Professional Financial Computing Using Excel and VBA
Bibliographic record
Abstract
Professional Financial Computing Using Excel and VBA is an admirable exposition that bridges the theoretical underpinnings of financial engineering and its application which usually appears as a black-box software application. The book opens the black-box and reveals the architecture of risk-modeling and financial engineering based on industry-standard stochastic models by utilizing Excel and VBA functionality to create a robust and practical modeling tool-kit. Financial engineering professionals who purchase this book will have a jumpstart advantage for their customized financial engineering and modeling needs. Dr. Cameron Wicentowich Vice President, Treasury Analytics Canadian Imperial Bank of Commerce (CIBC) Spreadsheet modeling for finance has become a standard course in the curriculum of many Quantitative Finance programs since the Excel-based Visual Basic programming is now widely used in constructing optimal portfolios, pricing structured products and managing risks. Professional Financial Computing Using Excel and VBA is written by a unique team of finance, physics and computer academics and practitioners. It is a good reference for those who are studying for a Masters degree in Financial Engineering and Risk Management. It can also be useful for financial engineers to jump-start a project on designing structured products, modeling interest term structure or credit risks. Dr. Jin Zhang Director of Master of Finance Program and Associate Professor The University of Hong Kong Excel has been one of the most powerful tools for financial planning and computing over the last few years. Most users utilize a fraction of its capabilities. One of the reasons is the limited availability of books that cover the advanced features of Excel for Finance. Professional Financial Computing Using Excel and VBA goes the extra mile and deals with the Excel tools many professionals call for. This book is a must for professionals or students dealing with financial engineering, financial risk management, computational finance or mathematical finance. I loved the way the authors covered the material using real life, hands-on examples. Dr. Isaac Gottlieb Temple University Author, Next Generation Excel: Modeling in Excel for Analysts and MBAs
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".