Impact of news sentiment and topics on IPO underpricing: US evidence

Fedorova, Elena; Druchok, Sergei; Drogovoz, P. A.

doi:10.1108/ijaim-06-2021-0117

Cited by 14 publications

(5 citation statements)

References 60 publications

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“…The parallel combination model predicts data values through multiple models and compares them with actual values. The error is larger, the weight is smaller [4].…”

Section: Introduction To the Methods Of Combining Models Based On Fus...mentioning

confidence: 99%

“…Therefore, on the basis of traditional analysis methods, the sentiment analysis section is designed, which combines the differential integration moving average autoregressive model and backpropagation feedforward neural network model to obtain a new combination algorithm model for sentiment analysis fusion, thereby improving the accuracy of stock prediction by the network model. At the same time, the neural network model after sentiment analysis is fused, can further verify the direct relationship between stock market changes and emotional tendencies [4]. There are many current studies on stock prediction, and most of them only improve the prediction model or perform sentiment analysis prediction from the perspective of sentiment tendency, and very few studies that combine the two exist.…”

Section: Introductionmentioning

confidence: 99%

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ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

Gong

2024

SCPE

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In recent years, the prediction of stocks has mainly focused on improving and combining stock prediction algorithms, or analyzing news sentiment tendencies to simulate subjective investor consciousness. However, both methods have shortcomings in practicality and comprehensiveness. Therefore, based on the use of stock data, the sentiment propensity of vocabulary in the article was processed, and a new algorithm model was obtained by combining the differential integration moving average autoregressive model and backpropagation feedforward neural network model. Finally, sentiment propensity was integrated into the combination model to obtain an algorithm model that integrates sentiment analysis. After optimizing the sentiment vocabulary of news. The algorithm has improved its ability to recognize emotional tendency words, while traditional algorithms have been improved to improve the accuracy of stock prediction, further verifying the relationship curve between emotional tendency and stock prediction fluctuations. The experimental results show that the combined model of sentiment analysis is close to the true value in predicting stock results, with an error of less than 1.5%. The accuracy and stability of the model’s prediction results are significantly better than the uncombined model and traditional prediction models. The new combination model provides better judgment basis for investors through experimental prediction results, creating conditions for investors to avoid stock market risks and improve investment value.

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“…The parallel combination model predicts data values through multiple models and compares them with actual values. The error is larger, the weight is smaller [4].…”

Section: Introduction To the Methods Of Combining Models Based On Fus...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

Gong

2024

SCPE

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“…We consider FLAIR to be a more preferable text processing method comparing to more traditionally used techniques because this library is simpler in terms of its usage on real data (versus, e.g. neural networks transformers like GPT and BERT (Fedorova et al , 2022)) and does not require large data sets. Moreover, in comparison with classical methods, like an extremely popular bag of words, using FLAIR eliminates the problem of subjectivity when considering libraries, their limitedness in terms of size and the problem of text consideration, as it uses neural networks in its base.…”

Section: Methodsmentioning

confidence: 99%

“…As has already been mentioned, in addition to exogenous factors, it has become quite popular to use variables stemming from behavioural finance. Generally, macro risks, which IPO underpricing are usually depicted by mass media, as shown by Fedorova et al (2022), affect IPO underpricing. If we take a micro approach, one should pay attention to corporate press releases, public reactions of management (Feng et al, 2022), as well as prospectuses (Yan et al, 2019;Zhang et al, 2022).…”

Section: Literature and Hypotheses Developmentmentioning

confidence: 99%

COVID-19: the impact of the pandemic fear on IPO underpricing

Fedorova

Chertsov²,

Kuzmina³

2022

JFRA

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Purpose The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic. Design/methodology/approach The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model. Findings The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model. Originality/value At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

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“…Early empirical explanations of this anomaly have focused on quantifiable market and financial statement data (Beatty and Ritter 1986;Hanley 1993;Brau et al 2014). Fedorova et al (2022) demonstrate the influence of behavioral characteristics of management on IPO pricing. Finally, Brau et al (2016) focused on the pricing of soft information on IPO pricing using a strategic word dictionary.…”

Section: Literature Reviewmentioning

confidence: 99%

A Textual Analysis of Logograms in Chinese IPO Roadshows: How Agreement between Investors and Management Relates to Pricing and Performance

Brau

Cicon

Owen

2022

IJFS

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We analyze the interaction between management and investors during Chinese IPO roadshows through Jaccard Similarity analysis of written Chinese logograms. We provide evidence that when agreement is high, investor optimism increases, leading to relatively large first-day underpricing. We further show that high agreement biases investors to systematically overestimate IPO prospects leading to poor long-run abnormal performance. Jaccard Similarity is different from current content analysis methodologies because it is language and culture agnostic, requiring no a priori construction of thematic dictionaries. Elimination of such dictionaries removes the danger that the researcher has imposed predispositions upon the study.

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Impact of news sentiment and topics on IPO underpricing: US evidence

Cited by 14 publications

References 60 publications

ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

COVID-19: the impact of the pandemic fear on IPO underpricing

A Textual Analysis of Logograms in Chinese IPO Roadshows: How Agreement between Investors and Management Relates to Pricing and Performance

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