Factor Fund - Hedged

Wryght Research and Capital Pvt Ltd
PMS Multi Cap & Flexi Cap
Benchmark
S&P BSE 500 TRI
Inception Date
24 August 2023
Min. Investment
₹50,00,000
SIP Available
Yes
STP Available
Yes

Investment Strategy

Investment Approach

The Wright Factor Fund - Hedged implements a systematic, quantitative strategy that utilizes a diversified set of ten distinct factor groups. These factors, including but not limited to value, momentum, growth, and quality, are chosen based on their historical efficacy in driving portfolio performance.

Investment Objective

The Wright Factor Fund - Hedged is designed to achieve sustained capital growth over the long term. This is accomplished through a dual strategy: proactive investment in a curated selection of Indian equities, complemented by robust downside risk mitigation via hedging techniques. Our investment methodology employs a systematic, factor-driven framework aimed at consistently surpassing the benchmark index. This approach focuses on exploiting market inefficiencies and identifying meticulously selected investment prospects.

Performance Returns

Returns as on 2025-12-31

Trailing Returns

Period Strategy Benchmark Outperformance
1 MONTH -2.48% -0.24% -2.24%
3 MONTHS 3.26% 5.02% -1.76%
6 MONTHS -1.02% 1.63% -2.65%
1 YEAR -17.16% 7.63% -24.79%
2 YEAR 5.35% 11.64% -6.29%
SINCE INCEPTION 11.23% 16.31% -5.08%

Calendar Year Performance

Year Return
CY 2025 -17.16%
CY 2024 33.99%

Financial Year Performance

Year Return
FY 26_YTD 5.37%
FY 25 -1.02%

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

Fund Managers

Sonam Srivastava, a seasoned professional with over a decade of experience in investment research and portfolio management, founded Wright Research. This India-domiciled entity operates as a SEBI-registered Robo-advisor and Portfolio Management Service (PMS). Wright Research is dedicated to wealth creation in the digital domain, employing scientific, data-driven methodologies to identify and capitalize on opportunities within public markets across various asset classes for client benefit. The platform has garnered significant traction, particularly among millennial investors, serving over 50,000 clients and managing assets exceeding ₹300 crore. Over the past three years, Wright Research has demonstrated strong performance, achieving an outperformance of over 90% relative to its benchmark index. Ms. Srivastava's expertise encompasses systematic strategies, long-short strategies, and algorithmic trading. Her career began at Forefront Capital in Mumbai, which was subsequently acquired by Edelweiss. Within Edelweiss, she contributed as an algorithm designer on the institutional equity broking desk. She further honed her quantitative skills at HSBC Europe, developing factor-driven portfolio solutions. Prior to establishing Wright Research, she was involved in portfolio management at Qplum, an artificial intelligence-driven Robo-advisor. Ms. Srivastava holds a master's degree in Financial Engineering from Worldquant University and is an alumnus of IIT Kanpur. She is recognized globally as a researcher and contributes as visiting faculty at the AI in Finance Institute, New York, and BSE Institute Limited.

Portfolio Details

Portfolio Composition

Large Cap
0%
Mid Cap
0%
Small Cap
0%
Cash & Equivalent
0%

Top 5 Holdings

  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A

Top 5 Sectors

  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A
  • Undisclosed N/A

Portfolio Characteristics

total number of stocks
N/A
top 5 stocks percent
N/A
top 5 sectors percent
N/A
average market cap
N/A
portfolio age
2 Yrs, 4 Months
sip
Available
stp
Available

Fee Structure

Fixed Fee

AMC: 1.5%

Variable Fee

AMC: No Option

Hurdle: 10% or Benchmark

Profit Sharing: 15% Profit Sharing above 10% hurdle or Benchmark

Exit Load

Year 1: 0%

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)