High-Dimensional Data

Bayesian Analysis of High Dimensional Vector Error Correction Model ArXiv ID: 2312.17061 “View on arXiv” Authors: Unknown Abstract Vector Error Correction Model (VECM) is a classic method to analyse cointegration relationships amongst multivariate non-stationary time series. In this paper, we focus on high dimensional setting and seek for sample-size-efficient methodology to determine the level of cointegration. Our investigation centres at a Bayesian approach to analyse the cointegration matrix, henceforth determining the cointegration rank. We design two algorithms and implement them on simulated examples, yielding promising results particularly when dealing with high number of variables and relatively low number of observations. Furthermore, we extend this methodology to empirically investigate the constituents of the S&P 500 index, where low-volatility portfolios can be found during both in-sample training and out-of-sample testing periods. ...

Ten Financial Applications of Machine Learning (Seminar Slides) ArXiv ID: ssrn-3197726 “View on arXiv” Authors: Unknown Abstract Financial ML offers the opportunity to gain insight from data:* Modelling non-linear relationships in a high-dimensional space* Analyzing unstructured d Keywords: Financial ML, machine learning, non-linear modeling, high-dimensional data, unstructured data analysis, General Financial Markets Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 4.0/10 Quadrant: Philosophers Why: The content is conceptual, emphasizing high-level ML applications and data insights (e.g., non-linear relationships, meta-labeling) without presenting specific equations, derivations, or implementation details. It lacks backtest metrics, code, or datasets, focusing more on theoretical justification and conceptual frameworks than on hands-on empirical validation. flowchart TD A["Research Goal Apply ML to Finance"] --> B["Key Methodology Non-linear & High-dimensional Modeling"] B --> C{"Data Inputs"} C --> D["Unstructured & Market Data"] C --> E["Structured Financial Data"] D & E --> F["Computational Processes ML Algorithms"] F --> G["Key Outcomes Insight Generation"] G --> H{"General Financial Markets Application"} H --> I["Improved Prediction"] H --> J["Risk Management"]