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1EMBRACING BIG DATA AND AI METHODS FOR MORE ACCURATE STOCK VALUATION: AN EXAMINATION OF EMERGING TRENDS
Zoirjonova Maftuna
Master's student at the Banking and Finance Academy zoirjonovamaftuna@gmail.com
Annotation: This article explores how combining Big Data and AI can revolutionize stock valuation, discussing emerging trends in financial analysis. Accurate stock valuation is crucial for investment decisions, influencing strategies, capital raising, and informed choices. This research examines the synergy between traditional and Al-driven methods, offering valuable insights for analysts, investors, and firms to adapt to evolving financial markets and shape the future of stock valuation.
Keywords: stock valuation, technical analysis, alternative data sources, behavioral economics, Big Data integration, artificial intelligence, financial analysis, predictive analytics.
AKSIYALARNI ANIQROQ BAHOLASH UCHUN KATTA HAJMDAGI MA'LUMOTLAR VA SUN'IY INTELLEKT USULLARINI QO'LLASH: RIVOJLANAYOTGAN TENDENSIYALAR BILAN TANISHISH
Zoirjonova Maftuna Ulug'bek qizi
Bank-moliya akademiyasi magistranti zoirjonovamaftuna@gmail.com
Annotatsiya: Ushbu maqolada big data va sun'iy intellektning birlashmasi qimmatli qog'ozlarni baholashni qanday o'zgartirishi mumkinligini o'rganadi va moliyaviy tahlilda paydo bo'layotgan tendensiyalarni muhokama qiladi. Investitsiya qarorlarini qabul qilish, strategiyalarga ta'sir ko'rsatish, kapitalni oshirish va ongli tanlovlar uchun aktsiyalarni to'g'ri baholash juda muhimdir. Ushbu tadqiqot an'anaviy va sun'iy intellektga asoslangan usullar o'rtasidagi sinergiyani o'rganadi, tahlilchilar, investorlar va firmalar uchun rivojlanayotgan moliyaviy bozorlarga moslashish va aktsiyalarni baholashning kelajagini shakllantirish uchun qimmatli tushunchalarni taqdim etadi.
Kalit so'zlar: qimmatli qog'ozlar baholash, texnik tahlil, alternativ ma 'lumotlar manbalari, xulq-atvor iqtisodiyoti, Big Data integratsiyasi, sun'iy intellekt, moliyaviy tahlil, prognozli tahlil.
ИСПОЛЬЗОВАНИЕ БОЛЬШИХ ДАННЫХ И МЕТОДОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ БОЛЬШЕ ТОЧНОЙ ОЦЕНКИ АКЦИЙ: ИЗУЧЕНИЕ НОВЫХ ТЕНДЕНЦИЙ
Зоиржонова Мафтуна Улугбек кизи
Магистрант Банковско-финансовой академии zoirjonovamaftuna@gmail.com
Аннотация: В этой статье рассматривается, как сочетание больших данных и искусственного интеллекта может революционизировать оценку акций, а также обсуждаются новые тенденции в финансовом анализе. Точная оценка акций имеет решающее значение для принятия инвестиционных решений, влияния на стратегии, привлечения капитала и осознанного выбора. В этом исследовании рассматривается синергия между традиционными методами и методами, основанными на искусственном интеллекте, что дает аналитикам, инвесторам и компаниям ценную информацию, необходимую для адаптации к развивающимся финансовым рынкам и формирования будущего оценки акций.
Ключевые слова: оценка акций, технический анализ, альтернативные источники данных, поведенческая экономика, интеграция больших данных, искусственный интеллект, финансовый анализ, прогнозная аналитика.
Introduction
The field of stock valuation has witnessed remarkable transformations over the decades. Traditional methods, such as fundamental analysis and technical analysis, have long been the cornerstone of investment decision-making. However, as financial markets have become increasingly complex and interconnected, the limitations of these conventional approaches have become evident [1]. In today's digital age, the proliferation of data, both structured and unstructured, presents both challenges and opportunities for stock valuation. Stock valuation is a critical aspect of investment decision-making, as it serves as the foundation for determining the fair market value of a company's shares. Accurate stock valuation not only influences investment strategies but also impacts a company's ability to raise capital and make informed financial decisions. In recent years, emerging trends have transformed the traditional methods of stock valuation, paving the way for more accurate and insightful approaches. This article explores these emerging trends that are reshaping the landscape of stock valuation, ultimately leading to better-informed investment decisions.
Main part
The digital age has revolutionized the way financial data is collected, analyzed, and utilized in stock valuation. With the advent of big data and advanced analytics, investors now have access to an unprecedented wealth of information. This data includes not only traditional financial statements but also alternative data sources like social media sentiment analysis, satellite imagery, and web scraping. Machine learning algorithms and artificial intelligence are harnessed to process this massive volume of data, enabling investors to identify patterns, correlations, and anomalies that were previously unattainable. This data-driven approach allows for a more comprehensive understanding of a company's performance and market dynamics, leading to more precise valuations [2].
In the domain of stock price prediction, three prominent technical challenges emerge. Firstly, the intricate and volatile nature of stock markets, influenced by an array of factors like national policies, economic conditions, and industry developments, renders accurate price forecasting difficult, primarily due to incomplete and asymmetric information. Secondly, stock prices, represented as time-series data, exhibit non-linear and non-stationary attributes, influenced by both external and intrinsic factors, necessitating a forecasting model with strong non-linear problemsolving capabilities. Lastly, the inherent randomness within the stock market, stemming from investors' susceptibility to emotional swings induced by various sources, significantly impacts decision-making and contributes to price fluctuations [3]. Consequently, the presence of data noise further complicates the task of achieving precise stock price predictions.
Many existing stock price prediction methods primarily rely on historical trading data, neglecting the valuable insights embedded in textual information such as financial news, company earnings reports, and stock bar comments, which can significantly influence investor decisions. Consequently, it is imperative to give due consideration to the impact of textual data on the stock market. Deep learning techniques offer a promising avenue for enhancing stock price forecasting. To harness this potential, we propose a comprehensive approach that involves quantifying textual data, including investor sentiment and financial news, amalgamating investor attention signals, and integrating these sources with historical trading data. By employing these multifaceted information sources, we construct predictive models that enable more robust and accurate stock market analysis and forecasting.
Environmental, Social, and Governance (ESG) factors have gained prominence in stock valuation, reflecting the increasing importance of sustainability and corporate responsibility. Investors are now considering a company's ESG performance alongside traditional financial metrics. ESG ratings and data provide insights into a company's
impact on the environment, its relationships with stakeholders, and its governance practices [4]. As socially responsible investing becomes more popular, companies with strong ESG profiles are often rewarded with higher valuations, reflecting a shift in investor sentiment and preferences.
Understanding human behavior and psychology has become an integral part of stock valuation. Behavioral economics explores how cognitive biases and emotional decision-making influence investor sentiment and, consequently, stock prices. Researchers and analysts are incorporating principles from behavioral economics to assess market sentiment, investor overreactions, and irrational exuberance. This allows for a more nuanced valuation approach that accounts for the human element in financial markets [5].
The utilization of artificial intelligence (AI) and predictive analytics in stock valuation has grown substantially. Machine learning models are trained to predict future stock prices and market movements based on historical data and various indicators. These models can identify potential market trends, risk factors, and investment opportunities with a high degree of accuracy. Moreover, Al-driven sentiment analysis of news articles, social media, and financial reports can provide realtime insights into market sentiment and help investors make timely decisions.
The utilization of artificial intelligence (AI) and predictive analytics in stock valuation has grown substantially. Machine learning models are trained to predict future stock prices and market movements based on historical data and various indicators. These models can identify potential market trends, risk factors, and investment opportunities with a high degree of accuracy. Moreover, AI-driven sentiment analysis of news articles, social media, and financial reports can provide realtime insights into market sentiment and help investors make timely decisions.
Big Data can be effectively utilized in the stock valuation process to enhance decision-making and gain a competitive edge in the financial markets. Data Collection and Integration plays important role in making good fundamentals to start with [6]. We need to gather and aggregate a wide variety of data sources, including financial statements, economic indicators, social media sentiment, news articles, and market data. The more diverse the data, the better the insights. Next we may use Natural Language Processing (NLP) techniques to analyze news articles, social media posts, and online forums to gauge market sentiment. This can help identify potential market-moving events and investor sentiment. Next stage is employing machine learning algorithms to build predictive models that can forecast stock prices, volatility, and other relevant financial metrics. These models can incorporate a wide range of variables and historical data to make predictions. Utilizing real-time data analytics to monitor market conditions and adjust valuation models accordingly allows for rapid response to changing market dynamics.
Big Data can be employed to assess and manage risks associated with stock investments. This includes analyzing the correlation between assets, tracking market events, and stress-testing portfolios. Financial data can be combined with Big Data analytics to improve earnings estimation models, which can help investors make more accurate predictions about future earnings and growth prospects [7]. Insights from behavioral finance can be incorporated using Big Data to understand and model how investor psychology and biases can influence stock prices. The good extensions are the event detection algorithms that can automatically identify and assess the impact of corporate events such as mergers, acquisitions, earnings releases, and regulatory changes. One of the main issues can be maintaining robust data security and compliance protocols to protect sensitive financial information and adhering to regulatory requirements.
Considering AI techniques, we can line out the Machine Learning Models like Linear regression, polynomial regression, and support vector regression (SVR). They can be used to model the relationship between stock prices and various fundamental or technical indicators. Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) are powerful for capturing sequential data and can be used to model stock price movements. Creating relevant features from raw data, such as moving averages, Relative Strength Index (RSI), and other technical indicators is another example of integrationg AI techniques. To create trading strategies that adapt to changing market conditions and maximize returns we can apply reinforcement learning algorithms. Advanced AI techniques like Deep Reinforcement Learning (DRL) can be used to build trading bots that learn optimal trading strategies by interacting with financial markets. AI systems should be designed to continuously learn from new data and adapt to changing market conditions. Ensuring that AI models and strategies comply with relevant financial regulations and ethical guidelines is essential [8].
Deep Learning algorithms have exerted a significant influence on modern technology, particularly in the development of time series-based prediction models. There are various models such as ARIMA, LSTM, CNN, Hybrid LSTM, and Hybrid CNN. LSTM and Hybrid LSTM models outperform in predicting future stock prices, while CNN and Hybrid CNN models excel in forecasting stock trends. The hybrid prediction strategy is a potent and accurate tool for forecasting future stock prices [9]. It's worth noting that stock market sentiment is profoundly influenced by public sentiment, facilitated by the internet's ease of communication and information sharing. Various social media platforms like Facebook, Twitter, blogs, and financial news websites significantly impact market trends, underscoring the importance of sentiment analysis for intraday and short-term trading. Natural Language Processing (NLP) plays a pivotal role in analyzing stock-related sentiment, as negative reviews, for instance,
can influence future trends. Data extracted from sentiment analysis feeds into Deep Neural Networks, contributing to stock trend and price predictions. This analytical approach also holds substantial sway in stock selection and the pursuit of significant profits in daily trading.
Conclusion
In today's rapidly evolving financial landscape, the traditional methods of stock valuation can be supplemented. The integration of Big Data and Artificial Intelligence (AI) techniques has ushered in a new era of stock valuation, one that holds immense promise for investors and financial analysts. Big Data can revolutionize the stock valuation process by providing access to vast amounts of information, enhancing predictive capabilities, and enabling more informed investment decisions. However, it's essential to have a clear strategy, well-defined goals, and robust data analytics and modeling techniques in place to effectively utilize Big Data in the stock market. Combining multiple techniques, using domain expertise, and staying informed about market developments are essential components of a successful stock valuation strategy. Additionally, risk management is crucial to protect investments in the highly volatile world of stock markets. Stock valuation has evolved, no longer static and formulaic, now harnessing the power of data, technology, and behavioral insights to enable more informed and nuanced investment decisions. Traditional valuation methods persist but are complemented by a broader toolkit encompassing data-driven analysis, ESG considerations, behavioral insights, relative valuation, and AI-powered predictive analytics. Staying abreast of these emerging trends is essential for investors navigating the complexities of stock valuation in the modern era as the financial landscape continues to evolve.
References
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3. Progress and prospects of data-driven stock price forecasting research, Chuanjun Zhao. 2023
4. Chasing the ESG factor. Abraham Lioui, Andrea Tarelli, 2022
5. Behavioral Economics and the Effects of Psychology on the Stock Market, Dr. Frederick Floss, 2017
6. Creating Strategic Business Value from Big Data Analytics: A Research Framework, Varun Grover, Roger H.L. Chiang, Ting-Peng Liang & Dongsong Zhang, 2018
7. Big data, artificial intelligence and machine learning: A transformative symbiosis in favor of financial technology, Duc Khuong Nguyen, Georgios Sermpinis, Charalampos Stasinakis, 2022
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2022
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