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Academic literature on the topic 'Loughran McDonald Sentiment Classifier'
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Journal articles on the topic "Loughran McDonald Sentiment Classifier"
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Rai, Prakhyath, Bellipady Shamantha Rai, Permanki Guthu Rithesh Pakkala, and R. Akhila Thejaswi. "Forecasting Business Status of Organizations by Analyzing Historic Earnings Call Transcripts with the Aid of Text Refinement Framework." Indian Journal Of Science And Technology 17, no. 24 (2024): 2469–77. http://dx.doi.org/10.17485/ijst/v17i24.347.
Full textAbstract:
Objectives: The research work focuses on providing an effective framework for automated text refinement that aids in financial condition projections for the company based on prior transcripts of earnings calls. The proposed framework captures the ad hoc advancements of the organizations described in the earnings call as sentiments and computes a score based on the captured sentiments. The sentiment score is then used as prime parameter to predict the stock values of the organizations. Methods: The framework is equipped with sentiment analysis or opinion mining to identify and extract the subjective content using text mining and Natural Language Processing (NLP). The extracted sentiments help in yielding a sentiment score to aid in the process of stock projection. The research also illustrates how the sentiment score-based stock prediction enhances in projections of stock compared to existing ML frameworks like LSTM, Random Forest, ARIMA and Regression models. Findings: The proposed work has an accuracy score of 93%, precision 96% and recall 95% which is comparatively better than existing ML frameworks framed on LSTM, Random Forest, ARIMA and Regression models. Novelty: The research framework overcomes the influence of regular features and test data in stock prediction by using the computed sentiment prediction score from the extracted sentiment phase to aid in prediction stock values and determine the financial status of organizations. The existing frameworks project the stock price based on trained model from previous stock price repository, which tend to fail capturing ad hoc changes incurring in the organization such as change of management or any economic disaster which can poses a high impact on stock projections, the proposed research work captures these organizational changes from the earnings call transcripts as sentiments and build a score to yield the stock projection framework. Keywords: Natural Language Processing, Text Refinement, Loughran McDonald Sentiment Classifier, Term Frequency Inverse Document Frequency, Stock Price
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Prakhyath, Rai, Shamantha Rai Bellipady, Guthu Rithesh Pakkala Permanki, and Akhila Thejaswi R. "Forecasting Business Status of Organizations by Analyzing Historic Earnings Call Transcripts with the Aid of Text Refinement Framework." Indian Journal of Science and Technology 17, no. 24 (2024): 2469–77. https://doi.org/10.17485/IJST/v17i24.347.
Full textAbstract:
Abstract <strong>Objectives:</strong> The research work focuses on providing an effective framework for automated text refinement that aids in financial condition projections for the company based on prior transcripts of earnings calls. The proposed framework captures the ad hoc advancements of the organizations described in the earnings call as sentiments and computes a score based on the captured sentiments. The sentiment score is then used as prime parameter to predict the stock values of the organizations. <strong>Methods:</strong> The framework is equipped with sentiment analysis or opinion mining to identify and extract the subjective content using text mining and Natural Language Processing (NLP). The extracted sentiments help in yielding a sentiment score to aid in the process of stock projection. The research also illustrates how the sentiment score-based stock prediction enhances in projections of stock compared to existing ML frameworks like LSTM, Random Forest, ARIMA and Regression models. <strong>Findings:</strong> The proposed work has an accuracy score of 93%, precision 96% and recall 95% which is comparatively better than existing ML frameworks framed on LSTM, Random Forest, ARIMA and Regression models.<strong> Novelty:</strong> The research framework overcomes the influence of regular features and test data in stock prediction by using the computed sentiment prediction score from the extracted sentiment phase to aid in prediction stock values and determine the financial status of organizations. The existing frameworks project the stock price based on trained model from previous stock price repository, which tend to fail capturing ad hoc changes incurring in the organization such as change of management or any economic disaster which can poses a high impact on stock projections, the proposed research work captures these organizational changes from the earnings call transcripts as sentiments and build a score to yield the stock projection framework. <strong>Keywords:</strong> Natural Language Processing, Text Refinement, Loughran M
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Jin, Zekai, Martin Ma, Zihao Zhou, Shulan Gan, and Yuhan Min. "Correlation Between News and Stock Price Based on Stock Market Indices: Can News Classification Be Used as a Tool to Make Better Decisions?" Advances in Economics, Management and Political Sciences 82, no. 1 (2024): 131–41. http://dx.doi.org/10.54254/2754-1169/82/20230971.
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The stock market is influenced by various factors, including news events, economic indicators, and investor sentiment. Understanding the correlation between news and stock price movements interests market participants and researchers. In this paper, we explore the relationship between news sentiment and stock market trends using stock market indices. We employ natural language processing (NLP) techniques to classify news articles and analyze their impact on stock market indices, focusing on the S&P 500 and the Dow Jones Industrial Average. We utilize sentiment analysis and machine learning algorithms, including Random Forest, Loughran-McDonald (2014) Financial Sentiment Word Lists (Extended), and AFINN Lexicon, to predict stock market trends based on news sentiment. Our findings demonstrate that positive news sentiment has a more significant impact on stock prices than negative sentiment. The Random Forest model achieves the highest accuracy, while domain-specific lexicons provide valuable insights into financial news sentiment. However, predicting negative trends remains a challenge across all methods. Our research contributes to the growing knowledge of the relationship between news and stock prices and provides valuable insights for market participants and researchers.
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Cho, Su-Ji, Heung-Kyu Kim, and Cheol-Won Yang. "Building the Korean Sentiment Lexicon for Finance (KOSELF)." Korean Journal of Financial Studies 50, no. 2 (2021): 135–70. http://dx.doi.org/10.26845/kjfs.2021.04.50.2.135.
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This study aims to verify and establish a Korean sentiment lexicon suitable for corporate financial analysis. When analyzing existing sentiment lexicons, the KOSAC and KNU (Kunsan University) dictionaries developed based on Korean are weak because they are used for general purposes. The Harvard IV and Loughran and McDonald (2011) have the disadvantage of being translated from English. In this study, the Korean Sentiment Lexicon for Finance (KOSELF) is constructed and presented. To verify its usefulness, text data from about 20,000 analyst reports published in Korea from 2016 to 2018 are collected from the Hankyung Consensus web page. After calculating the sentiment variables of negative and positive word frequency using five sentiment lexicons for each report, the recommendation and target price changes are regressed on these sentiment variables. The sentiment variables from the newly-constructed KOSELF in this study have a significant relationship with the analyst’s recommendation and target price change. Even when the sentiment variables calculated through the other four sentiment lexicons are added, it shows better performance. Our work has practical significance in that it proposes a Korean sentiment dictionary that can be used for finance.
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Segawa, Arnold. "Sentimental Outlook for the Monetary Policies of South African Reserve Bank." International Journal of Finance & Banking Studies (2147-4486) 10, no. 3 (2021): 37–56. http://dx.doi.org/10.20525/ijfbs.v10i3.1298.
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The South African Reserve Bank (SARB) migrated to inflation targeting in 2000 and has since embarked on a trajectory of transparency. This has taken the shape of releasing Monetary Policy Committee (MPC) statements other forms of communication. This paper examines SARB’s MPC statements’ tone and sentiment between 2000 and 2021 using the Besigye-Segawa’s TextBlob polarity and subjectivity calculator which measures central bank communication tone and sentiment using the Loughran-McDonald dictionary’s word classification to gauge polarity and subjectivity. The study goes on to explore causality of SARB’s MPC statements’ tone and sentiment on inflation expectation results from the Bureau of Economic (BER) results survey. The systematic analysis shows a causality of SARB’s MPC statements’ tone and sentiment on succeeding BER’s inflation expectations results therein justifying the need for effective communication as SARB’s MPC communications’ polarity and subjectivity ultimately have a causal effect on inflation expectations. therein justifying the need for effective communication. As central bank tone and sentiment studies are only emerging in many emerging and frontier markets, this study lays a foundation for future exploration of effects of central bank communication on the expectations channel.
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Singh, Purva. "Intelligent Portfolio Management via NLP Analysis of Financial 10-k Statements." International Journal of Artificial Intelligence & Applications 11, no. 6 (2020): 13–25. http://dx.doi.org/10.5121/ijaia.2020.11602.
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The paper attempts to analyze if the sentiment stability of financial 10-K reports over time can determine the company’s future mean returns. A diverse portfolio of stocks was selected to test this hypothesis. The proposed framework downloads 10-K reports of the companies from SEC’s EDGAR database. It passes them through the preprocessing pipeline to extract critical sections of the filings to perform NLP analysis. Using Loughran and McDonald sentiment word list, the framework generates sentiment TF-IDF from the 10-K documents to calculate the cosine similarity between two consecutive 10-K reports and proposes to leverage this cosine similarity as the alpha factor. For analyzing the effectiveness of our alpha factor at predicting future returns, the framework uses the alphalens library to perform factor return analysis, turnover analysis, and for comparing the Sharpe ratio of potential alpha factors. The results show that there exists a strong correlation between the sentiment stability of our portfolio’s 10-K statements and its future mean returns. For the benefit of the research community, the code and Jupyter notebooks related to this paper have been open-sourced on Github1.
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Gao, Xiang, Weige Huang, and Hua Wang. "Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility." Virtual Economics 4, no. 1 (2021): 7–18. http://dx.doi.org/10.34021/ve.2021.04.01(1).
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This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.
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Koelbl, Marina. "Is the MD&A of US REITs informative? A textual sentiment study." Journal of Property Investment & Finance 38, no. 3 (2020): 181–201. http://dx.doi.org/10.1108/jpif-12-2019-0149.
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PurposeThis study examines whether language disclosed in the Management Discussion and Analysis (MD&A) of US Real Estate Investment Trusts (REITs) provides signals regarding future firm performance and thus generates a market response.Design/methodology/approachThis research conducts textual analysis on a sample of approximately 6,500 MD&As of US REITs filed by the SEC between 2003 and 2018. Specifically, the Loughran and Mcdonald (2011) financial dictionary, and a custom dictionary for the real estate industry created by Ruscheinsky et al. (2018), are employed to determine the inherent sentiment, that is, the level of pessimistic or optimistic language for each filing. Thereafter, a panel fixed-effects regression enables investigating the relationship between sentiment and future firm performance, as well as the markets’ reaction.FindingsThe empirical results suggest that higher levels of pessimistic (optimistic) language in the MD&A predict lower (higher) future firm performance. Hereby, the use of a domain-specific real estate dictionary, namely that developed by Ruscheinsky et al. (2018) leads to superior results. Corresponding to the notion that the human psyche is affected more strongly by negative than positive news (Rozin and Royzman, 2001), the market responds solely to pessimistic language in the MD&A.Practical implicationsThe results suggest that the market can benefit from textual analysis, as investigating the language in the MD&A reduces information asymmetries between US REIT managers and investors.Originality/valueThis is the first study to analyze exclusively US REITs, whether language in the MD&A is predictive of future firm performance and whether the market responds to textual sentiment.
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Cho, Su-Ji. "A Study on Information Value of Analyst Report Text Based on the Analysts’ Herding Behavior Under Globalized Financial Markets." Korea Association for International Commerce and Information 25, no. 2 (2023): 3–25. http://dx.doi.org/10.15798/kaici.2023.25.2.3.
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Recently, the investment information is spreading in near real-time due to the global integration of the international financial market. At this point, it is essential to resolve the information asymmetry in the capital market to vitalize domestic and foreign investment. Analysts improve market efficiency, by performing information intermediary role between the capital market and investors. However, the herding behavior among analysts in the market can weaken the information quality and lead to inefficiency in the financial market. In this study, the relationship between analyst herding behavior and the text of the report is analyzed. In addition, the effect of the text of the report body on the information power according to the analyst's herding behavior was verified. To this end, the positive and negative emotions shown in the body of the analyst report were extracted as words of up to 2-gram units and merged with the list of positive and negative words of Loughran and McDonald(2011), a sentiment dictionary widely used in the financial field. As a result of the analysis, when an analyst presents a 'bold' opinion that deviates from the consensus, the sentiment of the report body is more extreme than that of an ‘herd’ opinion. In addition, it was confirmed that positive emotion weakens the report information power according to the analyst's herding behavior. This study is expected to alleviate information asymmetry between investors and the financial market. Furthermore, the text mining methodology used in this study can be readily applied to any other kind of text, including News, Social Network Services, or IR reports.
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Fedorova, E. A., D. O. Afanasyev, A. V. Sokolov, and M. P. Lazarev. "Impact of disease information (Ebola and COVID-19) on the pharmaceutical sector in Russia and USA." FARMAKOEKONOMIKA. Modern Pharmacoeconomic and Pharmacoepidemiology 14, no. 2 (2021): 213–24. http://dx.doi.org/10.17749/2070-4909/farmakoekonomika.2021.054.
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Objective: identification of the relationship between the news coverage of global diseases and the dynamics of the return on shares of the pharmaceutical sector for Russia and the United States.Material and methods. The empirical base of the study includes more than 700 thousand tweets on Ebola and COVID-19 in Russian and English, news of the RBC news agency. The sentiment of the text was assessed on the basis of five English and four Russian-language dictionaries, the influence of fundamental and textual variables on the profitability of pharmaceutical companies' shares was carried out using the ARMAX-GARCH econometric model.Results. It has been proven that the dynamics of the stock index of pharmaceutical companies is explained by fundamental (economic) and sentimental factors. News of any epidemics negatively affects the pharmaceutical sector in the US and Russia, that is, there are no industries that benefit from this situation. Pandemic news affects US pharmaceutical companies more than Russian companies. The effect of news influence depends on the level of spread of the disease. News influences not only at the moment of their publication, but also after: there is a "delayed effect". Ebola news affects the American pharmaceutical market for 2 weeks, and the dynamics of the increase in influence can be traced. News on the COVID pandemic amplifies its impact during 1 week for the Russian pharmaceutical market and for 2 weeks for the US pharmaceutical companies. As for news sources, the elastic network has identified more significant variables based on publications from RBC; therefore, Internet publications generate more publicity, shaping a more significant overall sentiment in the markets.Conclusion. The models developed in the framework of the study and the economic conclusions obtained have not only theoretical, but also practical significance, and can also be used for further research in this area. It is possible to give recommendations on the practical use of dictionaries to assess the sentiment of the text. In our study, the elastic network method chose the Loughran–McDonald dictionary for evaluating economic texts in English and the EcSentiThemeLex dictionary (designed in R and Python programming environments). Avenues for further investigation may include analysis of other sources of information about the pandemic.
Conference papers on the topic "Loughran McDonald Sentiment Classifier"
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Arshad, Asma. "THE ROLE OF FED SPEECH SENTIMENT SIGNALS IN SHAPING US MARKET RESPONSE." In 2024 SoRes Dubai – International Conference on Interdisciplinary Research in Social Sciences, 26-27October. Global Research & Development Services, 2024. https://doi.org/10.20319/icssh.2024.400411.
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The Federal Reserve's communication shapes US investor decisions and market dynamics. This paper examines the impact of the Fed governor speeches' sentiments signals on the US equity market performance from June 1996 to Sep 2023. The sentiment index is calculated using individual Lexicon dictionaries (AFINN, Bing, NRC, and Loughran McDonald) and their combined PCA scores. Our findings revealed a negative relationship suggesting that a positive (negative) sentiment brings a significant decrease (increase) in the cumulative abnormal return on the event window (+2). These results provide valuable insights into the dynamic nature of the US equity market in response to the Federal Reserve’s communication for regulators, policymakers, and other stakeholders of the equity market.