Model and forecast stock market behavior integrating investor sentiment analysis and transaction data

2017 ◽  
Vol 20 (1) ◽  
pp. 789-803 ◽  
Author(s):  
Gaowei Zhang ◽  
Lingyu Xu ◽  
Yunlan Xue
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Investor Sentiment ◽  
Market Behavior ◽  
Transaction Data

Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa B L ◽  
Shambhavi B R
Keyword(s):  
Stock Market ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Moving Average ◽  
Investor Sentiment ◽  
Keyword Extraction ◽  
Stock Market Prediction ◽  
Content Type ◽  
Technical Indicator ◽  
News Sentiment

PurposeStock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.Design/methodology/approachThis paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.FindingsThe performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.Originality/valueThis paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

scholarly journals Research on the Application of Generative Adversarial Networks in the Generation of Stock Market Forecast Trend Images

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Daiyou Xiao
Keyword(s):  
Stock Market ◽  
Prediction Model ◽  
Investor Sentiment ◽  
Image Features ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Generation Model ◽  
Stock Market Prediction ◽  
Transaction Data ◽  
Model Based

Investors make capital investment by buying stocks and expect to get a certain income from the stock market. When buying stocks, they need to draw up investment plans based on various information such as stock market historical transaction data and related news data of listed companies and collect and analyze these data. The data are relatively cumbersome and require a lot of time and effort. If you only rely on subjective analysis, the reference factors are often not comprehensive enough. At the same time, Internet social media, such as the speech in stock forums, also affect the judgment and behavior of investors, and investor sentiment will have a positive or negative effect on the stock market. This has an impact on the trend of stock prices. Therefore, this article proposes a stock market prediction model that uses data preprocessing technology based on past stock market transaction data to establish a stock market prediction model, and secondly, an image description generation model based on a generative confrontation network is designed. The model includes a generator and a discriminator. A time-varying preattention mechanism is proposed in the generator. This mechanism allows each image feature to pay attention to the image features of other stock markets to predict stock market trends so that the decoder can better understand the relational information in the image. The discriminator is based on the recurrent neural network and considers the degree of matching between the input sentence and the 4 reference sentences and the image features. Experiments show that the accuracy of the model is higher than that of the stock pretrend forecast model based on historical data, which proves the effectiveness of the data used in this paper in the stock price trend forecast.

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