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Quant vs. Traditional Fund Performance: A Comparative Analysis (USA) and Quant Adoption Simulation (India)

In this article

Executive Summary:

  1. Analysis of the US market (2019-2024) shows quant funds outperformed traditional funds (S&P 500) on a risk-adjusted basis.
  2. Simulation suggests incorporating quant funds into Indian portfolios could enhance returns and Sharpe ratios.
  3. However, adoption in India faces barriers; international solutions should be considered.

Introduction

  1. Quant investing uses algorithms and data for trading, unlike traditional methods relying on human judgment.
  2. Global interest in quant is rising due to technology and data availability.
  3. Quant adoption in India is growing, with investors seeking alternatives.
  4. This report compares US quant and traditional fund performance (2019-2024) and simulates Indian retail investor impact using provided data.

US Quant vs. Traditional Fund Performance (2019-2024)

  1. Investment performance is influenced by market cycles (bull vs. bear).
  2. The S&P 500 experienced several phases between 2019 and 2024:
    • Prolonged bull market (Mar 2009 – Feb 2020)
    • Brief bear market (Feb 2020 – Mar 2020) – COVID-19 impact
    • Strong bull market (Mar 2020 – Jan 2022) – stimulus and recovery
    • Sustained bear market (Jan 2022 – Oct 2022) – inflation and rate hikes
    • New bull market (Oct 2022 – End 2024) – significant gains in 2023/24.
  3. Annual Returns: Refer to Table 1: Annual Returns of Quant Funds vs. S&P 500 (USA, 2019-2024).
    • Some quant funds (e.g., D.E. Shaw funds) showed consistent positive returns, even in the 2022 bear market.
    • Renaissance – Institutional Equities Fund had a significant loss in 2020 but strong gains afterward.
    • The S&P 500 showed positive returns in most years but a substantial drop in 2022, highlighting its sensitivity to market downturns.
    • Some quant strategies demonstrated an ability to navigate adverse market conditions profitably.
  4. Overall Performance Metrics: Refer to Table 2: Overall Performance Metrics: Quant vs. Traditional Fund (USA, 2019-2024).
    • The average quant fund had a higher average return (19.970%) and a lower standard deviation (8.360%) compared to the S&P 500 (13.4975% average return, 12.468% standard deviation).
    • The Sharpe ratio (risk-adjusted return, assuming zero risk-free rate) was significantly higher for the average quant fund (2.389) than the S&P 500 (1.083).
    • This suggests quant funds provided better returns per unit of risk with less volatility during this period.
  5. Consistency of Returns:
    • Individual quant fund returns varied, reflecting different strategies (see Table 1).
    • The S&P 500’s negative return in 2022 shows its direct link to broad market movements (see “Input_Data” table).
    • While average quant performance was strong, investors should note that individual quant fund consistency can vary.

Quant Adoption Simulation for common investor in India

  1. A normalization approach was used to estimate potential Indian quant fund returns based on US data.
  2. Normalization ratio (1.116) was calculated by comparing 10-year average returns of S&P 500 (0.114) and Nifty 50 (0.128).
  3. Nifty 50 data served as a proxy for the Traditional Fund in the Indian simulation.

     

  4. Simulated Performance: Refer to the table titled “DATA“.
    • The simulated Quant Fund showed a higher average return (22.289%), lower standard deviation (9.331%), and a better Sharpe ratio (2.389) compared to the Traditional Fund (Nifty 50 proxy: 12.358% average return, 10.760% standard deviation, 1.148 Sharpe ratio).
    • This normalization provides a simplified estimate and doesn’t fully capture Indian market nuances.
  5. Portfolio Allocation Scenarios: Refer to the table titled “Quant Adoption Simulation“.
    • Five scenarios (A to E) explored different allocations to quant and traditional funds.
    • Higher quant allocations generally led to increased returns. Portfolio A (0% quant) had a 12.358% return, while Portfolio E (100% quant) reached 22.289%.
    • The Sharpe ratio peaked at a 50% quant allocation (2.900).
    • Mixed portfolios (B, C, D) often showed lower volatility than pure traditional or pure quant portfolios, suggesting diversification benefits.
  6. Resilience During Downturns:
    • Drawing from the US market in 2022 (Table 1), quant funds showed resilience.
    • Higher Sharpe ratios in the Indian simulation for quant-allocated portfolios suggest potential for better risk-adjusted returns during downturns.
  7. Investment Growth Simulation (₹10,000,000 Initial Investment): Refer to tables “Quant Adoption Simulation-2019” to “Quant Adoption Simulation-2024” and corresponding “DATA-2019” to “DATA-2024“.
    • Simulations of investments made annually from 2019 to 2024 and held until 2024 generally indicate that higher quant allocations resulted in greater accumulated amounts.
    • This suggests quant-heavy portfolios might offer better long-term outcomes, potentially by mitigating volatility.
  8. Long-Term Forecast: Refer to the table titled “Quant Adoption Simulation (Forecasted)“.
    • Projections based on average returns suggest significantly higher potential returns for portfolios with greater quant allocations over longer periods (1 to 20 years).

Early performace Insights:

Early Performance Insights:

  1. US analysis (2019-2024, Table 2) indicates selected quant funds outperformed the S&P 500 on a risk-adjusted basis (higher returns, lower volatility).
  2. Indian simulation (Quant Adoption Simulation table) suggests incorporating quant funds could improve returns and Sharpe ratios for retail investors.
  3. Investment growth simulations (Quant Adoption Simulation-2019 to -2024 tables) hint at the potential for quant-heavy portfolios to outperform, possibly showing more resilience in volatile markets.
  4. However, widespread quant adoption in India faces challenges: lack of understanding, model/data risk concerns, liquidity issues, and preference for traditional options.
  5. Learning from international solutions focusing on financial literacy, user-friendly tech, supportive regulations, and transparency is crucial for promoting quant investing in India.

Table 1: Annual Returns of Quant Funds vs. S&P 500 (USA, 2019-2024)

Fund2019(%)2020(%)2021(%)2022(%)2023(%)2024(%)
D.E. Shaw – Oculus Fund11.7025.4015.0020.007.8036.00
D.E. Shaw – Composite Fund10.4019.4018.5024.709.6018.00
Renaissance – Medallion Fundn/a76.00n/an/an/a30.00
Renaissance – Institutional Equities Fundn/a-19.90n/an/an/a22.70
Schonfeld – Strategic Partnersn/an/an/an/a3.0019.70
AQR – Helix Fundn/an/an/a49.1014.3017.90
AQR – Apex Fundn/an/an/a17.1016.2015.10
Citadel – Wellington Fundn/a24.4026.3038.1012.6015.10
Millennium Managementn/a26.0013.5010.2010.0015.00
Two Sigma – Absolute Return Enhanced Strategyn/an/an/an/an/a14.30
CFM – Stratus Fundn/an/an/an/an/a14.22
CFM – Discus Fundn/an/an/an/an/a12.01
S&P 5009.2518.1617.76-12.0825.0122.88

Key Insights from the Data highlights the varying annual performance of individual quant funds compared to the S&P 500, showcasing that while some quant funds maintained positive returns even during downturns, others experienced more volatility.

Table 2: Overall Performance Metrics: Quant vs. Traditional Fund (USA, 2019-2024)

MetricQuant Fund (Average)Traditional Fund (S&P500)
Average Return (%)19.97013.4975
Standard Dev (%)8.36012.468252
Sharpe Ratio2.3887992751.082550549

Key Insights from the Data provides a clear quantitative comparison, demonstrating that, on average, the analyzed quant funds in the US achieved higher average returns and Sharpe ratios with lower standard deviation than the traditional S&P 500.

Graph 1: Showing annual return comparison of quant vs traditional fund from year 2019 to 2024.
Showing-annual-return-comparison-of-quant-vs-traditional-fund-from-year-2019-to-2024
Graph 2: Showing different parameters for comparison between quant & traditional fund in USA
Showing-different-parameters-for-comparison-between-quant-traditional-fund-in-USA

Table 3: Indian Market Simulation Parameters

MetricS&P 500Nifty 50
Average Monthly Return0.91%0.010056223
Average Yearly Return0.114302930.127577978
Normalization Ratio1.116139174

Key Insights from the Data: The Indian Market Simulation Parameters (Table 3) establish the normalization ratio used to adapt US quant fund data for the Indian context, based on historical S&P 500 and Nifty 50 returns.

Table 4: Quant Adoption Simulation Results (India)

TypeQuant Fund(%)Traditional Fund(%)Returns(%)Std Dev(%)Sharpe Ratio(%)
A0%100%12.357807%0.1076027171.148466048
B20%80%14.344012%0.0824459181.739808657
C50%50%17.323321%0.0597447192.899556799
D80%20%20.302629%0.0712311962.850243988
E100%0%22.288834%0.0933055972.388799275

Key Insights from the Data: Results from the Quant Adoption Simulation in India indicate a positive correlation between the allocation to quant funds and the potential returns and Sharpe ratios of a portfolio.

Graph 3: Return % based on Type of portfolio (Quant/Traditional Fund) for the year 2024.
Type-of-portfolio-Quant-Traditional-Fund-for-the-year-2024
Graph 4: Sharpe ratio based on Type of portfolio (Quant/Traditional Fund) for the year 2024.
Sharpe-ratio-based-on-Type-of-portfolio-Quant-Traditional-Fund-for-the-year-2024

Table 5: Projected Investment Value by Year and Allocation (India, Investment of ₹10,000,000 in 2019)

Allocation (Quant/Traditional)Amount in 2024 (₹)
A (0%/100%)1,95,30,085.65
B (20%/80%)2,25,82,064.45
C (50%/50%)2,76,86,495.11
D (80%/20%)3,34,31,877.13
E (100%/0%)3,76,18,649.36

Key Insights from the Data: shows the projected growth of a ₹10,000,000 investment under different quant/traditional allocations, suggesting that higher quant allocations could lead to greater accumulated value over the analyzed period.

Graph 5: ₹10,000,000 invested in 2019 value in 2024 based on Type (Quant/Traditional) of portfolio 
₹10000000-invested-in-2019-value-in-2024-based-on-Type-Quant-Traditional-of-portfolio

Table 6: DATA

Fund NameAvg Return(%)Std Dev(%)Sharpe Ratio
Quant Fund22.28883%9.33056%2.388799275
Traditional Fund12.35781%10.76027%1.148466048

Table 7: Quant Adoption Simulation

TypeQuant FundTraditional FundRetirns(%)Std Dev(%)Sharpe Ratio
A0%100%12.357807%0.1076027171.148466048
B20%80%14.344012%0.0824459181.739808657
C50%50%17.323321%0.0597447192.899556799
D80%20%20.302629%0.0712311962.850243988
E100%0%22.288834%0.0933055972.388799275

Table 8: Input_Data (for Indian Market Simulation)

S&P 500NIFTY 50
Average monthly return0.91%0.010056223
Average yearly return0.114302930.127577978
Normalisation ratio1.116139174

Table 9: Input_Data (for Forecasting)

S No.YearQuant Fund Return(%)YearTraditional Fund Return(%)
12019A11.05000000%2019A3.423522%
22020A34.24000000%2020A21.459861%
32021A18.32500000%2021A23.171197%
42022A26.53333333%2022A-0.207038%
52023A10.50000000%2023A24.462764%
62024A19.16916667%2024A1.836535%

Table 10: DATA-2019

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 12.33334% 9.33056% 1.32182187
Traditional Fund 3.42352% 10.76027% 0.318163192

Table 11: Quant Adoption Simulation-2019

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% 3.423522% 10342352.24 0.107602717 0.318163192 ₹ 1,95,30,085.65
B 20% 80% 5.205485% 10520548.55 0.082445918 0.631381832 ₹ 2,25,82,064.45
C 50% 50% 7.878430% 10787843.01 0.059744719 1.318682274 ₹ 2,76,86,495.11
D 80% 20% 10.551375% 11055137.48 0.071231196 1.481285645 ₹ 3,34,31,877.13
E 100% 0% 12.333338% 11233333.79 0.093305597 1.32182187 ₹ 3,76,18,649.36

Table 12: DATA-2020

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 38.21661% 9.33056% 4.095853469
Traditional Fund 21.45986% 10.76027% 1.994360481

Table 13: Quant Adoption Simulation-2020

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% 21.459861% 12145986.07 0.107602717 1.994360481 ₹ 2,35,51,813.17
B 20% 80% 24.811210% 12481120.96 0.082445918 3.00939211 ₹ 2,49,98,475.62
C 50% 50% 29.838233% 12983823.30 0.059744719 4.994287982 ₹ 2,70,58,236.60
D 80% 20% 34.865256% 13486525.64 0.071231196 4.894661114 ₹ 2,89,54,451.68
E 100% 0% 38.216605% 13821660.53 0.093305597 4.095853469 ₹ 3,01,11,344.17

Table 14: DATA-2021

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 20.45325% 9.33056% 2.19207111
Traditional Fund 23.17120% 10.76027% 2.153402569

Table 15: Quant Adoption Simulation-2021

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% 23.171197% 12317119.68 0.107602717 2.153402569 ₹ 1,58,65,572.25
B 20% 80% 22.627608% 12262760.75 0.082445918 2.744539444 ₹ 1,73,95,093.28
C 50% 50% 21.812224% 12181222.36 0.059744719 3.650904061 ₹ 1,97,74,790.91
D 80% 20% 20.996840% 12099683.96 0.071231196 2.947702819 ₹ 2,22,38,819.20
E 100% 0% 20.453250% 12045325.04 0.093305597 2.19207111 ₹ 2,39,17,377.35

Table 16: DATA-2022

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 29.61489% 9.33056% 3.173967447
Traditional Fund -0.20704% 10.76027% -0.019240922

Table 17: Quant Adoption Simulation-2022

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% -0.207038% 9979296.25 0.107602717 -0.019240922 ₹ 1,26,48,614.82
B 20% 80% 5.757349% 10575734.85 0.082445918 0.698318197 ₹ 1,35,49,891.27
C 50% 50% 14.703928% 11470392.76 0.059744719 2.461125927 ₹ 1,48,90,895.52
D 80% 20% 23.650507% 12365050.67 0.071231196 3.320245636 ₹ 1,62,08,414.42
E 100% 0% 29.614893% 12961489.27 0.093305597 3.173967447 ₹ 1,70,67,939.13

Table 18: DATA-2023

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 11.71946% 9.33056% 1.256029831
Traditional Fund 24.46276% 10.76027% 2.273433625

Table 19: Quant Adoption Simulation-2023

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% 24.462764% 12446276.36 0.107602717 2.273433625 ₹ 1,26,74,856.53
B 20% 80% 21.914103% 12191410.31 0.082445918 2.657997334 ₹ 1,28,37,928.24
C 50% 50% 18.091112% 11809111.25 0.059744719 3.028068904 ₹ 1,30,49,404.56
D 80% 20% 14.268122% 11426812.18 0.071231196 2.003072057 ₹ 1,32,21,123.39
E 100% 0% 11.719461% 11171946.13 0.093305597 1.256029831 ₹ 1,33,13,515.11

Table 20: DATA-2024

Fund Name Return (%) Std Dev (%) Sharpe Ratio
Quant Fund 21.39546% 9.33056% 2.293051921
Traditional Fund 1.83653% 10.76027% 0.170677345

Table 21: Quant Adoption Simulation-2024

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount in 2024
A 0% 100% 1.836535% 10183653.46 0.107602717 0.170677345 ₹ 1,01,83,653.46
B 20% 80% 5.748319% 10574831.93 0.082445918 0.697223024 ₹ 1,05,74,831.93
C 50% 50% 11.615996% 11161599.62 0.059744719 1.944271644 ₹ 1,11,61,599.62
D 80% 20% 17.483673% 11748367.32 0.071231196 2.454496659 ₹ 1,17,48,367.32
E 100% 0% 21.395458% 12139545.78 0.093305597 2.293051921 ₹ 1,21,39,545.78

Table 22: Quant Adoption Simulation (Forecasted)

Type Quant Fund Traditional Fund Returns % 1 year Return Std Dev (%) Sharpe Ratio Amount after 5 years Amount after 10 years Amount after 15 years Amount after 20 years
A 0% 100% 12.35780677% 11235780.68 10.760271741% 1.148466048 ₹ 1,79,06,729.19 ₹ 3,20,65,095.04 ₹ 5,74,18,097.34 ₹ 10,28,17,031.99
B 20% 80% 13.88016208% 11388016.21 8.244591844% 1.683547511 ₹ 1,91,53,157.63 ₹ 3,66,84,344.72 ₹ 7,02,62,103.70 ₹ 13,45,74,114.77
C 50% 50% 16.16369505% 11616369.50 5.974471861% 2.705460068 ₹ 2,11,52,031.78 ₹ 4,47,40,844.82 ₹ 9,46,35,977.14 ₹ 20,01,74,319.55
D 80% 20% 18.44722802% 11844722.80 7.123119575% 2.589768124 ₹ 2,33,14,414.19 ₹ 5,43,56,190.91 ₹ 12,67,28,274.88 ₹ 29,54,59,549.05
E 100% 0% 19.96958333% 11996958.33 9.330559699% 2.140234239 ₹ 2,48,51,679.98 ₹ 6,17,60,599.80 ₹ 15,34,85,466.17 ₹ 38,14,37,168.73

Conclusion:

The analysis of US quant and traditional fund performance between 2019 and 2024 indicates a notable advantage for quant funds in terms of risk-adjusted returns, as summarized in Table 2. The simulation for the Indian market further suggests the potential benefits of incorporating quant strategies into retail investors’ portfolios, as detailed in Table 4. Moreover, the projected investment values under different allocation scenarios, presented in Table 5 and further elaborated in tables from Table 11 to Table 21, suggest that portfolios with a higher allocation to quant funds may have the potential for greater growth. The long-term forecasts in Table 22 also support this trend. However, significant barriers to adoption persist in India. Addressing these challenges by drawing lessons from international solutions focused on education, technology, regulation, and transparency will be crucial for enabling wider participation in quantitative investing within the Indian market.

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