Machine Learning in Finance - KIT Financial Economics & Risk Management Report Template - Guidance

Sep 13, 2026

Upload by Fez

Technical report template and submission guidance for a KIT Financial Economics & Risk Management student project. Provides recommended LaTeX and PowerPoint resources for consistent formatting, and outlines a complete structure covering introduction, mathematical formulation, use-case scenario, system architecture, unified layered model, algorithm specification with pseudocode, experimental setup, results and visualization, evaluation metrics, discussion, reproducibility checklist, conclusion, plus code appendix and unit tests. Includes placeholders for author, project details, and repository links.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages14

Reading Time5 min

AccessFree to download

1 Introduction

1. Problem Statement & Motivation

2. Objectives

3. Background & Related Work

4. Contributions

2 Mathematical Formulation (replace with your own)

3 Use-Case Scenario (replace with your own)

4 System Architecture (replace with your own)

5 Unified Layered Model (ULM) (replace with your own)

6 Algorithm Specification & Pseudocode

6.1 Plain-Language Overview

6.2 Pseudocode (replace with your own)

6.3 Reference Implementation

6.4 Complexity Analysis

6.5 Executable Example

7 Experimental Setup

7.1 Hyper-Parameter Search Protocol (replace with your own)

8 Results & Visualisation

8.1 Formatting Conventions

9 Evaluation Metrics

9.1 Regression Metrics

10 Discussion

11 Reproducibility Checklist

12 Conclusion

A Code Appendix

B Unit Tests

C Authorship Contribution Matrix

Technical Report Template & Guidance for Submission to the Chair of Financial Economics & Risk Management Student Name (ID: 123456) July 31, 2025 LaTeX Template Recommendation & KIT Presentation Guidelines For consistency with KIT formatting standards, we recommend using the official KSP LATEX class file (ksp. cls) and the SDQ PowerPoint template for final presentations. More details can be found at: [https://www. bibliothek. kit. edu/ksp-toolbox-dokumentvorlagen. php](https://www. bibliothek. kit. edu/ksp-toolbox-dokumentvorlagen. php) [https://sdq. kastel. kit. edu/wiki/Dokumentvorlagen](https://sdq. kastel. kit. edu/wiki/Dokumentvorlagen) Contents 1 Introduction 2 2 Mathematical Formulation (replace with your own) 2 3 Use-Case Scenario (replace with your own) 2 4 System Architecture (replace with your own) 2 5 Unified Layered Model (ULM) (replace with your own) 3 6 Algorithm Specification & Pseudocode 3 6.1 Plain-Language Overview .......................... 3 6.2 Pseudocode (replace with your own) .................... 3 6.3 Reference Implementation 4 6.4 Complexity Analysis 4 6.5 Executable Example 4 7 Experimental Setup 4 7.1 Hyper-Parameter Search Protocol (replace with your own) ........ 5 8 Results & Visualisation 6 8.1 Formatting Conventions ........................... 6 9 Evaluation Metrics 6 9.1 Regression Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 10 Discussion 6 11 Reproducibility Checklist 7 12 Conclusion 7 A Code Appendix 7 B Unit Tests 8 C Authorship Contribution Matrix 11 Report Title Placeholder Machine Learning in Finance BSc / MSc / PhD Programme Author: Student Name Matriculation No. : 123456 Submission: 2025-07-31 Supervisor: Name Course / Module: Name / Code Git Repository: [https://github. com/username/project](https://github. com/username/project) Abstract A concise summary of the problem, technical approach, key quantitative results, and main conclusions. Avoid undefined acronyms; write for a technically literate but non-expert reader. 1 Introduction 1. Problem Statement & Motivation 2. Objectives 3. Background & Related Work 4. Contributions 2 Mathematical Formulation (replace with your own) Present the governing equations of your method. Each equation must be numbered, and every symbol must be introduced immediately after the equation. Use a short description list as shown in the loss equation. N L (θ) = −Xhyi log ˆyi + (1 − yi ) log(1 − ˆyi)i (1) i=1 L Binary cross-entropy loss. θ Trainable model parameters. N Number of training samples. yi True label of sample i. ˆyi Predicted probability for sample i. 3 Use-Case Scenario (replace with your own) Briefly describe the application context, business motivation and the key performance indicators (KPIs) . Figure 1: Illustrative use-case diagram. 4 System Architecture (replace with your own) Provide a top-level architecture highlighting data sources, processing layers, and deployment targets. Figure 2: High-level system architecture. 5 Unified Layered Model (ULM) (replace with your own) If applicable, map the system into Data Layer, Model Layer, and Service Layer. Figure 3: Unified Layered Model. 6 Algorithm Specification & Pseudocode 6.1 Plain-Language Overview Summarize the core idea of the algorithm and outline why it is suitable for the stated problem. 6.2 Pseudocode (replace with your own) Provide your algorithm in readable pseudocode form using the ‘algorithm2e‘ environment. Every variable must be defined in-line or in a short caption, and the algorithm should be referenced in the text as an algorithm. Algorithm 1: Mini-batch Gradient Descent (example) Input: Initial parameters θ (0) , learning rate η, batch size B, dataset D Output: Optimised parameters θ (∗) for t ← 0 to T − 1 do Sample mini-batch B ⊂ D of size B; Compute gradient g ← ∇θ L (θ(t); B); Update parameters θ (

FAQs

What formatting tools are recommended for KIT submissions?

Use the official KSP LaTeX class file (ksp.cls) and the SDQ PowerPoint template for the final presentations to ensure consistency with KIT formatting standards.

What sections must be included in the technical report?

Include introduction, mathematical formulation, use-case scenario, system architecture, unified layered model, algorithm specification with pseudocode, experimental setup, results and visualization, evaluation metrics, discussion, reproducibility checklist, and conclusion, plus appendices for code and unit tests.

How should the hyper-parameter search protocol be documented?

Describe the optimization approach, model capacity exploration and overfitting safeguards, specify the library/tool and fixed random seed, log all trial configurations and metrics to an experiment-tracking platform, and state the trial cap and early-stopping rule.

Sep 13, 2026

Upload by Fez