Portfolio risk management model based on machine learning
نویسندگان
چکیده
This paper comprehensively analyzes many domestic and foreign literatures related to machine learning, financial risk management, the investment portfolio, etc. Domestic literature covers flood forecasting, portfolio construction optimization, intelligence, application of big data technology in hospital archives management. The involves multi-factor semi-parameter distribution international practice countermeasures customs autonomous navigation based on learning so on. From these literatures, it can be found that has shown a wide range prospects e-commerce marketing other fields. In terms management control, research discusses internal fraud identification model, enterprise information strategies, as well addition, optimization focuses perspective genetic algorithm selection multi-risk assets. At same time, emerging fields are also mentioned literature, such status plant factories, smart finance control universities.[1]
منابع مشابه
mortality forecasting based on lee-carter model
over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...
15 صفحه اولPortfolio Optimization Based on Cross Efficiencies By Linear Model of Conditional Value at Risk Minimization
Markowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave been done to remove theses drawbacks. On one hand several better risk measures havebeen introduced and proper models have been ...
متن کاملMachine Learning and Portfolio Optimization
We modify two popular methods in machine learning, regularization and cross-validation, for the portfolio optimization problem. First, we introduce performance-based regularization (PBR), where the idea is to constrain the sample variances of the estimated portfolio risk and return. The goal of PBR is to steer the solution towards one associated with less estimation error in the performance. We...
متن کاملModel-based machine learning
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations....
متن کاملA hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements
Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majori...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Financial engineering and risk management
سال: 2023
ISSN: ['2523-2576']
DOI: https://doi.org/10.23977/ferm.2023.060910