The three-factor model and artificial neural networks: predicting stock price movement in China

Cao, Qing; Parry, Mark E.; Leggio, Karyl B.

doi:10.1007/s10479-009-0618-0

Cited by 42 publications

(25 citation statements)

References 62 publications

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“…After addressing the input-output relationship from the social media data, ANN can be used to formulate such a relationship mathematically. ANN is a non-linear regression model which has been widely used for regression and predictive purposes such as stock market forecasting [26], credit rating [27], and consumer analysis [28]. Its application to social media data however is somehow limited.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

An ANN-based approach of interpreting user-generated comments from social media

Wong

Chan

Lacka

2017

Applied Soft Computing

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The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

An ANN-based approach of interpreting user-generated comments from social media

Wong

Chan

Lacka

2017

Applied Soft Computing

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“…Wei et al () proposed an ANN‐based fuzzy model to predict the stock price trend in the Taiwan Stock Exchange and found that the proposed model has superior prediction power compared with previous fuzzy models, such as those of Yu () and Chen (). Cao et al () compared the predictive ability of ANN models with the dynamic versions of a single‐factor capital asset pricing model (CAPM)‐based model and Fama–French's three‐factor model in predicting stock price movement in China and reported that the ANN model outperformed the CAPM‐based models and proved to be a useful tool for stock price prediction in emerging markets. Hsieh et al () also investigated the forecasting of stock price index's direction using an ANN‐based model and successfully applied it to the DJIA Index, London FTSE‐100 Index, Tokyo Nikkei‐225 Index (Nikkei), and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).…”

Section: Artificial Neural Network Modelling and Literature Reviewmentioning

confidence: 99%

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Fadlalla

Amani

2014

Intell. Sys. Acc. Fin. Mgmt.

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Accurate prediction of stock market price is of great importance to many stakeholders. Artificial neural networks (ANNs) have shown robust capability in predicting stock price return, future stock price and the direction of stock market movement. The major aim of this study is to predict the next trading day closing price of the Qatar Exchange (QE) Index using historical data from 3 January 2010 to 31 December 2012. A multilayer perceptron ANN architecture was used as a prediction model with 10 market technical indicators as input variables. The experimental results indicate that ANNs are an effective modelling technique for predicting the QE Index with high accuracy, outperforming the well-established autoregressive integrated moving average models. To the best of our knowledge, this is the first attempt to use ANNs to predict the QE Index, and its performance results are comparable to, and sometimes better than, many stock market predictions reported in the literature. The ANN model also revealed that the weighted and simple moving averages are the most important technical indicators in predicting the QE Index, and the accumulation/distribution oscillator is the least important such indicator. The analysis results also indicated that the ANNs are resilient to stock market volatility.

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“…There are 598 price values in each company, thus a company with 0.5% missing data will have 3 missing weekly price values from the year 2000 to 2011. 5 5 Missing data are estimated by the linear interpolation using formulas (1), (2) and (3).…”

Section: Estimated Missing Datamentioning

confidence: 99%

“…The grouped portfolios are shown below in table (2). There are 13 portfolios and each contains over 10 companies.…”

Section: Portfoliosmentioning

confidence: 99%

Price returns efficiency of the Shanghai A-Shares

Long

Jaaman²,

Samsudin³

2014

AIP Conference Proceedings

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Beta measured from the capital asset pricing model (CAPM) is the most widely used risk to estimate expected return. In this paper factors that influence Shanghai A-share stock return based on CAPM are explored and investigated. Price data of 312 companies listed on Shanghai Stock Exchange (SSE) from the year 2000 to 2011 are investigated. This study employed the Fama-MacBeth cross-sectional method to avoid weakness of traditional CAPM. In addition, this study improves the model by adjusting missing data. Findings of this study justifies that systematic risk can explain the portfolios' returns of China SSE stock market.

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The three-factor model and artificial neural networks: predicting stock price movement in China

Cited by 42 publications

References 62 publications

An ANN-based approach of interpreting user-generated comments from social media

An ANN-based approach of interpreting user-generated comments from social media

Predicting Next Trading Day Closing Price of Qatar Exchange Index Using Technical Indicators and Artificial Neural Networks

Price returns efficiency of the Shanghai A-Shares

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