Mean–variance portfolio optimization using machine learning-based stock price prediction

Chen, Wei; Zhang, Haoyu; Mehlawat, Mukesh Kumar; Jia, Lifen

doi:10.1016/j.asoc.2020.106943

Cited by 143 publications

(50 citation statements)

References 57 publications

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“…Literature [17] studies have found that the accuracy of the neural network model [18][19][20] in predicting nonlinear time series data is much higher than the ARIMA model. Literature [21] compares the prediction effects of the Bayesian estimation and neural network model with different standards. The results show that the prediction effect of the neural network model is better.…”

Section: Stock Forecastingmentioning

confidence: 99%

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

Yuan

2021

Wireless Communications and Mobile Computing

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The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

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Section: Stock Forecastingmentioning

confidence: 99%

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

Yuan

2021

Wireless Communications and Mobile Computing

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“…Speculators, especially investors always want to increase the chance of earning more profit with the help of market patterns analysis. In the past, it was an assumption in efficient market hypothesis (EMH) that the stock prices have all information related to stock patterns, and that the best possible and the most natural way for the stock market should be is random walk [1,2]. However, there is an argument by the researchers in behavioral finance that hypothesis may be wrong due to the outrageous behavior of players, which can be affected by various types of market information and the psychological interpretation of information by the individuals [3].…”

Section: Introductionmentioning

confidence: 99%

A deep learning method DCWR with HANet for stock market prediction using news articles

Albahli

Awan

Nazir

et al. 2022

Complex Intell. Syst.

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Stock movement prediction is a challenging problem to analyze in both academic and financial research areas. The advancement of deep learning (DL) techniques has grasped the attention of researchers to employ them for predicting the stock market’s future trends. Few frameworks can understand the financial terms in literature, and the volatile nature of stock markets further complicates this process. This paper has tried to overcome the existing challenges by introducing a DL-based framework using financial news articles to forecast the stock market. After performing preprocessing step, the deep contextualized word representation (DCWR) approach is applied for feature extraction. In the next step, the independent component analysis (ICA) method is used for feature reduction. Finally, the resultant features train the hierarchical attention networks (HANet) classifier to predict the stock movements. The proposed scheme is evaluated over the 7 years of data from a publicly available dataset gathered from the Reuter’s website and attained an average prediction accuracy of 92.5% which shows our framework’s robustness.

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“…However, many studies show that a single prediction model is not sufficient to achieve very accurate predictions and high returns. A new approach to portfolio construction using a hybrid model based on machine learning for stock prediction and a mean variance (MV) model for portfolio selection was published by [18]. The results obtained show that the proposed method is better than traditional methods (without stock prediction) and benchmarks in terms of returns and risks.…”

Section: Introductionmentioning

confidence: 99%

Underestimating risk leading to the collapse of a market leader in tourism

2022

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The topic of this paper, underestimating risk leading to the collapse of the market leader in tourism, is demonstrated on the example of the British travel agency Thomas Cook, which at one time was one of the oldest and largest travel agencies in the world. The aim of this paper is to analyze the development of the stock prices of Thomas Cook from May 13, 2018 to May 19, 2019 and the factors that had an impact on the share price of this company in the monitored period. The base source of data are the share prices of the travel agency Thomas Cook in the specified period from May 13, 2018 to May 19, 2019 published by MarketWatch. A statistical description of time series is used, a moving average trend line is displayed, and a cause-and-effect analysis evaluating the impact of the published information on the value of Thomas Cook’s stocks is carried out. The general lesson for companies resulting from this contribution is that every negative event, announcement or piece of information has a negative impact on the value of a company’s shares and a collapse could happen even to the leader of a given industry. The collapse of Thomas Cook provides lessons for companies doing business in tourism, so that in the event of a planned merger, a suitable company is selected, the company’s funds are under control and development trends in the field are monitored.

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Mean–variance portfolio optimization using machine learning-based stock price prediction

Cited by 143 publications

References 57 publications

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

A deep learning method DCWR with HANet for stock market prediction using news articles

Underestimating risk leading to the collapse of a market leader in tourism

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