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TITLE: Risk Management and Survilleance
COMPANY NAME : Angel Broking Ltd
Submitted by
Students Name: Mohd Asif Anis
Class: MBA (IB)
Enrolment no: A7002008092
Specialization: Finance
Under guidance of:
Industry Guides : Himanshu Singh Faculty Guide Prof. Anil
Sharma
Designation: Financial Advisor ABS, Lucknow
Organization: Angel Broking
(SUMMER INTERNSHIP REPORT IN PARTIAL FULFILLMENT OF THE AWARD OF FULL TIME
MASTERS IN BUSINESS ADMINISTRATION (2008-10)
AMITY BUSINESS SCHOOL
AMITY UNIVERSITY UTTAR PRADESH LUCKNOW
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Students Certificate
Certified that this report is prepared based on the dissertation reportundertaken by me in Angel Broking ,under the able guidance of MrAnil Sharma in partial fulfillment of the requirement for award ofdegree of master of business Administration from Amity UniversityUttar Pradesh .
Date :30/07/09
Signature Signature
Name ; Mohd Asif Anis prof Anil SharmaEnrollment :A7002008092 Faculty guide
SignatureProf R.P SinghDirector A.B.S
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Certificate of faculty Guide
This is to certify that mr Mohd Asif Anis student of MBA 3rd
semester of amity university ,Uttar Pradesh has under gone adissertation report under myb guidance. The report entitled Riskmanagement in portfolio selection has been completed by the student
to my entire satisfaction.
Date: 30/07/09
Prof Anil SharmaFaculty Guide
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D E C L A R A T I O N
I here by declare that this dissertation report submitted by me to
Amity Business school in my own and it has not been submitted to
any other university or published at any time before .
Mohd Asif Anis
Place :Lucknow
Date :30/07/09
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A C K N O W L E D G E M E N T
I am highly indebted to Mr. Ejaz Mohyi, Branch Manager,
Angel Broking Ltd., Lucknow, for his invaluable time and advice given to
me from his busy schedule in completion of this project successfully.
He not only told me about various policies of Angel Broking
Ltd. but of others also, he helped me to get to the market so that I may
easily collect data and helped me peep into the working style of Angels
employees.
Besides each and every member of the team, were very supportive and
kind to me during the whole training period.
I am also highly thankful to my industry guides Mr. Himanshu
Singh, Equity Advisor, Angel Broking Ltd and Mr. Ali Asad, Sales, Angel
Broking Ltd for their efforts in guiding me as and when I required their
guidance.
In the end, I am thankful to my parents; friends & teachers who directly
or indirectly helped me while preparing the report.
Executive Summary
The activities of large, internationally active financial institutions have grown
increasingly complex and diverse in recent years. This increasing complexity has necessarilybeen accompanied by a process of innovation in how these institutions measure and monitor
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their exposure to different kinds of risk. One set of risk management techniques that has
attracted a great deal of attention over the past several years, both among practitioners and
regulators, is "stress testing", which can be loosely defined as the examination of the
potential effects on a firms financial condition of a set of specified changes in risk factors,
corresponding to exceptional but plausible events.
This report represents the findings of a Working Group on Macro Stress Testing
established by the Committee on the Global Financial System. The group was asked to
investigate the current use of stress testing at large financial institutions, in line with the
Committees overall mandate to improve central banks understanding of institutional
developments relevant to global financial stability. The term "macro" in the groups name
indicates another element of the groups mandate, namely to explore the possibility thataggregating financial firms stress test results might produce information that is of use to
central banks, other financial regulators, and private-sector practitioners.
Members of the group interviewed risk managers at more than twenty large,
internationally active financial institutions, both in their home countries and as a group at a
meeting hosted by the Banque de France. From these interviews, the group gained a
substantial base of knowledge on the current "state of the art" in the design and
implementation of risk management and on the role of stress testing in risk management
decisions at the corporate level.
Drawing on this knowledge, the group then considered some of the issues relating to
the aggregation of the results of risk management conducted at different financial firms. The
group concluded that, under ideal circumstances, aggregate stress tests could potentially
provide useful information in a number of areas. Aggregate risk management might be used
by financial firms to help make ex ante assessments of market liquidity risk under stress
when evaluating the riskiness of a trading strategy. Central banks and financial regulators
might use them to more effectively monitor broad patterns of risk-taking and risk-
intermediation in financial markets. However, the group also noted that it is as yet unclear
whether such ideal circumstances prevail. In particular, it is unclear whether an appropriate
reporting population can be assembled, whether the stress tests currently conducted by
financial firms are compatible with one another, and whether the information obtained would
justify the reporting burden.
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This report represents practices application of risk management techniques in the
portfolio section. Risk management can be integrate by fundamental and technical method
which are used for calculation of annul return and E.P.S for the portfolio .
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I N D E X
Particular Page
Student certificate 2
Certificate of faculty Guide 3
Declaration 4
Acknowledgement 6
Executive summary 7
Chapter 1 10
THEORETICAL PRESENTATION OF THE TOPIC
Chapter 2 42
ORGANIZATIONAL PROFILE OF THE COMPANY
Chapter3 60
PRESENTATION OF DATA AND ANALYSIS
Chapter4 62
FINDINGS, CONCLUSIONS AND SUGGESTIONS
Conclusion 93
Bibliography 94
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CHAPTER I
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RISK MANAGEMENT IN PORTFOLIO
SELECTION
Objective
WHY DO I INTEND TO TAKE UP THIS STUDY ?
1. Analyse risk factors affecting different portfolios,
2.Analyse the different portfolio preference of various income
groups,
3.Select best suited portfolio for different risk appetite clients,
4.Minimize risk exposure to the present portfolio,
5.Minimize losses at the time of extreme shock,
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INTRODUCTION
Over the past fifteen years, several investors have suffered huge losses
due to extreme events.
Barings Bank failed in 1995, Long Term Capital Management collapsed
In 1998, and Enron went bankrupt in 2001. Furthermore, the terrorist attacks in
the U.S. (2001), Spain (2004), and the U.K. (2005) and the most recent
American economy collapse causing slow down (2008-09) have tremendously
affected Indian financial markets.
Extreme market moves and distress condition throughout the world have
occurred since the beginning of organized market even so, 1998 was
distinguished by the number of spectacular market stresses. Many market
participants should have learned powerful lessons. But 1998 and in 2008 and
shows that many of us are still ill prepared.
Extreme incidences that makes the world economies to slow down also
effect the india stock
Market causing the fall in index from 20 to 60 percent some of the
extreme events that affect the Indian stock market.
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Moments of crisis often present unusual but fleeting
opportunities to profit from strategic repositioning.
The following is a comprehensive list of actions that should be considered:
Buy protection or insurance for risks that can be immunized.
Restructure business, client, or product mix.
Price differently to include previously unidentified risk.
Get out of the position, market, or business.
Dont change the business but systematically monitor and manage the business
through more stress
Testing, and develop contingency plans for the shocks.
Evaluate the returns over the life cycle of the business for the total economics.
Beware of the industry herd mentality and the resulting concentration of risks.
Be careful of the .greater fool. Assumption.
Prepare for liquidity and funding issues that naturally occur in stressful market
environments by increasing
Credit/counterparty lines/ limits and funding sources, and managing liability structure for
adequate short-, medium-, and long-term funding in a crisis.
In general, a capital charge is not a useful tool for dealing with the results of stress tests. One
or more of the above solutions should provide the protection more effectively.
Taken together, the above seeks to first ensure that the firm can survive the stress
events (which include the impact on capital adequacy, reported earnings, firm liquidity,
credit ratings, and customer and investor confidence). In addition, the actions aim to preserve
enough resilience in distressed market conditions and to enable the firm to take the offensive
and move quickly, because moments of crisis often present unusual but fleeting opportunities
to profit from strategic repositioning.
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Risk Management
Since the occurrence of these incidents, the importance of risk management has been
extensively recognized by banks and securities firms when deciding the amount of risk they
are willing to take.
Moreover, bank regulators now put an emphasis on risk management practices in
attempting to reduce the fragility of financial and banking system.
Setting up of risk management cell is been practiced by various banks, brokerage
houses and other financial firms. Basic objective of this department was to eliminate risk
exposure to the firm and the clients portfolio as much as possible.
In volatile financial markets, both market participants and market regulators need
models for measuring, managing and containing risks. Market participants need risk
management models to manage the risks involved in their open positions. Market regulators
on the other hand must ensure the financial integrity of the stock exchanges and the clearing
houses by appropriate margining and risk containment systems.
The successful use of risk management models is critically dependent upon estimates
of thevolatility of underlying prices. The principal difficulty is that the volatility is not
constant overtime - if it were, it could be estimated with very high accuracy by using a
sufficiently long sample of data. Thus models of time varying volatility become very
important. Practitioners and econometricians have developed a variety of different models forthis purpose. Whatever intuitive or theoretical merits any such model may have, the ultimate
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test of its usability is how well it holds up against actual data. Empirical tests of risk
management models in the Indian stock market are therefore of great importance in the
context of the likely introduction of index futures trading in India.
There are several risk management models available, but the
most popular in them are :-
Value-at-Risk,
Stress Testing,
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Value-at-Risk,
In financial risk management, Value at Risk (VaR) is a widely used measure of
the risk of loss on a specificportfolio of financial assets. For a given portfolio,
probability and time horizon, VaR is defined as a threshold value such that the probability
that the mark-to-market loss on the portfolio over the given time horizon exceeds this value
(assuming normal markets and no trading in the portfolio) is the given probability level.
For example, if a portfolio of stocks has a one-day 5% VaR of $1 million, there is a
5% probability that the portfolio will fall in value by more than $1 million over a one day
period, assuming markets are normal and there is no trading. Informally, a loss of $1 million
or more on this portfolio is expected on 1 day in 20. A loss which exceeds the VaR threshold
is termed a VaR break.
VaR has five main uses in finance: risk management, risk measurement,
financial control, financial reporting and computing regulatory capital. VaR is sometimes
used in non-financial applications as well.
Varieties of VaR
The definition of VaR is no constructive, it specifies a property VaR must have, but
not how to compute VaR. Moreover, there is wide scope for interpretation in the
definition. This has led to two broad types of VaR, one used primarily in risk
management and the other primarily for risk measurement. The distinction is not sharp,
however, and hybrid versions are typically used in financial control,financial reporting and
computing regulatory capital.
To a risk manager, VaR is a system, not a number. The system is run periodically
(usually daily) and the published number is compared to the computed price movement in
opening positions over the time horizon. There is never any subsequent adjustment to the
published VaR, and there is no distinction between VaR breaks caused by input errors
(including Information breakdowns, fraud and rogue trading), computation errors (including
failure to produce a VaR on time) and market movements.
A frequentist claim is made, that the long-term frequency of VaR breaks will equal
the specified probability, within the limits of sampling error, and that the VaR breaks will
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be independent in time and independent of the level of VaR. This claim is validated by
aback test, a comparison of published VaRs to actual price movements. In this interpretation,
many different systems could produce VaRs with equally good back tests, but wide
disagreements on daily VaR values.
For risk measurement a number is needed, not a system. A Bayesian probability claim
is made, that given the information and beliefs at the time, the subjective probability of a
VaR break was the specified level. VaR is adjusted after the fact to correct errors in inputs
and computation, but not to incorporate information unavailable at the time of
computation. In this context, backtest has a different meaning. Rather than comparing
published VaRs to actual market movements over the period of time the system has been in
operation, VaR is retroactively computed on scrubbed data over as long a period as data areavailable and deemed relevant. The same position data and pricing models are used for
computing the VaR as determining the price movements.
Although some of the sources listed here treat only one kind of VaR as legitimate,
most of the recent ones seem to agree that risk management VaR is superior for making
short-term and tactical decisions today, while risk measurement VaR should be used for
understanding the past, and making medium term and strategic decisions for the future.
When VaR is used forfinancial control orfinancial reporting it should incorporate elements
of both. For example, if a trading deskis held to a VaR limit, that is both a risk-management
rule for deciding what risks to allow today, and an input into the risk measurement
computation of the desks risk-adjusted return at the end of the reporting period.
Risk measure and Risk metric
The term VaR is used both for a riskmeasure and a riskmetric. This sometimes
leads to confusion. Sources earlier than 1995 usually emphasize the risk measure, later
sources are more likely to emphasize the metric.
The VaR risk measure defines risk as mark-to-market loss on a fixed portfolio over a
fixed time horizon, assuming normal markets. There are many alternative risk measures in
finance. Instead of mark-to-market, which uses market prices to define loss, loss is often
defined as change in fundamental value. For example, if an institution holds a loan that
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declines in market price because interest rates go up, but has no change in cash flows or
credit quality, some systems do not recognize a loss. Or we could try to incorporate
the economic cost of things not measured in daily financial statements, such as loss of market
confidence or employee morale, impairment of brand names or lawsuits.
Rather than assuming a fixed portfolio over a fixed time horizon, some risk measures
incorporate the effect of expected trading (such as a stop loss order) and consider the
expected holding period of positions. Finally, some risk measures adjust for the possible
effects of abnormal markets, rather than excluding them from the computation.
The VaR risk metric summarizes the distribution of possible losses by a quantile, a
point with a specified probability of greater losses. Common alternative metrics are standard
deviation, mean absolute deviation, expected shortfall and downside risk.
VaR risk management
Supporters of VaR-based risk management claim the first and possibly greatest
benefit of VaR is the improvement in systems and modeling it forces on an institution. In
1997, Philippe Jorion wrote:
The greatest benefit of VAR lies in the imposition of a structured methodology for
critically thinking about risk. Institutions that go through the process of computing their VAR
are forced to confront their exposure to financial risks and to set up a proper risk
management function. Thus the process of getting to VAR may be as important as the
number itself.
Publishing a daily number, on-time and with specified statistical properties holds
every part of a trading organization to a high objective standard. Robust backup systems and
default assumptions must be implemented. Positions that are reported, modeled or priced
incorrectly stand out, as do data feeds that are inaccurate or late and systems that are too-
frequently down. Anything that affects profit and loss that is left out of other reports will
show up either in inflated VaR or excessive VaR breaks. A risk-taking institution that does
notcompute VaR might escape disaster, but an institution that cannotcompute VaR will
not.
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The second claimed benefit of VaR is that it separates risk into two regimes. Inside
the VaR limit, conventional statistical methods are reliable. Relatively short-term and
specific data can be used for analysis. Probability estimates are meaningful, because there are
enough data to test them. In a sense, there is no true risk because you have a sum of
many independent observations with a left bound on the outcome. A casino doesn't worry
about whether red or black will come up on the next roulette spin. Risk managers encourage
productive risk-taking in this regime, because there is little true cost. People tend to worry
too much about these risks, because they happen frequently, and not enough about what
might happen on the worst days.
Outside the VaR limit, all bets are off. Risk should be analyzed with stress
testing based on long-term and broad market data.[14]
Probability statements are no longermeaningful. Knowing the distribution of losses beyond the VaR point is both impossible and
useless. The risk manager should concentrate instead on making sure good plans are in place
to limit the loss if possible, and to survive the loss if not.
One specific system uses three regimes.
1. Out to three times VaR are normal occurrences. You expect periodic VaR
breaks. The loss distribution typically has fat tails, and you might get more than one
break in a short period of time. Moreover, markets may be abnormal and trading may
exacerbate losses, and you may take losses not measured in daily marks such as
lawsuits, loss of employee morale and market confidence and impairment of brand
names. So an institution that can't deal with three times VaR losses as routine events
probably won't survive long enough to put a VaR system in place.
2. Three to ten times VaR is the range forstress testing. Institutions should be
confident they have examined all the foreseeable events that will cause losses in this
range, and are prepared to survive them. These events are too rare to estimate
probabilities reliably, so risk/return calculations are useless.
3. Foreseeable events should not cause losses beyond ten times VaR. If they do
they should be hedged or insured, or the business plan should be changed to avoid
them, or VaR should be increased. It's hard to run a business if foreseeable losses are
orders of magnitude larger than very large everyday losses. It's hard to plan for these
events, because they are out of scale with daily experience. Of course there will be
unforeseeable losses more than ten times VaR, but it's pointless to anticipate them,
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you can't know much about them and it results in needless worrying. Better to hope
that the discipline of preparing for all foreseeable three-to-ten times VaR losses will
improve chances for surviving the unforeseen and larger losses that inevitably occur.
"A risk manager has two jobs: make people take more risk the 99% of the time it is safe to do
so, and survive the other 1% of the time. VaR is the border."
VaR risk measurement
The VaR risk measure is a popular way to aggregate risk across an institution.
Individual business units have risk measures such as duration for a fixed
incomeportfolio orbeta for anequitybusiness. These cannot be combined in a meaningful
way.It is also difficult to aggregate results available at different times, such as positions
marked in different time zones, or a high frequency trading desk with a business holding
relatively illiquid positions. But since every business contributes to profit and loss in
an additive fashion, and many financial businesses mark-to-market daily, it is natural to
define firm-wide risk using the distribution of possible losses at a fixed point in the future.
In risk measurement, VaR is usually reported alongside other risk metrics such
as standard deviation, expected shortfall and greeks (partial derivatives of portfolio value
with respect to market factors). VaR is a distribution-free metric, that is it does not depend on
assumptions about the probability distribution of future gains and losses. The probability
level is chosen deep enough in the left tail of the loss distribution to be relevant for risk
decisions, but not so deep as to be difficult to estimate with accuracy.
Risk measurement VaR is sometimes called parametric VaR. This usage can be
confusing, however, because it can be estimated either parametrically (for
examples, variance-covarianceVaR or delta-gamma VaR) or nonparametrically (for
examples, historical simulation VaR orresampled VaR). The inverse usage makes more
logical sense, because risk management VaR is fundamentally nonparametric, but it is
seldom referred to as nonparametric VaR.
VaR has been controversial since it moved from trading desks into the public eye in
1994. A famous 1997 debate between Nassim Taleb and Philippe Jorion set out some of themajor points of contention. Taleb claimed VaR:[20]
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1. Ignored 2,500 years of experience in favor of untested models built by non-
traders
2. Was charlatanism because it claimed to estimate the risks of rare events,
which is impossible
3. Gave false confidence
4. Would be exploited by traders
More recently David Einhorn and Aaron Brown debated VaR in Global Association of
Risk Professionals Review[12][21] Einhorn compared VaR to an airbag that works all the time,
except when you have a car accident. He further charged that VaR:
1. Led to excessive risk-taking and leverage at financial institutions
2. Focused on the manageable risks near the center of the distribution and
ignored the tails
3. Created an incentive to take excessive but remote risks
4. Was potentially catastrophic when its use creates a false sense of security
among senior executives and watchdogs.
New York Times reporterJoe Nocera wrote an extensive piece Risk Mismanagement on
January 4, 2009 discussing the role VaR played in the Financial crisis of 2007-2008. After
interviewing risk managers (including several of the ones cited above) the article suggests
that VaR was very useful to risk experts, but nevertheless exacerbated the crisis by giving
false security to bank executives and regulators. A powerful tool for professional risk
managers, VaR is portrayed as both easy to misunderstand, and dangerous when
misunderstood.
A common complaint among academics is that VaR is not subadditive. That means the
VaR of a combined portfolio can be larger than the sum of the VaRs of its components. To a
practicing risk manager this makes sense. For example, the average bank branch in the
United States is robbed about once every ten years. A single-branch bank has about 0.004%
chance of being robbed on a specific day, so the risk of robbery would not figure into one-
day 1% VaR. It would not even be within an order of magnitude of that, so it is in the range
where the institution should not worry about it, it should insure against it and take advice
from insurers on precautions. The whole point of insurance is to aggregate risks that are
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beyond individual VaR limits, and bring them into a large enough portfolio to get statistical
predictability. It does not pay for a one-branch bank to have a security expert on staff.
As institutions get more branches, the risk of a robbery on a specific day rises to within
an order of magnitude of VaR. At that point it makes sense for the institution to run internal
stress tests and analyze the risk itself. It will spend less on insurance and more on in-house
expertise. For a very large banking institution, robberies are a routine daily occurrence.
Losses are part of the daily VaR calculation, and tracked statistically rather than case-by-
case. A sizable in-house security department is in charge of prevention and control, the
general risk manager just tracks the loss like any other cost of doing business.
As portfolios or institutions get larger, specific risks change from low-probability/low-
predictability/high-impact to statistically predictable losses of low individual impact. That
means they move from the range of far outside VaR, to be insured, to near outside VaR, to be
analyzed case-by-case, to inside VaR, to be treated statistically.
Even VaR supporters generally agree there are common abuses of VaR.
1. Referring to VaR as a "worst-case" or "maximum tolerable" loss. In fact, you
expect two or three losses per year that exceed one-day 1% VaR.
2. Making VaR control or VaR reduction the central concern of risk
management. It is far more important to worry about what happens when losses
exceed VaR.
3. Assuming plausible losses will be less than some multiple, often three, of
VaR. The entire point of VaR is that losses can be extremely large, and sometimes
impossible to define, once you get beyond the VaR point. To a risk manager, VaR is
the level of losses at which you stop trying to guess what will happen next, and start
preparing for anything.
4. Reporting a VaR that has not passed abacktest. Regardless of how VaR is
computed, it should have produced the correct number of breaks (within sampling
error) in the past. A common specific violation of this is to report a VaR based on the
unverified assumption that everything follows a multivariate normal distribution
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Risk management in Indian capital market
Categorisation of stocks for imposition of margins
Stock are classifed into three categories on the basis of their liquidity and impact cost.
The Stocks which have traded at least 80% of the days for the previous six months
shall constitute the Group I and Group II.
Out of the scrips identified above, the scrips having mean impact cost of less than orequal to 1% are categorized under Group I and the scrips where the impact cost is
more than 1, are categorized under Group II.
The remaining stocks are classified into Group III.
The impact cost is calculated on the 15th of each month on a rolling basis considering
the order book snapshots of the previous six months. On the basis of the impact cost
so calculated, the scrips move from one group to another group from the 1st of the
next month.
For securities that have been listed for less than six months, the trading frequency and
the impact cost are computed using the entire trading history of the security.
Categorisation of newly listed securities
For the first month and till the time of monthly review a newly listed security is
categorised in that Group where the market capitalization of the newly listed security exceeds
or equals the market capitalization of 80% of the securities in that particular group.
Subsequently, after one month, whenever the next monthly review is carried out, the actual
trading frequency and impact cost of the security is computed, to determine the liquidity
categorization of the security.
In case any corporate action results in a change in ISIN, then the securities bearing
the new ISIN are treated as newly listed security for group categorization.
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Margins
Daily margins payable by members consists of the following:
1. Value at Risk Margin
2. Extreme Loss Margin
3. Mark to Market Margin
Daily margin, comprising of the sum of VaR margin, Extreme Loss Margin and mark to
market margin is payable.
Value at Risk Margin
All securities are classified into three groups for the purpose of VaR margin
For the securities listed in Group I, scrip wise daily volatility calculated using the
exponentially weighted moving average methodology is applied to daily returns. The
scrip wise daily VaR is 3.5 times the volatility so calculated subject to a minimum of
7.5%.
For the securities listed in Group II, the VaR margin is higher of scrip VaR (3.5
sigma) or three times the index VaR, and it is scaled up by root 3.
For the securities listed in Group III the VaR margin is equal to five times the index
VaR and scaled up by root 3.
The index VaR, for the purpose, is the higher of the daily Index VaR based on S&P CNX
NIFTY or BSE SENSEX, subject to a minimum of 5%.
NSCCL may stipulate security specific margins from time to time.
The VaR margin rate computed as mentioned above is charged on the net outstanding
position (buy value-sell value) of the respective clients on the respective securities across all
open settlements. There is no netting off of positions across different settlements. The net
position at a client level for a member is arrived at and thereafter, it is grossed across all the
clients including proprietary position to arrive at the gross open position.
For example, in case of a member, if client A has a buy position of 1000 in a security and
client B has a sell position of 1000 in the same security, the net position of the member in the
security is taken as 2000. The buy position of client A and sell position of client B in the
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same security is not netted. It is summed up to arrive at the members open position for the
purpose of margin calculation.
The VaR margin is collected on an upfront basis by adjusting against the total liquid
assets of the member at the time of trade.
The VaR margin so collected is released on completion of pay-in of the settlement.
Extreme Loss Margin
The Extreme Loss Margin for any security is higher of:
1. 5%, or
2. 1.5 times the standard deviation of daily logarithmic returns of the security price in
the last six months. This computation is done at the end of each month by taking the
price data on a rolling basis for the past six months and the resulting value is
applicable for the next month.
The Extreme Loss Margin is collected/ adjusted against the total liquid assets of the member
on a real time basis.
The Extreme Loss Margin is collected on the gross open position of the member. The
gross open position for this purpose means the gross of all net positions across all the clients
of a member including its proprietary position.
There is no netting off of positions across different settlements. The Extreme Loss
Margin collected is released on completion of pay-in of the settlement.
Mark-to-Market Margin
Mark to market loss is calculated by marking each transaction in security to the
closing price of the security at the end of trading. In case the security has not been traded on
a particular day, the latest available closing price at NSE is considered as the closing price. In
case the net outstanding position in any security is nil, the difference between the buy and
sell values shall be is considered as notional loss for the purpose of calculating the mark to
market margin payable.
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The mark to market margin (MTM) is collected from the member before the start of the
trading of the next day.
The MTM margin is collected/adjusted from/against the cash/cash equivalent
component of the liquid net worth deposited with the Exchange.
The MTM margin is collected on the gross open position of the member. The gross
open position for this purpose means the gross of all net positions across all the clients of a
member including its proprietary position. For this purpose, the position of a client is netted
across its various securities and the positions of all the clients of a member are grossed.
There is no netting off of the positions and setoff against MTM profits across two
rolling settlements i.e. T day and T+1 day. However, for computation of MTM profits/losses
for the day, netting or setoff against MTM profits is permitted.
Trade for Trade segment Surveillance segment
In case of securities in Trade for Trade Surveillance segment (TFT-S segment) the
upfront margin rates (VaR Margin + Extreme Loss Margin) applicable is 100 % and each
trade is marked to market based on the closing price of that security.
The details ofall marginsVAR, extreme loss margin and mark to market as at end of
each day are downloaded to members in their respective Extranet directory.
Margins collection from Client
Members should have a prudent system of risk management to protect themselves
from client default. Margins are likely to be an important element of such a system. The same
should be well documented and be made accessible to the clients and the Stock Exchanges.
However, the quantum of these margins and the form and mode of collection are left to the
discretion of the members.
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Margin Shortfall
In case of any shortfall in margin:
The members shall not be permitted to trade with immediate effect.
There is a penalty for margin violation
Penalty applicable for margin violation is levied on a monthly basis based on slabs as
mentioned below:
Instances of
Disablement Penalty to be levied
1st instance 0.07% per day
2nd to 5th instance
of disablement0.07% per day +Rs.5000/- per instance from 2nd to 5th instance
6th to 10th instance
of disablement
0.07% per day+ Rs. 20000 ( for 2nd to 5th instance) +Rs.10000/- per
instance from 6th to 10th instance
11th instance
onwards
0.07% per day +Rs. 70,000/- (for 2nd to 10th instance) +Rs.10000/- per
instance from 11th instance onwards. Additionally, the member will be
referred to the Disciplinary Action Committee for suitable action
Instances as mentioned above shall refer to all disablements during market hours in a
calendar month. The penal charge of 0.07% per day shall is applicable on all disablements
due to margin violation anytime during the day.
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Liquid assets
Embers are required to provide liquid assets which adequately cover various margins
and Security Deposit requirements. A member may deposit liquid assets in the form of cash,
bank guarantees, fixed deposit receipts, approved securities and any other form of collateral
as may be prescribed from time to time. The total liquid assets comprise of the cash
component and the non cash component wherein the cash component shall be at least 50% of
liquid assets.
1. Cash Component:
a. Cash
b. Bank fixed deposits (FDRs) issued by approved banks and deposited withapproved
custodiansor NSCCL.
c. Bank Guarantees (BGs) in favour of NSCCL from approved banks in the specified
format.
d. Units of money market mutual fund and Gilt funds where applicable haircut is 10%.
2. Non Cash Component:
a. Liquid (Group I) Equity Shares in demat form, as specified by NSCCL from time to
time deposited with approved custodians.
b. Mutual fund units other than those listed under cash component decided by NSCCL
from time to time deposited with approved custodians.
Margins for institutional deals
Nstitutional businesses i.e., transactions done by all institutional investors
are margined from T+1 day subsequent to confirmation of the transactions by the
custodians. For this purpose, institutional investors include
Foreign Institutional Investors registered with SEBI. (FII)
Mutual Funds registered with SEBI. (MF)
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Public Financial Institutions as defined under Section 4A of the Companies Act,
1956. (DFI)
Banks, i.e., a banking company as defined under Section 5(1)(c) of the Banking
Regulations Act, 1949. (BNK)
Insurance companies registered with IRDA. (INS)
Pension Funds registered with PFRDA (PNF)
Levy of margins:
Institutional transactions are identified by the use of the participant code at the time of
order entry.
In respect of institutional transactions confirmed by the custodians the margins are
levied on the custodians.
In respect of institutional transactions rejected/not accepted by the custodians the
margins are levied on the members who have executed the transactions.
The margins are computed and levied at a client (Custodial Participant code) level in
respect of institutional transactions and collected from the custodians/members.
Retail Professional Clearing Member:
In case of transactions which are to be settled by Retail Professional Clearing
Members (PCM), all the trades with PCM code are included in the trading members
positions till the same are confirmed by the PCM. Margins are collected from respective
trading members until confirmation of trades by PCM.
On confirmation of trades by PCM, such trades are reduced from the positions of
trading member and included in the positions of PCM. The PCMs are then liable to pay
margins on the same.
Exemption upon early pay-in of securities
In cases where early pay-in of securities is made prior to the securities pay-in, such
positions for which early pay-in (EPI) of securities is made are exempt from margins.
Members are required to provide client level early pay-in file in a specified format. The EPI
of securities is allocated to clients having net deliverable position, on a random basis unless
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specific client details are provided by the member/ custodian. However, member/ custodian
shall ensure to pass on appropriate early pay-in benefit of margin to the relevant clients.
Additionally, member/custodian can specify the clients to whom the early pay-in may be
allocated.
Exemption upon early pay-in of funds
In cases where early pay-in of funds is made prior to the funds pay-in, such positions
for which early pay-in (EPI) of funds is made are exempt from margins based on the client
details provided by the member/ custodian in the specified format . Early pay-in of funds
specified by the member/custodians for a specific client and for a settlement is allocated
against the securities in the descending order of the net buy value of outstanding position of
the client.
Cross Margin
Salient features of the cross margining available are as under:
1. Cross margining benefit is available across Cash and Derivatives segment
2. Cross margining benefit is available to all categories of market participants
3. For client/entities clearing through same clearing member in Cash and Derivatives
segments, the clearing member is required to intimate client details through a file
uploadthrough Collateral Interface for Members (CIM) to avail the benefit of Cross
margining
4. For client/entities clearing through different clearing member in Cash and Derivatives
segments they are required to enter into necessary agreements for availing cross
margining benefit.
5. For the client/entities who wish to avail cross margining benefit in respect of
positions in Index Futures and Constituent Stock Futures only, the entitys clearing
member in the Derivatives segment has to provide the details of the clients and not
the copies of the agreements. The details to be provided by the clearing members in
this regard are stipulated in the Format
1. Positions eligible for cross-margin benefit
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2. Entities/clients eligible for cross margining
3. Facility of maintaining two client accounts
4. Computation of cross margining benefit
5. Provisions in respect of default
6. Additional Reports for Cross Margin
1. Positions eligible for cross-margin benefit:
Cross margining is available across Cash and F&O segment and to all categories of market
participants. The positions of clients in both the Cash and F&O segments to the extent they
offset each other are being considered for the purpose of cross margining as per the following
priority
a. Index futures and constituent stock futures in F&O segment
b. Index futures and constituent stock positions in Cash segment
c. Stock futures in F&O segment and stock positions in Cash segment
i. In order to extend the cross margin benefit as per (a) and (b) above, the basket
of constituent stock futures/ stock positions should be a complete replica of
the index futures. NSCCL specifies the number of units of the constituent
stocks/ stock futures required in the basket to be considered as a complete
replica of the index on the website of the exchange
(www.nseindia.com/NSCCL/Notification) from time to time.
ii. The number of units are changed only in case of change in share capital of the
constituent stock due to corporate action or issue of additional share capital or
change in the constituents of the index.
iii. The positions in F&O segment for the stock futures and index futures should
be in the same expiry month to be eligible for cross margining benefit.
iv. The position in a security is considered only once for providing cross
margining benefit. E.g. Positions in Stock Futures of security A used to set-
off against index futures positions will not be considered again if there is an
off-setting positions in the security A in Cash segment.
v. Positions in option contracts are not considered for cross margining benefit.
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2. Entities/clients eligible for cross margining
The clearing member has to inform NSCCL the details of client to whom cross
margining benefit is to be provided. The cross margining benefit is available only if clearing
members provide the details of clients in such manner and within such time as specified by
NSCCL from time to time.
1. Client/entity settling through same clearing member in both Cash
and F&O segment
i. The clearing member has to ensure that the code allotted (code used while
executing client trade) to client/entity in both Cash and F&O segment is same
ii. The clearing member must inform the details of clients to whom cross
margining benefit is to be provided through a file upload facility provided in
Collateral Interface for Members (CIM).
2. Client/entity settling through different clearing member in Cash and F&O
segment
i. In case a client settles in the Cash segment through a trading member /
custodian and clears and settles through a different clearing member in F&Osegment, then they are required to enter into necessary agreements.
ii. In case where the client/entity settles through Custodian in Cash segment, then
the client/entity, custodian and the clearing member in F&O segment are
required to enter into a tri-partite agreement as per the format
iii. In case where the client/entity clears and settles through a member in Cash
segment, and a different clearing member in F&O segment, then the member
in Cash segment and the clearing member in F&O segment have to enter into
an agreement as per theformat. Further, the client/entity must enter into an
agreement with the member as per the format.
iv. The clearing member in the F&O segment must intimate to NSCCL thedetails
of the client/entity in F&O segment along-with letter from trading
member/custodian giving details of client/entity in Cash segment who wish to
avail cross margining benefit.
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3. Facility of maintaining two client accounts
As specified by SEBI, a client may maintain two accounts with their respective members to
avail cross margin benefit only. The two accounts namely arbitrage account and a non-
arbitrage account may be used for converting partially replicated portfolio into a fully
replicated portfolio by taking opposite positions in two accounts. However, for the purpose
of compliance and reporting requirements, the positions across both accounts shall be taken
together and client shall continue to have unique client code.
4. Computation of cross margining benefit
i. The computation of cross margining benefit is done at client level on an online real
time basis and provided to the trading member / clearing member / custodian, as the
case may be, who, in turn, shall pass on the benefit to the respective client.
ii. For institutional investors the positions in Cash segment are considered only after
confirmation by the custodian on T+1 basis and on confirmation by the clearing
member in F&O segment.
iii. The positions in the Cash and F&O segment are considered for cross margining only
till time the margins are levied on such positions.
iv. While reckoning the offsetting positions in the Cash segment, positions in respect of
which margin benefit has been given on account of early pay-in of securities or funds
are not considered.
v. The positions which are eligible for offset, are subject to spread margins. The spread
margins are 25% of the applicable upfront margins on the offsetting positions or such
other amount as specified by NSCCL from time to time.
vi. The difference in the margins on the total portfolio and on the portfolio excluding off-
setting positions considered for cross margining, less the spread margins is considered
as cross margining benefit. Example
5. Provisions in respect of default
In the event of default by a trading member / clearing member / custodian, as the case
may be, whose clients have availed cross margining benefit, NSCCL may:
i. Hold the positions in the cross margin account till expiry in its own name.
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ii. Liquidate the positions / collateral in either segment and use the proceeds to meet the
default obligation in the other segment.
iii. In addition to the foregoing provisions, take such other risk containment measures or
disciplinary action as it may deem fit and appropriate in this regard.
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Stress testing in risk management
Stress testing defines a scenario and uses a specific algorithm to determine the expected
impact on a portfolio's return should such a scenario occur. There are three types of
scenarios:
Extreme event: hypothesize the portfolio's return given the recurrence of a historical
event. Current positions and risk exposures are combined with the historical factor returns.
Risk factor shock: shock any factor in the chosen risk model by a user-specified
amount. The factor exposures remain unchanged, while the covariance matrix is used to
adjust the factor returns based on their correlation with the shocked factor.
External factor shock: instead of a risk factor, shock any index, macro-economic
series (e.g., oil prices), or custom series (e.g., exchange rates). Using regression analysis, new
factor returns are estimated as a result of the shock.
In an exponentially weighted stress test, historical periods more like the defined
scenario receive a more significant weighting in the predicted outcome. The defined decay
rate lets the tester manipulate the relative importance of the most similar periods. In the
standard stress test, each period is equally weighted.
Instead of doing financial projection on a "best estimate" basis, a company may do stress
testing where they look at how robust a financial instrument is in certain crashes, a form
ofscenario analysis. They may test the instrument under, for example, the following stresses:
What happens if the market crashes by more than x% this year?
What happens if interest rates go up by at least y%?
What if half the instruments in the portfolio terminate their contacts in the fifth year?
What happens if oil prices rise by 200%?
This type of analysis has become increasingly widespread, and has been taken up by
various governmental bodies (such as the FSA in the UK) as a regulatory requirement on
certain financial institutions to ensure adequate capital allocation levels to cover potential
losses incurred during extreme, but plausible, events. This emphasis on adequate, risk
adjusted determination of capital has been further enhanced by modifications to banking
regulations such as Basel II. Stress testing models typically allow not only the testing of
individual stressors, but also combinations of different events. There is also usually the
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ability to test the current exposure to a known historical scenario (such as the Russian debt
default in 1998 or9/11 attacks) to ensure the liquidity of the institution.
Stress testing reveals how well a portfolio is positioned in the event forecasts prove true.
Stress testing also lends insight into a portfolio's vulnerabilities. Though extreme events are
never certain, studying their performance implications strengthens understanding.
Stress Testing Approaches
The following comprise a fairly comprehensive set of approaches for stress testing:
Historical event analysis.
What happens if the severe market event happens again? For example,
what is the impact on your portfolio if the Reliance industries drops 23% as it did on
October 19, 1987?
Scenario analysis based on historical events.
Develop scenarios based on historical events but update them for current conditions.
Institution-specific scenario analysis.
Identify scenarios based on the institution.s portfolio, businesses,and structural risks. This
seeks to identify the vulnerabilities and the worst-case loss events specific to the firm.
Extreme standard deviation scenarios.
Identify extreme moves and construct the scenarios in which such losses can occur. For
example, what can cause a 5-, 6-, 7-, 8-, 9-, 10-standard-deviation loss event?
Extreme incremental market moves and tail risk.
This approach does not identify the scenarios but just quantifies a set of progressively severe
market moves and the resultant loss. For example,
what is the potential loss if all equity markets markets gap by plus and minus 5%, 10%,
15%, 20%,etc.?
Quantitative evaluation of distributions of tail events and extreme value theory.
Based on observed historical market events, quantify the impact of a series of tail
events to evaluate the severity of the worst case losses. This approach also evaluates the
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distribution of tail events to determine if there are any patterns that should be used for
scenario analysis.
Specific Stress Tests by Category
Stress tests can be categorized by the types of assumptions they challenge, the
types of things that can go wrong, the nature of the surprises or market moves,model
parameters, product complexity, credit, sea changes.
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PORTFOLIO SELECTION
THE PROCESS OF SELECTING a portfolio may be divided into two stages.
The first stage starts with observation and experience and ends with beliefs about the
future performances of available securities.
Thesecond stage starts with the relevant beliefs about future performances and ends
with the choice of portfolio.
This report is concerned with the second stage. We first consider the rule that the
investor does (or should) maximize discounted expected, or anticipated, returns. This rule is
rejected both as a hypothesis to explain, and as a maximum to guide investment behavior.
We next consider the rule that the investor does (or should) consider expected return a
desirable thing andvariance of returnan undesirable thing. This rule has many sound points,
both as amaxim for, and hypothesis about, investment behavior. We illustrate relations
between beliefs and choice of portfolio according to the "expected returns-variance of
returns" rule.One type of rule concerning choice of portfolio is that the investor does (or
should) maximize the discounted (or capitalized) value of future returns.
. Since the future is not known with certainty, it must be "expected" or "anticipatded7'returns which we discount. Variations of this type of rule can be suggested. Following Hicks,
we could let "anticipated" returns include an allowance for risk.
We could let the rate at which we capitalize the returns from particular securities vary
with risk.
The hypothesis (or maxim) that the investor does (or should)maximize discounted
return must be rejected. If we ignore market imperfections the foregoing rule never implies
that there is a diversified portfolio which is preferable to all non-diversified portfolios.
Diversification is both observed and sensible; a rule of behavior which doesnot imply the
superiority of diversification must be rejected both as a maxim.There is a rule which implies
both that the investor should diversify and that he should maximize expected return. The rule
states that the investor does (or should) diversify his funds among all those securities which
give maximum expected return. The law of large numbers will insure that the actual yield of
the portfolio will be almost the same as the expected yield.5 This rule is a special case of the
expected returnsvariance of returns rule (to be presented below). It assumes that there is a
portfolio which gives both maximum expected return and minimum variance, and it
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commends this portfolio to the investor.This presumption, that the law of large numbers
applies to a portfolio of securities, cannot be accepted. The returns from securities are too
intercorrelated. Diversification cannot eliminate all variance.
The portfolio with maximum expected return is not necessarily the one with
minimum variance. There is a rate at which the investor can gain expected return by taking
on variance, or reduce variance by giving
Up expected return.We saw that the expected returns or anticipated returns rule is
inadequate. Let us now consider the expected returns-variance of returns (E-V) rule. It will
be necessary to first present a few elementary concepts and results . We will then show some
implications of the E-V rule. After this we will discuss its plausibility.In our presentation we
try to avoid complicated mathematical statements and proofs. As a consequence a price is
paid in terms of rigor and generality. The chief limitations from this source are (1) we do not
derive our results analytically for the n-security case; instead, we present them geometrically
for the 3 and 4 security cases;
(2) we assume static probability beliefs. In a general presentation we must recognize that
the probability distribution of yields of the various securities is a function of time.
The writer intends to present, in the future, the general,mathematical treatment which
removes these limitations.The concepts "yield" and "risk" appear frequently in
financialwritings. Usually if the term "yield" were replaced by "expected yield" or
"expected return," and "risk" by "variance of return," little change of apparent
meaning would result.
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Integrating Risk Management with Portfolio selection
Practical implementation of risk management at the selecting or creating the portfolio
can be a great tool which can insure the better performance of portfolio even in the
extremely volatile stock market . Risk management cannot protect you from losses in
extreme shock but it can minimize the risk and surprises . Risk measures such as VAR
provide useful base-case information. Risk capital serves as a last line of defense. Stress
testing, together with daily management dialogue and decision making, provides proactive
and dynamic management of risk. No risk management can prevent losses but the best can
minimize surprises. Stress testing is a powerful means of anticipating, understanding, and
preparing for shocks and the resulting potential losses.
Modern portfolio theory aims to allocate assets by maximizing the expected risk
premium per unit of risk. In a mean variance frame work risk is defined in term of the
possible variation of expected portfolio return. The focus on standard deviation as the
appropriate measure for risk implies that the investor weigh the probability of negative return
equally against positive return. However it is a stylized fact that the distribution of many
financial return series are non-normal Furthermore there is ample evidence that agents often
treat losses and gains asymmetrically. There is a wealth of experimental evidence for loss
aversion. The choice therefore of mean variance efficient portfolios is likely to give rise to an
inefficient strategy for optimizing expected returns for financial assets whilst minimizing
risk. It would therefore be more desirable to focus on a measure for risk that is able to
incorporate any non-normality in the return distributions of financial assets. Indeed risk
measures such as semi-variance were originally constructed in order to measure the negative
tail of the distribution separately.
Typically mainstream finance rests on the assumption of normality, so that a move
away from the assumption of normally distributed returns is not particularly favored; one
drawback often stated is the loss in the possibility of moving between discrete and
continuous time frameworks. However it is precisely this simplifying approach, whereby any
deviations from the square root of time rule are ignored, which needs to be incorporated into
current finance theory. The ability to focus on additional moments in the return distribution
enables additional risk factors to be included into the optimal portfolio selection
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CHAPTER II
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COMPANY PROFILE
Angel Broking ltd. is one of India's leading financial institutions, offering
complete financial solutions that encompass every sphere of life. From stock
broking, to mutual funds, to life insurance, to investment, the group caters to the
financial needs of individuals and corporate.
Angel group is leading Retail Broking service provider in the country.
The group has emerged as one of the top 3 retail stock broking house offering a
gamut of retail centric services like Research, investment Advisory ,wealth
Management Services ,E-broking & Commodities to the individual investor.
Angel has a wide and efficient network of 21 regional hubs,150 branchesand 2200+ business associates in 115 cities all over the country services more
than 1.9 lac individual investors. The group is promoted by Mr. Dinesh
thakar, who started these enterprises as a sub broker in 1987.
Angel Broking's tryst with excellence in customer relations began in
1987. Today, Angel has emerged as one of the most respected Stock-Broking
and Wealth Management Companies in India. With its unique retail-focusedstock trading business model, Angel is committed to providing Real Value for
Money to all itsclients.
The Angel Group is a member of the Bombay Stock Exchange (BSE),
National Stock Exchange (NSE) and the two leading Commodity Exchanges in
the country: NCDEX & MCX. Angel is also registered as a Depository
Participant with CDSL.
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Research Strength at Angel
Angel Broking is one of the leading retail brokerage houses with a
professional and qualified research team. We deploy state of the art research
metrics and international news services like Bloomberg/ Reuters etc. to remain
in touch with global / domestic developments.
Angels research has a proven track record of over 5 years. Emphasis on
providing best investment value for money to the retail client is the core
philosophy at angel .angel principally focuses on the individual investor
community and has an investment / advisory desk to give first hand information
/ guidance to them. Angels research and advisory team comprises of 80+
professionals working continuously to discover potential multi- bagger stocks
for you.
Angel broking Research center the special research cell where some of
Indias finest financial analyst bring you intensive research reports on how the
stock market is faring. When is the right time to invest, when to execute your
order and more. Depending on what kink of investor you are, we bring you
fundamental or basis research and technical research. As an investor with angel
broking ltd., you get access to these research reports exclusively. You get
access to the following reports.
Intraday Calls
These calls are provided according to changing market situations. Be it
news, momentum or technical perspectives be updated with what are experts
advise you to do during the market hours.
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Daily Technical view
A technical view summarizing the previous day movement and what is
expected to happen on the current day. This report will also provide you withtechnical calls for trading along with various supports and resistances of chosen
stocks.
Sectorial Reports
Deciding which sector to invest in, our super sector report can guide you.
Know details including the effect of government policies and regulations and
estimates about how the sector is expected to behave.
M connect
At last but not the least you can get these expert tips and
recommendations as SMS on to your mobile phone.
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Angel broking limited
The journey so far (milestones)
February, 2008 Crossed the 400,000 mark in unique trading accounts
November, 2007 Received "Major Volume Driver" award for FY07
March, 2007 Crossed the 200,000 mark in unique trading accounts
December, 2006 Crossed the 2,500 mark in terms of business associates.
October, 2006 Received "Major Volume Driver" award for FY06
September, 2006 Commenced Mutual Fund and IPO distribution business
July, 2006 Formally launched the PMS function
March, 2006 Crossed the 100,000 mark in unique trading accounts
October, 2005 Received the prestigious "Major Volume Driver" award for FY05
September, 2004 Launch of Online Trading Platform
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Services of Angel Broking Ltd.
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Competitors
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KARVY
KARVY, is a premier integrated financial services provider, and ranked
among the top five in the country in all its business segments, services over 16
million individual investors in various capacities, and provides investor services
to over 300 corporate, comprising the who is who of Corporate India. KARVY
covers the entire spectrum of financial services such as Stock broking,
Depository Participants, Distribution of financial products - mutual funds,
bonds, fixed deposit, equities, Insurance Broking, Commodities Broking,
Personal Finance Advisory Services, Merchant Banking & Corporate Finance,
placement of equity, IPOs, among others. Karvy has a professional management
team and ranks among the best in technology, operations and research of
various industrial segments.
The birth of Karvy was on a modest scale in 1981. It began with the
vision and enterprise of a small group of practicing Chartered Accountants who
founded the flagship company Karvy Consultants Limited. We started with
consulting and financial accounting automation, and carved inroads into the
field of registry and share accounting by 1985. Since then, we have utilized our
experience and superlative expertise to go from strength to strengthto better
our services, to provide new ones, to innovate, diversify and in the process,
evolved Karvy as one of Indias premier integrated financial service enterprise.
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ICICI direct .com
ICICI Bank Demat Services boasts of an ever-growing customer base of
over 11.5 lacs account holders. In our continuous endeavor to offer best of the
class services to our customers we offer the following features:
E-Instructions: You can transfer securities 24 hours a day, 7 days a week
through Internet & Interactive Voice Response (IVR) at a lower cost. Now with
"Speak to transfer", you can also transfer or pledge instructions through ourcustomer care officer.
Consolidation Demat Account: Dematerialise your physical shares in
various holding patterns and consolidate all such scattered holdings into your
primary demat account at reduced cost.
Digitally Signed Statement: Receive your account statement and bill by
email.
Corporate Benefit Tracking: Track your dividend, interest, bonus through
your account statement.
Mobile Request: Access your demat account by sending SMS to enquire
about Holdings, Transactions, Bill & ISIN details.
Mobile Alerts: Receive SMS alerts for all debits/credits as well as for anyrequest which cannot be processed.
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