Stock Market Portfolio Valuation Using CPU Based Parallelism

In the past decade, the volume and variety of financial instruments has grown exponentially. Hence, portfolio valuation has become a computationally intensive task, especially for large portfolios and complex financial models. This research explores the application of CPU-based parallelism to accelerate the valuation process. By utilizing the multi-core architecture of modern processors, we aim to significantly reduce computation time without compromising accuracy. The study investigates the implementation of parallel algorithms for various valuation methodologies, pricing models, and risk analysis. We develop a system where we calculate a user's portfolio using parallel computation. We divide a portfolio into sub-portfolios and using Python's multiprocessing module, distribute the workload across multiple cores of the CPU. The suggested method's performance is assessed across a range of portfolio sizes and contrasted with serial valuation techniques. The results show that parallel processing speeds up the calculation by 77% compared to serial mode of processing. Parallel method of valuation was also found to be more efficient for large portfolio sizes. The findings contribute to the development of efficient and scalable portfolio valuation systems, enabling faster decision-making and risk management. The findings of this study accelerate the development of high-performance computing solutions for the financial industry.