The new methods for equity fund selection and optimal portfolio
construction
- URL: http://arxiv.org/abs/2004.10631v1
- Date: Mon, 20 Apr 2020 08:24:12 GMT
- Title: The new methods for equity fund selection and optimal portfolio
construction
- Authors: Yi Cao
- Abstract summary: We show how to produce a long-short portfolio from a smaller pool of stocks from mutual fund top holdings.
As these methods are based on statistical evidence, we need closely monitoring the model validity, and prepare repair strategies.
- Score: 3.0166620288400776
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We relook at the classic equity fund selection and portfolio construction
problems from a new perspective and propose an easy-to-implement framework to
tackle the problem in practical investment. Rather than the conventional way by
constructing a long only portfolio from a big universe of stocks or macro
factors, we show how to produce a long-short portfolio from a smaller pool of
stocks from mutual fund top holdings and generate impressive results. As these
methods are based on statistical evidence, we need closely monitoring the model
validity, and prepare repair strategies.
Related papers
- A Case Study of Next Portfolio Prediction for Mutual Funds [0.0]
This work frames mutual fund portfolio prediction as a Next Novel Basket Recommendation (NNBR) task.
We create a benchmark dataset using publicly available data and evaluate the performance of various recommender system models on the NNBR task.
arXiv Detail & Related papers (2024-10-08T12:49:00Z) - Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer [1.4061979259370274]
We implement the PolyModel theory for constructing a hedge fund portfolio.
We create quantitative measures such as Long-term Alpha, Long-term Ratio, and SVaR.
We also employ the latest deep learning techniques (iTransformer) to capture the upward trend.
arXiv Detail & Related papers (2024-08-06T17:55:58Z) - Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives.
Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
arXiv Detail & Related papers (2024-03-25T12:04:03Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Model-Free Market Risk Hedging Using Crowding Networks [1.4786952412297811]
Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies.
We analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks.
Our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization.
arXiv Detail & Related papers (2023-06-13T19:50:03Z) - HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and
Regime-Switch VAE [113.47287249524008]
It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting.
We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors.
Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
arXiv Detail & Related papers (2023-06-05T12:58:13Z) - Joint Latent Topic Discovery and Expectation Modeling for Financial
Markets [45.758436505779386]
We present a groundbreaking framework for financial market analysis.
This approach is the first to jointly model investor expectations and automatically mine latent stock relationships.
Our model consistently achieves an annual return exceeding 10%.
arXiv Detail & Related papers (2023-06-01T01:36:51Z) - E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction.
Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
arXiv Detail & Related papers (2023-05-25T10:27:07Z) - Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights.
Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
arXiv Detail & Related papers (2022-10-22T14:47:11Z) - Asset Allocation: From Markowitz to Deep Reinforcement Learning [2.0305676256390934]
Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets.
We conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques.
arXiv Detail & Related papers (2022-07-14T14:44:04Z) - Precise Stock Price Prediction for Robust Portfolio Design from Selected
Sectors of the Indian Stock Market [0.0]
We have built the minimum variance portfolio and optimal risk portfolio for all the five chosen sectors.
A comparative study of minimum variance portfolio and optimal risk portfolio with equal weight portfolio is done by backtesting.
arXiv Detail & Related papers (2022-01-14T17:24:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.