We started with a simple observation. Most finance courses teach theory but skip the messy reality of actual data work. Our programs focus on what you'll actually do when analyzing markets, building models, and making sense of complex financial information.
From a small workshop series to comprehensive training programs that prepare finance professionals for real analytical challenges.
Three finance professionals in Phitsanulok noticed a gap. People knew financial theory but struggled with actual analysis work. We ran monthly workshops teaching practical techniques—the kind you need when your data doesn't behave like textbook examples.
By mid-2020, we had refined our approach based on what actually worked. Built comprehensive curriculum modules that combined statistical methods with market context. Started focusing on progressive skill development rather than isolated techniques.
Added advanced analytical frameworks covering derivatives pricing, risk modeling, and quantitative research methods. Worked directly with finance teams to understand which skills made the biggest difference in their daily work.
Current programs emphasize interpreting results over just running calculations. We teach how to question assumptions, validate models, and communicate findings effectively. Our autumn 2025 curriculum integrates machine learning techniques with traditional financial analysis.
Our programs build analytical capabilities through progressive modules. Each stage develops specific skills while connecting to broader financial analysis context.
Working with financial datasets, cleaning messy information, and building reliable data workflows. You'll handle real market data that doesn't come pre-formatted.
Applied statistical techniques for financial analysis. Regression modeling, hypothesis testing, and probability distributions in actual market scenarios.
Building and testing valuation frameworks. Understanding when models work, when they break, and how to adjust for real market conditions.
Quantifying and interpreting various risk factors. Portfolio risk measurement, scenario analysis, and stress testing techniques used in practice.
Working with temporal financial data. Forecasting methods, trend analysis, and dealing with autocorrelation and non-stationarity issues.
Combining techniques in comprehensive analysis projects. You'll work through complete analytical workflows from data gathering to presenting recommendations.
Our programs run six to twelve months because developing analytical judgment can't be rushed. You need time to work through different market conditions, test various approaches, and understand why certain methods work better in specific contexts.
We've found that spacing out learning allows people to apply techniques in their current work, see what happens, and then build on that experience. The gap between modules isn't empty time—it's when you actually develop practical skill.
Our instructors come from quantitative finance backgrounds. They've built models for trading desks, risk departments, and investment teams—which means they know what works outside controlled academic environments.
Designing analytical frameworks that answer specific business questions. We teach research methodology that works with incomplete data and real market constraints.
Testing whether analytical models actually work. Understanding model assumptions, identifying failure modes, and knowing when results should be trusted.
Interpreting market behavior through quantitative lenses. Connecting statistical patterns to actual market dynamics and institutional flows.
Explaining complex analysis to non-technical audiences. Presenting findings clearly, acknowledging limitations, and supporting decision-making processes.
Spent eight years building risk models for regional banks before transitioning to education. Warren focuses on teaching analytical techniques that remain useful when market conditions shift. His approach emphasizes understanding why methods work rather than just following procedures.
Programs use real financial datasets with all their quirks—missing values, outliers, structural breaks, and reporting inconsistencies. This matters because clean academic datasets don't prepare you for actual analysis work.
You'll spend time understanding data quality issues, deciding how to handle anomalies, and determining when imperfect data is still useful versus when it's genuinely unreliable.
We evaluate skills through analytical projects rather than exams. You'll complete assignments that mirror real analysis work—messy data, ambiguous requirements, and the need to make judgment calls about methodology.
Projects get reviewed for analytical rigor, appropriate method selection, and clear communication of findings. Feedback focuses on improving analytical thinking rather than just correcting technical errors.
Our autumn 2025 cohort begins in early September and runs through March 2026. Applications open in June. We keep groups small—around fifteen participants—so everyone gets detailed feedback on their analytical work.
If you're interested in developing practical financial analysis skills through hands-on work with real data, reach out at info@shocknetly.com or call +66 2 835 3737. We can discuss whether our approach matches what you're looking for.