Specialized Document Embeddings for Aspect-based Similarity of Research Papers
- Computer Science2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
The approach of aspect-based document embeddings mitigates potential risks arising from implicit biases by making them explicit and can, for example, be used for more diverse and explainable recommendations.
SHOWING 1-10 OF 87 REFERENCES
Financial Signal Processing and Machine Learning
- Computer Science
This book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects and highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches.
A hybrid stock trading framework integrating technical analysis with machine learning techniques
- Computer Science
Clustering financial time series: an application to mutual funds style analysis
- Computer ScienceComput. Stat. Data Anal.
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
- Computer ScienceExpert Syst. Appl.
Can Machine Learning Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification
The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the…
Clustering Indian stock market data for portfolio management
- Computer Science, EconomicsExpert Syst. Appl.
Sparsity and stability for minimum-variance portfolios
- Computer ScienceRisk Management
It is found that simply using LASSO is insufficient to lower turnover when the model’s tuning parameter can change over time, and it is possible to maintain the low-risk profile of efficient estimation methods while automatically selecting only a subset of assets and further inducing low portfolio turnover.
Clustering Stock Data for Multi-objective portfolio Optimization
- Computer Science, EconomicsInt. J. Comput. Intell. Appl.
A model that can efficiently suggest a portfolio that is worth investing is proposed and the multi-objective genetic algorithm is used to build portfolio optimization with highest return rate and lowest risk.
Clustering with t-SNE, provably
- Computer ScienceSIAM J. Math. Data Sci.
It is proved that t-SNE is able to recover well-separated clusters and suggests novel ways for setting the exaggeration parameter α and step size h to improve the quality of embedding of topological structures.
Machine Learning and Portfolio Optimization
- Computer ScienceManag. Sci.
It is shown that the PBR models can be cast as robust optimization problems with novel uncertainty sets and establish asymptotic optimality of both sample average approximation SAA and PBR solutions and the corresponding efficient frontiers.