SEBI Study: 91% of Traders Are Losing Money in F&O | Proprietary Firms Cash In

The study from the document titled “Analysis of Profits & Losses in the Equity Derivatives Segment (FY22-FY24)” provides a detailed investigation into the performance of traders in India’s F&O (Futures and Options) segment over three fiscal years. Here’s a comprehensive overview, broken down into key sections, and statistical insights presented in a tabular format for clarity:

Key Findings

  1. High Proportion of Loss-Makers Among Individuals:

    • FY24: 91.1% of individual traders (approximately 73 lakh) faced losses, with an average net loss of ₹1.20 lakh per person.
    • Over the period from FY22 to FY24, 1.13 crore individual traders incurred cumulative losses amounting to ₹1.81 lakh crore. Specifically, FY24 saw individual traders losing ₹75k crore.
    • Only 7.2% of individual traders made profits, with just 1% earning more than ₹1 lakh after accounting for transaction costs.
  2. Profit-Makers (FPIs and Proprietary Traders):

    • Proprietary traders earned ₹33,000 crore in gross profits in FY24.
    • FPIs (Foreign Portfolio Investors) garnered ₹28,000 crore in gross profits.
    • Majority of these profits came from algorithmic trading. About 97% of FPIs’ profits and 96% of proprietary traders’ profits were generated by Algo entities.
  3. Transaction Costs:

    • Individual traders incurred more than ₹50,000 crore in transaction costs between FY22 and FY24.
    • Of this, more than half (₹25,000 crore) was in the form of brokerage, followed by ₹13,800 crore in Government taxes (STT, GST, and stamp duty), and ₹10,200 crore in exchange fees.
    • On average, individual traders spent ₹26,000 per person in transaction costs in FY24, with 71% of these costs being attributed to brokerage and exchange fees.
  4. Demographic Insights:

    • The proportion of young traders (under 30 years) rose significantly from 31% in FY23 to 43% in FY24. However, 93% of these young traders incurred losses.
    • Female participation in F&O trading decreased from 14.9% in FY22 to 13.7% in FY24. However, fewer female traders incurred losses compared to male traders.
    • A large portion of traders, about 76%, reported annual incomes below ₹5 lakh, with 93% of these traders incurring losses.
  5. Geographic Distribution:

    • B30 cities (beyond the top 30 cities) accounted for 72.2% of F&O traders in FY24, with a significant contribution to the total turnover and losses.
    • The states with the largest number of F&O traders were Maharashtra, Gujarat, Uttar Pradesh, and Rajasthan.
    • The highest average per-person losses were recorded in southern states like Telangana (₹1.97 lakh), Andhra Pradesh (₹1.45 lakh), Tamil Nadu (₹1.37 lakh), and Karnataka (₹1.35 lakh).

Statistical Summary (Tabular Format)

Category FY24 Key Data
Total Individual Traders 95.7 lakh
Percentage of Loss-Makers 91.1%
Average Net Loss per Person ₹1.20 lakh
Total Losses by Individuals (FY22-FY24) ₹1.81 lakh crore
Profitable Traders (FY22-FY24) 7.2%
Transaction Costs ₹50,000 crore (₹26,000 per person in FY24)
Top States by F&O Traders Maharashtra, Gujarat, UP, Rajasthan
Top States by Average Loss Telangana, Andhra Pradesh, Tamil Nadu, Karnataka

Breakdown of Loss-Makers by Trading Category (FY24)

Category Percentage of Loss-Makers Average Loss (₹)
Futures 60% ₹60,000
Options 91.5% ₹91,000

Geographic Insights (FY24)

State Number of Traders (in lakh) Average Loss (₹)
Maharashtra 18.8 ₹74,000
Gujarat 10.1 ₹88,000
UP 9.3 ₹83,000
Rajasthan 5.4 ₹76,000

Distribution of Traders Based on Turnover Size (FY22-FY24)

Turnover Category % of Traders % of Total Losses % Loss-Makers
Small Traders (<₹1 lakh) 30% 1% 91.5%
Mid-Size Traders (₹1 lakh - ₹1 crore) 53% 18.4% 93.8%
High-Value Traders (>₹1 crore) 17% 81% 95%

Conclusion

The findings of this study highlight the overwhelming proportion of loss-making individual traders in India’s F&O segment, particularly younger traders and those from lower income brackets. The majority of profits are concentrated among a small number of proprietary and FPI traders, especially those utilizing algorithmic trading strategies. Transaction costs also represent a significant burden on individual traders. As a result, there’s a strong case for improving trader education and risk management practices, especially given the rapid growth in F&O participation from B30 cities.

For detailed information, read the full story here: SEBI | Study - Analysis of Profits & Losses in the Equity Derivatives Segment (FY22-FY24)

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But transaction charges were increased in the last budget :slightly_smiling_face:

No wonder people are losing money in options. Options were never meant to be a primary tool for making money; their main purpose is hedging.

When I used an AI model to calculate the possible combinations of an “Opening Range Breakout” strategy for option trading, here’s what I found:

Mathematical Combinations:

Each parameter in the strategy can have multiple variations, and the total number of possible strategies can be calculated by multiplying the number of choices for each parameter.

For example:

N time frames (e.g., 5, 10, 15, 30, 60 minutes)

3 directional strategies (Long, Short, Both)

X entry/exit methods (including stop-loss, take-profit, trailing stops, etc.)

Y filters (based on volume, trend, etc.)

Z time-based conditions

The total number of strategies can be expressed as:

For example, if you have:

5 time frames (5, 10, 15, 30, 60 minutes)

3 directional choices (Long, Short, Both)

10 different entry/exit methods

5 filter options

3 time-based conditions

The number of possible strategy combinations would be:

5 times 3 times 10 times 5 times 3 = 2250 possible combinations

And this is just one strategy with a few parameters! The potential variations are vast.

As I mentioned earlier, paradoxically, option trading is the easiest hard way to make money.

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SEBI has turned into more of a data analytics firm.

Milch has to be milked to the hilt. :crazy_face:

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All brace up for more transaction charges

@t7support Expect exchange transaction charges to decrease from the same time or just around it. I’m hoping that will make the impact of new STT near to zero.

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