Applications

Share BuyBack Pretrade Tool

https://sharebuybackpretradesimulator.streamlit.app/

A Tool for Quantitative Analysis of Share Buyback Execution

Share repurchase programs are a common method of capital allocation. The outcome of a buyback is influenced by its execution strategy. A given strategy can result in a higher or lower average purchase price depending on market behavior, which in turn affects the value returned to shareholders. This simulator is designed to facilitate pre-trade analysis of different execution strategies under various market conditions.

This Share Buyback Pre-Trade Simulator is an analytical tool for finance professionals who use quantitative methods in their capital management process.

The Challenge: Modeling Execution Risk

When planning a share buyback, several questions arise:

• What are the potential outcomes of different methods for deploying capital over the program's duration?

• What is the trade-off between buying aggressively on price dips versus maintaining a steady, predictable pace?

• How might market volatility impact the average purchase price?

• Which execution strategy offers a favorable risk-adjusted performance profile for a specific market view?

Using single-point forecasts or simple historical averages may not fully capture the range of potential market outcomes or the path-dependent nature of execution performance.

A Solution: A Probabilistic Approach to Pre-Trade Analysis

The Share Buyback Pre-Trade Simulator uses a Monte Carlo simulation engine to model potential outcomes. By generating thousands of potential future stock price paths based on the principles of Geometric Brownian Motion, the tool allows users to test a strategy against a wide spectrum of possible market scenarios.

This probabilistic approach provides a distribution of potential outcomes, which can be used to assess the risks and potential rewards associated with a given strategy.

Key Features

• Monte Carlo Engine: Simulates thousands of unique stock price paths based on user inputs for market drift (expected return) and volatility.

• Strategy Library: Allows for comparison of four distinct execution strategies, from passive approaches to dynamic, volatility-aware algorithms.

• Performance Attribution: A methodology is included to decompose the total performance of each strategy into Timing Alpha (the value from daily decisions) and Duration Alpha (the value from changing the program's length).

• Interactive Visualization Suite: A full suite of interactive charts allows users to explore the results, from high-level statistical summaries to the behavior of a single simulated path.

• Real-Time Interface: Built with a modern tech stack, the simulator provides responsive feedback, allowing users to adjust parameters and see the impact on performance distributions.

The Strategies: A Spectrum of Execution Logic

Test and compare a range of execution strategies to analyze their potential fit for different objectives:

1. Daily Fixed Amount (DCA): The baseline strategy. A fixed dollar amount is deployed every day, providing a simple, predictable benchmark.

2. Adaptive DCA: A classic mean-reversion strategy. It buys more when the stock price is below its running average and less when it's above.

3. Dynamic Adaptive DCA: A more aggressive version of the adaptive strategy. It dynamically targets either the minimum or maximum program duration based on daily market conditions, leading to a highly variable participation rate.

4. Volatility-Scaled Adaptive: This model adjusts its execution speed based on how far the current price has deviated from its average, scaled by market volatility. This allows it to be more aggressive in calm markets and more cautious during periods of high turbulence.

Advanced Analytics: Decomposing Alpha

The simulator can be used to analyze why a strategy performs the way it does. For every adaptive strategy, the total outperformance is decomposed into two key components:

• Timing Alpha: How much value did the strategy's day-to-day buy/sell logic add?

• Duration Alpha: How much value was created by the strategy's decision to accelerate or decelerate the entire program?

This insight can help in understanding the drivers of performance and selecting a strategy whose logic aligns with corporate goals.

Intended Audience

The Share Buyback Pre-Trade Simulator is a tool for:

• Chief Financial Officers (CFOs) involved in capital allocation and shareholder value analysis.

• Corporate Treasurers tasked with the management and execution of repurchase programs.

• Investor Relations (IR) Professionals who articulate execution strategy and potential outcomes to the board and the market.

• Quantitative Finance Teams looking for a platform to test and analyze execution algorithms.

A Tool for Data-Informed Decisions

This simulator provides a framework for data-driven, probabilistic pre-trade analysis. It can be used to quantify risks, understand strategic options, and analyze the potential outcomes of a share repurchase program

Share BuyBack Analyzer

A web-based tool to analyze the average cost of corporate share repurchase programs using the harmonic mean rather than the arithmetic mean.
Key features:

• Daily execution modeling based on simulated data

• Comparison of harmonic vs. arithmetic mean purchase prices

• Built using Python, Streamlit, and Plotly

Application:
Used to design or evaluate buyback strategies, especially when prices fluctuate significantly. The harmonic mean approach reflects the actual cost per share more accurately in volatile markets.

📎 Access the tool: sharebuyback-harmonic-mean.streamlit.app

This chart shows the distribution of basis-point differences between the arithmetic and harmonic mean of daily execution prices under a fixed-notional share buyback strategy (10,000 simulations). On average, the harmonic mean is 51.5 bps lower than the arithmetic mean (σ = 46.4 bps). This demonstrates a structural bias: because more shares are bought when prices are low, the harmonic mean better reflects the true average cost.

Using the arithmetic mean as a benchmark overstates the cost and creates the illusion of good performance. In contrast, the harmonic mean sets a more accurate and harder-to-beat benchmark. This chart highlights why the arithmetic average is unsuitable for evaluating fixed-notional buyback execution.