Quantis Investment Management developed the Q-Select system to balance subjective and objective factors in fund selection. The process first filters funds based on criteria like tracking error and fund size. Managers are then scored on people, process, performance, risk monitoring and more. Scores are translated into an expected alpha using a formula accounting for tracking error, breadth, and other factors. A final alpha forecast combines this quantitative forecast with qualitative assessments and past performance to balance type 1 and type 2 errors. Portfolios are constructed to optimize expected alpha and diversification based on the quantitative analysis.
Risk and Return: Portfolio Theory and Assets Pricing ModelsPANKAJ PANDEY
Discuss the concepts of portfolio risk and return.
Determine the relationship between risk and return of portfolios.
Highlight the difference between systematic and unsystematic risks.
Examine the logic of portfolio theory .
Show the use of capital asset pricing model (CAPM) in the valuation of securities.
Explain the features and modus operandi of the arbitrage pricing theory (APT).
The Global Market Portfolio Composition Studygjohnsen
Eastgate Advisors, llc recently conducted a review of published literature on the likely composition of the global markets portfolio which theory says is the most mean variance efficient portfolio an investor can hold. Our purpose in doing so was to help update our strategic global asset allocation benchmarks.
Feel free to contact Greg Johnsen, CFA with comments or questions.
Risk and Return: Portfolio Theory and Assets Pricing ModelsPANKAJ PANDEY
Discuss the concepts of portfolio risk and return.
Determine the relationship between risk and return of portfolios.
Highlight the difference between systematic and unsystematic risks.
Examine the logic of portfolio theory .
Show the use of capital asset pricing model (CAPM) in the valuation of securities.
Explain the features and modus operandi of the arbitrage pricing theory (APT).
The Global Market Portfolio Composition Studygjohnsen
Eastgate Advisors, llc recently conducted a review of published literature on the likely composition of the global markets portfolio which theory says is the most mean variance efficient portfolio an investor can hold. Our purpose in doing so was to help update our strategic global asset allocation benchmarks.
Feel free to contact Greg Johnsen, CFA with comments or questions.
A ratio is simple arithmetical expression of the relationship of one number to another. It may be defined as the indicated quotient of two mathematical expressions. According to Accountant’s Handbook by Wixon, Kell and Bedford, “a ratio is an expression of the quantitative relationship between two numbers”.
Ratio analysis is the process of determining and presenting the relationship of items and group of items in the statements. According to Batty J. Management Accounting “Ratio can assist management in its basic functions of forecasting, planning coordination, control and communication”.
It is helpful to know about the liquidity, solvency, capital structure and profitability of an organization. It is helpful tool to aid in applying judgement, otherwise complex situations.
Discusses various risks involved in capital budgeting - useful to the students of under graduate, post graduate and professional course students in finance and management
A Study on the Performance of Mutual Fund Scheme in IndiaIJAEMSJORNAL
A mutual fund is a trust that encompasses the savings of a number of investors who share a common financial goal. The money thus collected is then invested in capital market instruments such as shares, debentures and other securities. The income earned through these investments and the capital appreciation realized is shared by its unit holders in proportion to the number of units owned by them. Thus, Mutual Fund is one of the most effective instruments for the small & medium investors for investment and offers opportunity to them to participate in capital market with low level of risk. It also provides the facility of diversification i.e. investors can invest across different types of schemes. Indian Mutual Fund has achieved a lot of popularity since last two decades. For a long time UTI enjoyed the monopoly in mutual fund industry. But with the passage of time many new players came in the market and thus the mutual fund industry faces a lot of competition. Now a day this industry has become the major player of the financial system. Therefore it becomes important to investigate the mutual fund performance at continuous basis. The wide variety of schemes floated by these mutual fund companies gave wide investment choice for the investors. Among wide variety of funds equity, diversified fund is considered as substitute for direct stock market investment. In present paper an attempt has been made to investigate the performance of the open ended, growth oriented, equity diversified schemes on the basis of return and risk evaluation. The analysis was achieved by assessing various financial tests like Average Return, Standard Deviation, Beta, Coefficient of Determination (R2), Alpha, Sharpe Ratio and Treynor Ratio whose results will be useful for investors for taking better investment decisions. The data has been taken from various websites of mutual fund schemes and from amfiindia.com. The analysis depicts that majority of funds selected for study have outperformed under Sharpe Ratio as well as Treynor Ratio.
A ratio is simple arithmetical expression of the relationship of one number to another. It may be defined as the indicated quotient of two mathematical expressions. According to Accountant’s Handbook by Wixon, Kell and Bedford, “a ratio is an expression of the quantitative relationship between two numbers”.
Ratio analysis is the process of determining and presenting the relationship of items and group of items in the statements. According to Batty J. Management Accounting “Ratio can assist management in its basic functions of forecasting, planning coordination, control and communication”.
It is helpful to know about the liquidity, solvency, capital structure and profitability of an organization. It is helpful tool to aid in applying judgement, otherwise complex situations.
Discusses various risks involved in capital budgeting - useful to the students of under graduate, post graduate and professional course students in finance and management
A Study on the Performance of Mutual Fund Scheme in IndiaIJAEMSJORNAL
A mutual fund is a trust that encompasses the savings of a number of investors who share a common financial goal. The money thus collected is then invested in capital market instruments such as shares, debentures and other securities. The income earned through these investments and the capital appreciation realized is shared by its unit holders in proportion to the number of units owned by them. Thus, Mutual Fund is one of the most effective instruments for the small & medium investors for investment and offers opportunity to them to participate in capital market with low level of risk. It also provides the facility of diversification i.e. investors can invest across different types of schemes. Indian Mutual Fund has achieved a lot of popularity since last two decades. For a long time UTI enjoyed the monopoly in mutual fund industry. But with the passage of time many new players came in the market and thus the mutual fund industry faces a lot of competition. Now a day this industry has become the major player of the financial system. Therefore it becomes important to investigate the mutual fund performance at continuous basis. The wide variety of schemes floated by these mutual fund companies gave wide investment choice for the investors. Among wide variety of funds equity, diversified fund is considered as substitute for direct stock market investment. In present paper an attempt has been made to investigate the performance of the open ended, growth oriented, equity diversified schemes on the basis of return and risk evaluation. The analysis was achieved by assessing various financial tests like Average Return, Standard Deviation, Beta, Coefficient of Determination (R2), Alpha, Sharpe Ratio and Treynor Ratio whose results will be useful for investors for taking better investment decisions. The data has been taken from various websites of mutual fund schemes and from amfiindia.com. The analysis depicts that majority of funds selected for study have outperformed under Sharpe Ratio as well as Treynor Ratio.
Numerical Methods was a core subject for Electrical & Electronics Engineering, Based On Anna University Syllabus. The Whole Subject was there in this document.
Share with it ur friends & Follow me for more updates.!
Product Profitability - Out of the ShadowsDavid Gerbino
Product profitability has become the stepchild to organizational profitability and, more recently, customer profitability. Led by perspectives from an industry consultant and a banking practitioner, you’ll discover the critical role it plays in organizational focus, product development, marketing resource allocation, sales efforts, and as a prerequisite to customer/relationship profitability. Learn some of the "sausage making" of product profitability and how banks can and do use it. Key points and takeaways include:
• Ways your bank can grow revenues while margins shrink and compliance costs escalate
• How product profitability fits into strategic decision making and strategy execution
• A demystification of the product profitability “black box.”
“Over” and “Under” Valued Financial Institutions: Evidence from a “Fair-Value...Ilias Lekkos
The aim of the study is to present our approach that allows us to evaluate relative over- and under-valuation of financial institutions based on the distance between their market-based price to book ratios and our estimated "fair-value" P/Bs.
Building a Holistic Capital Management FrameworkCognizant
For banks, capital management strategy is a complex process that must take into account a vast range of regulatory and financial factors. Adopting the holistic approach detailed here will enable banks to provide sustainable value to their clients.
Making Long Term FM Decisions - Integrative Case Title An.docxsmile790243
Making Long Term FM Decisions - Integrative Case
Title: Analyzing Long Term Financial Decision Making in the Firm (Learning Demonstration 3)
Initial Steps to Completion:
1. Organize your team, choose a leader, and accept accountability for being the lead analyst for one or more parts of this list of tasks.
2. Complete your draft assigned task(s) and post in a common area for review by your team members.
3. Review, comment on, and suggest changes to draft completed tasks by the team.
4. Discuss and resolve differences and come to a consensus on the best responses.
5. Organize your analysis, conclusions, and recommendations
Course Deliverable: Write a report responding to the tasks assigned to your team. Clearly organize your report and effectively communicate the team’s analysis, conclusions and recommendations (if appropriate) associated with each task. Provide the details supporting your analysis as attachments. You should be completing tasks along the way – do not wait until the end of the course to complete your tasks.
Introduction: As a special analytical group set up by ACME Iron by the firm’s Controller, you have been tasked to respond to the following issues raised in a meeting with the CFO.
You and your team must look over several prospective financial strategies to aid in the successful growth of ACME Iron.
You are to work over an 8 to 12 week period on several projects, detail your work as you proceed on these projects, and assemble the report for the CFO to make to the board on the items listed while you work in a team environment. Management will be looking at the team over this period on how well they self-organize and analyze the research areas which will include:
Capital investment analysis
CAPM – Capital Asset Pricing Model determination for the company
WACC – Weighted Average Cost of Capital computations
EVA – Economic Value Analysis
MVA – Market Value Added
Capital structure of the company
Dividend policy
Stock repurchase and option pricing strategy
Bankruptcy risk analysis
Decision Tree Creation
Real option analysis of projects
The CFO wants to test your team out on a simple project in the first task before you get into preparing items for his board presentation in subsequent tasks and projects. He wants to see how well you perform tasks as a team as well as how accurate and thoughtful you are in your work. Details are important to him as well as good organization/presentation and communication.
Financial Statements for use on Tasks
ACME Iron
Balance Sheet
Assets
Current assets:
2014
2015
change
Cash
500,000
600,000
100,000
Investments
1,000,000
1,025,000
25,000
Inventories
110,000,000
117,000,000
7,000,000
Accounts receivable
11,750,000
12,500,000
750,000
Pre-paid expenses
2,500,000
2,600,000
100,000
Other
0
0
...
In this paper, we used financial statements as the main information to calculate the enterprise
value by discounted cash flow model. For the prediction of future cash flows in DCF model, a new method
based on the Markov chain is proposed to get the growth rates of future cash flows, instead of the fixed growth
rate method. The superior performance of it can be illustrated in empirical analysis. And the result shows that
we can improve the accuracy of the enterprise value evaluation with partial information by using the Markov
chain
1. pLAYERS www.citywireglobal.com
34
citywirEglobal.com
Selectors’ tool kit
It may look like alchemy but Quantis Investment
Management’s Q-Select system combines
subjective and objective facets of fund selection
to achieve a balanced result, says its creator
and the company’s CIO Attila Rébak
W
hen we launched Quantis Investment
Management, we realised we lacked the
economies of scale to practice stock picking on
a global scale. However, a structured fund selection process
can balance subjective and objective factors whether we
build a Chinese equity fund of funds or a Latin American
one. With this in mind we have set up our own process,
called Q-Select, which has similarities with other multi-
managers together with specialities and styles of its own.
The first filter
The first step in our process is to sift for potential funds
that fit into our fund of funds model. Filters used include
tracking error, fund size, consistency, turnover, manager
tenure and standard deviation. The aim is to reduce the
number of candidates to a manageable group of 20-40.
When we have the list of contenders we try to
understand the investment process and the proposition
of the given vehicle. Initially we go through the RFP stage
and roughly complete our scoring template; these points
can then be grouped under the following headings:
People, Process, Performance, Risk monitoring and
management.
This is a common step for many multi-manager
companies, but it is a very subjective one as well.
We are lucky at Quantis because we talk a common
language; there are only minor differences in scores
depending on who completes the scoring system.
We try to meet the decision makers to fully understand
their process, to know what to expect from the fund in
general and in different market situations in particular,
and to go through every ambiguous point with them.
After the meeting we often compare one another’s
scores for the same fund, to reduce the chance of
incorrect or subjective evaluation, and to have a
consensus on the fine-tuned scoring.
Crude alpha forecast
We realised that just a pure score is hardly enough to
structure our fund of funds and determine the weights
of the individual holdings. We turn to the Waring and
Ramkumar article1
to translate our scores into an
expected alpha, which is coupled with a forecasted
tracking-error and the correlation among the alpha of
the given funds, with which we can make a formal
optimisation for our fund of funds.
α = estimated equilibrium level of alpha of the given
manager;
IC = information coefficient of Quantis, which measures
the correlation between the predicted quality of the
managers and the actual quality of the manager;
σ = variability in the information coefficient of the
individual managers. As suggested by the original
paper we use 0.07 for all managers;
z = measures the quality of the manager. We translate
our qualitative score with the help of the standard
normal distribution;
Br = breadth of the individual managers, ie the
number of independent bets the managers take
each year;
ω = tracking error of the manager;
TC = transfer coefficient, how effectively could the
manager translate his view in practice – this very
much depends on the number of benchmark holdings
and the active risk of the manager.
We use the formula proposed by Grinold and Kahn for
long-only managers:
where γ=(53+N)^0.57 and N is the number of securities
in the benchmark portfolio.
The above mentioned translation helps us to establish
a few important links with which we agree:
Firstly, the skill alone (high score) is not enough to add
value; you should factor in risk (tracking error).
Secondly, we can judge whether the fee-structure is
attractive by taking into account the quality and the risk
of the fund. As we forecast before fee alpha with the
translation it is very easy to incorporate the fee structure
of the fund.
Finally, the link is not linear between higher tracking
error and higher (before-fee) alpha. Depending on the
depth of the universe and the turnover of the fund
manager, a lower transfer coefficient can easily eat away
the value added by a higher tracking error. This can be
a concern especially in emerging market countries and
regional funds, and less so for global developed market
or US funds.
As we want to use the tracking error forecast for the
alpha forecast and for risk management as well, we have
to balance the conflicting objectives of the two functions.
For alpha forecast we need a more stable tracking error
as we do not want to trade the underlying funds just
because the forecasted tracking error shrinks from one
A formula
for success
Mgr Mgr
1-γ(N)
Quantis=ICα ±σ z ωΒrIC TCMgr Mgr Mgr Mgr
TC=(—–)[—–———]ωMgr
1 (1+ )ωMgr –1
1– γ(Ν)
Mgr Mgr
1-γ(N)
Quantis=ICα ±σ z ωΒrIC TCMgr Mgr Mgr Mgr
TC=(—–)[—–———]ωMgr
1 (1+ )ωMgr –1
1– γ(Ν)
2. pLAYERS 35Issue 21: February 2012
Selectors’ tool kit
citywirEglobal.com
Got something to say on fund
selection? Call Jesús Segarra Sobral.
jsobral@citywire.co.uk +44 20 7840 2175
month to the next and thus the projected alpha is small
compared with other funds. But to be able to effectively
manage the risk of our fund, we need a tracking error
forecast which effectively takes into account every
piece of information and responds promptly to market
movements. After evaluating many potential candidates
we chose exponential smoothing from weekly returns.
Final alpha forecast
We understand our limitations as well; hence we use
other methods to generate the final alpha forecast for
the given funds. The aim with this method is to avoid
mistakes known in statistics as Type I and Type II errors.
Sometimes the manager cannot add value with a
well-thought-out investment process due to deficiencies
uncovered with our scoring system; or to the contrary
the manager can produce alpha because she/he employs
information efficiently in a framework of an average
investment process.
To achieve this goal (ie balance efficiently Type I and
Type II error) we combine a priori information (return
forecast based on Waring et al. article) with the posterior
information (the return achieved during the past 36
months) in a Bayesian fashion, that gives much more
weight to our qualitative assessment.
Portfolio construction
With the expected alpha and covariance matrix at hand
we can make a formal optimisation for our fund of funds.
There are several advantages to this method:
We can identify funds (portfolio composition) which
truly diversify our risk compared to the benchmark.
With the forecasted risk and expected alpha we can
identify the optimal number of funds in our portfolio.
Holding only a few funds represents a big risk for us,
while a larger number diversifies the expected alpha. A
surprisingly small number of funds (four to six) is enough
to achieve optimal diversification in case of single country
or regional funds. This is also true for global funds, where
the fund selector does not want to bet beyond the fund
selection (sector, country, region).
This quantitative method usually confirms the qualitative
assessment of the underlying funds. For example, our
optimisation for global developed market equity yielded
four funds as the ideal composition, where two of the
underlying managers are true stock pickers, one of them
tries to add value mostly with sector bets, while the last
one’s value-added comes mostly from regional calls.
We can use several well-defined constraints explicitly
during the optimisation process such as minimum weights
for individual holdings and tracking error range.
Making it work for you
As we do not make any asset allocation calls in our
strategy, we do not have to make any adjustment to
the aforementioned process, but for selectors who
want to control the asset allocation as well, our model
can be modified. Assuming that the correlation among
the alpha of different managers in different asset
classes (or regions, countries) is independent,
separating the asset allocation and fund selection
steps solves the problem.
We have been using Q-Select since the beginning
and have had positive feedback. Even though the
investment process is well structured I have to emphasise
the subjective factors, mainly during the selection and
evaluation phase. In the phase of fund selection, it is
essential to filter the right universe as the alpha forecast
depends on the tracking error, which makes sense only
when we compare the funds to the correct benchmark.
While in the evaluation phase we also need some
controlled subjectivity when we score the individual
funds and finalise our qualitative assessment.
1
M. Barton Waring and Sunder R. Ramkumar: ‘Forecasting Fund
Manager Alphas: The Impossible Just Takes Longer’, Financial
Analysts Journal March/April 2008.
‘We understand our limitations as
well; hence we use other methods
to generate the final alpha forecast
for the given funds’
Attila Rébak
CIO Quantis Investment Management