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Explainable Unsupervised Anomaly Detection with Random Forest
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection.
Can an unsupervised clustering algorithm reproduce a categorization system?
Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems.
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated industrial applications due to regulatory requirements and the need for transparency in business practices.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Quantile Regression using Random Forest Proximities
Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest.
Machine Learning-based Relative Valuation of Municipal Bonds
With nearly 2\% of securities from over a million securities outstanding trading daily, determining the value or relative value of a bond among its peers is challenging.
Open Set Recognition for Random Forest
In such scenarios, the classifiers should be able to i) perform classification on known classes, and at the same time, ii) identify samples from unknown classes.
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF can be mathematically formulated as an adaptive weighted K nearest-neighbors model.
Company Similarity using Large Language Models
Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution.
Quantifying Outlierness of Funds from their Categories using Supervised Similarity
Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning.
Learning Mutual Fund Categorization using Natural Language Processing
Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analysis, to quantifying portfolio diversification.
Supervised similarity learning for corporate bonds using Random Forest proximities
Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc.
Fund2Vec: Mutual Funds Similarity using Graph Learning
Identifying similar mutual funds with respect to the underlying portfolios has found many applications in financial services ranging from fund recommender systems, competitors analysis, portfolio analytics, marketing and sales, etc.
Machine Learning Fund Categorizations
Given the surge in popularity of mutual funds (including exchange-traded funds (ETFs)) as a diversified financial investment, a vast variety of mutual funds from various investment management firms and diversification strategies have become available in the market.
