financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2023-27-6-136-147finance-2518Research ArticleФИНАНСОВЫЙ МЕНЕДЖМЕНТFINANCIAL MANAGEMENTАнализ настроений с использованием машинного обучения для прогнозирования тенденций на индийских фондовых рынках: краткий обзорSentiment Analysis using Machine learning for forecasting Indian stock Trend: A brief Surveyhttps://orcid.org/0000-0001-6359-2160ДашА.С.DashA.S.

Анупа Секхар Даш — аспирант 

Пуна 

Anupa S. Dash — PhD Scholar 

Pune 

anups.dash@gmail.comhttps://orcid.org/0000-0002-3291-0143МишраУMishraU.

Уджвал Мишра — PhD, профессор финансов, академик 

Пуна 

Ujjwal Mishra — PhD, Prof. of Finance, academician 

Pune 

ujjwalmmishra@gmail.comКолледж менеджмента Массачусетского технологического института, Университет искусств, дизайна и технологий Массачусетского технологического института,ИндияMIT College of Management, MIT Art, Design & Technology UniversityIndia202328122023276136147Copyright © Даш А., Мишра У., 20232023Даш А., Мишра У.Dash A., Mishra U.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/2518

Благодаря новому технологическому прогрессу машина может мыслить как человек-инвестор и выражать свое отношение к имеющейся финансовой информации. На основе анализа этих настроений могут быть созданы модели прогнозирования, которые помогут предсказать тенденции на индийском фондовом рынке. Цель исследования — выявить пробелы в существующих подходах к анализу настроений и моделях прогнозирования тенденций на индийском фондовом рынке, что может повысить точность прогнозирования динамики индийских акций. Представлен обзор литературы по анализу настроений финансовой информации с использованием лексических методов, методов машинного обучения и прогнозирования для индийского фондового рынка на основе данных анализа настроений. Рассматриваются научные работы, доклады конференций, диссертации, книги и статьи, опубликованные учеными за период с 2015 по 2021 г. Наборы данных, опубликованные на индийских фондовых биржах, свидетельствуют о росте в последнее время участия в индийском фондовом рынке розничных инвесторов. Чтобы помочь инвесторам в принятии решений, существуют различные модели прогнозирования, основанные на финансовой информации. Результаты исследования показали, что настроения инвесторов на основе микроэкономической и макроэкономической информации, связанной с акциями, оказывают влияние на движение цены акции. Поэтому для прогнозирования будущего тренда или цены необходим анализ настроений на основе имеющейся финансовой информации. Сделан вывод, что при помощи машинного обучения для извлечения настроений из финансовой информации можно делать более точные прогнозы, чем при анализе настроений на основе лексикона. Результаты данного исследования могут быть полезны студентам и начинающим специалистам в области анализа тональности финансовой информации и прогнозирования на фондовом рынке, которые хотят познакомиться с данной областью, выявить проблемные вопросы и создать модели прогнозирования принятия решений.

Due to new technical advances, the machine can think as a person-investor and express its reaction to readily available financial information. Forecasting models for the Indian stock market can be developed based on the analysis of these sentiments. The purpose of the study is to identify gaps in existing approaches to the analysis of sentiments and models of forecasting trends in the Indian stock market, which can improve the accuracy of the prediction of the dynamics of Indian stocks. The paper presents an overview of the literature on the analysis of sentiments of financial information using lexical methods, machine learning methods and forecasting for the Indian stock market based on sentiment analysis data. The scientific works, conference reports, dissertations, books and articles published by scientists for the period from 2015 to 2021 are considered. The datasets published in Indian Stock Exchanges suggest increasing participation of retail investors in the Indian Stock market in recent times. To help investors in decisionmaking, various prediction models are available based on the financial information. The results of the survey showed that investors’ attitudes based on the microeconomic and macroeconomic information associated with stocks influence the movement of the stock price. Therefore, forecasting a future trend or price requires a sentiments analysis based on available financial information. It was concluded that using machine learning to extract sentiment from financial data allows for more accurate forecasts than sentiment analysis based on vocabulary. The results of this study can be useful for students and new professionals in the field of financial information data analysis and stock market predictions who want to get connected with this area, identify problem concerns, and develop models for predicting decision-making.

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The authors declare that there are no conflicts of interest present.