Financial Modeling and Quantitative Analysis

Bayesian optimization provides an elegant framework for approaching problems to find the global minimum in the smallest number of steps.

Bayesian optimization (BO) (Greenhill et al., 2020; Shahriari et al., 2015) is one of the major approaches to inverse material design and involves gradually optimizing material-design parameters through repeated experiments (Balachandran et al., 2016; Doan et al., 2020; Lookman et al., 2017, 2019). Although the process resembles human-based trial-and-error, design parameters in BO are determined based on a machine-learning model, which is updated after each experiment and gets smarter through repeated updates (Shahriari et al., 2015). Accordingly, BO can effectively accelerate complex optimization problems and is useful, particularly for material-design problems involving time-consuming experiments.
Bayesian optimization for goal-oriented multi-objective inverse .... https://www.cell.com/iscience/pdf/S2589-0042(21)00749-5.pdf

Predictive data mining is data mining that is done for the purpose of using business intelligence or other data to forecast or predict trends. This type of data mining can help business leaders make better decisions and can add value to the efforts of the analytics team. Utilization of this data is provided from a company’s operating systems such as POS, Timekeeping, social media, and marketing platforms including reservation systems for restaurants and hotels. Much of this data in leveraged and processed for actionable initiatives for both sales’ growth and customer retention.

Data warehouses make working with big data easier — particularly for businesses with large customer bases, sales and billing reports, and resource plans. Through data warehousing, companies can segment and target customers from vast collections of sales orders, product searches, or loyalty program registrations. They also can store and analyze various data points, even social media posts about products and businesses.
Data warehousing also consolidates various data sources into one place, making mining and decision-making more efficient and saving businesses time and money.

Before being loaded into a data warehouse, the data goes through a three-stage process known as ETL – extract, transform, and load:
Extract: Data, which can be structured (names, dates, credit card numbers, etc.) or unstructured (photos, videos, audio files, social media posts), is copied and moved from its source to a warehouse staging area.
Transform: In this step, data is validated and then formatted to fit the warehouse through filtering and cleaning data via removing errors.
Load: In the final stage, we load the transformed data into the data warehouse. The ETL process repeats as new data is received.

Association rule mining, a/k/a market basket analysis, involves the employment of machine learning models to analyze information for discovering exceptional relations and patterns between variables in large databases. The methodology identifies the “if” or “then” associations, known as the association rules. Association rules are used in discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements. Within big data, the value of “what” is being purchased together is more valuable than “why” then why the items are purchased together. Description text goes here

holt-winters exponential smoothing, Moving Averages, Exponential Smoothing, ARIMA Modeling

Valuation Analysis calculates the value of a company or asset based on the future cash flows of the business or deal, discounted back to present value. This process identifies the asset or business value on present terms. AnalysisLAB uses a multi-tiered approach of Discounted Cash Flows (DCF), Net Present Values, and Exit Multiples (P/E) to establish the valuation of a project or business.  Our valuation analysis derives the current value of future cash flow projections through these three valuation methodologies to derive a more accurate valuation of the deal or business. This type of modeling is utilized in the acquisition of a business, an investment in a business, and ascertaining the lifetime valuation of a stated project. We incorporate a terminal-value analysis and exit-multiple methodologies for a comparative exit outcome.