Abstract:

This research presents a comprehensive and sophisticated integrated approach that expands upon Dr. Glen Brown’s esteemed Market Expected Moves Hypothesis (MEMH). By incorporating Fibonacci factors, break-even point analysis, Dynamic Adaptive ATR Trailing Stops (DAATS), and reverse-engineering of past winning trades, the enhanced MEMH offers an advanced predictive model for estimating price movements in the financial market. This novel framework provides traders and investors with an enhanced level of precision, detail, and confidence in making informed decisions.

Introduction:

The accurate prediction of price movements in the dynamic and complex financial market remains a formidable challenge. Dr. Glen Brown’s MEMH has gained recognition for its efficacy in forecasting market trends by employing the concept of expected moves utilizing DAATS. To further refine and augment MEMH, this study introduces an innovative integrated approach that incorporates Fibonacci factors, break-even point analysis, and the reverse-engineering of past winning trades. By integrating these components, the enhanced MEMH introduces a novel methodology for anticipating market fluctuations with heightened accuracy.

The Market Expected Moves Hypothesis:

Dr. Glen Brown’s MEMH serves as the foundation for our integrated approach. The hypothesis leverages the adaptive nature of DAATS, which adjusts to market volatility, to estimate the likely extent of price fluctuations. The MEMH formula is expressed as follows:

Market Daily Average Expected Moves (MDAEM) = 0.6375 * Average Dynamic Adaptive ATR Trailing Stops (DAATS) on M1440.

Dr. Glen Brown’s current preference at the time of writing is to use a 200-Period Average True Range (ATR) for each trading timeframe. According to Dr. Glen Brown’s methodology, the ATR multipliers for each timeframe are as follows:

• 1-Minute Trading Timeframe: 34. Hence DAATS = 34ATR200
• 5-Minute Trading Timeframe: 27. Hence DAATS = 27ATR200
• 15-Minute Trading Timeframe: 22. Hence DAATS = 22ATR200
• 30-Minute Trading Timeframe: 17. Hence DAATS = 17ATR200
• 60-Minute Trading Timeframe: 13. Hence DAATS = 13ATR200
• 240-Minute Trading Timeframe: 11. Hence DAATS = 11ATR200
• 1440-Minute Trading Timeframe: 9. Hence DAATS = 9ATR200
• 10080-Minute Trading Timeframe: 7. Hence DAATS = 7ATR200
• 43200-Minute Trading Timeframe: 5. Hence DAATS = 5ATR200

These formulas represent theoretical percentages of expected moves, forming the core of the MEMH.

Integrating Fibonacci Factors:

To enhance MEMH, Fibonacci factors are seamlessly integrated into the model. Fibonacci retracement levels, including 23.6%, 38.2%, 50.0%, 61.8%, and 78.6%, are assigned corresponding factors. These factors are derived by multiplying the MEMH Fib Factor (0.6375) with each respective Fibonacci level:

• 23.6%: MEMH Fib Factor = 0.6375 * 23.6% = 0.15042
• 38.2%: MEMH Fib Factor = 0.6375 * 38.2% = 0.24355
• 50.0%: MEMH Fib Factor = 0.6375 * 50.0% = 0.31875
• 61.8%: MEMH Fib Factor = 0.6375 * 61.8% = 0.393885
• 78.6%: MEMH Fib Factor = 0.6375 * 78.6% = 0.501015

These Fibonacci factors are then applied to the DAATS values, enabling the estimation of expected moves at different Fibonacci retracement levels. This integration empowers traders with an unparalleled level of precision and depth in assessing potential market movements.

Break-Even Point Analysis:

In addition to Fibonacci factors, break-even point (BEP) analysis is incorporated into the integrated MEMH framework. The average break-even point is derived from the average MEMH Fibonacci Expected Moves. It represents the theoretical threshold at which neither profit nor loss is incurred.

Our Fixed Normal Trailing Stop is also set at 0.31875MAEM, providing a critical reference point for assessing the effectiveness of trading strategies.

Reverse-Engineering of Past Winning Trades:

To further refine the MEMH framework, the reverse-engineering of past winning trades is incorporated. This process involves analyzing successful trades to identify common patterns and factors that contributed to their success. By understanding these elements, traders can enhance their predictive models and improve future trade outcomes.

• Identification of Winning Trades: Begin by identifying a sample of past winning trades. These trades should be well-documented, including entry and exit points, trade duration, and the rationale behind each trade.
• Pattern Recognition: Analyze the identified trades to discern common patterns. Look for recurring elements such as specific market conditions, technical indicators, price action, and volume trends that preceded successful trades.
• Factor Analysis: Identify key factors that contributed to the success of these trades. Factors may include adherence to trading rules, risk management strategies, market sentiment, and external economic events.
• Integration with MEMH: Incorporate the identified patterns and factors into the MEMH framework. Adjust the DAATS, Fibonacci levels, and BEP calculations to reflect insights gained from reverse-engineering past winning trades.
• Validation and Testing: Validate the enhanced MEMH framework by applying it to historical market data. Conduct backtesting to evaluate its predictive accuracy and adjust the model as needed based on the results.

Enhanced Risk Management Models:

1. Dynamic Position Sizing:Position sizing is crucial for managing risk and maximizing returns. Dynamic Position Sizing involves adjusting the size of a trade based on the volatility of the asset and the trader’s risk tolerance. By integrating DAATS with position sizing, traders can dynamically adjust their positions based on the current market conditions.
   • Formula: Position Size = (Account Equity * Risk Per Trade) / DAATS
   • Example: For an account with $100,000 equity and a risk per trade of 1%, if the DAATS value is 50 pips, the position size would be (100,000 * 0.01) / 50 = 20,000 units.
2. Volatility-Based Stop Loss:Volatility-based stop loss levels help in setting stop loss orders at levels that are less likely to be triggered by normal market noise. This model uses the DAATS value to set stop loss levels based on the current market volatility.
   • Formula: Stop Loss Level = Entry Price – (DAATS * Multiplier)
   • Example: For an entry price of $50 and a DAATS value of 2, with a multiplier of 1.5, the stop loss level would be $50 – (2 * 1.5) = $47.
3. Fibonacci Retracement Stop Loss:Incorporating Fibonacci retracement levels into stop loss settings provides a systematic approach to risk management. By using the retracement levels calculated from the MEMH framework, traders can set stop losses at key Fibonacci levels.
   • Formula: Stop Loss Level = Entry Price – (Entry Price * Fibonacci Level)
   • Example: For an entry price of $100 and a 38.2% Fibonacci retracement level, the stop loss would be $100 – (100 * 0.382) = $61.8.
4. Trailing Stop Loss with MEMH:A trailing stop loss adjusts as the market price moves in favor of the trade, locking in profits while limiting losses. By using MEMH, traders can set trailing stops that adapt to market movements.
   • Formula: Trailing Stop Level = Current Price – (MEMH * Trailing Stop Multiplier)
   • Example: For a current price of $120 and an MEMH value of 3 with a trailing stop multiplier of 1, the trailing stop level would be $120 – 3 = $117.
5. Risk-Reward Ratio Analysis:Ensuring a favorable risk-reward ratio is essential for long-term trading success. By integrating MEMH with risk-reward analysis, traders can evaluate potential trades to ensure that the expected reward justifies the risk.
   • Formula: Risk-Reward Ratio = Potential Reward / Potential Risk
   • Example: If the potential reward is $500 and the potential risk (as defined by DAATS or MEMH) is $100, the risk-reward ratio would be 5:1.
6. Diversification Strategy:Diversification helps in spreading risk across different assets. By using MEMH to assess the expected moves and risks of various assets, traders can construct a diversified portfolio that minimizes risk while maximizing returns.
   • Strategy: Allocate capital across assets with uncorrelated price movements, adjusting allocations based on the MEMH-derived risk assessments for each asset.
7. Stress Testing and Scenario Analysis:Stress testing involves simulating extreme market conditions to assess the impact on a portfolio. Scenario analysis using MEMH can help traders understand how their positions would perform under various market scenarios.
   • Process: Identify key risk factors (e.g., interest rates, economic indicators), create scenarios, and apply MEMH to estimate potential impacts on the portfolio.

Conclusion:

The enhanced risk management models presented in this study provide traders with a comprehensive set of tools for managing risk effectively. By integrating MEMH with advanced risk management techniques such as dynamic position sizing, volatility-based stop loss, Fibonacci retracement stop loss, trailing stop loss, risk-reward ratio analysis, diversification strategy, and stress testing, traders can make informed decisions that enhance their trading performance. The inclusion of reverse-engineering past winning trades adds a valuable layer of insight, allowing traders to leverage successful strategies for future trades. It is important to use these models alongside other analysis techniques and risk management strategies to form a comprehensive and robust trading strategy.

Disclaimer:

This paper is intended for educational and informational purposes only. The views and strategies described may not be suitable for all readers or investors. The information contained herein does not constitute and should not be construed as investment advice, an endorsement, or an offer or solicitation to buy, sell, or hold any securities, other investments, or to adopt any investment strategy. The strategies, concepts, and techniques discussed are complex and may not be understood completely without a thorough understanding of finance, investments, and risk management.

The data and information presented are believed to be accurate but are not guaranteed. Past performance is no guarantee of future results. Investments in financial markets are subject to risk, including the potential loss of principal. The author, Dr. Glen Brown, and any associated entities will not be held responsible or liable for any decisions made based on the information provided in this paper.

Readers and investors are urged to consult with their own financial advisors before making any investment decisions. It is the responsibility of the reader or investor to carefully consider their particular investment objectives, risk tolerance, and financial circumstances before investing.

About the Author:

Dr. Glen Brown is a seasoned finance and accounting professional with an impressive track record spanning over 25 years in the industry. As the President & CEO of both Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., he steers organizations with a clear focus on bridging the gap between the fields of accountancy, finance, investments, trading, and technology. His leadership has positioned these entities as globally recognized multi-asset class professional proprietary trading firms.

Dr. Brown is an alumnus of distinguished educational institutions, holding a Doctor of Philosophy (Ph.D.) in Investments and Finance. His broad spectrum of expertise encompasses financial accounting, management accounting, finance, investments, strategic management, and risk management.

Besides his executive responsibilities, Dr. Brown wears several other hats — Chief Financial Engineer, Head of Trading & Investments, Chief Data Scientist, and Senior Lecturer in a range of financial disciplines. These diverse roles highlight his dual commitment to the practical application of financial knowledge and the advancement of academic learning in his field.

Dr. Brown’s guiding philosophy is a testament to his leadership style and personal commitment: “We must consume ourselves in order to transform ourselves for our rebirth. We are blessed with subtlety, creative imaginations, and outstanding potential to attain spiritual enlightenment, transformation, and regeneration.” This belief is the driving force behind his dedication to innovation, personal growth, and the pursuit of excellence in finance and investments.

With his unique blend of extensive experience and a philosophical approach, Dr. Glen Brown continues to cultivate a culture of innovation and success at Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. Through his stewardship, these organizations offer pioneering solutions to complex financial challenges, setting the gold standard in the industry.

General Disclaimer:

This paper is intended for educational and informational purposes only. The views and strategies described may not be suitable for all readers or investors. The information contained herein does not constitute and should not be construed as investment advice, an endorsement, or an offer or solicitation to buy, sell, or hold any securities, other investments, or to adopt any investment strategy. The strategies, concepts, and techniques discussed are complex and may not be understood completely without a thorough understanding of finance, investments, and risk management.

The data and information presented are believed to be accurate but are not guaranteed. Past performance is no guarantee of future results. Investments in financial markets are subject to risk, including the potential loss of principal. The author, Dr. Glen Brown, and any associated entities will not be held responsible or liable for any decisions made based on the information provided in this paper.

Readers and investors are urged to consult with their own financial advisors before making any investment decisions. It is the responsibility of the reader or investor to carefully consider their particular investment objectives, risk tolerance, and financial circumstances before investing.