10 core principles of risk management according to William O’Neil

Within the context of stock investing, William J. O’Neil has developed an influential risk management approach that is not aimed at speculation, but at capital preservation and rational decision-making. In contrast to many popular investment strategies, which focus on returns, O’Neil formulates his approach based on the necessity to limit losses and to structurally integrate discipline into the investment process.
This article displays his 10 core principles briefly. 

1. Sell a position at a loss of 7–8%

O’Neil’s basic rule involves applying a loss limit per position: as soon as a stock trades 7 to 8 percent below the purchase price, it must be sold (O’Neil, 2009). This rule is intended to prevent cumulative capital destruction. Within prospect theory (Kahneman & Tversky, 1979), loss aversion is a central component, whereby investors irrationally hold on to losing positions. A predetermined stop-loss limit breaks this behavioural pattern (Shefrin & Statman, 1985).

2. Invest only during an upward market trend

O’Neil advises investors to be active only during periods in which the general market trend is positive. This is supported by empirical research into trend-following strategies, in which positive momentum in market indices correlates with an increased likelihood of profitable transactions (Moskowitz, Ooi & Pedersen, 2012). In periods of heightened market volatility or accumulation of “distribution days,” exposure is actively reduced.

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3. Select only stocks with strong fundamental and technical characteristics

An important aspect of risk reduction is the selection of stocks that demonstrably deliver strong operational performance. O’Neil emphasizes the combination of earnings growth, revenue growth, and relative strength within sectors. This corresponds with empirically proven anomalies such as the momentum effect (Jegadeesh & Titman, 1993) and earnings revision strategies (Chan, Jegadeesh & Lakonishok, 1996).

4. Limit the size of individual positions to approximately 10% of total capital

A position sizing rule limits the risk that a single erroneous transaction has a disproportionate impact on the entire portfolio. According to modern portfolio theory (Markowitz, 1952), diversification leads to a reduction of unsystematic risk. By maintaining an upper limit of approximately 10% per position, risk concentration is mitigated and the liquidity of portfolio management is enhanced.

5. Sell losing positions early and without emotion

In addition to setting loss limits, O’Neil emphasizes the importance of psychological discipline. Research into the disposition effect shows that investors tend to hold on to losing investments for too long, which leads to suboptimal portfolio construction (Shefrin & Statman, 1985). By applying predefined exit rules, such biases are eliminated.

6. Identify exit moments based on technical signals

O’Neil makes use of technical sell indicators such as the downward breach of moving averages, increased sell volume, and climax formations. These signals are recognized in the literature as triggers within technical allocation models (Lo, Mamaysky & Wang, 2000), in which price movement and trading volume function as early indicators of trend reversal.

7. Avoid the use of borrowed capital in retail investing

Although O’Neil does not explicitly systematize this as a separate rule, his work shows a dismissive attitude toward the use of leverage. Scientific literature confirms that the use of margin or borrowed capital exponentially increases the volatility of returns, as well as the likelihood of forced liquidations during market corrections (Black, Jensen & Scholes, 1972).

8. Use liquidity (cash) as a strategic risk position

O’Neil does not consider cash a passive reserve, but a strategic instrument when waiting for suitable market conditions. This approach is supported by allocation theories in which risky and risk-free assets are cyclically exchanged based on market dynamics (Ilmanen, 2011). Liquidity here functions as protection against drawdowns.

9. Systematically analyze and evaluate all transactions

An essential component of risk control is self-evaluation. O’Neil advises investors to systematically document transactions, including entry rationale, outcome, and deviation from the plan. This approach aligns with the concept of “deliberate practice,” in which reflection on executed actions leads to improved expertise (Ericsson, Krampe & Tesch-Römer, 1993).

10. Perform risk assessment before entering a position

Finally, responsible risk management requires that risk analysis takes place before purchasing a stock. O’Neil integrates risk information such as historical volatility, correlation with existing positions, liquidity, and news sensitivity. This corresponds with quantitative techniques such as Value at Risk (VaR), in which potential losses within a confidence interval are simulated in advance (Jorion, 2006).

Conclusion

William O’Neil’s risk management rules provide a systematic framework for managing downside risk in growth investing. Through a combination of behavioural psychological discipline, technical indicators, and positional risk control, a consistent pattern of action emerges that aligns with academic insights from portfolio theory, behavioural finance, and market structure research. His approach legitimizes the importance of limiting losses as a structural foundation for capital growth and is applicable within both retail and institutional contexts.

REFERENCES

1. Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. In Handbook of the Economics of Finance (Vol. 1, pp. 1053–1128). Elsevier.
2. Black, F., Jensen, M. C., & Scholes, M. (1972). The capital asset pricing model: Some empirical tests. In Studies in the theory of capital markets, 81(3), 79–121.
3. Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681–1713.
4. Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
5. Ilmanen, A. (2011). Expected Returns: An Investor’s Guide to Market Forecasting. Wiley.
6. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91.
7. Jorion, P. (2006). Value at Risk: The New Benchmark for Managing Financial Risk (3rd ed.). McGraw-Hill.
8. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
9. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705–1765.
10. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228–250.
11. O’Neil, W. J. (2009). How to Make Money in Stocks: A Winning System in Good Times and Bad (4th ed.). McGraw-Hill.
12. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. The Journal of Finance, 40(3), 777–790.

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