Stock Price Forecasting Using Monte Carlo Simulation in Alteryx

• Analytics & Modeling - Digital Twin / Simulation
• Application Infrastructure & Middleware - Middleware, SDKs & Libraries

• Equipment & Machinery
• Finance & Insurance

• Logistics & Transportation

• Inventory Management
• Virtual Reality

The case study revolves around the use of Monte Carlo simulation for forecasting stock prices. The challenge was to create a sample Alteryx workflow that sources stock price data, performs analysis of the historical prices, uses these metrics to perform Monte Carlo simulations, and then analyzes the output of these simulations to drive business decision making. The aim was to provide an Alteryx template for Monte Carlo simulation-based forecasting that could be used and further enriched by the Alteryx community. The challenge also involved sourcing stock prices from Yahoo Finance, calculating daily percentage change in the stock price, preparing metrics for the simulation, and running the simulation multiple times.

The customer in this case study is not explicitly mentioned. However, it can be inferred that the customer is a user or member of the Alteryx community who would benefit from the Alteryx template for Monte Carlo simulation-based forecasting. The customer could be anyone interested in statistical forecasting, particularly in the context of stock price movements. They could be financial analysts, data scientists, or other professionals who use Alteryx for data analysis and forecasting. The customer would be interested in extending the workflow and customizing it to their specific use case.

The solution involved several steps. First, historical stock prices were sourced from Yahoo Finance using the Python library 'pandas_datareader'. The daily percentage change in the stock price was calculated. Next, simulation parameters were prepared using the collected stock information. These parameters included the daily mean change in the stock price, daily volatility for the change in stock price, and the projection period for simulated prices. The Monte Carlo simulation was then performed using either the Python NumPy library or the Alteryx tool 'Simulation Sampling'. The simulation was run multiple times to build a basis for decision making. Finally, the simulation results were analyzed to drive decision making. Various reporting and data analysis tools in Alteryx were used to understand the forecasted prices. A dynamically generated report was prepared that included Price Projections Interactive chart, Histogram of the prices, and associated commentary generated dynamically using various metrics calculated in the workflow.

• The operational results of this project were significant. The workflow forced the author to think of every corner of the Alteryx tools to achieve the final result, teaching them new skills along the way. The ability in Alteryx to inspect outcomes at every step and scale it to run for multiple iterations was very handy throughout the development of this sample use case. The sample can be extended further to add on many more features such as letting the user choose the stock to analyze, analyzing multiple stocks at the same time, building an application that lets you choose iterations for the simulation, and building a more intuitive reporting user interface to let the user dynamically analyze the simulation outcome. However, the workflow has its own challenges and can be improved further. For instance, the workflow takes a lot of time to execute. For 1000 simulations, it took about 45 minutes on an i7, 16GB RAM desktop. The reporting in the workflow is very basic for illustration purpose and can be further enhanced.