Machine Learning Equity Quant

ArthAlpha LLP
PMS Mid & Large Cap
Benchmark
S&P BSE 500 TRI
Inception Date
29 October 2024
Min. Investment
₹50,00,000
SIP Available
Yes
STP Available
Yes

Investment Strategy

Investment Approach

ArthAlpha's Quantitative Equity Strategy leveraging Machine Learning.

Investment Objective

This strategy is designed to generate superior risk-adjusted returns over the medium to long term through investments in equities and equity-linked instruments.

Performance Returns

Returns as on 2025-12-31

Trailing Returns

Period Strategy Benchmark Outperformance
1 MONTH -0.81% -0.23% -0.58%
3 MONTHS 0.57% 5.03% -4.46%
6 MONTHS -4.59% 1.63% -6.22%
1 YEAR 4.26% 7.63% -3.37%
SINCE INCEPTION 3.65% 5.30% -1.65%

Calendar Year Performance

Year Return
CY 2025 4.26%

Financial Year Performance

Year Return
FY 26_YTD 11.65%

Past performance is not indicative of future results. Returns are subject to market risks.

Fund Managers

Rohit Beri

Co-Founder and CIO

Rohit, a founding member and Chief Investment Officer at ArthAlpha, possesses significant expertise as a data scientist and investment professional, with a specialization in AI/ML-powered quantitative investing, financial engineering, and data science. Prior to establishing ArthAlpha, he held the position of CIO at True Beacon. In this role, he was instrumental in launching India's inaugural AI-driven long-only quantitative equity fund. This fund integrated fundamental analysis with machine learning methodologies to generate distinct returns and appeal to institutional and high-net-worth investors. Rohit boasts over two decades of experience in financial markets across India, Singapore, and the United States, demonstrating a proven ability to develop quantitative investment strategies for both Indian and international markets. Previously, Rohit co-founded Riemann Capital in Singapore. At Riemann Capital, he provided advisory services to institutional investors and family offices on financial data science and executed trades in Indian equity derivatives utilizing deep learning models. He also founded Roaring Numbers, a US-based quantitative hedge fund, which was acquired by a family office within its first year of operation. Rohit's prior professional experience includes roles at prominent global and regional banking institutions such as ICICI Bank, Citibank, Credit Agricole CIB, and ANZ. In his final banking role at ANZ, he established and managed the wealth solutions business for Asia. This involved assembling a high-performing team responsible for driving innovation in investment and financing solutions across various asset classes. Rohit is a Chartered Accountant who transitioned into data science. He holds the CFA (US) charter and is a Sloan Fellow (MS-Business) from the Stanford Graduate School of Business. Furthermore, he has earned graduate certificates in AI/ML and Data Science from MIT and Harvard. Rohit's perspectives on the application of AI in finance and market strategy are frequently published in leading Indian financial media, including The Economic Times, Mint, and Moneycontrol.

Portfolio Details

Portfolio Composition

Large Cap
28.82%
Mid Cap
66.76%
Small Cap
1.32%
Cash & Equivalent
3.12%

Top 5 Holdings

  • GE Vernova T&D India Limited 5.32%
  • Fortis Healthcare Ltd 4.31%
  • ACC Ltd 3.81%
  • Hitachi Energy India Limited 3.67%
  • Adani Energy Solutions Limited 3.61%

Top 5 Sectors

  • Financial Services 24.77%
  • Capital Goods 14.17%
  • Automobile and Auto Components 8.71%
  • Power 8.25%
  • Healthcare 6.90%

Portfolio Characteristics

total number of stocks
N/A
top 5 stocks percent
20.72%
top 5 sectors percent
62.80%
average market cap
N/A
portfolio age
1 Yrs, 2 Months
sip
Available
stp
Available

Fee Structure

Variable Fee

AMC: 1.5%

Hurdle: 10%

Profit Sharing: 15%

Exit Load

Year 1: 1%

Year 2: 0%

Year 3: 0%

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Disclaimer

Returns are as of 2025-12-31. Returns for periods up to one year are not annualized. Returns for periods exceeding one year are annualized. Returns are computed using the Time Weighted Rate of Return (TWRR) methodology and are provided by the respective Asset Management Companies (AMCs). 'ND' denotes 'No Data', and 'NA' denotes 'Not Applicable'. The performance of the respective Benchmark Indices is presented above. Benchmark(s) are as defined in SEBI circular no. SEBI/HO/IMD/IMD-PoD-2/P/CIR/2022/172 dated December 16, 2022, APMI circular no. APMI/2022-23/02 dated March 23, 2023, and Revised Annexure-1.

This page is provided solely for general informational purposes in relation to the strategy and is based on publicly available information. While reasonable care has been taken to ensure accuracy, the information may not reflect the most recent updates.

Nothing on this page constitutes investment, financial, or legal advice. Investment decisions should be made after consulting a qualified advisor and reviewing official scheme documents.

Last Updated: January 2026 · Published by Affluense AI (affluense.ai)