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Our price forecasting model for analyzing Share targets employs a detailed, data-driven approach to determine monthly price projections. This methodology integrates classic analytical tools, including long-term pivot point analysis, historical performance metrics, and volatility assessment. Below, we outline the key components and processes that constitute our forecasting framework. It is essential to recognize that these price estimates are purely mathematical and should not be considered financial advice. Stock markets are highly dynamic, influenced by multiple unpredictable factors that no single model can comprehensively capture.

Long-Term Pivot Point Analysis

At the core of our methodology lies long-term pivot point analysis, complemented by Fibonacci series calculations. These pivot points help determine critical support and resistance levels, providing a structured framework for anticipating potential price movements.

            Pivot = Previous Close
            Resistance_n = Pivot + (Range × F_n)
            Support_n = Pivot - (Range × F_n)
        

Where:

• F_n represents Fibonacci multipliers (e.g., 0.382, 0.618, 1.000).
• Range is the difference between the highest and lowest prices over a specific period.

Historical Performance Analysis

A stock's historical performance plays a vital role in predicting its future behavior. Our model conducts an extensive analysis of past data to determine average returns over various timeframes, capturing both short-term fluctuations and long-term trends.

            Average Return = (1/N) Σ R_i
        

Where:

• N is the number of periods (e.g., months or quarters).
• R_i represents the return in the i-th period.

Volatility Assessment

Market volatility is a crucial factor in assessing risk and uncertainty associated with stock price movements. Our methodology incorporates a comprehensive evaluation of stock volatility, measured by the standard deviation of historical returns.

            σ = √[(1/(N-1)) Σ (R_i - μ)^2]
        

Where:

• μ is the mean return.
• R_i is the return in the i-th period.
• N is the total number of returns.

Integrated Predictive Modeling

Our forecasting model integrates pivot point analysis, historical performance, and volatility assessments through advanced predictive techniques, ensuring data-driven and adaptable price projections.

• Calibration Based on Historical Performance: Utilizing past average returns and volatility metrics to align future price targets with the stock’s established patterns.
• Mathematical Optimization: Applying techniques such as the Least Squares Method (LSM) to optimize projections, reducing errors and enhancing forecast precision.
• Dynamic Adjustment: Incorporating real-time market data to refine predictions, ensuring forecasts remain relevant and up-to-date.

Target Price Calibration

The final step in our methodology is the precise calibration of monthly price targets, ensuring they align with analytical insights and market conditions.

            Target Price_next month = Current Price × (1 + Adjusted Growth Rate)
        

Where:

• Adjusted Growth Rate is derived from historical average returns and volatility, refined through our integrated predictive modeling approach.

This ensures that price targets account for both growth potential and associated risks, providing balanced and actionable forecasts.