Prediction-Based Portfolio Optimization Models Using Deep Neural Networks

Yilin Ma · Ruizhu Han · Weizhong Wang

104 citations0/0 files storedIEEE Access

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Financial Markets and Investment Strategies Risk and Portfolio Optimization

Abstract

Portfolio optimization is a hot research topic, which has attracted many researchers in recent decades. Better portfolio optimization model can help investors earn more stable profits. This paper uses three deep neural networks (DNNs), i.e., deep multilayer perceptron (DMLP), long short memory (LSTM) neural network and convolutional neural network (CNN) to build prediction-based portfolio optimization models which own the advantages of both deep learning technology and modern portfolio theory. These models first use DNNs to predict each stock's future return. Then, predictive errors of DNNs are applied to measure the risk of each stock. Next, the portfolio optimization models are built by integrating the predictive returns and semi-absolute deviation of predictive errors. These models are compared with three equal weighted portfolios, where their stocks are selected by DMLP, LSTM neural network and CNN respectively. Also, two prediction-based portfolio models built with support vector regression are used as benchmarks. This paper applies component stocks of China securities 100 index in Chinese stock market as experimental data. Experimental results present that the prediction-based portfolio model based on DMLP performs the best among these models under different desired portfolio returns, and high desired portfolio return can further improve the performance of this model. This paper presents the promising performance of DNNs in building prediction-based portfolio models.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 104 citations exist upstream.

References

1952PORTFOLIO SELECTION*2017Deep learning with long short-term memory networks for financial market predictions 1991Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies 2016Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 1984Models of Stock Returns--A Comparison 2018Deep learning-based feature engineering for stock price movement prediction

Cited by

2022Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection 2023Harnessing a Hybrid CNN-LSTM Model for Portfolio Performance: A Case Study on Stock Selection and Optimization 2023Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.