Why You Do Not Need to Be Right Most of the Time to Win
Learn how a 40% win-rate with a 2:1 risk-reward ratio easily beats a 60% win-rate with poor risk management using simple expectancy math.
Many retail investors believe that successful investing requires predicting the future correctly 80% or 90% of the time. They spend hours searching for the perfect stock tip or the ultimate technical indicator. But the truth is far more liberating: you can be wrong more often than you are right, and still build a highly profitable portfolio. It all comes down to a simple mathematical concept called expectancy.
The Trap of the High Win-Rate
Imagine two investors, Amit and Priya. Amit is right 60% of the time. He feels great because most of his trades are winners. However, when he wins, he makes a small profit of ₹5,000. When he loses, he lets his losses run, losing ₹10,000 on average. Priya is right only 40% of the time. She feels the sting of losing more often. But because she cuts her losses quickly and lets her winners run, she makes ₹20,000 when she is right, and loses only ₹10,000 when she is wrong. Let us look at how their portfolios perform over 10 trades.
| Metric | Amit (High Win-Rate) | Priya (High Risk-Reward) |
|---|---|---|
| Win Rate | 60% (6 wins out of 10) | 40% (4 wins out of 10) |
| Average Win | ₹5,000 | ₹20,000 |
| Average Loss | ₹10,000 | ₹10,000 |
| Total Gains | ₹30,000 (6 × ₹5,000) | ₹80,000 (4 × ₹20,000) |
| Total Losses | ₹40,000 (4 × ₹10,000) | ₹60,000 (6 × ₹10,000) |
| Net Result | -₹10,000 (Loss) | +₹20,000 (Profit) |
Understanding R Multiples
To achieve Priya's results, you need to think in terms of R multiples. 'R' stands for your initial risk—the maximum amount of money you are willing to lose on a trade. If you buy a stock at ₹100 and set a stop-loss at ₹90, your risk (1R) is ₹10 per share. If you sell that stock at ₹120, your profit is ₹20 per share. Since your risk was ₹10, your reward is 2 times your risk. In trading terms, you made a 2R profit.
A Worked Example: Calculating Your Expectancy
A positive expectancy means your strategy makes money over time, while a negative expectancy means it slowly drains your capital. Let us calculate the expectancy for both Amit and Priya using our formula.
- Step 1: Define 1R (Risk) as ₹10,000 for both investors.
- Step 2: Amit's average win is ₹5,000 (0.5R). His average loss is ₹10,000 (1R). Win rate is 60% (0.60).
- Step 3: Calculate Amit's expectancy: (0.60 × 0.5R) - (0.40 × 1R) = 0.3R - 0.4R = -0.1R. Amit loses 0.1R (₹1,000) on average per trade.
- Step 4: Priya's average win is ₹20,000 (2R). Her average loss is ₹10,000 (1R). Win rate is 40% (0.40).
- Step 5: Calculate Priya's expectancy: (0.40 × 2R) - (0.60 × 1R) = 0.8R - 0.6R = +0.2R. Priya gains 0.2R (₹2,000) on average per trade.
Even though Priya is wrong 60% of the time, her positive expectancy of +0.2R ensures that she remains profitable in the long run. Amit, despite his high 60% win-rate, has a negative expectancy of -0.1R, meaning he is mathematically guaranteed to lose money over a large sample of trades.
Stop focusing on being right every time. Focus on keeping your losses small (1R) and letting your winning trades reach at least twice your risk (2R) to secure a positive expectancy.
You can easily model these risk-reward scenarios and track your portfolio's potential expectancy using the interactive planning tools on the stock-analyze.com stock analysis page.