Stock Market Analysis Research Articles

An ordered approach on geometric vitality function for risk measurement and its application in stock market analysis

In reliability analysis, the residual life of a component or system plays a key role in explaining a component's present condition as well as how long it will continue to function as intended under specific circumstances, thereby preventing premature failure and saving money. Based on this, Nair and Rajesh [Geometric vitality function and its applications to reliability. IAPQR Trans. 2000;25(1):1–8] proposed a new measure called the geometric vitality function that explains the system's failure pattern. In the present study, we have introduced the geometric vitality function in the context of order statistics by means of the relevation transform and also analysed the monotone and ordering properties of the proposed measure. The application of the proposed measure in the stock markets are explored in the present study. Finally, to estimate the proposed measure using a real life data set, a simple estimator has been introduced as well.

Stock Market Forecasting Using a Neural Network Through Fundamental Indicators, Technical Indicators and Market Sentiment Analysis

AbstractThe objective of this research is to provide evidence that it is possible to obtain a prediction that better aligns with the future performance of a stock if a neural network model is trained with stock market analysis variables and qualitative variables. As a case study, thirty-three companies’ representative of the S&P 500 are selected, and a multilayer perceptron artificial neural network is built and trained with input parameter indicators of fundamental analysis, technical analysis, and market sentiment. By incorporating the latter as an additional variable, the model's accuracy increases by 1.5% for 66% of the companies analyzed. The results confirm the crucial role played by the selection of the neural network model and its variables depending on the type of company to be analyzed. The main contributions of this research are the identification of the best variables combination to train a neural network model depending on the market sector to be analyzed, likewise it is demonstrated that, by using market sentiment, it is possible obtain a high accuracy or increase the accuracy to an existing model.

A Rise in the No. of Initial Public Offer’s in the Indian Public Markets

The research paper is an analysis of the Indian stock market and analyses an increase in Initial Public Offerings (IPOs) made between 2012 to 2024 The growth is also attributed to factors such as positive economic data, growing investor interest, and regulatory softening. No doubt that there has been some change, but the trend has overall remained stronger riding high on bullish market sentiment as well as confidence from investors. The study stresses that this growth is only possible if the continued regulatory oversight and investor protection measures in place remain following the growth.

An Exploratory Study on the Effect of Macroeconomic Indicators on the Stock Market Performance among 6-ASEAN Countries

The stock market's performance has been impacted by the global economy's recovery from the COVID-19 pandemic. This study investigates the impact of various factors on stock market performance, with a focus on the global economic recovery from the COVID-19 pandemic. The stock market has experienced positive growth, driven by low interest rates, ample liquidity, and sustained investor optimism, despite concerns over rising inflation and interest rates. This research aims to analyze stock market performance across six ASEAN countries, using a range of data sources, including historical stock prices, financial reports, and macroeconomic indicators. The findings reveal that the real exchange rate exerts the most significant influence on stock market performance. The insights from this study provide valuable information for investors, policymakers, and government bodies, aiding in strategic decision-making and investment planning in these markets.

Leveraging corporate governance characteristics for stock crash risk assessment

Causing considerable losses for individual investors, stock crashes are also potential triggers for wider financial crises. In contrast to existing research that primarily focuses on specific corporate governance characteristics in relation to stock crash risk, in this study, we construct a set of corporate governance attributes and employ machine learning techniques for a comprehensive assessment of stock crash risk. Through empirical analysis in the Chinese stock market, we find that our corporate governance attributes are effective indicators of stock crash risk, yielding statistically significant and economically meaningful crash risk assessment. Additionally, our heterogeneity tests suggest that firms with less concentrated shareholding result in superior assessment accuracy. This observation is consistent with prior findings that increased retail investor participation enhances corporate governance transparency, thereby contributing to better assessment performance. Industry-wise, the performance of crash risk assessment for real estate companies is higher than that for cultural, sports, and entertainment sectors. We speculate that these variations stem from industry-specific factors, such as business logic, operational models, and profit mechanisms. Our results also suggest that characteristics related to shareholder meetings are pivotal in assessing crash risks for manufacturing companies, while board-related features are essential for real estate firms. Overall, this study contributes to current literature by validating the feasibility of employing corporate governance characteristics to assess crash risk and by exploring the economic value of crash risk assessment. Furthermore, we discuss several valuable insights into the Chinese stock market through heterogeneity analysis.

The influence of European MiCa regulation on cryptocurrencies

This research investigates the relationship between impending regulatory measures, specifically the introduction of European MiCa regulations, and their influence on cryptocurrency markets. Analysing stock market responses, we highlight significant shifts in liquidity, variance, and return dynamics post-MiCa-related announcements. Specifically, such announcements are associated with significant negative cryptocurrency returns and elevated liquidity. The findings reveal defined heterogeneity across different cryptocurrency sub-classes, each uniquely affected by its inherent attributes and susceptibility to regulatory changes. This work offers insights into the nature of market responses to regulatory intervention, providing an invaluable perspective on the equilibrium between cryptocurrency market behaviour and the evolving European regulatory landscape.

The Bears & Bulls of Fundamental Analysis Vs Technical Analysis

In the intriguing realm of the Indian Stock Market, significant fortunes are amassed and aspirations materialize. In the realm of financial markets, two predominant methodologies govern the analysis of markets: fundamental analysis and technical analysis. This discussion aims to delve into the core principles of each approach, elucidate their respective merits and limitations, and underscore the advantages of amalgamating them to form a comprehensive market analysis framework.  Biography of the Author I am Vansh Thirani, a high school student from New Delhi, India. Stock Market is something I have a lot of interest in. I am very passionate about building a career in this field and with my continuous learning and self- improving skillset, I am confident enough to excel in this field. This paper can help a lot of young teenagers who aspire to gain knowledge and pursue a career in the field of Stock Market Analysis. Being a teenager myself, I understand how challenging it might be for students to start analysing at first, but I believe after students go through this paper, it might become easier for them to analyse and make investment decisions.

Machine Learning Based Stock Market Trend Prediction and Analysis

Machine Learning Based Stock Market Trend Prediction and Analysis

IFRS Adoption and Performance of Stock Markets: A Comparative Analysis of Frontier Stock Markets in Sub‐Saharan Africa

ABSTRACTThe International Financial Reporting Standards (IFRS) is a set of global accounting standards that tries to provide truer and fairer accounting figures by recommending more realistic measurement and recognition criteria, increasing the disclosure of relevant information, and increasing the comparability of financial statements across borders. Most of the literature on IFRS adoption addresses its impact on the value relevance of accounting figures but ignores the wider picture in terms of impact on stock market performance. The few studies that address IFRS adoption and stock market performance focus on advanced economies and lack proper theoretical underpinning. This study addresses the impact of IFRS adoption on stock market performance in Sub‐Saharan Africa. The study applied the autoregressive distributed lag model to test the research hypotheses. The Johannesburg, Nigerian, and Nairobi stock exchanges were selected as the sample markets because they constitute the major frontier markets for foreign investors wishing to penetrate the Sub‐Saharan African market. The sample period was from 1990 to 2020. The results of the study showed that IFRS adoption had a positive and significant impact on market efficiency and size for all the selected countries. IFRS adoption had a positive and significant impact on market liquidity in Kenya only. The study recommends that IFRS adopters should implement stock market policies that encourage the participation of foreign investors following the increase in financial statements comparability brought about by IFRS adoption.

Application of Financial Engineering in Quantitative Investment Strategy

This paper mainly discusses the application of financial engineering in quantitative investment strategy. Firstly, the importance and function of financial engineering in quantitative investment strategy are introduced, and then the principle and technology of quantitative investment strategy are expounded. Then, the application of financial engineering in quantitative investment strategy is analyzed in detail, including stock market analysis, financial risk management, portfolio optimization, market liquidity analysis and financial innovation. Finally, the contribution of financial engineering in quantitative investment strategy and its future development trend are summarized.

Application of Copula Models in Stock Market Analysis

Objectives. The objective of the study is to use copula models to analyze shares of the Russian stock market and describe changes in the relationship between the shares before and during the coronavirus infection (COVID-19).Methods. An algorithm for using copulas and functions of the R programming language in its implementation is presented. To model the dynamics of financial series the ARMA-GJR-GARCH process (autoregressive moving average Glosten-Jagannathan-Runkle model with generalized autoregressive conditional heteroskedasticity) is used. The selection of optimal families and parameters of copula models is carried out. The adequacy of the obtained models is checked and the results of the study of the relationship between the series are analyzed.Results. An algorithm has been developed for a relatively new approach to using copulas in conjunction with the ARMA-GJR-GARCH model. The approach was used to study the impact of coronavirus in the context of the Russian economy. It is revealed that during the COVID-19 period the dependence between different stocks increases. It is shown that the effect of volatility in financial series increases after the outbreak of the pandemic.Conclusion. The research algorithm using copula models in conjunction with the ARMA-GJR-GARCH process has shown its feasibility. This approach can be used with other GARCH-type models to study finance and other areas.

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

Green Economic Growth: A Global Analysis of Fiscal Expenditures, Institutional Quality, and Stock Markets

Green Economic Growth: A Global Analysis of Fiscal Expenditures, Institutional Quality, and Stock Markets

Revisiting the January effect anomaly: evidence from international stock markets

A recurring theme in the literature and discussion of stock market analysis is the concept of the January effect which essentially implies buy in December and sell in January. The January effect is based on the perceived seasonality trend where short term investors hope to take advantage of the arbitrage opportunity between December and January. However, this perceived anomaly continues is seen as a myth and has been strongly rejected by many market participants in the current dispensation. The purpose of this paper was to investigate the January effect in the Nikkei 225, JSE, CAC 40, DAX, and the NASDAQ Index from February 2019 to February 2024. A F-test statistics was used to explore the phenomenon. The findings indicated that there is no significant difference between the realised returns in January and those of the other month for the most recent five years. This led to the conclusion that, while the January effect may have been observed, it is no longer evident in financial markets, hence market participants should avoid using this type of arbitrage approach because it may result in massive losses

A Causality Perspective: Investments in Green Energy Business vs. Conventional Business

Abstract At the level of the European Union numerous sustainability programs are being created. Considering these the main role f the research is to explore the potential of green energy production along with its limitations. Studies have found that by combining multiple sources of green energy and harnessing technological advances, there is the possibility to believe that one can reduce human’s dependence on fossil fuels. The aim of this study is to create a stock market analysis in order to identify future investment trends by assessing whether there is greater interest in investing in renewable electricity companies. The current paper explores the impact of investing in renewable electricity companies versus investing in the stock market of fossil fuel-based energy companies. The dataset includes closing stock price data for two major companies, one of which is the British-Dutch company SHELL.L Global (known in the oil industry as well as the energy sector) and the other company is Vestas Wind Systems (a Danish company that manufactures wind turbines for renewable energy parks around the world). This dataset covers the period 3rd of January 2011 to 24th of November 2023 and is used together with time series statistical tests. The purpose is to highlight the main objective of this article: to demonstrate through Granger causality whether investment in the renewable electricity market affects investment in the conventional energy market. This result will shed light on whether long-term investments in fossil fuel-based businesses are worth investors’ attention going forward. While the major purpose of the paper is the research mentioned above, there is also a secondary objective, that of carrying out a descriptive analysis of the two listed companies and their evolution in order to identify the key periods that led to significant fluctuations in the series temporal.

Long Short Term Memory Method and Social Media Sentiment Analysis for Stock Price Prediction

The stock market is a complex arena of interest yet uncertainty. Trading stocks, binaries, gold, and bitcoin is growing in popularity, but is prone to price fluctuations influenced by economic and political factors. Social media, particularly Twitter, is where views on companies are shared. Social media sentiment analysis can provide additional insights to evaluate potential future stock price movements, preventing unwanted speculation. The purpose of this research is to develop a Tesla stock price prediction model by integrating the Long Short-Term Memory (LSTM) method and social media sentiment analysis from Twitter to improve prediction accuracy. Stock price data is obtained from Kaggle and Twitter sentiment data is processed through pre-processing. Evaluation values such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are lower in the model with sentiment indicating the ability of the model to more accurately model the dynamics of stock price movements. Lower MSE and RMSE indicate that the model's predictions are closer to the true values, and therefore, the model can be considered more reliable in projecting future stock price changes. These results provide support for the use of Twitter sentiment analysis as a useful source of additional information in improving the prediction accuracy of LSTM regression models in the context of stock market analysis

Impact of Subprime and Covid-19 crises on WAEMU stock exchange efficiency: a classic Hurst vs dynamic Hurst approach

Financial markets are alternative sources of financing, above all, in the current context of financial globalization and the development of information and telecommunication technologies. In the WAEMU zone in particular, the size of the economies seemed insufficient to supply national markets without high costs. The union’s authorities therefore set up a regional market that seemed to be a beneficial outcome : the WAEMU stock market. This stock exchange has undergone significant modernizations since its inception, with the aim of achieving optimal resource allocation. The informational efficiency of this stock exchange has become a hot topic of interest among researchers and professionals, giving rise to a growing number of publications on the subject. Our article is set in this context and aims to analyze the WAEMU’s weak-form efficiency. Based on the limitations of traditional methods, we use new approaches. Firstly, the classic Hurst is a tool for characterizing market structure and analyzing efficiency. The results highlight the multifractal nature of the WAEMU’s market and the rejection of the null hypothesis of no memory in the return series. Given the resurgence of crises, it would be interesting to analyze stock market behavior during such events. We also study the impact of crises (subprime and covid-19) on efficiency, using the dynamic Hurst exponent to account for efficient market varying with time. The results show that during periods of crisis, the stock market alternates between efficiency and inefficiency. They also reveal that Covid-19’s impact was faster than subprime crisis. They also reveal that covid impact crisis was faster than that of subprimes. However, despite the big spike linked to the covid context, the composite index picked up and continued in the same vein.

Comparative Analysis of Stock Price Prediction Accuracy: A Machine Learning Approach with ARIMA, LSTM, And Random Forest Models

This research investigates the comparative effectiveness of three distinct predictive models – ARIMA (Auto Regressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Random Forest – in forecasting stock prices. Focusing on Tata Motors and Infosys stocks, historical data spanning a significant timeframe is collected using the finance library. These models are trained on a diverse set of features including open, close, high, and low prices to capture the underlying market dynamics. The evaluation of model performance is centred on their ability to forecast stock prices over varying prediction horizons. Metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are utilized to quantify the accuracy and reliability of the predictions. Through rigorous analysis, this study provides insights into the strengths and limitations of each model, offering valuable guidance to investors and market analysts. The findings underscore the significance of selecting appropriate predictive models in financial forecasting and contribute to advancing the understanding of predictive modelling techniques in stock market analysis. Additionally, the research delves into the implications of long-term dependencies in stock price forecasting, shedding light on the challenges and opportunities inherent in predicting market trends over extended periods.

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

This paper investigates the distributional characteristics of daily returns of the Nifty 50 index, a benchmark index comprising 50 large-cap stocks traded on the National Stock Exchange of India. Utilizing historical data spanning a specified time period, we conduct normality testing to assess the adequacy of the normal distribution assumption underlying many financial models. Our analysis provides insights into the departure from normality. As a result of departure from Normality, it may affect the tail behaviour, and volatility dynamics of Nifty 50 daily returns, offering implications for risk management, option pricing, portfolio management, and market efficiency. By synthesizing empirical findings with theoretical considerations, this research contributes to a deeper understanding of the statistical properties of Nifty 50 daily returns and informs practitioners in finance about the challenges associated with Modeling and analysing stock market data.

The impact of announcements of regulatory and law enforcement penalties on stock market valuation of US banks from 2000 to 2022

PurposeThis paper aims to analyze stock market reactions to announcements of regulatory and law enforcement penalties imposed on banks operating in the USA.Design/methodology/approachThis paper examines abnormal stock market returns around penalty announcements for banks operating in the USA from 2000 to 2022. The authors use a comprehensive data set of nearly 600 penalties to conduct their event study.FindingsThis paper finds evidence of positive and statistically significant abnormal returns on the day of the penalty announcement. However, the authors also observe negative and statistically significant abnormal returns days later, violating the semi-strong efficient market hypothesis.Originality/valueBy accounting for confounding events and analyzing subsamples, the authors reconcile conflicting results from prior literature that have variously shown negative, null or positive stock market reactions to penalty announcements.