The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests

Gupta, Rangan; Pierdzioch, Christian; Vivian, Andrew; Wohar, Mark E.

doi:10.1016/j.frl.2018.08.013

Cited by 24 publications

(17 citation statements)

References 29 publications

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“…Using U.K. stock data from March 1977 to March 2016, they analyze inequality measures and conditional distribution of stock returns to explore factors causing inequality of income and wealth. Their findings can be a reference for other countries when addressing issues about economic development [35].…”

Section: Random Forestmentioning

confidence: 80%

Analysis of Information Security News Content and Abnormal Returns of Enterprises

Hung

2019

BDCC

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As information technologies and the Internet have rapidly evolved, businesses have begun to use them to improve communication efficiency within and outside the organization. However, applications of information technologies are accompanied by information delivery, personal data protection, and information security problems. There are potential risks inherent in any application of information technologies. Moreover, with the improvement of networking and computing capabilities, the impact of attacks from hackers and malicious software has also increased. A breach or leakage of important corporate data may not only damage the firm’s image but also sabotage the firm’s operation, resulting in financial losses. In this study, the content of information security news reports was analyzed in an attempt to clarify the association between information security news and corporate stock prices. Methods including decision trees, support vector machines (SVMs), and random forests were used to explore the associations of news related variables with abnormal returns. Results indicate that the news source and the presence of negative words in the news have an impact on abnormal returns.

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Section: Random Forestmentioning

confidence: 80%

Analysis of Information Security News Content and Abnormal Returns of Enterprises

Hung

2019

BDCC

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“…Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), ANNs and adaptive neuro-fuzzy inference systems, the proposed model performs best based on two error measures, namely mean squared error and mean absolute percent error. Gupta et al (2018) use quantile random forests to study the predictive value of various consumption-based and income-based inequality measures across the quantiles of the conditional distribution of stock returns. Results suggest that the inequality measures have predictive value for stock returns in sample, but do not systematically predict stock returns out of the sample.…”

Section: Literature Reviewmentioning

confidence: 99%

INVESTMENT PERFORMANCE OF MACHINE LEARNING: ANALYSIS OF S&P 500 INDEX

Chen¹,

Chen²,

Liu³

2020

IJEFI

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This study aims to explore the prediction of S&P 500 stock price movement and conduct an analysis of its investment performance. Based on the S&P 500 index, the study compares three machine learning models: Artificial neural networks (ANN), support vector machines (SVM), and random forest. With a performance evaluation of S&P 500 index historical data spanning from 2014 to 2018, we find: (1) By overall performance measures, machine learning models outperform benchmark market index. (2) By risk-adjusted measures, the empirical results suggest that Random Forest generates the best performance, followed by SVM and ANN.

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“…Empirical results show that a novelty of the current work is about the selection of technical indicators and their use as features, with high accuracy for medium to long-run prediction of stock price direction. Gupta et al (2018) contribute to research on the predictability of stock returns in two ways. First, they use quantile random forests to study the predictive value of various consumption-based and income-based inequality measures across the quantiles of the conditional distribution of stock returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Untitled

2019

IJEFI

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This paper addresses problem of predicting direction of movement of stock price index for Taiwan stock markets. The study compares four prediction models, artificial neural network (ANN), support vector machine, random forest and naive-Bayes with two approaches for input to these models. The first data preprocess approach involves computation of ten technical parameters using stock trading data while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on 19 years of historical data from 2000 to 2018 of Taiwan stock market index. The experimental results suggest that for the first approach of input data where ten technical parameters are represented as continuous values, ANN outperforms other three prediction models on overall performance. Experimental results also show that the performance of all the prediction models improve when these technical parameters are represented as binary trend deterministic data.

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The predictive value of inequality measures for stock returns: An analysis of long-span UK data using quantile random forests

Cited by 24 publications

References 29 publications

Analysis of Information Security News Content and Abnormal Returns of Enterprises

Analysis of Information Security News Content and Abnormal Returns of Enterprises

INVESTMENT PERFORMANCE OF MACHINE LEARNING: ANALYSIS OF S&P 500 INDEX

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