A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points

Tang, Huaping; Dong, Peiwu; Shi, Yong

doi:10.1016/j.asoc.2019.02.039

Cited by 60 publications

(37 citation statements)

References 34 publications

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“…If the value is too large, the end of a rising trend would be anticipated and the price would be expected to move downward. In [17], [26], most of the above indicators are constructed using technique indicators of exchange rate price and are used as features to predict the movement of the exchange rate price. In this paper, we focus on the short-term prediction of the stock index.…”

Section: ) Collected Indicatorsmentioning

confidence: 99%

“…[25] used 6 popular indicators and trade volume as input indicators to predict the Shanghai Stock Exchange Composite Index and the Shenzhen Stock Exchange Component Index in the short. [26] used the weighted SVM to predict the turning point of stock price change with 14 technical indicators and combined the relative strength index (RSI). The experimental results showed that the trading income was relatively stable.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Stock index price forecasting is a consistent focus of business intelligence. Various factors influence stock index price forecasting, such as technical indicators, financial news, business status, and the macroeconomics situation. In addition, many studies have shown that the exchange rate is related to the stock index price; however, no study has examined whether the exchange rate can be used to forecast stock index prices. Therefore, this paper focuses on this topic and uses exchange rate to predict China stock index price for the first time. Firstly, we compare the association of China stock index price with different data sources to illustrate the feasibility of using the exchange rate to predict stock index prices. Then, we generate some additional technical features of the exchange rate and propose a strategy to predict the stock index price. Finally, we compare the forecast results of China's stock index price based on four data sources, i.e., technical indicators, exchange rate data, US market index data and finance news data from January 3, 2017 to March 20, 2019. Experimental results demonstrate that the performance of exchange rate data for stock index prediction is comparable to other popular data sources and that, in some prediction periods, the exchange rate outperforms such data sources. The results confirm that the exchange rate could be used for forecasting the Shanghai Composite Index prices. INDEX TERMS Stock index prediction, exchange rate, finance news, technical indicators, SZ50, rolling window.

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Section: ) Collected Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Due to the inefficiency of the SVM and ANN based stock prediction models, Patel et al [19] suggested prediction models using fusion or hybrid algorithms of SVM, ANN and Random forests for stock prediction. Similarly, Tang et al [20] proposed a stock price forecasting model using a combination of piecewise linear representation (PLR) and weighted support vector machine (WSVM). This PLR-WSVM model uses a fitness function to select the automatic threshold of PLR and then employs the oversampling method for the stock price turning points.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

Bhuvaneshwari,

Beena

2020

IJEAT

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Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.

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“…Although the abovementioned approaches can describe and evaluate the relationship between variables through statistical inference, there are still some limitations. On one hand, since these methods are based on the assumption of linear relationship of model structure, they can hardly capture the nonlinear variation of the stock price [8,9]. On the other hand, these approaches assume that the data have constant variance, while the financial time series have high-noisy, time-varying, dynamic properties, and so on [10].…”

Section: Introductionmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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A new approach of integrating piecewise linear representation and weighted support vector machine for forecasting stock turning points

Cited by 60 publications

References 34 publications

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

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

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

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