Portfolio optimization with return prediction using deep learning and machine learning

Ma, Yilin; Han, Ruizhu; Wang, Weizhong

doi:10.1016/j.eswa.2020.113973

Cited by 177 publications

(71 citation statements)

References 54 publications

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“…The research can also consider the combination of the BS model, the classic binomial model and the classic difference method for different experimental data. The dual-hybrid model proposed in the article has broad application prospects and has huge potential in time series analysis, and it can be applied to portfolio management and asset allocation in the future [41][42][43][44].…”

Section: Discussionmentioning

confidence: 99%

Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

2022

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The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expectations due to its limited conditions. Combining an economic model with a deep learning model to establish a hybrid model provides a new method to improve the prediction accuracy of the pricing model. This includes the usage of real historical data of about 10,000 sets of CSI 300 ETF options from January to December 2020 for experimental analysis. Aiming at the prediction problem of CSI 300ETF option pricing, based on the importance of random forest features, the Convolutional Neural Network and Long Short-Term Memory model (CNN-LSTM) in deep learning is combined with a typical stochastic volatility Heston model and stochastic interests CIR model in parameter models. The dual hybrid pricing model of the call option and the put option of CSI 300ETF is established. The dual-hybrid model and the reference model are integrated with ridge regression to further improve the forecasting effect. The results show that the dual-hybrid pricing model proposed in this paper has high accuracy, and the prediction accuracy is tens to hundreds of times higher than the reference model; moreover, MSE can be as low as 0.0003. The article provides an alternative method for the pricing of financial derivatives.

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Section: Discussionmentioning

confidence: 99%

Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

2022

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“…In [25], the authors used a hybrid deep neural network architecture to forecast stock price to enhance prediction accuracy. In a very recent study [26], the authors used return prediction to optimize portfolio formation. In [27], a multilayer and multi-ensemble stock trader was discussed, and the authors validated their approach in a real-world trading context.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Trading Based on Market Sentiments and Reinforcement Learning

Suhail¹,

Sankar²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Stock market is a place, where shares of different companies are traded. It is a collection of buyers' and sellers' stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends in stock prices. The sentiment analysis module generates a unified score as a measure of the daily news about sentiments. This score is then fed into the RL module as one of its inputs. The RL section gives decisions in the form of three actions: buy, sell, or hold. The objective is to maximize long-term future profits. We have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect trading. The stock price of any company rises, when significant positive news become available in the public domain. Our results reveal the influence of market sentiments on forecasting of stock prices.

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“…Some other studies use prediction models first to predict each asset's future dynamics and then use the predictions to find optimal assets. [8,27,44] investigate a range of machine learning methods from simpler methods (Linear Regression & Support Vector Regression) to more complex methods (XGBoost & LSTMs 1 ). Reinforcement Learning (RL) methods have also been studied, in which an agent learns how to construct appropriate portfolios by behaving in the environment [46].…”

Section: A Related Workmentioning

confidence: 99%

“…A variety of computational methods have started to rise. Some methods first predict each asset changes in the future and use the results for portfolio construction [8,27,44]. Other methods have also been used to directly construct the portfolios by training on previous data, such as Reinforcement Learning [46], and Deep Learning [47].…”

Section: Introductionmentioning

confidence: 99%

RPS: Portfolio Asset Selection using Graph based Representation Learning

Fazli¹,

Alian²,

Owfi³

et al. 2021

Preprint

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Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection (RPS) by redefining the distance matrix of financial assets using Representation Learning and Clustering algorithms for portfolio selection to increase diversification. RPS proposes a heuristic for getting closer to the optimal subset of assets. Using empirical results in this paper, we demonstrate that widely used portfolio optimization algorithms, such as MVO, CLA, and HRP, can benefit from our asset subset selection.

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Portfolio optimization with return prediction using deep learning and machine learning

Cited by 177 publications

References 54 publications

Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

Dual-Hybrid Modeling for Option Pricing of CSI 300ETF

Stock Market Trading Based on Market Sentiments and Reinforcement Learning

RPS: Portfolio Asset Selection using Graph based Representation Learning

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