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INVASSET GROWTH FUND

INVASSET GROWTH FUND is a Equity Portfolio Management Service launched by INVASSET LLP on January 2020. Managed by Anirudh Garg, the fund holds ₹351 Cr in AUM and benchmarks against BSE 500 TRI. Minimum investment is ₹5000000 lakh. Trailing returns: 0.52% (1Y), 25.2% CAGR (3Y), 26.77% since inception. Performance data as of July 2026.

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INVASSET GROWTH FUND

INVASSET LLP

351.11 CR AUM

INVASSET GROWTH FUND

INVASSET GROWTH FUND

INVASSET LLP

351.11 CR AUM

BSE 500 TRI
Benchmark
6yr 6mo
Age
EQUITY
Strategy
NIL
Fixed Fees
NIL
Variable Fees
NIL
Exit Load
BSE 500 TRI
Benchmark
6yr 6mo
Age
EQUITY
Strategy
NIL
Fixed Fees
NIL
Variable Fees
NIL
Exit Load
Fund manager

anirudh garg

Fund Manager
EQUITYStrategies

Anirudh Garg stands at the forefront of financial innovation as the architect of InvAsset. His academic credentials include a Bachelor of Business Studies from Shaheed Sukhdev College and a Masters in Investment Management from the ICMA Centre. Anirudh's foray into the financial world began at his family's proprietary desk established in 1992, with his journey taking a significant turn in 2007. Navigating through the challenging financial landscape of 2008, he gained invaluable insights into the intricacies of market dynamics. His extensive research between 2008 and 2010, which involved in-depth conversations with a diverse range of investors, culminated in the creation of InvAsset Aid. This pioneering algorithm, developed over three years and rigorously tested since 1987, is at the heart of his investment philosophy, emphasizing forward-looking market analysis, bias reduction, and the critical balance of alpha preservation and generation. The successful application of this algorithm from 2013 onwards in managing proprietary funds led to the establishment of InvAsset in 2020. Today, InvAsset boasts the management of 500 crore in proprietary funds and 250 crore in client assets, showcasing Anirudh's exceptional expertise and the effectiveness of his innovative investment strategies

Anirudh Garg stands at the forefront of financial innovation as the architect of InvAsset. His academic credentials include a Bachelor of Business Studies from Shaheed Sukhdev College and a Masters in Investment Management from the ICMA Centre. Anirudh's foray into the financial world began at his family's proprietary desk established in 1992, with his journey taking a significant turn in 2007. Navigating through the challenging financial landscape of 2008, he gained invaluable insights into the intricacies of market dynamics. His extensive research between 2008 and 2010, which involved in-depth conversations with a diverse range of investors, culminated in the creation of InvAsset Aid. This pioneering algorithm, developed over three years and rigorously tested since 1987, is at the heart of his investment philosophy, emphasizing forward-looking market analysis, bias reduction, and the critical balance of alpha preservation and generation. The successful application of this algorithm from 2013 onwards in managing proprietary funds led to the establishment of InvAsset in 2020. Today, InvAsset boasts the management of 500 crore in proprietary funds and 250 crore in client assets, showcasing Anirudh's exceptional expertise and the effectiveness of his innovative investment strategies

What is this fund's investment objective?

Invasset launched with a dream to make people achieve their financial freedom. We are a SEBI registered portfolio management service provider ( SEBI: INP000006907) consisting of a specialized research team having 25+ years of professional experience and an in-house developed INVasset AAIDTM: ( Algo, AI & Data) based approach for selection of companies and sectors that will lead to Alpha generation.

How has INVASSET GROWTH FUND performed?

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Wealth Creation Calculator

1M IA return
-3.54%
1M benchmark return
1.73%
Alpha
-5.27%
1M Wealth multiple
0.96x

How has this fund performed vs its benchmark?

2026
2026
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Wealth Creation Calculator

1M IA return
-3.54%
1M benchmark return
1.73%
Alpha
-5.27%
1M Wealth multiple
0.96x

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