“Multi Asset Dynamic Portfolio”

v

www.plindia.com

“Multi Asset Dynamic Portfolio”

Multi Asset Dynamic Portfolio (MADP) is a 100% quant-

based tactical asset allocation strategy created by Quantifi

that invests only in passive instruments (index funds and

ETFs). Quantifi is Prabhudas Lilladher’s specialized

quantitative research and investment management arm.

The strategy is only available as a discretionary PMS to

avoid greed and fear of investors.

Our Strategy is based on 3Pillars

Downside

Protection

Upside

Participation

Consistent

Performance

PL PMS - Multi Asset Dynamic Portfolio 2

Strategy Overview

Long Only EquityMulti Asset – comprises of 7 asset classes

Strategic Asset Allocationwith moderate Shifts

Tactical asset allocationwith radical shifts

DiscretionaryQuant & Rules Based that is unbiased

Relies only on Fundamental data andFund Managers’ judgment

Quantamental Strategy that relies on varieddatasets - Fundamental, Alternative &

Technical data

Stock or Sector SelectionInvests in ETFs

Rebalances at fixed time intervalsirrespective of market conditions

Trigger Based Rebalances

Limited rotation and allocationbetween asset classes due to size, classification and tax constraints

such as in mutual funds

SEBI regulated PMS, offering higher flexibilityas it can make aggressive allocations and

rotations between asset classes, to be ableto utilize Tactical opportunities.

What MADP Is? What MADP Isn’t? VS

1

2

3

4

5

6

7


Source: *Brinson, Hood, Beebower. “Determinants of Portfolio Performance”. Financial Analysts Journal. July-August 1986; Brinson, Singer, Beetbower. “Determinants of Portfolio Performance II: An Update”. Financial Analysts Journal. May-June 1991.

● Despite the attention that stock selection typically gets, it is not the major driver of investor outcomes.

● Asset allocation explains about 92% of the variability of results for diversified investment pools such as pension funds*.

● Only 8% is explained by other factors such as security selection, market timings etc.

● Significant percentage of volatility of investment performance is driven by asset allocation decisions

4.6% Security Selection

2.1% Other Factors

1.8% Market Timing

91.5% Assets Allocation

Asset Allocation Matters The Most


Asset Allocation Matters The Most

Note: Equity returns include price performance and does not include dividend yields. Calendar month convention is used for YoY returns calculation. For Large, mid and small caps correspond to BSE S&P Sensex, BSE S&P mid cap and BSE S&P small cap respectively. Gold prices in INR are from World Gold Council. S&P corporate bond index is taken to calculate corporate bond returns. 10-year sovereign g-sec price index data is taken from BSE website. Liquid/ Cash is the DSP Liquid Growth Fund (Regular) Source: BSE, World Gold Council, Bloomberg, Yahoo Finance, PL.

Did You Know ? “All” asset classes have certain periods of underperformance.

OK So? We generate returns by allocating money to the Right Asset at Right Time !

How !? Our Proprietary Quant models ensure that you remain invested in the Right Asset ETFs at the Right Time.

Do you see the same asset winning consistently every year? No, Right?

YEAR WISE ANNUAL RETURNS OF INVESTMENT AVENUES

Year Gold Large Cap Mid Cap Small Cap Corp Bonds G-sec Nasdaq 100 Liquid Winner

2007 17.5% 47.1% 68.6% 93.7% 6.8% 7.1% 18.7% 7.0% Small Cap

2008 28.9% -52.4% -67.0% -72.4% 13.7% 9.1% -41.9% 8.7% Gold

2009 19.4% 73.0% 107.7% 126.9% 8.0% 3.5% 53.5% 4.4% Small Cap

2010 24.2% 22.9% 16.1% 15.7% 9.5% 4.0% 19.2% 5.5% Gold

2011 29.4% -24.6% -34.2% -42.6% 5.2% 2.5% 2.7% 8.9% Gold

2012 11.7% 25.7% 38.5% 33.0% 11.7% 10.7% 16.8% 9.7% Mid Cap

2013 -18.0% 9.0% -5.7% -11.2% 6.8% -0.3% 35.0% 9.2% Nasdaq 100

2014 2.2% 29.9% 54.7% 69.2% 13.4% 14.1% 17.9% 9.0% Small Cap

2015 -7.9% -5.0% 7.4% 6.8% 9.1% 7.4% 8.4% 8.3% Corp Bonds

2016 10.9% 1.9% 8.0% 1.8% 11.1% 14.2% 5.9% 7.6% G-sec

2017 6.0% 27.9% 48.1% 59.6% 6.8% -0.6% 31.5% 6.6% Small Cap

2018 8.4% 5.9% -13.4% -23.5% 7.5% 6.7% -1.0% 7.4% Gold

2019 21.1% 14.4% -3.0% -6.8% 10.0% 10.0% 38.0% 6.5% Nasdaq 100

2020 28.3% 15.8% 19.9% 32.1% 12.2% 9.6% 47.6% 4.2% Nasdaq 100

The strategy has given positive returns on gross basis over last 15 years and has performed average or better compared to other asset classes.

Note: Back-tested results of Quant Model & not actual returns. Above returns are only for understating purpose and there is no assurance or guarantee that the objectives of the investment will be achieved as investment in Securities is subject to market risk. Returns net of management fees, performance fees ,transaction costs. and other expenses. Impact cost not included. Data as on 15th November 2021. Benchmark = 80% 50:50 Crisil Hybrid Moderate Index + 20% Gold


Performance

YEAR QUANTIFI

ETF BASED MADP FUND

NIPPON INDIA NIFTY BEES

ICICI PRU NEXT 50

ETF

NIPPON INDIA NIFTY

MIDCAP 150

MOTILAL OSWAL

SMALLCAP 250 INDEX FUND

MOTILAL OSWAL

S&P 500 INDEX FUND

MOTILAL OSWAL

NASDAQ 100 ETF

NIPPON INDIA GOLD BEES

NIPPON INDIA LONG TERM

GILT ETF

BHARAT BOND ETF - APRIL 2025

NIPPON INDIA

LIQUID BEES

BENCHMARK

2007 35.4% 52.9% 75.7% 76.9% 94.9% -7.5% 6.5% 17.4% 6.6% 6.8% 6.4% 30.1%

2008 22.9% -51.9% -63.5% -59.4% -69.1% -24.2% -27.9% 25.6% 22.8% 13.7% 6.6% -19.9%

2009 86.6% 74.6% 127.9% 99.0% 113.9% 17.9% 47.6% 22.7% -4.9% 8.0% 2.8% 36.6%

2010 15.6% 18.8% 17.8% 19.2% 16.3% 8.9% 15.7% 21.7% 6.0% 9.5% 4.1% 14.3%

2011 4.6% -23.9% -31.9% -31.0% -36.0% 18.3% 23.4% 30.3% 5.8% 5.2% 6.1% -1.0%

2012 20.6% 27.0% 48.1% 39.2% 38.2% 17.4% 17.4% 10.9% 11.4% 11.7% 6.0% 18.1%

2013 7.3% 6.6% 4.8% -5.1% -9.5% 46.6% 54.9% -14.1% 2.9% 6.8% 6.0% -1.6%

2014 20.8% 31.2% 44.4% 55.9% 69.6% 12.3% 22.6% 0.8% 16.3% 13.4% 6.0% 19.8%

2015 0.7% -4.1% 7.0% 6.5% 9.6% 5.0% 13.5% -7.8% 8.2% 9.1% 5.0% 1.8%

2016 15.9% 3.9% 7.1% 7.1% -0.4% 9.4% 8.1% 10.7% 15.5% 11.1% 4.5% 9.7%

2017 27.7% 30.0% 44.8% 47.3% 57.1% 11.4% 22.2% 2.9% 1.1% 6.8% 4.0% 16.5%

2018 13.1% 4.6% -8.8% -15.4% -27.6% 0.7% 5.6% 6.9% 7.3% 7.5% 4.2% 4.5%

2019 21.6% 13.5% 1.4% 0.0% -7.8% 33.0% 41.6% 22.9% 11.1% 10.0% 3.7% 12.6%

2020 49.4% 15.8% 14.8% 25.3% 25.9% 0.5% 51.5% 26.2% 11.1% 10.4% 2.5% 19.3%

2021 YTD 21.4% 31.0% 37.4% 52.9% 60.0% 27.7% 29.3% -2.6% 1.1% 4.6% 2.1% 14.1%


Quantamental Investing Approach

Quantamental Investing Approach is a blend of fundamental analysis and quantitative techniques. This approach uses Fundamental + Alternative + Quantitative + Technical (FAQT) methods to make investment decisions. Quantamental research is qualitative and quantitative data driven approach. Quantamental research explores the deeper and wider insights of the data enabling agile decision-making process.

Merits of Quantamental Investing Approach • Data Driven trigger based Agile decision making

• Zero intervention of Fund Manager’s bias

• Quantifies market sentiments and Euphoria

• Provides greater insights of factors affecting prices

• Over-rides rule based decision making in times of events not

captured by historical data such as Covid-19 market crash.

Outcomes of Quantamental Investing Approach • More consistent returns resulting to faster compounding

• Eliminates emotional biases like panic decision making,

Loss-Aversion etc.

• Eliminates behavioral biases and Heuristics

• Constant monitoring and seamless decision making

• Optimized and Reliable back tested investment strategies

In the US, the AUM of Quant based mutual funds has grown a drastic 19.2% CAGR from 2010-2017 and has done so in an exponential manner. This shows the rising popularity of quant funds in the United States as the 8-year CAGR is expected to rise beyond 20%.

Source: Morgan Stanley Research

Source: AceMF, Data as on 30th July 2021


Quantitative Funds in the US and India

In India, Quant funds still represent a miniscule percentage of total funds managed by MFs. This space is still nascent with a lot of upcoming small investment firms following quant-based strategies. India Quant Fund Landscape: Total AUM Size – Rs. 5920 Cr approx. Top players by AUM

● DSP Quant Fund – Rs. 988 Cr

● Tata Quant Fund – Rs. 64 Cr

● Nippon Quant Fund – Rs. 30 Cr

● Quant Money Managers – Rs. 3207 Cr

● ICICI Pru Quant Fund – Rs. 69 Cr.

● Axis Quant Fund – Rs. 1552 Cr


The Rise of Quant

Proportion of Hedge Fund Launches that Use Quantitative Framework

Source: Preqin

35.8

23.7

17.2

12.1 10.1

0.5 0.4 0.2 0

5

10

15

20

25

30

35

40

ActiveLarge

Growth

ActiveLargeBlend

ActiveLargeValue

ActiveOther

PassiveLargeBlend

PassiveOther

PassiveLarge

Growth

PassiveLargeValue

Source: Morningstar

Percentage of Assets in US equity funds by Investment Type

As on December 1998

33.0

17.0

12.3 11.3 9.4 9.1

4.0 3.8

0

5

10

15

20

25

30

35

PassiveLargeBlend

ActiveLarge

Growth

ActiveOther

ActiveLargeBlend

ActiveLargeValue

PassiveOther

PassiveLargeValue

PassiveLarge

Growth

As on April 2019

89%

11%

As on December 1998

Active Passive

50% 50%

As on April 2019

Active Passive


Passive Goes Massive

Source: Investment Company Institute

Cumulative Flows from US Active to Passive Funds


Passive Goes Massive

Data as on April 2019 Source: Morningstar


USA is Automating…

33.0

17.0

12.3 11.3

9.4 9.1

4.0 3.8

0

5

10

15

20

25

30

35

PassiveLargeBlend

ActiveLarge

Growth

ActiveOther

ActiveLargeBlend

ActiveLargeValue

PassiveOther

PassiveLargeValue

PassiveLarge

Growth

Percentage of Assets in US equity funds by Investment Type

24.3%

40.6% 7.7%

14.7%

7.4%

2.9% 2.4%

35.1%

Active Managed Funds Others

Mutual index funds Institutional Index

ETF Index Smart ETFs

Quant Funds

Automated Managed Funds

Others: Held by companies Government, Insurance, Foreigners Source: The Economist

Percentage of Total Public Equities (worth USD 31trn)


… So Will India

0%

2%

4%

6%

8%

10%

12%

14%

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May

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Jun

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Jul-

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Sep

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Jan

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Feb

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Mar

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Ap

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May

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Jun

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Sep

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Oct

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Passive AUM has risen from 7.9% in April 2020 to 11.9% in October 2021

Note: Passive AUM includes Index funds, ETFs & FoFs investing overseas Source: AMFI

Size of Passive + Quant Funds in Total MF AUM

11.9%

12.7%

34.8%

40.6%

Passive +Quant

Hybrid

ActiveEquity

Fixedincome

Note: Passive AUM includes Index funds, ETFs & FoFs investing overseas, Quant includes Quant Equity funds Source: AMFI


Passive Trends in India

Source: AMFI Source: Bloomberg; Deutsche Bank; Thomson Reuters, trackinsight.com

Source: SPIVA April 2021, S&P Dow Jones, MorningStar


The Active vs. Passive Debate- Who Wins?

FUND CATEGORY COMPARISON INDEX 1-YEAR (%) 3-YEAR (%) 5-YEAR (%)

Indian Equity Large-Cap S&P BSE 100 86.21 86.67 82.72

Indian ELSS S&P BSE 200 56.66 76.19 76.19

Indian Equity Mid-/Small-Cap

S&P BSE 400 Mid SmallCap Index

57.14 48.65 69.57

Indian Government Bond

S&P BSE India Government Bond Index

70.83 51.85 71.43

Indian Composite Bond S&P BSE India Bond

Index 50.00 97.90 97.87

Percentage of Funds Outperformed by the Index (Based on Absolute Return)


ETF vs Direct Equity

Criteria Index ETF Investing Direct Equity Investing

Stock Selection Risk Tracks an index which is inherently rule-based. Hence, does not involve any stock selection risk.

There is a need to do extensive research, selecting stocks and entering at the right time.

Monitoring & Rebalancing The monitoring/rebalancing is done by the Index creator.

One needs to monitor stocks and keep researching new investments.

Company-specific - Unsystematic Risk Eliminates individual company-specific risk. It is very well diversified.

An undiversified portfolio can be vulnerable to company-specific risks.

Costs Since there is no active selection, the total expense ratio is usually very low.

There can be high trading costs due to frequent trading.

Tax Efficiency Yields relatively better after-tax returns due to low number of rebalances.

Frequent trading can lead to lower after-tax returns and high short term capital gains tax.

Emotional Biases It is a disciplined approach to investing. Subject to human and emotional biases.

Drawdowns & Survivorship Bias Lower drawdowns and survivorship bias working in favor of index investors

Higher drawdowns and survivorship bias working in against of direct equity investors in under performing stocks


Our Investment Style – Tactical Asset Allocation, Passive Instruments & Quant Methods

Investment Selection Style

Manager Based - Tactical Asset Allocation ● Human bias

● Security selection risk

● Performance dependent on fund manager

Manager Based- Strategic Asset Allocation ● Human bias

● Security Selection Risk

● Static Allocation, prone to drawdowns in line with market falls

Index Based- Tactical Asset Allocation + Quant Methods ● No security selection risk

● No Human Bias – Rule based investing

● Not Fund manager dependent

Index Based- Strategic Asset Allocation ● Static Allocation, prone to drawdowns

in line with market falls

● Does not capture market moves - limited upsides

● Emphasis on minimizing market risk

Ac

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Sto

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s)

Pa

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ive

(In

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Tactical (Dynamic) Strategic (Static)

Asset Allocation Style

The new holy trinity of the investing world


Tactical vs. Strategic Asset Allocation

Risk based

Static

Unchanging

Outcome Oriented

Active Rebalancing

Opportunistic

Risk Management Emphasis

Diversification &

Tactical Asset Allocation

Domestic Equities ● Fastest growing large emerging economy ● Broad coverage of entire listed market (includes Large and mid caps) ● Diversified across sectors and industries ● Helps in managing portfolio beta by going overweight for growth &

underweight for protection

Fixed income ● Brings in portfolio stability ● Capital Preservation and Steady Income ● Practically ‘Nil’ credit risk (invests in AA & above)

US Equities – Nasdaq 100/ S&P 500 ● Access to world’s largest GDP ● Exposure to biggest and fastest growing tech stocks ● Provides USD exposure by creating a dollar asset to capture dollar

appreciation vs INR ● Geographical risk diversification

Gold ● Safe Haven asset class held by global Central Banks ● Negative correlation with most asset classes ● Hedge against inflation


Rationale For Each Asset Class

1. The strategy takes exposure to asset classes via investing in the following instruments based on Liquidity, AUM, Tracking Error, Vintage, Impact Cost, Expense ratio,

market share and asset class representation.

ASSET CLASS ETFs

Large Cap

Mid cap

Gold

Corporate bonds

G-sec securities

Liquid Funds

International Equities

Nippon India Nifty Bees/ Nippon India Junior Bees

Nippon India Gold BEes

Bharat Bond April 2025 ETF/ April 2023 ETF

Nippon India Long Term Gilt ETF/ ICICI Pru Constant Maturity Gilt Fund

Nippon India Liquid Bees

Motilal Oswal Nasdaq 100 ETF/ S&P 500 Index Fund

Nippon India Midcap 150 ETF


Instrument Details

2. Benchmark: 80% Crisil Hybrid 50:50 – Moderate Index + 20% Domestic Gold Price in INR

*Note: ETFs and index funds are subject to change in accordance with fund manager’s discretion

10%-50%

0%-34%

5%-35%

10%-32%

0%-32%

0%-11%

5%-31%

Min-Max Allocations

Output Frequency Tenure Number ofIndicators

Type

Output in terms ofStrong Growth,Steady Growth,Deceleration,Recovery &Slowdown

Output in terms ofInvest in Equities,

Invest in Gilt, Investin Corp bonds,Invest in Debt +

Equity

Output in terms ofDeeply undervalued,Undervalued, FairlyValued, Overvalued,Highly Overvalued

Output in terms ofDeeply undervalued,Undervalued, FairlyValued, Overvalued,Highly Overvalued

Gives the asset classesthat shows the highest

momentum

Gives output as Investin Gold or invest inother asset classes

Signals Buy or Sell inNifty 50

Signals Buy or Sell inequity market

Output in terms ofRisk On or Risk OffC

on

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LeadingConcurrent

Co

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ConcurrentConcurrent

Concurrent

La

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ed

Lagg

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Sh

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Te

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Short

Term

MediumTerm

Med

ium

Term

MediumTerm

Medium

Term

Mediu

m

Term

Lo

ng

Te

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erm

Da

ily

Daily

Daily

Weekly

WeeklyWeekly

Daily

Mo

nth

ly

Mon

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25

2

3

59

5

10

Methodology25 2

6

The strategy uses 9 proprietary meters as a toolbox to determine the allocation between the asset classes:

Macrometer: Economic Cycle

It focuses on identifying the level of the economic cycle and its direction, using 25

monthly economic indicators. By combining the level and the momentum of the economic activity, we differentiate periods into 5 macro

regimes namely, Strong Growth, Steady Growth, Deceleration, Recovery and Slowdown

Monetary Meter: Monetary cycle

It quantifies the steepness of the yield curve and the liquidity in the markets that affect bond yields and hence

bond prices. This meter tracks interest rate regime and money supply

Cyclometers: Equity market cycle

Cyclometer tracks the equity market cycle by quantifying valuation zones and trend of Nifty 50 index using high frequency valuation indicators

Multi Asset Momentum: Trend

This indicator captures the trend across asset classes. We follow a variation of Dual Momentum strategy

where we consider the absolute and relative trend of asset classes & is used as a confirmation tool

Gold Meter: Gold momentum It captures momentum in gold by comparing price performance of Gold against other asset classes such as EM and DM equities, commodities and net long positions held by the investors

Technometer: Technical Risk- Reward It evaluates Equity Market Cycle – for Nifty 50 as well as Nasdaq 100, from a Technical Risk Reward Perspective to look for reversals and breakouts using a combination of technical indicators

Sentimeter: Sentiment It captures the sentiment in domestic equity market. Using high frequency market sentiment indicators, we create an index which gives us bullish and bearish signals

Global RORO: Risk Appetite This risk on- risk off indicator tracks global risk appetite by evaluating relative risk-reward across equity and debt instruments of developed & emerging markets

Using combination of these proprietary meters, we arrive at our final asset allocation by following a trigger based and not a time based rebalance mechanism. The rebalance across assets is triggered for tactically balancing risk and return.

Relative Value meter Relative value meter tracks relative attractiveness of small & midcaps vs. large caps


Methodology


Macrometer – Quantifying health of the economy

Macrometer’ is based on identifying the level of the economic cycle and its momentum, using 25 monthly economic

indicators. By combining the level and the momentum of the economic activity, we differentiate periods into 5 macro

regimes – Strong growth, steady growth, deceleration, slowdown and recovery. Given the time lag with which the official

number for GDP comes out, Macrometer acts as a leading indicator of the economic growth which tells about where the

economy is heading much before the government releases official numbers. The indicator is updated monthly.

Data as on 6th November 2021


Monetary meter for Debt Valuations

Monetary meter indicates quantifies the steepness of the yield curve and the liquidity in the markets. Since the economic

data comes with a lag, we replace the lagged data with data available at higher frequency. For example, we take change in

crude price as proxy for current account balance, and change in money supply as a proxy for inflation. The indicator is

updated monthly.



Cyclometers: Capturing the equity value at the right time

Cyclometer tells us if Nifty 50 index, at any point, is in

Highly overvalued, Overvalued, Fairly Valued,

Undervalued or Deeply undervalued zone. The

indicator is updated daily.



Relative Value Meter

Relative Value Meter captures the relative attractiveness in terms of risk –reward of investing in mid and smallcaps

compared to large caps. It tells us if Nifty Smallcap 250 index at any point, is in Highly overvalued, Overvalued, Fairly

Valued, Undervalued or Deeply undervalued zone vis a vis Nifty 50 index. The indicator is updated daily.



Multi Asset Momentum: Capturing Trend

Using an algorithm, we mark a time periods as follows: Invest in domestic equities if the model says assign 59% or more of the weight to Equities. Invest in Fixed income if cumulative weight of G-sec, corporate bonds and liquid funds is more than 45% in the

portfolio Invest in Gold if model says invest 29% or more in Gold Distribute equally, otherwise The left chart shows the zone of investing in different asset classes while the right chart shows the performance of MAM vs. Nifty 50



Technometer: Technical Risk- Reward

It evaluates Equity Market Cycle – for Domestic and International equity market, from a Technical Risk Reward

Perspective to look for reversals and breakouts using a price data analysis independently.

Technometer

Parameter Domestic Market International

Market

Back-testing 14 Years 14 Years

Buy and Hold Returns 478.5% 781.2%

Technometer Returns 2259.6% 1411.9%

Max Drawdown 26.7% 17.6%

Winning Trades 24 29

Loosing Trades 5 3

Avg Win / Avg Loss 2.422 4.741

Profitability Percentage 82.8% 90.6%



Sentimeter: Quantifying market sentiment

It captures the sentiment in domestic equity market. Using high frequency market sentiment indicators, we create an index

which gives us bullish and bearish signals. Sentimeter processes canalized data from market valuations, sectoral analysis,

adaptive indicators, international markets, market volatility and euphoria and dequantifies into seamless qualitative outputs

for agile decision making engine.



Global & Domestic RORO: Risk Appetite


• In crisis events, emerging markets track developed markets closely. Hence, it is important to measure how markets across the globe are behaving.

• For Global RORO, we not only track equity markets, but also debt and commodity markets across the globe. • The indicator comprises of 26 assets, covering emerging & developed market equities, government securities • Negative rolling correlation indicates risk off and positive correlation indicates risk on environment in capital markets

globally.

The strategy generates a CAGR of around 23% based on back-test conducted going back to July 2006 with standard deviation lesser than that of equity asset class.

PERIOD

MULTI ASSET

DYNAMIC PORTFOLIO


NIPPON INDIA

JUNIOR BEES

NIPPON INDIA NIFTY

MIDCAP 150

MOTILAL OSWAL

S&P 500 INDEX FUND

MOTILAL OSWAL

NASDAQ 100 ETF



GILT ETF

BHARAT BOND ETF

- APRIL 2025

NIPPON INDIA

LIQUID BEES

BENCH-MARK

3M 5.87% 9.9% 12.6% 14.4% 5.2% 7.2% 5.3% 1.1% 0.8% 0.6% 6.2%

6M 14.1% 24.4% 23.8% 30.7% 15.6% 26.1% 1.9% 0.9% 3.1% 1.2% 10.9%

1 YR 37.9% 44.0% 52.4% 73.4% 32.0% 37.8% -4.0% 1.9% 5.4% 2.4% 18.6%

CAGR (3Y) 32.0% 20.8% 18.3% 24.9% 15.5% 35.5% 15.5% 8.8% 8.9% 2.9% 16.3%

CAGR (5Y) 26.9% 18.7% 16.5% 19.1% 14.5% 30.5% 9.1% 6.3% 8.0% 3.4% 13.1%

CAGR (7Y) 21.6% 12.7% 13.8% 15.9% 12.4% 24.3% 8.8% 8.2% 8.8% 3.8% 11.3%

CAGR (10Y) 19.2% 14.2% 16.9% 17.4% 16.1% 26.0% 4.3% 8.9% 9.4% 4.5% 10.5%

CAGR (12Y) 18.3% 12.0% 13.5% 14.0% 15.4% 25.1% 8.2% 8.2% 9.0% 4.5% 10.6%

CAGR (15Y) 23.0% 11.2% 13.2% 13.6% 10.5% 19.9% 10.8% 8.0% 9.0% 4.7% 10.9%

Sharpe Ratio (3Y) 2.30 0.66 0.57 0.91 0.42 1.12 0.67 0.65 1.37 1.13

Standard Deviation (3Y)

11.3% 22.3% 21.4% 20.7% 22.6% 26.3% 14.2% 4.4% 2.1% 0.1% 9.1%



Performance

Note: Back-tested results of Quant Model & not actual returns. Above returns are only for understating purpose and there is no assurance or guarantee that the objectives of the investment will be achieved as investment in Securities is subject to market risk. Returns net of management fees, performance fees ,transaction costs. and other expenses. Impact cost not included.


Inflation-Beating Alpha

YEAR YoY

INFLATION% 1 Yr FD RATE BENCHMARK MADP

1 Yr FD RATE (Inflation Adjusted)

MADP (Inflation Adjusted)

Alpha due to tactical rotation

(MADP – Benchmark)

2007 6.2% 8.4% 30.1% 35.4% 2.2% 27.0% 5.4%

2008 8.0% 8.4% -19.9% 22.9% 0.4% 14.5% 42.7%

2009 10.3% 6.5% 36.6% 86.6% -3.8% 80.1% 50.0%

2010 11.4% 8.6% 14.3% 15.6% -2.8% 7.0% 1.3%

2011 8.5% 9.1% -1.0% 4.6% 0.6% -4.5% 5.6%

2012 8.9% 8.9% 18.1% 20.6% 0.0% 11.7% 2.5%

2013 10.5% 9.0% -1.6% 7.3% -1.5% -1.7% 8.9%

2014 6.5% 8.6% 19.8% 20.8% 2.1% 12.2% 1.0%

2015 4.8% 7.4% 1.8% 0.7% 2.6% -6.7% -1.1%

2016 4.8% 6.9% 9.7% 15.9% 2.1% 9.0% 6.2%

2017 3.3% 6.6% 16.5% 27.7% 3.3% 21.1% 11.2%

2018 3.9% 6.8% 4.5% 13.1% 2.9% 6.3% 8.6%

2019 3.6% 5.6% 12.6% 21.6% 2.0% 16.0% 9.0%

2020 6.4% 5.2% 19.3% 49.4% -1.2% 44.2% 30.1%

Asset classes are represented by underlying ETFs. Return is the average annualized pre-tax returns for the last 3 years and till 15th November 2021. Risk is the annualized std deviation of daily returns for the last 3 years and till 15th November 2021 Note: Back-tested results of Quant Model & not actual returns. Above returns are only for understating purpose and there is no assurance or guarantee that the objectives of the investment will be achieved as investment in Securities is subject to market risk. Returns net of management fees, performance fees ,transaction costs. and other expenses. Impact cost not included. Data as on 15th November 2021. Benchmark = 80% 50:50 Crisil Hybrid Moderate Index + 20% Gold


Higher Returns with Lower Risks than Equities


Monthly Returns


Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly

Returns

2007 1.0% -0.7% 0.6% 6.1% 4.5% 3.8% 1.4% 2.5% 4.7% 3.9% 0.3% 2.9% 35.4%

2008 1.7% 2.4% -2.6% 7.6% 1.7% -1.3% 0.3% 4.1% 3.5% -5.9% 2.8% 7.2% 22.9%

2009 0.9% -9.1% 10.5

% 13.2% 25.9% 2.0% 6.9% 1.6% 7.9% -1.9% 8.6% 1.6% 86.6%

2010 -4.1% 3.8% 2.4% 2.4% -0.1% 0.8% 0.5% 1.1% 7.5% 0.9% -0.8% 0.4% 15.6%

2011 -2.6% 0.3% 1.9% 2.3% -0.3% -0.9% 1.5% 3.7% -1.2% 2.6% -1.6% -1.0% 4.6%

2012 7.9% 4.9% 1.0% -1.3% -0.4% 1.2% 0.8% 1.2% 1.6% -1.8% 3.4% 0.8% 20.6%

2013 0.6% -3.1% 1.4% -1.6% 1.2% 0.0% 0.0% 5.7% -3.2% 2.8% 0.8% 2.9% 7.3%

2014 -4.7% 2.9% -1.5% -0.7% 9.2% 6.7% -1.0% 2.4% 2.3% 0.9% 2.0% 1.2% 20.8%

2015 2.0% -0.4% -1.3% 0.3% 2.2% -3.2% -1.9% 1.9% -0.1% 1.4% 0.9% -0.7% 0.7%

2016 0.0% 5.8% 1.2% 1.1% 1.7% 3.4% 4.9% 2.1% -0.3% 1.2% -4.6% -1.5% 15.9%

2017 5.6% 3.7% 2.7% 3.1% -0.2% 0.1% 4.3% -0.6% -0.7% 5.7% -0.2% 1.5% 27.7%

2018 3.2% 1.3% 0.9% 2.4% -1.5% -1.2% 3.4% 2.5% -0.1% 0.9% 0.8% 0.0% 13.1%

2019 -1.3% 0.9% 4.8% -0.7% -0.4% 2.6% 2.1% 4.1% 1.5% 4.8% 1.2% 0.5% 21.6%

2020 2.1% 1.7% -0.6% 6.4% -0.8% 7.9% 4.2% 4.4% 0.3% -0.2% 11.5% 4.5% 49.4%

2021 -0.6% 10.1% -2.7% 2.2% 4.6% 0.4% 1.4% 2.1% 0.8% 1.4% NA NA 21.4%


Monthly Relative Performance vs. Benchmark



Returns

2007 -0.5% 3.2% -0.1% 3.4% 2.5% 2.6% -1.9% 3.1% -3.0% -3.3% -0.3% -1.9% 5.4%

2008 6.4% 1.2% 3.3% 4.0% 2.6% 7.4% -3.2% 4.3% 5.5% 7.6% 3.8% -1.4% 42.7%

2009 1.0% -7.1% 6.2% 6.7% 14.1% 1.7% 4.0% 0.6% 3.6% 0.1% 1.7% 2.8% 50.0%

2010 -1.9% 1.3% 1.9% 0.5% -0.5% -1.5% 0.9% -1.2% 2.8% 0.9% -1.5% -0.3% 1.3%

2011 2.2% -1.2% -0.3% 0.9% 0.1% -0.4% -0.1% 2.0% 1.0% -1.5% -1.5% 4.3% 5.6%

2012 1.1% 3.8% 1.1% -2.1% 1.3% -2.1% 0.5% 0.2% -2.7% -0.9% 1.0% 1.0% 2.5%

2013 0.4% -0.6% 1.6% -1.4% 0.0% 2.6% 0.7% 3.5% -1.9% -1.2% 2.2% 2.9% 8.9%

2014 -4.7% 0.2% -2.9% -1.4% 5.9% 2.1% -0.9% 1.0% 2.5% -0.7% -0.7% 1.0% 1.0%

2015 -1.7% -0.8% 0.6% 1.3% 0.3% -2.9% -2.0% 2.6% -0.1% 0.2% 2.1% -0.7% -1.1%

2016 0.6% 4.5% -1.1% -0.9% 1.0% -0.4% 2.4% 1.3% -0.4% 1.2% -2.2% -0.2% 6.2%

2017 1.9% 2.7% 1.0% 0.6% -1.7% -0.1% 1.3% -0.9% -0.2% 3.5% 0.4% -0.1% 9.5%

2018 1.4% 3.6% 0.8% 0.1% -1.1% -0.5% 0.9% 0.6% 2.7% 1.7% -0.7% -1.8% 8.6%

2019 -1.8% 0.9% 2.0% -0.6% -2.3% 1.2% 3.3% 1.9% 1.0% 1.9% 0.8% -0.4% 9.0%

2020 1.2% 2.9% 7.2% -1.0% -1.2% 3.4% -0.9% 3.9% 0.6% -1.3% 7.1% 1.0% 30.1%

2021 0.5% 7.5% -1.7% 0.7% 0.5% -0.4% 0.3% -0.7% -0.3% 0.4% NA NA 7.3%


Monthly Relative Performance vs. Nifty



Returns

2007 0.1% 7.7% -2.1% -1.0% -0.5% 3.1% -3.5% 3.8% -8.8% -12.4% 2.6% -3.7% -17.4%

2008 18.1% 0.6% 6.8% -1.4% 7.3% 18.6% -11.1% 3.3% 13.8% 20.9% 9.9% -3.2% 74.8%

2009 3.5% -0.4% -4.6% -1.7% -2.1% 4.3% 1.0% 1.0% -1.2% 5.4% 0.0% 0.0% 12.0%

2010 2.1% -0.4% -0.8% 1.8% 3.4% -2.8% -1.8% 0.4% -6.2% 2.9% 0.2% -2.5% -3.2%

2011 7.6% -0.3% -3.5% 3.7% 2.8% -2.5% 3.9% 12.3% 0.0% -5.2% 5.7% 5.3% 28.5%

2012 -4.7% 1.8% 3.9% -0.4% 5.4% -6.5% 1.6% 0.5% -7.2% -0.1% -1.2% 0.4% -6.4%

2013 -1.6% 2.1% 3.3% -6.0% -0.2% 1.2% 2.4% 10.3% -9.0% -6.2% 2.8% 0.8% 0.7%

2014 -1.3% 0.7% -8.1% -0.6% 1.1% 0.8% -2.5% -0.7% 2.4% -3.9% -0.7% 4.4% -10.5%

2015 -4.3% -1.7% 4.2% 3.9% -1.0% -3.8% -3.1% 8.4% 0.1% -0.1% 2.2% -0.6% 4.8%

2016 4.9% 8.4% -4.1% -0.3% -2.4% 1.0% 1.1% 0.3% 1.7% 0.8% 0.6% -1.4% 12.0%

2017 1.1% -0.3% -0.5% 0.3% -3.7% 0.8% -1.8% 0.9% 0.6% 0.0% 1.8% -2.5% -4.0%

2018 -1.6% 6.3% 4.0% -3.7% -1.7% -1.3% -2.8% -0.5% 5.6% 6.4% -4.0% 0.1% 8.5%

2019 -1.1% 0.4% -2.3% -1.8% -2.0% 2.8% 8.1% 4.7% -1.6% 0.1% -0.3% -0.4% 8.1%

2020 3.8% 8.6% 22.1% -8.3% 1.9% -1.0% -2.1% 1.4% 0.1% -2.6% -0.9% -2.2% 33.6%

2021 1.9% -1.9% 0.8% 2.5% -2.1% -0.7% 1.0% -6.7% -1.7% 0.4% NA NA -9.6%


35

Rolling Best and Worst Performance

Performance MADP Benchmar

k


ICICI PRU NEXT 50

ETF

NIPPON INDIA NIFTY

MIDCAP 150

MOTILAL OSWAL


MOTILAL OSWAL

S&P 500 INDEX FUND

MOTILAL OSWAL

NASDAQ 100 ETF



GILT ETF

BHARAT BOND ETF

- APRIL 2025

NIPPON INDIA

LIQUID BEES

Asset Class Wise

3 Years CAGR

Best 47.7% 22.4% 27.8% 41.7% 36.9% 35.5% 31.7% 37.2% 34.8% 14.5% 11.9% 6.0%

Worst 7.3% 3.9% -4.7% -6.0% -11.2% -17.9% -10.0% -1.1% -8.3% 2.1% 7.2% 2.9%

Median 16.5% 9.9% 9.6% 12.6% 12.1% 8.2% 11.8% 21.4% 10.6% 8.2% 9.2% 4.6%

Average 21.4% 10.3% 10.0% 12.8% 11.8% 8.8% 11.9% 20.5% 10.8% 8.3% 9.3% 4.8%

5 Years CAGR

Best 35.1% 15.5% 19.8% 27.9% 24.6% 25.3% 26.9% 33.8% 27.6% 11.4% 11.2% 5.8%

Worst 9.7% 5.4% -1.2% -1.0% -2.4% -7.7% -3.2% 5.9% -3.9% 3.9% 8.0% 3.4%

Median 15.8% 9.5% 10.3% 12.6% 11.2% 9.8% 13.3% 22.0% 7.4% 8.6% 9.3% 5.1%

Average 19.1% 9.9% 9.8% 12.9% 11.8% 9.3% 13.1% 21.1% 9.3% 8.4% 9.3% 4.9%

Time period Wise

3 Years CAGR

Best 47.7% 14.7% 13.7% 18.9% 19.3% 13.6% -5.0% 5.4% 19.5% 8.6% 9.5% 4.7%

Worst 7.3% 4.7% -1.0% -4.4% -9.1% -14.1% 26.5% 29.8% 13.0% 6.8% 8.0% 6.0%

5 Years CAGR

Best 35.1% 13.8% 13.7% 18.2% 17.8% 14.1% 0.1% 10.0% 19.6% 7.6% 8.5% 5.2%

Worst 9.7% 7.1% 5.3% 7.9% 6.3% 4.9% 19.9% 25.8% 4.7% 9.0% 9.3% 5.8%

The chart below shows the time periods of model being overweight and underweight in equities. The grey areas show the times when the model had more than 60% into equities (including International Equities). The blue areas show when the model has less 30% exposure into equities.

MADP Portfolio protects you from the Equity Down-Cycles and participates in the Equity Up-Cycles



Entry & Exit out of Equities



Examples of Timely Protection & Participation

Time Period

Large Cap

Mid cap US

Equities Gold

Gilt funds

Corporate

bonds MADP

7-Sep-07 Allocation 10.0% 0.0% 5.0% 35.0% 15.0% 32.0%

Performance 5.1% 2.0% -7.8% 27.1% 3.9% 5.7% 10.9%

9-Apr-08 Allocation 40.0% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 8.1% 11.0% 10.5% -0.8% 0.9% 0.8% 8.5%

7-May-08 Allocation 10.0% 0.0% 5.0% 35% 15.0% 32.0%

Performance -13.3% -20.5% -4.9% 7.8% -4.9% -2.3% 0.3%

24-Jul-08

Allocation 40.0% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 2.1% 5.2% 3.2% -5.0% 0.3% 0.5% 3.2%

7-Aug-08 Allocation 10.0% 0.0% 5.0% 35% 15.0% 32.0%

Performance -35.7% -42.2% -20.9% 17.9% 21.2% 10.7% 7.8%

9-Feb-09 Allocation 40.0% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 79.1% 130.8% 38.1% 18.1% -0.4% 11.2% 67.5%

5-May-10 Allocation 17.0% 0.0% 8.0% 30.0% 30.0% 15.0%

Time Period

Large Cap

Mid cap US

equities Gold

Gilt funds

Corporate

bonds MADP

9-Nov-18 Allocation 40% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 11.1% 0.9% 7.8% -1.6% 6.3% 5.0% 5.5%

7-May-19

Allocation 10.0% 0.0% 5.0% 35.0% 15.0% 32.0%

Performance -5.8% -9.2% 2.4% 23.0% 7.9% 4.7% 10.0%

22-Aug-19

Allocation 40% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 13.2% 21.0% 20.6% 3.7% 2.7% 4.1% 13.5%

6-Feb-20 Allocation 10.0% 0.0% 5.0% 35.0% 10.0% 40.0%

Performance -24.9% -28.8% -5.9% 13.0% 0.5% 1.9% 1.0%

9-Apr-20 Allocation 40% 30.0% 15.0% 5.0% 0.0% 10.0%

Performance 65.2% 90.8% 50.1% -3.1% 6.0% 5.8% 52.6%

22-Jun-21

Allocation 26.0% 10.0% 14.0% 20.0% 4.0% 20.0%

Global Financial Crisis Covid-19 Crash

₹ Capital Growth in 15 years

MADP ₹ 26.4x

Nasdaq 100 ₹ 16.9x

Mid Cap ₹ 8.7x

Large Cap ₹6.1x

Gold ₹ 4.6x

Corporate Bonds ₹ 3.7x

Gilt Funds ₹ 3.2x



Performance Trajectory of MADP

Note: Back-tested results of Quant Model & not actual Standard deviations.


Risk Analysis

1 Year rolling daily standard deviation is higher for all other assets and MADP stands at lowest volatility delivering stable higher risk adjusted returns.

Associated Risks

• Risks of underperformance: There is a risk of

underperformance of quantitative strategies

underperforming the broader markets in shorter or

medium horizon, especially during the period of

exuberance or FOMO buying in capital markets

• Higher transaction charges: Since the rebalance is

only carried out when the meters trigger rebalance,

there could be frequent rebalances in certain time

periods. The higher turnover leads to higher

transactions charges such as brokerage and

impact cost.

• Tax Inefficiency: Being a tactical strategy, the

average holding period of each asset class is

mostly lesser than 1 year, that attracts short term

capital gain tax.


Effective Rebalances

Note: Back-tested results of Quant Model & not actual returns. Above returns are only for understating purpose and there is no assurance or guarantee that the objectives of the investment will be achieved as investment in Securities is subject to market risk. Returns net of management fees, performance fees ,transaction costs. and other expenses. Impact cost not included. Data as on 22nd October. Benchmark = 80% 50:50 Crisil Hybrid Moderate Index + 20% Gold

The portfolio is rebalanced only when the model triggers change in allocation by more than 9% in atleast 3 asset classes. This rule is incorporated to avoid unnecessary rebalances in the portfolio unless the model is indicating a drastic change in allocations. The back test indicates 50 such rebalances in the past 15 years. Due to this rule, the portfolio often takes tectonic rotation between the asset classes to fully utilize the opportunities.

A Universal Strategy

That Deserves a Place in

Every Portfolio

Invests in only liquid ETFs

Takes care of diversification in

one portfolio

Fit for all categories of risk profiles

Market Cycle Agnostic

Zero Entry & Exit load


A Universal Strategy That Deserves a Place in Every Portfolio

An all season product


Taxation for MADP

Fund type ETFs/ Index Funds Holding Period Short-term

capital gains Long-term

capital gains Set Off Rules

Equity funds/ shares

Nippon India Nifty Bees ETF Nippon India Junior Bees ETF Nippon India Midcap 150 ETF

Less than 12 months – ST

More than 12 months – LT

15% Up to Rs 1 lakh

– Nil Above Rs 1 lakh – 10%

1. Long term capital loss can be set off against long term capital gains only. 2. Short term capital loss can be set off against short term capital gains or long term capital gains. 3. Unadjusted Capital Loss can be carried forward for 8 years. 4. The tax is deducted at the end of the financial year when the investor files for taxes and not when the gain or loss is realized.

Debt funds/ securities

Bharat Bond 2025 ETF Bharat Bond 2023 ETF Nippon India Gilt ETF

ICICI Constant Maturity Fund Nippon India Liquid Bees ETF



Taxed at the investor’s

income tax slab rate

20% with Indexation

Gold Nippon India Gold Bees ETF





20% with Indexation

International funds

Motilal Oswal Nasdaq 100 ETF Motilal Oswal S&P 500 Index Fund





20% with Indexation

In the tax efficient Portfolio, we invest in Arbitrage Funds instead of Fixed income funds. For simplification purpose, the returns from Arbitrage funds are taken same as

liquids fund returns.



Performance of Tax Efficient Portfolio

YEAR QUANTIFI ETF BASED MADP

FUND


ICICI PRU NEXT 50

ETF

NIPPON INDIA NIFTY

MIDCAP 150

MOTILAL OSWAL


MOTILAL OSWAL

S&P 500 INDEX FUND

MOTILAL OSWAL

NASDAQ 100 ETF



GILT ETF

BHARAT BOND ETF - APRIL 2025

NIPPON INDIA

LIQUID BEES

BENCHMARK

2007 33.9% 52.9% 75.7% 76.9% 94.9% -7.5% 6.5% 17.4% 6.6% 6.8% 6.4% 30.1%

2008 15.3% -51.9% -63.5% -59.4% -69.1% -24.2% -27.9% 25.6% 22.8% 13.7% 6.6% -19.9%

2009 85.5% 74.6% 127.9% 99.0% 113.9% 17.9% 47.6% 22.7% -4.9% 8.0% 2.8% 36.6%

2010 15.0% 18.8% 17.8% 19.2% 16.3% 8.9% 15.7% 21.7% 6.0% 9.5% 4.1% 14.3%

2011 6.2% -23.9% -31.9% -31.0% -36.0% 18.3% 23.4% 30.3% 5.8% 5.2% 6.1% -1.0%

2012 17.9% 27.0% 48.1% 39.2% 38.2% 17.4% 17.4% 10.9% 11.4% 11.7% 6.0% 18.1%

2013 8.0% 6.6% 4.8% -5.1% -9.5% 46.6% 54.9% -14.1% 2.9% 6.8% 6.0% -1.6%

2014 17.6% 31.2% 44.4% 55.9% 69.6% 12.3% 22.6% 0.8% 16.3% 13.4% 6.0% 19.8%

2015 -0.8% -4.1% 7.0% 6.5% 9.6% 5.0% 13.5% -7.8% 8.2% 9.1% 5.0% 1.8%

2016 13.6% 3.9% 7.1% 7.1% -0.4% 9.4% 8.1% 10.7% 15.5% 11.1% 4.5% 9.7%

2017 25.2% 30.0% 44.8% 47.3% 57.1% 11.4% 22.2% 2.9% 1.1% 6.8% 4.0% 16.5%

2018 10.9% 4.6% -8.8% -15.4% -27.6% 0.7% 5.6% 6.9% 7.3% 7.5% 4.2% 4.5%

2019 18.6% 13.5% 1.4% 0.0% -7.8% 33.0% 41.6% 22.9% 11.1% 10.0% 3.7% 12.6%

2020 46.6% 15.8% 14.8% 25.3% 25.9% 0.5% 51.5% 26.2% 11.1% 10.4% 2.5% 19.3%

2021 YTD 21.6% 31.0% 37.4% 52.9% 60.0% 27.7% 29.3% -2.6% 1.1% 4.6% 2.1% 14.1%

Being a 100% quant strategy eliminates emotional and behavioral, human biases that come with active management

The investment is only done in index ETFs which are low cost and have high liquidity.. No stock selection or sector selection bias

Exposure to domestic equities (large cap + midcap) limited to 79% at any point in time

The strategy is back tested going back to 15 years, covering at-least 3 macro cycle. Higher sharpe ratio & lower volatility than the underlying asset classes

Re-balancing is trigger based, not time based that lets you ride the full cycle

Exposure to defensive assets such as AAA corporate bonds and Gold at all times using min max allocations framework

Tactical asset allocation that focuses on investing in right asset class at the right time, instead of pre-defined fixed allocation into each asset

Our agile models track short + medium term indicators on a daily basis to capture changes in long term cycles

Provides exposure to international equities via high growth Nasdaq 100 index

In fixed income segment, exposure is taken only via AAA bonds or highly liquid G-sec bonds, hence eliminating credit risk and liquidity risk associated with bonds.


Mitigating Risks & Generating Returns

Why Invest in our

Tactical Multi

Asset Products?

Protection

Growth

Stability

Consistency

Reliability

Sustainability


Why Invest in our Tactical Multi Asset Products?


Fee Plans

Product Name Portfolios

Investment Objective

The investment objective is to generate consistent long term capital appreciation and managing associated risks by investing in a diversified multi asset portfolio comprising of Domestic Equity Index ETFs, International Equity Index ETFs, Debt and Money Market Instruments and Gold Index ETFs. However, there can be no assurance or guarantee that the investment objective of the Scheme would be achieved.

Investment Strategy

Our multi asset strategies are dynamic and use a tactical asset allocation strategy driven by our proprietary quant models and indicators. The models enable switching between domestic equities, international equities, gold, corporate bonds, gilt and liquid funds as per signals generated by our automated proprietary quantitative and fundamental models.

Risks Involved

Equity and Equity related instruments on account of its volatile nature are subject to price fluctuations on daily basis, Fixed income carries credit & interest rate risk Investment in Gold are subject to gold price movements due to several factors such as inflation expectations, demand & Supply of gold, Investment and trading activities of hedge funds and commodity funds etc.

Benchmark 80% Crisil Hybrid 50 + 50 – Moderate & 20% Gold

Fund Manager Siddharth Vora


Product Details

Amisha Vora is a Joint Managing Director and a key shareholder of the Prabhudas Lilladher Group (www.plindia.com). She is one of the leading women entrepreneurs in India today. A highly-sought opinion maker in the equity markets, she regularly features on leading business channels such as CNBC and ET Now for her views and insights.

AMISHA VORA

Joint Managing Director Prabhudas Lilladher Group

Over 20 + plus years of senior management experience in Financial markets with leading industry players. In depth understating of equities, an exceptional communicator with client first approach who believes that the transparent relationships are of utmost importance.

NUPUR PATEL

Principal Officer & Head PMS Sales & Marketing

Siddharth Vora is a Fund Manager with PL PMS and heads Investment Research & Product Strategy at Prabhudas Lilladher. He leads the Multi Asset & Equity Quant Investment Strategies.

A CA, CFA and an MSc (Management in Business Excellence) from the University of Warwick, UK, he is a SEBI-registered Research Analyst and Investment Advisor.

SIDDHARTH VORA

Ritika Chhabra is a Quant Portfolio Strategist at Prabhudas Lilladher. Ritika has obtained her Masters in Economics from IGIDR, Mumbai and B.Tech in Industrial Engineering from NIT Jalandhar. She has an experience of 7 years in Capital Markets covering equities as well as debt markets. Previously, she has worked with HSBC Global Research as Equity Strategy Analyst covering emerging markets.

RITIKA CHHABRA

Fund Manager & Head Quant Investment Strategies– PL PMS

Quant Portfolio Strategist Multi Asset Products


Meet our Team

https://www.plindia.com/






Data Partners

Strategy may invest substantially in equity, debt, gold and international securities.. Equity securities and equity related securities are volatile and proven to price

fluctuations. The liquidity of investments made in the portfolio may be restricted by trading volumes and settlement periods. Settlement period may be extended

significantly by unforeseen circumstances. The inability of the portfolio to make intended securities purchase due to settlement problems could cause the portfolio

miss certain investment opportunities. Similarly, the inability to sell securities, held in the strategies portfolio may result, at times, in potential losses to the strategy,

should there be a subsequent decline in the value of securities held in the strategies portfolio. Investment in Securities is subject to market risk and there is no

assurance or guarantee that the objectives of the investment will be achieved, as with investment in securities, the value of portfolio may go up or down depending

upon the factors and forces affecting in capital market and the portfolio manages is not responsible or liable for the losses resulting from the operations of the

portfolio. Investments in equity and equity related securities involve a degree of risk and investors should not invest in the strategy unless they can afford to take the

risk of losing their investment. performance related information is not verified by SEBI.


Disclaimer / Disclosures

CORPORATE OFFICE :

Prabhudas Lilladher Pvt. Ltd. 3rd Floor, Sadhana House, 570, P. B. Marg, Behind Mahindra Tower, Worli, Mumbai – 400 018. India.

+91 22 6632 2350

+91 98210 97856

[email protected] [email protected] www.plindia.com/QuantifiMADP

Co

nta

ct

Us

Documents

“Multi Asset Dynamic Portfolio”