Increasing the Explanatory Power of Investor Sentiment Analysis for Commodities in Online Media

Klein, Achim; Riekert, Martin; Kirilov, Lyubomir; Leukel, Joerg

doi:10.1007/978-3-319-93931-5_23

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Achim Klein, Martin Riekert, Lyubomir Kirilov and Joerg Leukel (2018) analyzed the relationship between investor sentiment from news and stock prices. [2] Zhigao Yi and Ning Mao (2009) constructed a comprehensive index that could better measure investor sentiment in China's stock market while controlling for the impact of economic fundamentals on sentiment, based on individual sentiment indicators such as the discount of closed-end funds, the number of IPOs and the first day of listing, the consumer confidence index and the number of new investors opening accounts. [3] Xingji Wei, Weili Xia and Dandan Sun (2014) selected 6 sentiment variables such as market turnover rate, and used the principal component analysis method to construct a monthly investor sentiment index in China's securities market.…”

Section: Literature Reviewmentioning

confidence: 99%

Investor Sentiment Measurement and Time Series Analysis

Bai¹

2023

SHS Web Conf.

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Investor sentiment is one of the destabilizing factors in the stock market. In the past, some scholars have proposed models for the measurement of emotions. On this basis, this paper selects 9 factors (closed-end fund discount rate, trading volume of the previous month, market turnover rate of the previous month, number of IPOs, first-day yield of IPOs, number of new investors opened accounts in the last month, Consumers Confidence index, Advance/Decline Line, Business Index of Macro-economic), and constructs the investor sentiment index through principal component analysis. Through statistical analysis of the index, it is found that it satisfies the conditions of a stationary time series, and a related formula is put forward. At the same time, the relationship between index and stock market is discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Investor Sentiment Measurement and Time Series Analysis

Bai¹

2023

SHS Web Conf.

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“…Similarly, text classifiers are evaluated on smaller domain datasets in several studies [15], although accuracy estimates on small datasets suffer from random errors and results of individual experiments for one dataset might not generalize to other datasets. Therefore, one classifier might perform better than the other classifier just by chance, which might lead to contradictory results.…”

Section: Effects Of Training Set Sizes On Design Factorsmentioning

confidence: 99%

Simple Baseline Machine Learning Text Classifiers for Small Datasets

Riekert

Klein

2021

SN COMPUT. SCI.

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Text classification is important to better understand online media. A major problem for creating accurate text classifiers using machine learning is small training sets due to the cost of annotating them. On this basis, we investigated how SVM and NBSVM text classifiers should be designed to achieve high accuracy and how the training sets should be sized to efficiently use annotation labor. We used a four-way repeated-measures full-factorial design of 32 design factor combinations. For each design factor combination 22 training set sizes were examined. These training sets were subsets of seven public text datasets. We study the statistical variance of accuracy estimates by randomly drawing new training sets, resulting in accuracy estimates for 98,560 different experimental runs. Our major contribution is a set of empirically evaluated guidelines for creating online media text classifiers using small training sets. We recommend uni- and bi-gram features as text representation, btc term weighting and a linear-kernel NBSVM. Our results suggest that high classification accuracy can be achieved using a manually annotated dataset of only 300 examples.

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Increasing the Explanatory Power of Investor Sentiment Analysis for Commodities in Online Media

Cited by 2 publications

References 26 publications

Investor Sentiment Measurement and Time Series Analysis

Investor Sentiment Measurement and Time Series Analysis

Simple Baseline Machine Learning Text Classifiers for Small Datasets

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