RD THNFO OPENS : 03 JANUARY, 2020 | NFO CLOSES : 17 JANUARY, 2020

A Machine Driven Investment Strategy

Introducing Tata Quant Fund

• Medium to Long Term Capital Appreciation.

This product is suitable for investors who are seeking*:

* Investors should consult their financial advisors if in

doubt about whether the product is suitable for them.

• Investment in equity & equity related instruments

selected based on quant model.

(An open-ended equity schemefollowing quant based investing theme)

QUANT FUND

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Ar�ficial Intelligence enabled Computer ‘Deep Blue’ to beat World Chess Champion Garry Kasparov in a 6-game match

ARTIFICIAL INTELLIGENCE IN LATE 90’s

Ability to Search and evaluate 700,000 Grandmaster games

Compu�ng power to evaluate 200 million posi�ons and strategy up to 40 or more moves into the future

Ability to recognize the best move & Strategy Pa�ern

e.g. Emo�ons impacted Kasparov’s performance in the MatchFree of emo�ons

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THE AGE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Did you know Google Maps uses Machine Learning at its core to let you know about

§ Traffic condi�ons &§ Es�mated �me to reach your

des�na�on

§ Define pr ice surge hours by predic�ng the rider demand

Did you know Ride apps use Machine Learning at its core to

§ Best routes to minimize the detours

Machine Learning!

Have you ever wondered how does your favourite Music App iden�fy what kind of songs you like?

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USE OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING HELP IN INVESTMENT PROCESS

Inconsistent processing &

inferencing of diverse

informa�on

Por�olio process for Investor

so much to think

Large universe of stocks

Company data

High volume

High velocity

Industry data

Short / long term trends

Interna�onal markets

Macro economic factors

Socio Poli�cal News

Quant Strategy - consistent, high velocity informa�on processing & structured decisioning

Secure large market impac�ng datasets

Employ sta�s�cs to iden�fy hidden pa�erns and correla�ons

Build sta�s�cal models to drive Quant investment strategy”

Run comprehensive model tes�ng for different real life scenarios

Create framework that evaluates and manages Quant Model’s predic�on accuracy over �me

Learn

Update

Feedback

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Fund with an ac�ve mul� factor investment model. Embedded with Ar�ficial Intelligence modules that

dynamically change factor strategies basis prevailing market condi�ons

(An open-ended equity schemefollowing quant based investing theme)

QUANT FUND

Key Highlights

§ The Fund would invest in stocks which form part of S&P BSE 200 or Equity Deriva�ve Segment stocks

§ Fund aims to

§ Consistently achieve be�er returns than the index

§ Avoid nega�ve absolute returns

§ Use factor Strategies viz. Value, Quality, Alpha for objec�ve stock selec�on & por�olio alloca�on

§ Periodically rebalance on the basis of latest economic & market informa�on. This helps in improving por�olio performance and

mi�gate risk

§ Embedded Machine Learning modules in the investment process so that the factor selec�on framework evolve as the market

changes

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UNDERSTANDING TATA QUANT FUND STRATEGY LANDSCAPE

Stocks from BSE 200 & Equity Deriva�ve Segment stocks classified

into various stock categories like Value, Quality & Alpha

Analyze and Evaluate Past Economic and Market data

Classifica�on of Stocks

Create Op�mal Por�olio

Es�mate Market

Direc�on

Hedge/Buy Op�mal Por�olio

Another Machine Learning algorithm used to predict direc�on (up / down)

of each candidate por�olio.

Analysis of Past Economic data like GDP, Interest rates etc. and Market condi�ons

like index returns

Machine Learning algorithm used to predict the factor combina�on

that will outperform in forthcoming month.

The model recommends taking hedged posi�on or buy

Recommended Por�olio

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TATA QUANT FUND – THE LANDSCAPE SIMPLIFIED

IMAGINE YOU ARE PLANNING A STRATEGY FOR A CRICKET MATCH

Let’s see what you must look while planning a strategy to win the match

Match Informa�on

Weather Condi�ons

Pitch Condi�ons

Opposi�on Team

Historical winning scores

Team Combina�on

There are many possible combina�on of players to choose from who could win in different scenarios and condi�ons

Number of Batsmen Number of Fast Bowlers

Number of Spin BowlersNumber of All Rounders

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TATA QUANT FUND – THE LANDSCAPE SIMPLIFIED

MACRO CONDITION ANALYSIS

Let’s see what you must look at while planning a strategy to win the match

Playing Condi�ons Analysis Investment condi�ons Analysis

Sunny, rainy or overcast Condi�ons favoring bowlers or BatsmenWeather

Condi�ons

Pitch Condi�ons

Grassy, Wet pitch and Hard pitch favoring Batsmen or Fast bowlers or Spinners

Opposi�on Team

Teams which have done well under similar condi�ons or Track record of Teams

GDP Growth rate, Infla�on, Interest rates indica�ng favorable condi�ons for equity markets Economic

Condi�ons

Category of stocks which perform well under prevailing market condi�ons and outlook

Factors of winning stocks

GoodValue

High Quality

StrongMomentum

Low Volatility

Markets at fair valua�on, or Bubble zone sugges�ng to invest or not

Equity Market Condi�ons

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SELECTION PROCESS

Let’s see how you would select players for your Team

Selectors look at Na�onal Talent pool across the Country to choose players from

Selec�on of Players Selec�on of Stocks

S& P BSE 200 & Equity Deriva�ve Segment stocks

Alpha Stocks Value Stocks Quality Stocks

Alpha-Quality Stocks

Value-Quality Stocks

Batsman Fast Bowler Spin Bowler

All Rounders

Team Combina�ons (TC)

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TEAM AND PORTFOLIO BUILD UP

Let’s see how you would Prepare Match Strategy

Match Strategy Investment Strategy

Win improves the

probability of leading the match strategy

Match Toss

Loss Prepare Strategy

to restrict the Opposi�on Team

Aim to Select Op�mal Por�olio Combina�on of Alpha, Value and Quality stocks

Aim to Select Best Team Combina�on ofBatsmen, All Rounders and Bowlers

Investment strategy of Quant Funds are essen�ally rule-based, driven by algorithms developed basis historical rela�ons of mul�ple factors with stock price movements. One of the risks in a quant-based model would be the �me taken by the algorithm to adapt to new development or change in how certain factors influence market or stock dynamics. The success of the model is based on systema�c investment approach and therefore it may not be able to leverage short term opportuni�es available in the market from �me to �me. Another risk that can emanate from a rule based systema�c investment strategy would be the inability to perfectly �me the market which might impact performance of the fund in the short term. There is no guarantee that the Quant model will generate higher returns as compared to the benchmark.

BullishInvest in the

Best Fit Por�olio

BearishHedge or

Conserva�ve Por�olioPredic�on of

Market Direc�on

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FACTOR STRATEGIES USED IN THE QUANT MODEL

ROCE – Return on Capital Employed • ROE – Return on Equity • EPS – Earnings Per Share • D/E – Dividend/Earnings • P/E – Price to Earnings

Linear combina�on of 4 parameters:PE, P/B, RoCE, Div Yield

Combina�on of 7 parameters ofValue and Quality

Combina�on of 3 Parameters:RoE, D/E, EPS growth variability

Single Factor Alpha

Value

Value Quality

QualityQualityAlpha

Combina�on of 4 Parameters from Quality & Alpha

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HOW DO WE CLASSIFY PORTFOLIO STRATEGY?

Alpha

Based on Stocks Jensen Alpha

Quality

Based on Stocks Fundamental StrengthValue

Based on Return on Capital and Rela�ve valua�on scores

Value Por�olio is created based on stocks score on ROCE (Return on Capital Employed), Price to Earning (P/E), Price to Book Value (P/B) and Dividend yield (D/P) parameters

Alpha score is calculated for all securi�es forming part of Scheme universe based on the stock’s Jensen Alpha score over Ni�y 50 Index over a period of 1 year.

SELECTION

WEIGHTS

Quality Por�olio is a combina�on of stocks with high scores on Return on Equity (ROE), Debt-to Equity (D/E) ra�o and Earning Per Share (EPS) growth variability in the previous 5 years

Stocks with the highest scores have the maximum weight

Based on the factor score, stocks are selected

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MACHINE LEARNING – THE COMPLEXITY BEHIND THE SIMPLICITY

Por�olio Strategy Selec�on01

Evaluates which factor Strategy (Alpha, Value & Quality etc.) worked in a Macro scenario and select an op�mal por�olio with a combina�on of top ranked stocks within the factor strategy.

Invest or Hedge Decision

Evaluates whether the Macro condi�ons and various strategy scenarios favor Factor strategy investment or may lead to fall in por�olio performance.

Based on the evalua�on, the Model makes the Buy or Hedge (Current por�olio) decision

02

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ARTIFICIAL INTELLIGENCE AND EMOTION FREE INVESTING

Machine learning avoids the pi�alls of emo�onal bias in Investment decision making as the en�re process is extremely objec�ve and Compu�ng based

BiasConfirma�on

Examples of Behavioral biases affec�ng investment decisions

If an investor believes that a stock, he has invested into will move up then he will keep looking for signals or informa�on which confirms to his belief.

Sunk Cost Fallacy Bias

Biases Tendencies Example

Regret Aversion Bias

A�er losing more than 70% of his stock value, the investor con�nues to hold the stock instead of exi�ng and inves�ng the same in quality stocks with good prospects.

A stock has earned in 3x mul�ple for the investor however, he prefers not to book the profit with tendency that he want to avoid regret selling it early in case the stock price moves up further

Tendency of people to con�nue to hold a losing stock with a fear of booking a loss even when the probability of recovering the lost amount is bleak

Tendency of people to not sell their profitable stock posi�on with a fear that he may lose further upside if it happens in the stock which he may regret

Tendency of people to favor informa�on that confirms their preexis�ng beliefs or hypothesis

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IS THIS ANOTHER RULES BASED INVESTMENT STRATEGY?

Rules Based Investment Model Machine Learning based Investment Model

Rigidity of Rules of Selec�onFlexibility in terms of selec�on of Op�mal por�olio Strategy

Model learns from new economic and market condi�ons and makes its own decision about the Op�mal Por�olio

Does not adapt and learn from new emerging market situa�ons

NFO Dates NFO OPENS: 3rd January, 2020 • NFO CLOSES: 17th January, 2020

Scheme Name TATA QUANT FUND

Investment Objec�ve The investment objec�ve of the scheme is to generate medium to long-term capital

apprecia�on by inves�ng in equity and equity related instruments selected based on a

quan�ta�ve model (Quant Model).

Type of Scheme An open-ended equity scheme following quant-based inves�ng theme

Min. Investment Amount Rs. 5,000/- and in mul�ple of Re.1/- therea�er

Addi�onal Investment: Rs 1,000/- and in mul�ple of Re 1/- therea�er.

Fund Manager Sailesh Jain

Exit Load: 1% of the applicable NAV, if redeemed/switched out on or before expiry of 365

days from the date of allotment.

Benchmark S&P BSE 200 TRI

Load Structure Entry Load (During NFO): N.A.

However, there is no assurance or guarantee that the investment objec�ve of the Scheme

will be achieved. The scheme does not assure or guarantee any returns.

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FUND DETAILS

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Mutual Fund Investments are subject to market risks, read all scheme related documents carefully

Tata Quant Fund is suitable for investors who are seeking*:

• Medium to Long Term Capital Appreciation.

• Investment in equity & equity related instruments selected based on

quant model.

* Investors should consult their financial advisors if in doubt about

whether the product is suitable for them.

DISCLAIMERS

Thank You

Call: 022 - 6282 7777 (Monday to Saturday 9:00 am to 5:30 pm)

www.tatamutualfund.com | Contact your Financial Advisor