Can the Exchange Rate Be Used to Predict the Shanghai Composite Index?

Zhang, Jun; Shao, Yuan‐Hai; Huang, Ling-Wei; Teng, Jie; Zhao, Yanguang; Yang, Z.; Li, Xinyang

doi:10.1109/access.2019.2962221

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…Sensoy and Tabak [6] analyzed the dependence between the stock market and exchange rate prices, and the experimental results showed that exchange rate prices were correlated with stocks. Zhang et al [7] used the SVM model to prove that the exchange rate price could be applied to the index prediction. In this study, the exchange rate price of RMB and USD for 7 years from January 1, 2013 to December 31, 2019 is chosen as the exchange rate characteristic.…”

Section: Introductionmentioning

confidence: 99%

Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion

Lin¹,

Lin²,

Li³

et al. 2023

Journal of Compurters

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<p> To some extent, stock prices can reflect national economic development and residents’ living standards. However, current stock forecasts are mainly analyzed using the stock’s own price. It is found that the exchange rate and technical indicators are closely related to the fluctuations of stocks and stock indexes. In this study, a generative adversarial network (GAN) financial forecasting method based on multi-model fusion is proposed, which introduces the exchange rate and technical indexes of stock and stock index into the input data, and combines the characteristics of its own highest and lowest price, the closing price of stock or stock index is predicted by using the generated antagonistic network. The methods are as follows: firstly, the exchange rate features are de-noised by wavelet transform technology, the overall trend of exchange rate features is extracted, and the dimensionality of technical indicators is reduced by Principal Component Analysis (PCA). Then, a convolutional neural network (CNN) is used in the generator to extract the local features of the input data, and the attention mechanism is used to improve the prediction effect of the model. Finally, the prediction was made by long short-term memory network (LSTM). Convolutional neural network and multilayer fully connected neural network are used in the discriminant, and gelu is used as activation function in the hidden layer. This study selects the data of Zhuhai Port (000507.SZ), Xiamen International Trade (600755.SH), Orient Venture (600278.SH) and Shanghai Stock Exchange Index (SSE, 000001.SH) in the export industry from January 1, 2013 to December 31, 2019 as the experimental data set. The exchange rate price between RMB and US dollar at the corresponding time is selected as the exchange rate feature. Compared with the best in-depth learning financial prediction comparison model, the results show that the generation confrontation network proposed in this paper has improved by 3% to 15% in each experimental data set.</p> <p> </p>

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Section: Introductionmentioning

confidence: 99%

Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion

Lin¹,

Lin²,

Li³

et al. 2023

Journal of Compurters

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show abstract

“…They indicated that reducing the number of TIs has a positive impact on the ability of prediction. Zhang et al [30] used the TIs as input values generated from the exchange rate and employed support vector machine (SVM) to predict the stock index price of China. The results confirmed that the technical indicators of the exchange rate can be used to improve the prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Chen

2021

IEEE Access

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Using the topological structure of financial networks to build a portfolio has attracted a wide range of research interests. A similarity matrix based on the technical indicators (TIs), and a correlation matrix based on the stock returns, are used to construct the financial networks. Hybrid topological measures are calculated for both networks to select the stocks that are further used to build portfolios. The random matrix theory (RMT) tool is used to filter the risk measurement in the Markowitz optimization process. A general result is presented in this study, which indicates that the portfolios composed of peripheral stocks consistently outperform those composed of central stocks. As shown in the empirical results, the RMT method can improve the Markowitz optimization process and further improve the performance of portfolios. By comparing the different portfolios established under different networks, the results reveal the advantage of the topological measure based on TIs. This study provides an important approach for constructing financial networks from the time series data, and it explores the impact of different similarity measurements on the network-based models for portfolio selection.

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“…Recently, different kinds of machine learning (ML) models have been used for short term financial market prediction [5]- [12] and have obtained satisfying results. This shows a promising direction to apply predictive future return as expected return in building portfolio models.…”

Section: Introductionmentioning

confidence: 99%

Prediction-Based Portfolio Optimization Models Using Deep Neural Networks

Han

Wang

2020

IEEE Access

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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. INDEX TERMS Deep neural network, Prediction-based portfolio, Semi-absolute deviation.

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Can the Exchange Rate Be Used to Predict the Shanghai Composite Index?

Cited by 14 publications

References 35 publications

Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion

Financial Forecasting Method for Generative Adversarial Networks based on Multi-model Fusion

Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Prediction-Based Portfolio Optimization Models Using Deep Neural Networks

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