Data Analysis

A K-means Algorithm for Financial Market Risk Forecasting ArXiv ID: 2405.13076 “View on arXiv” Authors: Unknown Abstract Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today’s society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate ...

Python Guide to Accompany Introductory Econometrics forFinance ArXiv ID: ssrn-3475303 “View on arXiv” Authors: Unknown Abstract This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches Keywords: Python, econometric techniques, software guide, dataset, data analysis, Multi-Asset Complexity vs Empirical Score Math Complexity: 5.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper is a practical Python guide with downloadable datasets and implementation code, indicating high empirical rigor, while the mathematics is introductory and applied, placing it in the low-to-moderate range. flowchart TD A["Research Goal: <br>Implement Econometrics for Finance"] --> B["Data/Inputs: <br>Freely Downloadable Datasets"] B --> C["Methodology: <br>Apply Econometric Techniques"] C --> D["Computational Process: <br>Python Implementation"] D --> E["Outcome: <br>Multi-Asset Data Analysis"] E --> F["Deliverable: <br>Software Guide & Insights"]

Stata forFinanceStudents ArXiv ID: ssrn-2318687 “View on arXiv” Authors: Unknown Abstract While MS-Excel is a default software for finance students, command line econometrics softwares make financial analysis easier, especially for repetitive tasks. Keywords: Financial Econometrics, Data Analysis, Statistical Software, Quantitative Finance, Quantitative Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on implementing standard financial and econometric methods using Stata commands and data access, making it highly practical and data-driven rather than theoretical. flowchart TD A["Research Goal: Stata for Finance Students"] --> B["Methodology: Survey & Comparative Analysis"] B --> C{"Inputs"} C --> D["Excel Usage Data<br/>Quantitative Finance Tasks"] C --> E["Stata Command-line Features<br/>Repetitive Task Efficiency"] D & E --> F["Computational Process:<br/>Statistical Software Comparison"] F --> G["Key Findings/Outcomes"] G --> H["Stata superior for<br/>financial econometrics"] G --> I["Command-line tools<br/>enhance analysis speed"] G --> J["Recommendation:<br/>Integrate Stata in curriculum"]