arriving at the future cash earnings of the company and thereby its intrinsic value.

In Finance the discounted cash flow (or DCF) approach describes a method of valuing a project, company, or asset using the concepts of the time value of money. All future cash flows are estimated and discounted to give their present values. The discount rate used is generally the appropriate cost of capital and may incorporate judgments of the uncertainty (riskiness) of the future cash flows.
Discounted cash flow analysis is widely used in investment finance, real estate development, and corporate financial management.
Mathematics
Discrete cash flows
The discounted cash flow formula is derived from the future value formula for calculating the time value of money and compounding returns.

The simplified version of the (for one cash flow in one future period) is expressed as:

where
• DPV is the discounted present value of the future cash flow (FV), or FV adjusted for the delay in receipt;
• FV is the nominal value of a cash flow amount in a future period;
• i is the interest rate, which reflects the cost of tying up capital and may also allow for the risk that the payment may not be received in full;
• d is the discount rate, which is i/(1+i), ie the interest rate expressed as a deduction at the beginning of the year instead of an addition at the end of the year;
• n is the time in years before the future cash flow occurs.
Where multiple cash flows in multiple time periods are discounted, it is necessary to sum them as follows:

for each future cash flow (FV) at any time period (t) in years from the present time, summed over all time periods. The sum can then be used as a net present value figure. If the amount to be paid at time 0 (now) for all the future cash flows is known, then that amount can be substituted for DPV and the equation can be solved for i, that is the internal rate of return.
All the above assumes that the interest rate remains constant throughout the whole period.
(1+i)^(-t) can of course also be expressed as exp(-it).
Continuous cash flows
With continuous cash flows, the summation in the above formula is replaced by an integration – nothing else changes:
DPV= integral over the required time period of FV(t) * (1-exp(-it)) dt
where FV(t) is now the rate of cash flow.
Example DCF
To show how discounted cash flow analysis is performed, consider the following simplified example.
• John Doe buys a house for Rs.100,000. Three years later, he expects to be able to sell this house for Rs.150,000.
Simple subtraction suggests that the value of his profit on such a transaction would be Rs.150,000 − Rs.100,000 = Rs.50,000, or 50%. If that Rs.50,000 is amortized over the three years, his implied annual return (known as the internal rate of return) would be about 14.5%. Looking at those figures, he might be justified in thinking that the purchase looked like a good idea.
1.1453 x 100000 = 150000 approximately.
However, since three years have passed between the purchase and the sale, any cash flow from the sale must be discounted accordingly. At the time John Doe buys the house, the 3-year US Treasury Note rate is 5% per annum. Treasury Notes are generally considered to be inherently less risky than real estate, since the value of the Note is guaranteed by the US Government and there is a liquid market for the purchase and sale of T-Notes. If he hadn’t put his money into buying the house, he could have invested it in the relatively safe T-Notes instead. This 5% per annum can therefore be regarded as the risk-free interest rate for the relevant period (3 years).
Using the DPV formula above, that means that the value of Rs.150,000 received in three years actually has a present value of Rs.129,576 (rounded off). Those future dollars aren’t worth the same as the dollars we have now.
Subtracting the purchase price of the house (Rs.100,000) from the present value results in the net present value of the whole transaction, which would be Rs.29,576 or a little more than 29% of the purchase price.
Another way of looking at the deal as the excess return achieved (over the risk-free rate) is (14.5%-5.0%)/(100%+5%) or approximately 9.0% (still very respectable). (As a check, 1.050 x 1.090 = 1.145 approximately.)
But what about risk?
We assume that the Rs.150,000 is John’s best estimate of the sale price that he will be able to achieve in 3 years time (after deducting all expenses, of course). There is of course a lot of uncertainty about house prices, and the outturn may end up higher or lower than this estimate.
(The house John is buying is in a “good neighborhood”, but market values have been rising quite a lot lately and the real estate market analysts in the media are talking about a slow-down and higher interest rates. There is a probability that John might not be able to get the full Rs.150,000 he is expecting in three years due to a slowing of price appreciation, or that loss of liquidity in the real estate market might make it very hard for him to sell at all.)
Under normal circumstances, people entering into such transactions are risk-averse, that is to say that they are prepared to accept a lower expected return for the sake of avoiding risk. See Capital asset pricing model for a further discussion of this. For the sake of the example (and this is a gross simplification), let’s assume that he values this particular risk at 5% per annum (we could perform a more precise probabilistic analysis of the risk, but that is beyond the scope of this article). Therefore, allowing for this risk, his expected return is now 9.0% per annum (the arithmetic is the same as above).
And the excess return over the risk-free rate is now (9.0%-5.0%)/(100% + 5%) which comes to approximately 3.8% per annum.
That return rate may seem low, but it is still positive after all of our discounting, suggesting that the investment decision is probably a good one: it produces enough profit to compensate for tying up capital and incurring risk with a little extra left over. When investors and managers perform DCF analysis, the important thing is that the net present value of the decision after discounting all future cash flows at least be positive (more than zero). If it is negative, that means that the investment decision would actually lose money even if it appears to generate a nominal profit. For instance, if the expected sale price of John Doe’s house in the example above was not Rs.150,000 in three years, but Rs.130,000 in three years or Rs.150,000 in five years, then on the above assumptions buying the house would actually cause John to lose money in present-value terms (about Rs.3,000 in the first case, and about Rs.8,000 in the second). Similarly, if the house was located in an undesirable neighborhood and the Federal Reserve Bank was about to raise interest rates by five percentage points, then the risk factor would be a lot higher than 5%: it might not be possible for him to make a profit in discounted terms even if he could sell the house for Rs.200,000 in three years.
In this example, only one future cash flow was considered. For a decision which generates multiple cash flows in multiple time periods, all the cash flows must be discounted and then summed into a single net present value.

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World Greatest Sales Man Quote

Posted by maheshpareek on December 11, 2008

Author OG MANDINO

Worlds Greatest Salesman

World's Greatest Salesman

British साहित्यकार इलियट ने एक बार लिखा था कि “रोजमर्रा की जिंदगी जीने में हम जीवन के अर्थ को भूल जाते हैं। बहुत अधिक ज्ञान के बीच हम अपनी बुद्धिमानी को भूल जाते हैं और बहुत अधिक सूचनाओं के बीच हम ज्ञान की झलक पाने से चूक जाते हैं।”

दूसरे शब्दों में हम यह कह सकते हैं कि “बहुत अधिक इनफॉर्मेशन फैक्ट्स एवं फीगर्स इकट्ठे करने का मतलब यह नहीं है कि हम ने नॉलेज अर्जित कर ली है। इसी तरह से बहुत अधिक नॉलेज अर्जित कर लेने से यह अर्थ नहीं कि हम बुद्धिमान बन गए हैं और यदि हम बहुत अधिक बुद्धिमान बन भी गए हैं, तो इसका अर्थ यह नहीं की हम अपने जीवन के अर्थ और उद्देश्यों को समझ चुके हैं।”

इस संसार में ऎसे लोग बहुत ही कम मिलते हैं जो ज्ञानी, बुद्धिमान और जीवन के अर्थ को समझने वाले हों। यह सलाह अक्सर दी जाती है कि यदि आपको संसार पर अपनी इच्छाओं का झंडा लहराना है तो एक सोची-समझी रणनीति के तहत लोगों के पास कुछ चुनी हुई सूचनाएं जबरदस्त प्रयासों के जरिए पहुंचानी चाहिए। ऎसा करने से उनकी सोच पर काबू पाया जा सकता है। सोच यदि कब्जे में है, तो यह संसार कब्जे में है। द्वितीय विश्वयुद्ध में एक अमरीकी कमांडर ने एक बार कहा था कि “हमारा टारगेट दुश्मन फौज के सैनिकों के शरीर नहीं हैं, हमारा वास्तविक टारगेट तो उनके कमांडर का माइंड है। यदि वह डर गया, तो समझो हमने युद्ध जीत लिया।” इसी तरह से एक अन्य सैन्य विशेषज्ञ ने कहा था कि “दुश्मन का अर्थ व्यक्तियों के समूह से है, जिसमें से कुछ को मार दिया जाता है, कुछ को या तो गिरफ्तार कर लिया जाता है या उन्हें छिपने के लिए मजबूर कर दिया जाता है और बाकी बचे नब्बे प्रतिशत लोगों की सिर्फ सोच बदली जाती है, ताकि उनके जीने का ढंग वैसा हो, जैसा आप चाहते हैं।” शायद इसीलिए भारत का कॉर्पोरेट वल्र्ड मैनेजमेंट विशेषज्ञों की सलाह पर लगभग तीस हजार करोड रूपए विज्ञापनों पर खर्च करता है, ताकि ग्राहक की सोच पर काबू पाया जा सके। तकरीबन दो लाख करोड रूपए वह मार्केटिंग पर खर्च करता है, ताकि उसके सेल्समैन, डीलर्स और डिस्ट्रीब्यूटर्स ग्राहक को हिप्नोटाइज करके ये बता सकें कि उनका प्रॉडक्ट बेस्ट है। इनफॉर्मेशन के इस वॉर में कॉर्पोरेट वल्र्ड ने साधारण परिवार वालों को भी अपनी सेना में शामिल कर लिया है। जो एम.एल.एम. मार्केटिंग आदि के तहत कब किस प्रॉडक्ट को बेच डालते हैं, पता ही नहीं चलता है। कॉर्पोरेट वल्र्ड में कुछ अपवादों को छोडकर अनगिनत ऎसे असफल उदाहरण मिलते हैं, जहां बिना विज्ञापन या सूचना प्रदान किए प्रॉडक्ट बेचने की कोशिश हुई, परन्तु उसका कोई फल प्राप्त नहीं हुआ।

Management is like a formula and the prime elements of the formula are planning, delegation, and control. Planning is the most important part of leadership. And it is essential about having a plan and a set of objectives, and building a team that has drive to attain them. An ideal plan should be able to provide a clear blue print for the members of an organization.

Planning is to be followed by delegation. No manager can do everything on his own. He has to delegate work to his team members and entrust them with responsibilities. This will help him to successfully operationalise his plan.

Now, it’s  the turn of the manager to review the progress of his plan on a periodic basis. This means that he should be always be in control of situation. This is necessary step as it will send out the right message th the team and make them aware of the fact that their work is always being monitored, and that they are answerable to someone at all stage of a plan’s implantation. 

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STOCK MARKET CHART

Posted by maheshpareek on December 11, 2008

MARKET WILL GO UP MARKET WAS GOING UP

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STEPS INVOLVE TO DEVLOPE IT SOLUTION

Posted by maheshpareek on December 9, 2008

AT A GLANCE

Summary:
_

Key Challenges:

_ Multiple stand-alone IT systems
_ No access to real-time data
_ Slow, imprecise decision making

Project Objectives:

_ Automate business processes
_ Provide a single point of access to all
organisational data
_ Provide access to real-time data
_ Manage operations systematically and
collaboratively at the strategic level
_ Improve service delivery

Solutions and Services:

Why this Solutions:

_ ?
_ ?
_ customer support?
_ product?

Implementation Highlights:
_ _____________ implemented in ____ months
_ ___________ for _____________ implemented in ____ months

Key Benefits:

_ Projected savings
_ Projected savings of __ lacs
from the elimination of ____________ systems
_ Real-time access to ___________ data
_ Seamless integration of data across all
business units
_ A single window for the entire organisation’s data,
knowledge, and resources with complete access
and control
_ Improved communication among ________ and
__________
_ Intangible qualitative benefits

Implementation Partner/Team:
_

Existing Environment:
_ Which software?

Database:

_ MS SQL/Access/My SQL

Hardware:

_

Operating System:
_

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