SEBI ran the numbers on India's full time and active individual traders so nobody has to guess. In the year to March 2025, individual traders in equity futures and options lost a combined 1,05,603 crore rupees. Ninety one per cent of them lost money. The average loser lost 1.1 lakh rupees. This was not a bad year, it was the second year running SEBI found roughly the same pattern, and the losses actually grew 41% from the year before. Before asking whether a monkey could do better, it is worth being precise about what a monkey would actually have to do, because the honest answer to this question is stranger and more useful than the joke version.
Chapter 1What would the monkey actually have to do to win this bet?
Strip away the cartoon image first. The monkey in this comparison is not a literal animal throwing darts, it is a stand in for zero skill, zero conviction, and critically, minimum activity. Burton Malkiel coined the image in his 1973 book A Random Walk Down Wall Street, arguing that a blindfolded monkey throwing darts at a newspaper's financial pages could select a portfolio that would do just as well as one carefully assembled by experts. The claim was never that randomness has insight. The claim was that most of what looks like skill in stock picking is noise, and that trading costs turn even a coin flip's worth of skill into a losing proposition once you act on it often enough.
Two real tests back this up better than any viral animal story does. The Wall Street Journal ran an actual Dartboard Contest for fourteen years, from 1988 to 2002, staff throwing darts against professional stock pickers every six months. The honest result cuts both ways at once. Professionals averaged a 10.2% gain against the darts' 3.5%, a real edge on paper. But the professionals also deliberately picked riskier, lower dividend stocks to chase that return, and the dart portfolio never got credit for the dividends it would have earned, meaning part of the pros' edge was simply taking more risk, not picking better. A separate, more rigorous academic exercise from Cass Business School constructed 10 million randomly weighted portfolios drawn from 1,000 stocks, every year from 1968 to 2011, and found the median random portfolio beat the market cap weighted index by a wide margin over that stretch. That result is real, peer reviewed adjacent, and considerably more useful than the popular stories about a chimpanzee named Raven supposedly beating 6,000 fund managers in 1999, or a Russian circus chimp called Lusha beating 94% of that country's mutual funds in 2010. Both of those specific claims circulate identically worded across dozens of personal finance blogs with no traceable original source, the exact pattern of a story that got retold into fact rather than one that was ever actually verified. Treat them as folklore, not data.
So the real monkey, defined properly, is simply a portfolio built with no strategy and, just as importantly, no urge to keep touching it. That second part turns out to matter more than the first.
Chapter 2What does an actual full time Indian trader earn, in SEBI's own numbers?
This is where the joke stops being a joke and becomes a regulatory filing.
SEBI's first major study, published in September 2024, covered FY22 through FY24 and found 93% of individual traders in equity futures and options lost money, with aggregate losses exceeding 1.8 lakh crore rupees across those three years. The top 3.5% of loss makers, roughly 4 lakh individual traders, lost an average of 28 lakh rupees each over the same period. SEBI's updated study, released in July 2025 and covering FY25, found the pattern had not improved, it had worsened. Net losses rose 41% year on year to 1,05,603 crore rupees, up from 74,812 crore rupees the year before. The share of traders losing money held at 91%, and the average individual loser was down 1.1 lakh rupees for the year. The study covered close to 96 lakh unique individual traders across India's thirteen largest brokers, not a small or unrepresentative sample.
The part of this that should sting more than the headline number is who was actually on the other side of these trades. SEBI's own study noted that most of the profits in the segment were generated by larger entities running proprietary trading desks, algorithmic infrastructure, and institutional scale, not by skilled individual humans picking better trades than everyone else. SEBI has been direct enough about the scale of the problem that it now mandates every broker display a specific warning at login, that nine out of ten individual traders lose money in the F&O segment. That is not a market commentator's opinion. That is a line the regulator forces onto the login screen.
Chapter 3Does this hold up outside India, or is this a uniquely Indian problem?
It holds up, and the strongest evidence for that comes from a data set an order of magnitude larger and more rigorously studied than anything SEBI has published, precisely because Taiwan's stock exchange gave academics its complete transaction history rather than a survey sample.
Brad Barber, Yi Tsung Lee, Yu Jane Liu, and Terrance Odean, working across a series of papers published in top finance journals, analysed 3.7 billion individual transactions on the Taiwan Stock Exchange between 1992 and 2006. Their central findings read like SEBI's report with the serial numbers filed off. Day traders lost an average of 23.9 basis points per day, net of fees, and aggregate day trading performance was reliably negative in 14 of the 15 years studied. Before costs, the average day trader still lost 7 basis points a day, a genuinely negative gross return before a single rupee, or dollar, of brokerage was even subtracted. Transaction costs then more than tripled that loss. Only about 5% of active day traders were profitable in any given period across the full sample, and a separate paper from the same research group found less than 1% of the day trading population could predictably and reliably earn a positive return net of fees, year after year, rather than by chance in a single lucky stretch.
The survival data from the same research is arguably the most brutal number in this entire piece. Of everyone who started day trading in the Taiwan sample, only 44% were still trading a year later. Only 24% remained after two years. Only 15% were still there after three.
This is not a study of bad traders in a bad market. It is a study of an entire national exchange's complete trading history, and it says the overwhelming majority of people who try to trade for a living stop being able to afford to within three years.
Chapter 4Does crypto make any of this better, or does leverage just make it faster?
Crypto derivatives add one ingredient the equity market mostly does not, leverage available at a scale that turns a survivable loss into an instant liquidation. On 10 and 11 October 2025, a geopolitical shock triggered the largest single deleveraging event in crypto market history, wiping out over 19 billion dollars in leveraged positions within roughly a day, with long positions accounting for somewhere between 80% and 90% of everything liquidated. Bitcoin fell about 14% in the worst phase of the crash. Some altcoins lost 40% to 80% of their value intraday. Binance's own 2025 research, cited in industry coverage, put the share of retail leverage traders who lose money at over 70%, broadly consistent with, if slightly less severe than, the equity derivatives picture in India and Taiwan.
A few more dramatic sounding claims circulate about crypto leverage specifically, that 95% of margin traders lose money and that the average 10x leverage account survives only 37 days, both traced back to a single exchange's own blog post citing an unnamed Cambridge study. These numbers may well be directionally right, leverage mathematically accelerates ruin, but they carry the same red flag as the Raven and Lusha stories, a suspiciously precise statistic from a source with an obvious incentive to make trading sound thrillingly dangerous rather than boringly unprofitable, with no independently checkable original citation behind it. The honestly documented number, the 19 billion dollar October 2025 event and Binance's own 70% figure, already makes the point without needing the embellishment.
Chapter 5So why does this actually happen? A framework, not just a pile of statistics
Three separate, well established bodies of theory explain why the monkey keeps winning this fight, and understanding all three is more useful than memorising any single statistic above.
The first is arithmetic, and it has a name, Charles Ellis called it the loser's game in a 1975 paper that remains one of the most cited pieces of investing theory ever published. Ellis's insight was that in a game dominated by professionals, like tennis at Wimbledon, points are won by brilliant shots. In a game where the field is largely amateur, like club tennis, points are won by whoever makes the fewest unforced errors, not by whoever hits the most winners. Trading against a market that is now dominated by algorithmic desks and institutions is closer to club tennis than Wimbledon for almost everyone reading this. Every trade also carries a real cost, brokerage, the bid ask spread, securities transaction tax, slippage, that a monkey holding a portfolio and doing nothing simply never pays. Once you accept that trading is close to a zero sum game before costs, since every rupee one trader gains is a rupee another trader loses, it becomes strictly negative sum after costs, meaning the average participant is mathematically guaranteed to lose money purely from the act of trading itself, before any question of skill even enters the picture.
The second is survivorship, and it explains why the 5% to 9% who do profit look so convincing on social media. In a population of 96 lakh traders, even pure random chance guarantees that some meaningful number will show large gains in any given year, exactly the way Cass Business School's own random portfolios sometimes beat the index by huge margins purely by the luck of the draw. Nobody makes a YouTube channel about the 91% who lost money quietly. The 9% who won, some through genuine skill and infrastructure, most through the same randomness that let some of Cass's ten million random portfolios crush the market, are the only ones anyone hears from, which distorts how achievable the outcome actually looks from the outside.
The third is behavioural, and it is the filter that explains why real humans do measurably worse than true randomness would predict, not merely no better. Barber and Odean's own earlier research on this exact question, separate from the Taiwan day trading papers, documented that individual investors are reliably overconfident about their own skill, trade far more often than a rational actor would, and show a well documented disposition effect, selling winning positions too early to lock in the good feeling and holding losing positions too long to avoid admitting the mistake. A monkey has none of these impulses. A monkey does not revenge trade after a loss, does not increase position size after a lucky win, and does not feel the specific, expensive emotion of watching a losing trade and needing it to become a winner before closing it. This is the genuinely humbling part of the whole comparison. The data does not just say humans fail to beat randomness. It says the average human, once leverage, overtrading, and the disposition effect are added in, performs worse than pure randomness would, which is a different and more damning finding than simply losing a fair fight.
Chapter 6So does the monkey actually win?
Against SEBI's own numbers, yes, decisively. A monkey that bought and held, doing nothing, would have avoided the 91% chance of loss that actual individual F&O traders faced in FY25, simply by not paying the transaction costs, not chasing the leverage, and not touching the position out of boredom or panic. Against the Taiwan data, yes again, since a monkey does not need to survive three years of day trading to avoid the fate that awaited 85% of the traders who tried. Against crypto leverage, the monkey wins by an even wider margin, mostly by refusing to use leverage at all, which a monkey has no ego invested in doing.
The uncomfortable, useful part of this framework is not that trading is impossible, a genuine, tiny fraction of participants in every single data set here did post real, repeatable, skill based gains. It is that the honest odds of being one of them are closer to the 1% Barber, Lee, Liu, and Odean found reliably profitable in Taiwan than to the story any individual trader tells themselves before they start. The monkey was never actually the insult in this comparison. Doing less, trading less, and feeling less about each individual position turns out to be the actual edge, and it is one every single person reading this can adopt starting today, no dartboard required.