Historical Market Research Articles

Decoding Market Emotions: The Synergy of Sentiment Analysis and AI in Stock Market Predictions

The stock market is influenced not only by traditional financial metrics but also by psychological factors such as emotions, opinions, and sentiments. In recent years, the integration of sentiment analysis and artificial intelligence (AI) has transformed stock market forecasting by enabling traders and investors to interpret market behavior more effectively. Sentiment analysis, a subset of natural language processing (NLP), analyzes textual data from diverse sources like news articles and social media to gauge public sentiment—positive, negative, or neutral—toward market conditions. This analysis bridges the gap between quantitative data and investor psychology, revealing insights that traditional metrics might not capture. With the rise of social media and online forums, the volume of opinion data has surged, necessitating advanced technologies for real-time processing and interpretation. AI, mainly through machine learning and deep learning models like GPT and BERT, is crucial in efficiently analyzing vast datasets, detecting patterns, and predicting market trends. These AI-powered tools can combine sentiment data with historical market trends, providing a holistic view of market dynamics. The advanced capabilities of AI models to comprehend nuances such as sarcasm and irony further enhance sentiment detection, allowing for more accurate predictions. While integrating sentiment analysis and AI in financial markets offers numerous advantages, it also faces challenges such as algorithmic bias, data privacy issues, and the unpredictability of human emotions. This study aims to explore the integration of sentiment analysis and AI in stock market predictions, assess the accuracy of AI-driven predictions compared to traditional methods, and analyze case studies of successful applications in financial markets. Through this, the study seeks to contribute to the evolving landscape of financial forecasting by demonstrating the potential of AI and sentiment analysis in shaping market behavior understanding and decision-making processes.

FINSYNC AI: Stock Market Analysis

The goal of the study "FINSYNC AI" is to enhance the accuracy and effectiveness of financial market forecasts by introducing a complex stock market prediction system. This work makes use of the frameworks Gradient Boosting Machine, Random Forest, Long Short-Term Memory (LSTM), and the proposed FINSYNC AI. The process involves acquiring historical stock market data, cleaning and normalizing it, and then feature engineering to produce new features like rolling means and trend components. These preprocessed data are then used to test and train several machine learning models in order to assess their predictive accuracy. Given the inadequacies of previous techniques, the FINSYNC AI is proposed as a means to improve their effectiveness. The project is broken down into three primary modules to guarantee a comprehensive process: creating and refining the framework, obtaining and prepping the data, and the prediction stage and evaluation. The Python implementation of the machine learning tasks makes use of scikit-learn, pandas for data management, yfinance for acquiring stock data, and matplotlib for visualization. The results show that the FINSYNC AI performs better than traditional one, making it a more useful tool for value trading in the stock market

AI-Powered Autonomous Trading: Enhancing Market Efficiency Through Predictive Analytics

Abstract: In the realm of financial markets, AI-powered autonomous trading systems are revolutionizing the way trades are executed and decisions are made. This paper presents a formal approach to developing an AI-powered trading system aimed at enhancing market efficiency through predictive analytics. The proposed system integrates machine learning algorithms, including deep learning and reinforcement learning, to analyze vast amounts of historical and real-time market data. Emphasis is placed on improving decision-making speed, accuracy, and risk management through techniques such as algorithmic trading, automated portfolio management, and sentiment analysis. The result is a robust, efficient, and scalable trading system that outperforms traditional models in terms of profitability, adaptability, and computational efficiency in high-frequency trading environments

Predictive Modeling of US Stock Market and Commodities: Impact of Economic Indicators and Geopolitical Events Using Machine

Comprehending the dynamics of the stock market is pivotal for investors, policymakers, and economists in the USA. The United States stock market being one of the most influential financial markets in the world, contributes significantly to shaping the world economy. This research project aimed to bridge the important gaps in understanding the interrelationship between economic indicators and geopolitical events concerning the performance of the US stock market and commodities. The prime objective was to assess how different economic indicators have an impact on stock market performance over a specific period. In this regard, the development of machine learning models facilitated the ability to forecast stock market and commodities trends. These models utilized economic indicators and geopolitical events from historical data to predict future movements in the market with higher accuracy than the traditional forecasting technique followed. The study considered several different datasets to comprehensively analyze the effects that economic indicators and geopolitical events have on the stock market and commodity performances. The key datasets used in this analysis involve historical stock market indices such as the S&P 500, Dow Jones Industrial Average, and NASDAQ, as well as commodity prices for gold, oil, and silver. These datasets were collected from reputed financial databases such as Bloomberg and Federal Reserve Economic Data-FRED, for metrics including GDP growth rates, unemployment rates, inflation figures, and interest rates. The researchers also gathered data on geopolitical events: elections, trade wars, and military conflicts, using usually reliable news archives like Reuters, Bloomberg News, and The New York Times. Linear Regression, Random Forest, and XG-Boost algorithms were selected to capture various facets of the data. The performance metrics used to evaluate the models in this study included Mean Absolute Error, Root Mean Squared Error, and R-squared. Random Forest Regressor outperformed the other models with the lowest RMSE, showcasing its ability to model complex relationships. XG-Boost Regressor equally delivered a strong balance between performance and computational efficiency, achieving similar accuracy to Random Forest. Results from this study therefore can inform policy makers, especially in regards to stabilizing the financial markets during periods of heightened economic or geopolitical uncertainty. Key recommendations include developing proactive policy measures that could dampen the effect of adverse economic indicators and geopolitical events on market stability.

AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities

Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that other quantitative models are unable to capture. We will discuss trading algorithms such as XGBoost, deep neural networks such as long short-term memory (LSTM) networks and convolutional neural networks (CNNs), how they can outperform traditional quantitative trading models and real-time decision making in stock price prediction, pattern recognition and trading strategy optimisation. We will also look at Al-enhanced predictive models that utilise deep learning and layered models, such as Natural language processing (NLP) sentiment analysis to capture the public sentiment in the market to forecast employing diverse datasets such as historical prices, market volatility, macroeconomic factors and social media sentiment to improve the forecasting accuracy. By going through several experiments and case studies, this paper will shed light on the impact of entrusting quantitative trading and market forecasting decisions to AI for improved performance and reduced errors. There are many challenges ahead but AI plays a constructive role in improving the trading strategies and forecasting market outcomes accurately.

Bridging the gap: Crypto users’ sense-making of a historical market crisis

The conflict between cryptocurrencies’ ideological promises and their practical reality has recently become a considerable topic of scholarly discussion. It has been framed in terms of the so-called ‘promissory gap’ of cryptocurrencies – a gap that is founded in the contrast between the promises of a ‘trust-free’ system and actual reality. Yet, while crypto’s notion of ‘trust-free’ money has been heavily questioned, there is little research on how users seek to overcome cryptocurrencies’ promissory gap. Contrary to the promises of being ‘trust-free’ by design, this paper demonstrates that struggles for trust and control represent a focal point of the community interactions surrounding crypto. By drawing upon Dervin’s sense-making methodology to conduct interviews with 28 crypto investors, this study examines how social actors attempt to construct ‘bridges’ over a ‘gap-filled’ reality related to the moment of a historical crisis in crypto. The results reveal crypto’s intricate social embeddedness: whilst unequal power dynamics, distrust, and conflicting interests represent primary elements of users’ struggles in the crypto space, users attempt to restore a sense of control by recalling personal motivations and connecting with like-minded people. The paper provides insights into crypto users’ strategies to construct meaning in a highly uncertain environment and contributes to the discussion of crypto as a socially structured phenomenon.

Machine Learning-Based Demand and Pricing Optimization for a Resilient Automotive Supply Chain

In the supply chain management of the automotive industry, this paper investigates the use of demand forecasting, supply optimization, and price optimization. Optimizing the supply chain is essential for satisfying changing customer needs, cutting expenses, and increasing revenue. This study aims to improve the accuracy of demand prediction, optimize supplier relationships, and simplify pricing strategies through the use of ML-driven models. To solve the difficulties faced by automakers in a volatile market, the suggested framework combines historical data analysis, market trends, and inventory management strategies. The results help the automotive industry's supply chain systems become more flexible and resilient.

Exploring Key Drivers of Sales Revenues Forecasting: Empirical Evidence from the Aluminium Industry

In the fast-paced and fiercely competitive banking industry and increasing demand for high forecasting accuracy for effective lending decision making, mastering the art of sales revenue forecasting is crucial for banking professionals who are concerned with efficient credit assessment and investment evaluation. This study delves deep into the main independent variables that impact sales revenue forecasting, moving beyond the widely used historical sales lagging approach. Through a comprehensive qualitative analysis utilizing semi-structured in-depth interviews technique with industry experts whereby study model is developed and subsequently validated using Multiple Linear Regression (MLR) analysis. Spanning a period from 2013 to 2022, this empirical research leverages secondary data from sample of 12 selected companies who manufacture primary aluminiumfrom different economics, accordingly with related macro and micro economic figures on quarterly basis. The findings revealed that interviews claimed six independent variables have significant impact on sales revenues forecasting including (Historical Trading metal price at the metal exchange, company strategy) while only four variables were (quantities sold, historical market selling prices, and historical producer price index and historical foreign exchange rates) have significant impact on sales revenue forecasting, empowering banking credit and investment teams with the critical knowledge necessary to make informed lending and investment decisions

An Analytical Study on Global Automotive Price Trends and Influencing Factors

This study analyzes global automotive price trends and the key factors influencing these trends from 2018 to 2023. Using a mixed-method approach, it examines historical data, market conditions, and future projections. The findings highlight the impact of technological advancements, economic conditions, government policies, and changing consumer preferences on automotive prices. The study reveals a consistent increase in electric vehicle (EV) adoption, driven by government incentives and environmental awareness, alongside rising new car prices influenced by higher production costs and advanced features. Inflation and raw material costs have significantly impacted vehicle prices, while government subsidies have facilitated the broader acceptance of EVs. The results underscore the importance of understanding these dynamics for stakeholders to navigate the automotive market effectively. Future research should explore the long-term effects of emerging technologies and evolving regulations on automotive prices. Insights from this study are useful for manufacturers, policymakers, and consumers to make informed decisions in a rapidly evolving industry.

Employee Salary Prediction in HRMS Using Regression Models

The study uses regression models to examine AI model engagement in employee salary prediction. Salary prediction is significantly critical for both employees and employers. Employees try to get maximum benefit for the services they provide, and employers emphasize achieving organizational goals through optimized employee salaries. Inconsistency in employee salaries may cause organizations financial losses and organizational objectivity failure. Historically, salary was determined by historical data, market surveys, and personal judgment, which often resulted in inconsistencies and biases. With the availability of large datasets from Human Resource Management Systems and advanced machine learning algorithms, there is an opportunity to enhance the fairness of Salary Prediction. To overcome this problem in organizations, we have proposed a regression model for salary prediction with a promising accuracy rate of 99% with regression models. The methodology includes data processing steps including EDA, data standardization, feature correlation, and feature engineering to enhance the accuracy of the models. This study used Random Forest Regressor, Gradient Boosting Regressor, and Light Gradient Boosting Machine Regressor models for employee salary prediction. This research paper provides a valuable understanding of HR analytics for HR professionals and organizations for salary prediction of employees in an organization. Also, this research investigates the use of Machine learning algorithms to predict employee salaries while comparing employee performance and eliminating biases. The aim is to develop robust, data-enriched frameworks in HRMS for organizations for accurate and transparent salary prediction.

Are investors attentive before a one-off holiday?

PurposeThis study investigates investor trading behaviour around regular and one-off public holidays on the Sydney Stock Exchange (SSX) from 1901 to 1950. The purpose is to examine whether trading patterns differ between regular holidays, which are known in advance, and one-off holidays, which are unexpected. The study provides insights into the predictability of holidays and its influence on market activity, contributing to the broader literature on investor inattention and market anomalies.Design/methodology/approachUsing a novel dataset constructed from handwritten share price lists covering 14,224 trading days, we perform quantitative analysis to assess trading volume before and after regular and one-off public holidays. Ordinary least squares regression models are employed to identify the presence of a holiday effect, accounting for various fixed effects and time-varying factors such as geopolitical events.FindingsWe find that trading volume is significantly lower on the day before regular holidays and higher on the day after, consistent with the investor inattention hypothesis. In contrast, no significant holiday effect is observed for one-off holidays. This suggests that predictability plays a crucial role in influencing investor behaviour, with irregular, less predictable holidays having less impact on trading patterns.Research limitations/implicationsThe study is limited by the historical nature of the data, which may not fully capture the diversity of modern trading environments. Additionally, the analysis is restricted to the SSX and may not be generalisable to other markets or time periods. Future research could explore similar effects in different contexts or with more recent data.Practical implicationsThis research provides valuable insights for market participants and regulators by demonstrating how the predictability of holidays influences market activity. Understanding these patterns could help in making more informed decisions during periods of expected low trading volumes.Social implicationsThe study underscores the role of public holidays in shaping investor behaviour, with broader implications for understanding how societal events influence financial markets. This is particularly relevant in discussions about the impact of unexpected events on market stability.Originality/valueThis is the first study to compare the effects of regular and one-off public holidays on trading volumes in a historical stock market context. Our findings highlight the importance of event predictability in financial markets, offering a new perspective on how historical market behaviours can inform current financial theories.

Artificial Intelligence in SME financial decision-making: Tools for enhancing efficiency and profitability

Small and medium-sized enterprises (SMEs) face significant challenges in financial decision-making due to resource constraints, limited access to capital, and unpredictable cash flow. To overcome these challenges, many SMEs are turning to artificial intelligence (AI) to improve efficiency and profitability. AI, through tools like machine learning and predictive analytics, has transformed how SMEs manage their finances, offering solutions that enhance accuracy, speed, and data-driven decision-making. AI-driven accounting tools are revolutionizing routine tasks such as bookkeeping, invoicing, and expense tracking. By automating these functions, SMEs can reduce human error, save time, and allocate resources more effectively. AI’s ability to integrate real-time data ensures that financial records remain up to date, enabling more accurate cash flow forecasting. For example, AI can predict liquidity needs based on historical data, seasonal trends, and market conditions, helping SMEs maintain healthy cash flow and avoid financial shortfalls. In addition to improving efficiency, AI plays a crucial role in optimizing revenue and profitability. AI-based pricing models allow SMEs to adjust prices dynamically, responding to market demand and competitor behavior in real-time. This data-driven approach ensures that businesses maximize revenue without compromising customer satisfaction. Moreover, AI helps SMEs identify high-value customers by analyzing purchasing patterns, preferences, and behaviors. By focusing on customer segmentation and tailored marketing strategies, SMEs can boost sales and customer retention. Despite the clear benefits, AI adoption in SMEs is not without challenges. High implementation costs, limited technical expertise, and concerns over data privacy can hinder the integration of AI tools. However, scalable, cost-effective AI solutions are becoming increasingly available, making it easier for SMEs to incorporate AI into their financial processes. As AI continues to evolve, it will play an even more significant role in helping SMEs navigate financial complexities, improve decision-making, and enhance long-term profitability.

Relationship between return on invested capital and market value of oil and gas companies

Subject. The article addresses approaches and methods to calculate the rate of return (profitability) on invested capital of oil and gas companies based on financial statements, taking into account the stock market conditions. Objectives. The study aims at reconciliation (elimination of gaps) of market value indicators and financial indicators of oil and gas companies, using the economic value added model. Methods. We apply methods of forecasting, financial and factor analysis, and statistical methods on the basis of financial statements figures of the studied companies. Results. We analyzed the dynamics of profitability indicators and cost of invested capital for oil and gas companies in comparison with their market capitalization for 2015–2022, identified key factors influencing changes in ROIC, offered an adjusted approach to calculating the indicator, including the initial cost of invested capital. We tested the methodology and provided explanation of its practical significance, using the case of historical financial data and market capitalization of ExxonMobil. Conclusions. The study enabled to confirm the hypothesis that it is necessary to calculate the cost of fixed assets by the amount of total actual investments, net of accumulated revaluations to calculate the return on invested capital. In the numerator of the formula for this indicator, it is advisable to use profit before interest expenses, depreciation, income tax, and exploration, less investments to maintain current assets, and in the denominator – invested capital net of cash, current and non-current non-debt liabilities.

Enhancing Agricultural Commodity Price Forecasting Using Generative Models: A Deep Learning Approach

Predicting stock market prices is a critical yet challenging task in finance. Technical analysis, a widely used methodology in investment theory, involves forecasting price movements by analyzing historical market data. Recently, deep learning has gained prominence for its exceptional ability to process complex data, making it a popular tool for financial applications such as stock prediction, portfolio optimization, financial information analysis, and trade execution strategies. In this study, we propose a novel deep learning architecture that integrates a Generative Adversarial Network (GAN) with a Convolutional Neural Network (CNN) as the discriminator and Gated Recurrent Units (GRU) as the generator. This framework generates distributions of daily stock prices through adversarial learning to predict stock closing prices. Using daily trading data from Ruchi Soya Industries Limited, an empirical analysis was performed across a broad time frame. Results show that the proposed GAN-based architecture significantly outperforms traditional deep learning models, including GRU, LSTM, and Bi-LSTM, in predicting stock closing prices. These findings demonstrate the potential of this novel approach for improving stock price forecasting accuracy.

Integration of Modified Heston Model (Jump and Sentiment Factor) with Artificial Neural Networks (ANN) for Volatility Forecasting in Financial Markets

This study examined a new methodology for predicting volatility in financial markets by combining the modified Heston model (JSF) with Artificial Neural Networks (ANN). Forecasting volatility is essential in various financial contexts, such as risk management, option pricing and portfolio optimization. Although the traditional Heston model is effective but it does not account for additional elements like jumps and investor sentiment factors, which impact asset price dynamics. To address this limitation, the extended Heston model with jump and sentiment variables helped in enhancing its ability to capture complex market trends. We utilized ANN's predictive capabilities on the modified Heston model to improve volatility predictions using historical random market data from NASDAQ Composite. The ANN was trained on simulated data from the modified Heston model and assessed on its effectiveness in forecasting volatility. This approach helped in developing a forecasting hybrid semi-parametric model known as the modified-Heston-ANN model. Our findings revealed that this integrated approach outperformed the individual model, offering more precise and robust volatility predictions

Comparative Analysis of ETF Arbitrage Strategy in Efficient and Inefficient Markets

This paper analyzes the different performance of an ETF arbitrage strategy in efficient and inefficient markets, the markets of the United States and Russia. It would initiate from the ETFs and their importance to the global financial market. Then, the paper introduced what the arbitrage strategy is, and how it works. Then, by comparing the key factors, it classifies efficient market, the U.S. market, and the inefficient market, the Russian market. In the paper, the S&P 500 ETF represents the U.S. market, and the VanEck Russia ETF (RSX) represents the Russian Market. The paper analyzes the performance in these two markets from 2016 to 2021 and ignores the transaction costs. The paper concluded that the ETF arbitrage strategy performed better in the inefficient market (Russia) compared to the efficient market (U.S.). Limitations are acknowledged in the paper, such as the effect of transaction costs, which would reduce the actual profits and returns, and that the historical market data might not strong enough in predicting the future performance of the market. These would lead to future research and further analysis to enhance the overall effectiveness of the project and make it aligns more closely with the real-world conditions.

Reframing approaches to workplace violence towards Pacific homecare workers in New Zealand and Australia

In Australia and New Zealand there is an emerging focus on the problems of violence and harassment for the large, feminised workforces of care and support workers in homecare settings. However, much of this research does not consider the power dynamics of workplaces and socio-political influences that impact how workers are supported when facing WPV. This is of particular concern in relation to the growing proportion of Pacific workers in Australia and New Zealand, many of whom have temporary migrant status. Using a narrative review method, we find that extant research largely fails to address the experiences of Pacific homecare workers, and that this, and the lack of attention to Pacific workers in practice, is informed by racist norms and attitudes based on historic and current labour market and migration policies. Drawing on socio-ecological models of workplace violence (WPV), we propose a research and policy analytical framework of WPV that centres Pacific voices and culture to better mitigate and prevent Pacific homecare workers’ experiences of WPV.

A Comprehensive Heritage BIM Methodology for Digital Modelling and Conservation of Built Heritage: Application to Ghiqa Historical Market, Saudi Arabia

A Comprehensive Heritage BIM Methodology for Digital Modelling and Conservation of Built Heritage: Application to Ghiqa Historical Market, Saudi Arabia

Comparative Analysis of Investment Value in the Media and Entertainment Industry

Propelled by technological development, the Media and Entertainment (M&E) industry has experienced a remarkable transformation and expansion over the past few decades, giving rise to numerous potential investment opportunities. In this context, this essay conducts a comparative analysis of value investing in the M&E industry, focusing on Paramount Global, Netflix, Fox Corporation, and The Walt Disney Company. This research utilizes value investing principles proposed by Benjamin Graham to evaluate the financial strength and profitability of four research subjects. The study suggests Netflix as the most desirable investment due to its robust revenue growth, EPS projection, and profitability trends while highlighting the fierce competition, high valuation risks, and cybersecurity threats faced by Netflix. This paper also acknowledges its limitations related to historical data, market dynamics, and quantitative analysis. The aim of this study is to explore implying value investing in the M&E industry and provide investors with a recommendation of the most desirable investment option in this sector.

Factor timing in the Chinese stock market

AbstractI conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well‐identified risk factors in 10 categories from the literature. The long–short portfolio of short‐term reversal exhibits strong out‐of‐sample predictability, which is robust across various models and all types of predictors. This predictability is significant both statistically and economically, with a simple investment strategy obtaining its return three times higher than the buy‐and‐hold return in the sample period and a significant annualized 20.4% CH‐3 alpha. Portfolio historical volatility and market volatility measurement predictors play crucial roles in the reversal factor premium's robust predictability. However, such results are not evident in predicting all other factors' long–short portfolios as well as all factors' long‐wing and short‐wing portfolios, and this failure cannot be attributed to their exposure to unpredictable market returns.