Advisory: Overview

Initially, WST started offering standardized training programs to financial institutions on Wall Street with an emphasis on critical thinking, strict adherence to best practices, and massive gains in efficiency. They covered virtually all corners of finance, ranging from fundamental analysis to the highly quantitative side. Investment banks, commercial banks, asset managers, securities regulators, and Fortune 500 companies alike have turned to us for our expertise backed by real-world experience.

However, over the years, our value proposition has quickly evolved beyond training. You can find some exemplary case studies in the Training+ section below. With multiple decades of presence in the industry across our team, we look forward to continue serving clients in the marketplace of global finance.

What is Training+ and how does it make us better trainers?
It all started with one little request to "build me the best model" ...

Project: Build a super-dynamic, scalable sector coverage model for a sell-side equity research analyst

Result: Combined qualitative drivers of growth & channel checks into quantitative financial model

Project: Standardize a US-based regulator's data analysis tools and capabilities post-2008 credit crisis

Result: Streamlined 12 different offices' methodologies to improve efficiency and effectiveness

Project: Integrate bank valuation model with macro-economic stress testing and cost analysis

Result: Helped a bank regulator design their approach to core, mission critical analysis and logic

Training+ is our way of staying active in financial services and constantly adapting our courses to industry dynamics. Our programs are carefully designed to go beyond your typical financial modeling and Excel class:

Market Insights

  • Direct insight to market participants and regulators
  • Incorporate new knowledge back into our training courses
  • Most up-to-date information on industry-wide best practices

Process Improvement

  • Workflow and process improvement beyond Excel
  • Improve efficiencies and internal standardization
  • Substantial and measurable leaps in productivity

Best Practices

  • Not only saves time, but adds another layer of quality control
  • Focus on client peace of mind by validating analyses
  • Strict adherence to technical modeling standards

Capital Markets

We advise firms on how to effectively tap into the capital markets across a variety of industries. As companies expand, they will find themselves in need of capital in different structures and from a diverse set of sources. Our strength as an independent party comes from being comprehensive and rigorous: we are here to guide you through financial projections, valuation, strategic positioning, and management presentations. With experience on both sides of the deal, we can anticipate the key questions that potential investors will have about your growth and help you maximize valuation.

Let's discuss the future of your business.


We work with firms to "explore strategic alternatives to maximize value-add by leveraging our clients' fully integrated platform." What exactly does that mean?

At WST, we have both breadth and depth when it comes to industry knowledge, from a product and relationship perspective. In short, we know who the buy-side and sell-side players are. The buy-side is always looking for good investment opportunities, and the sell-side is always looking to close deals. Many of them are our clients, so we act as the middle man to further facilitate transactions.

Contact us to learn more about how we can help your firm explore strategic alternatives.

Case Studies

Explore some of our recent Training+ projects below by clicking on some of our corporate client work below.

1. Quantifying Drivers of Growth (Sell-Side Research)

Client: A bulge bracket investment bank's sell-side research analyst who wanted to upgrade his team's financial modeling capabilities to better quantify their qualitative analysis.

Challenge: The head analyst was an industry expert who understood the drivers of growth in his coverage universe but wanted to improve his team's financial models to better quantify their catalysts. In addition, their models weren't standardized despite the consistency in the coverage companies' reporting metric and analysis required. The mandate was to "build me the best model on the Street for my sector."

Solution: We sat down with the analyst and his research associates and translated knowledge of key drivers into quantitative analysis by building an extremely scalable, robust model to facilitate sector coverage. Our success was validated when almost immediately after transitioning their coverage universe to the new template, they were the first team to identify a $2 billion liquidity shortfall for one of their names.

While we agree with the ages-old adage of "garbage in, garbage out," if you know your stuff inside and out, we'll build you the best model to go along with it.

2. Buy-Side Consulting (Asset Management)

Client: A large buy-side asset manager facing rapid growth in AUM who wanted to manage headcount growth without sacrificing quality.

Challenge: The challenge here was two-fold: (i) standardize best practices for each portfolio manager, and (ii) increase the number of coverage companies per research analyst while maintaining integrity of analysis.

Solution: A two-phase solution was implemented: first, WST provided financial modeling training courses to disseminate the best practices that we preach; then, we worked with the client to provide customized training & standardization for each team of portfolio manager and research analysts. A key hurdle that we overcame was that each team had their own approach and industry-specific drivers of growth. However, once the first phase (the 80% of the 80/20 rule) was implemented, the second phase was executed much quicker and painlessly than initially expected.

3. Advanced Data Analytics (U.S. Regulator)

Client: A federal regulatory agency responsible for policing the United States securities industry looking to upgrade their staff's technical capabilities in data analysis.

Challenge: Individual examiners on the same team and across different offices were analyzing the same data in multiple ways and formats (Microsoft Excel vs. Microsoft Access). On top of a lack of standardization across the agency, massive data dumps were taking too long to sift through individually and manually. The client needed a uniform approach that was the most efficient and easy to implement for some of the seasoned professionals.

Solution: We learned the client's various existing analyses and approaches to data analytics and custom designed a standardized approach and methodology. This required merging the best components of their existing approach and designing further streamlined procedures. We then delivered the new processes and recommendations by training the client's staff across all regional offices and assisted in implementing system-wide efficiencies.

4. Valuation Modeling (Private Equity)

Client: A top 5 private equity firm that needed assistance in internal valuation.

Challenge: The CFO's team of accountants and valuation experts were constrained with their existing accounting system and financial reporting structure. The lack of customization functionalities meant that several high-profile and time-sensitive analyses could not be constructed in a timely basis. Furthermore, additional complexities included the dynamic nature of the data, multiple hierarchies and cross holdings that could result in double- and triple-counting and constant need to produce updated reports.

Solution: Our innate understanding of how private equity firms are structured allowed us to quickly learn the multi-dimensional constraints with the client's data. Then we custom-built analyses and models via close collaboration with key members of the CFO's team, leading to a visualization of the entire number-crunching process required and an end result that enabled the rest of the team to implement long-term solutions via their accounting system.

This is a classic example of process re-engineering: by first understanding the existing process and constraints, coupled with the "wish list," we helped enable the client's ability to respond to dynamic and intensive reporting environment.

5. Corporate Business Development (Multinational Conglomerate)

Client: A Fortune 100 multinational conglomerate that is highly active in strategic acquisitions, joint ventures and divestitures of non-core assets.

Challenge: The client's global reach includes operations in more than 150 countries around the world. Small teams of corporate business development professionals are scattered across multiple locations and industry verticals. The challenge was how to efficiently disseminate a standardized, and yet customizable approach to the team's growing financial modeling needs.

Solution: We constructed custom-built modules on top of our most advanced merger modeling courses that mirrored the client's typical deal structures. We continue to train all their existing professionals and lateral and new hires, helping to further standardize the client's internal best practices. Thus far, we have trained the client's professional executives in all continents except Antarctica.

6. Emerging Market Development Modeling

Client: A family office that controls the oil assets of an emerging market economy and wanted to diversify their investments beyond oil.

Challenge: The client has a solid understanding of the economics of the country's main commodity, but needed some guidance on quantifying and sensitizing the returns of the assets they wanted to diversity into and further expand. In addition, the returns needed to be shown on a standalone basis as well as a consolidated basis.

Solution: We worked with the client's staff and key consultants to understand the nuances of each non-oil asset in their portfolio and built them a fully dynamic, robust model that was easily scalable. This allowed the client to quantify the risks and rewards of their current portfolio holdings. Moreover, the approach we took with the client mirrored our strict methodology to model building: "build me a dynamic model, not a static analysis!"