Stock Market Fluctuations Research Articles

Fractal momentum investment strategies based on liquidity non-linear fluctuations in Chinese stock market

Fractal momentum investment strategies based on liquidity non-linear fluctuations in Chinese stock market

A Comparative Study of NIFTY-50 Prediction Using Linear Regression and Regression Tree Models

It is important for investors to predict the tendency of stock. However, the stock market fluctuation is too hard to predict. A single stock may be inflected by many elements, so it would be much more feasible to study a stock index. A stock index may be decided by many stocks, which means that it would not be effected by some only one company. In this passage, NIFTY-50 would be analyzed to make a prediction. The linear regression and regression tree are used to predict the close price of NIFTY-50. To make sure the data size is big enough, the data includes the price from 2000 to 2021. By comparing two models, a proper model to predict stock index would be found. After the comparison of the MSE and R square index, it is easy to find that the regression tree is the model much more reliable. However, there is still long way to go before researchers find way to predict stock price precisely.

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.

Nexus Between Stock Return and Market Volatility in Indian Perspective

This research seeks to explore the volatility patterns for the SENSEX index to figure out the characteristics of volatility in Indian stock trading and to investigate the relationship between returns and volatility of the Indian market for the last ten years. The research adds to the existing knowledge about stock market fluctuations, their implications for investors, and their impact on developing the country's economy. The study involves using daily returns data from BSE SENSEX from 01 Jan 2014 to 31 Dec 2023. Daily closing prices are obtained from the official website of BSE, and returns are calculated based on these prices. The Dickey-Fuller and Phillips-perron are taken to make the time series static. ARCH, GARCH, and GARCH-M tests are utilized to capture volatility clustering, return and volatility relationship. The results reveal that Fluctuations in the Indian stock market, particularly shown in the SENSEX index of the BSE, were highest in the year 2020. Findings suggest that GARCH-M does not show any relation between expected returns and market volatility. The risk-premium parameter is positive but statistically insignificant. If you want to hedge against risk, the risk premium is not very high. It means that taking risks does not give you more returns. Durbin Watson's value of 1.981401 further supports the model's suitability.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Volatility in the Equity Stock Market in India from 2019 to 2024: An Empirical Analysis

This research investigates the volatility and fluctuations in the Indian equity stock market from 2019 to 2024, a period characterized by unprecedented global and domestic challenges. The Indian stock market, like many emerging markets, experienced heightened volatility due to a confluence of events, including the COVID-19 pandemic, geopolitical tensions, economic reforms, and shifts in global financial conditions. The study employs advanced econometric models, including GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and event study analyses, to capture the time-varying nature of market volatility and to identify the key factors driving these fluctuations. Our findings reveal that the COVID-19 pandemic triggered the most significant spikes in volatility, particularly during the early months of 2020, as uncertainty regarding the pandemic's impact on the economy led to sharp market corrections and investor panic. Additionally, the study highlights the influence of macroeconomic variables such as GDP growth, inflation, and monetary policy changes on market behavior. The paper also explores the role of investor sentiment and market liquidity in exacerbating volatility during crisis periods. The results provide valuable insights into the dynamics of the Indian equity market, offering implications for investors seeking to manage risk and for policymakers aiming to enhance market stability. The study underscores the need for robust risk management strategies and proactive regulatory measures to mitigate the adverse effects of such volatility, especially in an increasingly interconnected global financial environment. Keywords: Equity Stock Market, Volatility, India, COVID-19, Economic Reforms, 2019-2024

Precision forecasting in perilous times: stock market predictions leveraging google trends and momentum indicators during COVID-19

PurposeThis study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.Design/methodology/approachHybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.FindingsOur study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.Originality/valueTo the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Analysis of the Relationships between Fluctuation of Chinese Stock Market and Global Markets

Analysis of the Relationships between Fluctuation of Chinese Stock Market and Global Markets

Exploration of the Reasons for the Federal Reserve’s Interest Rate Hike in 2022 and Evaluation of Policy Effectiveness

After the global economy experienced the impact of the new crown epidemic, the US economy recovered in 2022, but inflation rose rapidly, prompting the Federal Reserve (FED) to take interest rate hikes. This paper analyzes the background and reasons for the Fed’s interest rate hike in 2022, and evaluates its policy effect, aiming to provide experience and lessons for future monetary policymaking. The main findings include: that the interest rate hike significantly reduced the inflation rate in the United States, from 8.0% in the first quarter to 5.67% in the fourth quarter; In the short term, interest rate hikes will hinder the economy, but in the long term, they will help promote stable growth; The interest rate hike led to stock market fluctuations and bond market yields rising, the credit market tightening, and increased the instability of the global financial market; Urge other countries to adjust their monetary policies and increase global economic uncertainty. The significance of this paper is to expand the theory of international financial and monetary policy and provide comparative research value for the policy-making of emerging market countries and developing countries. The policy recommendations include: the United States should strengthen policy transparency and financial supervision to promote the optimization of economic structure; Other countries should strengthen macro policy coordination, improve financial regulatory capacity, achieve economic diversification, maintain monetary policy flexibility, and promote the healthy development of cross-border capital flows.

Assessing the impact of the coronavirus pandemic and non-pharmaceutical interventions on Bursa Malaysia KLCI Index using GARCH-M (1,1) models

This study aims to explore the impact of coronavirus pandemic-related variables and non-pharmaceutical interventions on fluctuations in the Malaysian stock market during the period from January 7, 2020, to March 31, 2021. By employing GARCH-M (1,1) family models (GARCH-M, EGARCH-M, and PGARCH-M), the study seeks to understand the intricate dynamics of market volatility amidst the pandemic and associated interventions. The findings suggest that while past market volatility and conditional variance continue to influence current market fluctuations, their effects have diminished over time during the study period. Additionally, the EGARCH-M (1,1) model reveals a leverage effect, indicating increased market volatility following negative news compared to positive news. Interestingly, the EGARCH-M (1,1) model emerges as the optimal choice for accurately capturing data dynamics. Conversely, the PGARCH-M (1,1) model does not exhibit a statistically significant leverage effect. These insights contribute to a better understanding of market behavior during crises, informing future research and risk management strategies. AcknowledgmentThe authors are grateful to the Middle East University, Amman, Jordan, for the full financial support granted to this research paper.

A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms

Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators of a business’s financial health, both of which are adversely impacted by economic uncertainty. However, these metrics may diverge as distinct objectives drive them. There exists a gap in the literature regarding effective strategies for managing the trade-off between these metrics under economic uncertainty. This study addresses this gap by introducing a simulation-based optimization model that integrates system dynamics simulation and genetic algorithms. The proposed model aims to balance economic profitability and working capital efficiency within inventory management under partial trade credit. A recent real case study demonstrates the model’s applicability and reveals its superiority over conventional system dynamics simulation modeling. With its capacity to inform strategic and tactical decision-making, this model emerges as a valuable tool for supply chain and financial managers seeking to ensure financial stability amidst economic volatility.

Associations between stock market fluctuations and stress-related emergency room visits in China

Associations between stock market fluctuations and stress-related emergency room visits in China

A STUDY ON IMPACT OF STOCK INDICES ON FOREIGN DIRECT INVESTMENT

This study investigates the relationship between Foreign Direct Investment (FDI) inflows and the performance of key Indian stock indices, specifically the Nifty 50 and BSE Sensex, from April 2020 to December 2023. Despite FDI's crucial role in economic growth, existing research has often overlooked the nuanced interplay between stock market fluctuations and FDI within the Indian context. This study aims to fill this gap by analyzing the correlation and causal effects between these indices and FDI inflows, alongside a sectoral FDI trend analysis from 2020 to 2023. Utilizing secondary data, descriptive statistics, correlation, and regression analysis, the study found moderate positive correlations between the stock indices and FDI inflows; however, these relationships were not statistically significant, potentially due to the limited sample size. The regression models demonstrated weak predictive power and substantial unexplained variability, indicating that factors beyond stock market performance likely influence FDI inflows. Despite these limitations, the study highlights the need for more comprehensive data and analysis to better understand the dynamics between stock indices and FDI, providing valuable insights for policymakers to enhance India's investment climate and global competitiveness. Key Words: Moderate positive correlations, Limited sample size, statistically significant, Predictive power, Unexplained variability & Investment climate

Mean Variance Complex-Based Portfolio Optimization

Mean-Variance (MV) is a method that collects several assets using appropriate weight intending to maximize profits and to reduce risk. Stock market conditions are very volatile, mean variance method does not reach stock market fluctuation well because MV method is only limited to one time period. This study proposes a mean variance complex-based approach that transforms real returns into complex returns by using Hilbert transform to construct an optimal mean-variance portfolio based on complex returns and then find its dynamic asset allocation. The results show that with the same risk tolerance, the mean variance complex-based approach outperforms MV method in profits, losses, and portfolio performance tests.

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

Does Self-Control and Over-Confidence Influence Investment Decision: The Mediating Role of Fundamental and Technical Analysis

This study investigates the influence of self-control and over-confidence on investment decision-making, with a focus on the mediating role of fundamental and technical analysis. The respondents of the study are institutional investors operating in the Pakistan Stock Exchange Market (PSX), which includes the Lahore Stock Exchange, Karachi Stock Exchange, and Islamabad Stock Exchange. The behavior of these investors is assessed to determine the impact of stock market fluctuations on their investing decisions. The unit of analysis in this research is the institutional investors themselves. The study aims to examine the behavioral consequences of integrating self-control and overconfidence variables in investment decisions, particularly in relation to market instability and variations in fundamental and technical analysis. To select the sample, both stratified sampling and cluster sampling techniques were employed. Cluster sampling was used to save costs, while stratified sampling ensured that the sample accurately reflects the population. The states were divided into clusters to facilitate the use of cluster sampling. In terms of sample size, the study solely focuses on institutional investors in the Pakistan stock exchange PSX. Therefore, the sample consists of 375 institutional investors. Data collection for this study utilized a 5-point Likert scale, which is a commonly used measurement instrument for determining the relationship between variables. The scale allows respondents to provide their opinions on a continuum from strongly disagree to strongly agree. This approach enhances the originality and reliability of the data while minimizing respondent annoyance. Overall, this study aims to contribute to the understanding of how self-control and over-confidence influence investment decisions, with fundamental and technical analysis serving as mediators in the context of the Pakistan Stock Exchange Market. The findings from this research can provide valuable insights for institutional investors in their decision-making processes. Integrating self-control and over-confidence in investment decision-making aligns with SDG Goal 8: Decent Work and Economic Growth, by promoting responsible and sustainable investment practices.

Initial public offerings and the local economy: evidence of crowding out

Abstract We test the effect of going public on economic growth in the areas surrounding initial public offering (IPO) firms. We compare the effects of IPO filers that complete their IPOs with those that do not, using post-filing stock market fluctuations as an instrument for IPO completion. We show that IPOs that are large relative to the size of their counties lead to a 1.1 percentage point relative reduction in annual county-level establishment growth, with similar effects for employment and population growth. There are no corresponding effects for relatively small IPOs. These negative effects appear to be driven by a crowding out of local sector peers, but the crowding out also disrupts local agglomerations and slows down growth among other businesses that rely on local demand. Overall, our results indicate that macroeconomic gains from IPOs trade off against disruptions in local agglomeration economies where public firms originate.

Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic

Exploring the nexus between sectoral stock market fluctuations and macroeconomics changes before and during the COVID-19 pandemic

Uncovering the relationship between incidental emotion toward a disaster and stock market fluctuations: Evidence from the US market

Despite having potentially important implications, there has been little research on the relationship between the public's incidental emotion and the stock market. To that end, we construct a valence-based measure of incidental emotion using BERTweet's sentiment analysis and empirically investigate the association between collective incidental emotion toward the COVID-19 pandemic and the U.S. stock market. We employ multivariate time series autoregressive models to test the relationship between emotion polarity and stock market returns or trading volumes. The results reveal that societal sentiment toward the pandemic has a significant effect on the returns of the Dow Jones Industrial Average and S&P 500. In contrast, the macro-level emotion does not significantly affect the return for NASDAQ 100. The findings also suggest a significant association between incidental emotion and trading volumes. We conduct a battery of sensitivity tests that further support our conjecture. The study underscores the robust role of incidental emotion in investment decision-making, highlighting its significance as a distinctive feature that should be incorporated into financial decision support systems.

Does soft shareholder activism hold hard consequences?

This study investigates the impact of soft shareholder activism, as reported in media narratives, on stock market performance using an EGARCH methodology. Focusing on US-listed firms, we analyse how news articles discussing shareholder activism, ranging from formal proposals to informal expressions of investor concern, affect stock market returns and volatility. Our findings reveal that approximately one-fourth of the companies highlighted in these reports experience significant stock market fluctuations coinciding with media coverage. These effects are notably pronounced in firms with higher market capitalisation and robust financial metrics (ROE and ROA) but lower ESG scores and negative leverage. The study also establishes a strong correlation between firm performance metrics, particularly ROE and ROA, and market reactions around news publication dates. This pattern suggests that the market responds more intensely to companies with higher performance levels. The research contributes to understanding shareholder activism’s indirect influence on market dynamics through media, offering insights for companies, investors, and policymakers in the evolving landscape of shareholder activism and the media’s role therein.

Tracing the dynamic impact of energy transitions on equity market volatility in an era of financial turbulence

This study investigates the dynamic relationship between the shift towards clean energy and stock market fluctuations, covering the period from January 2004 to August 2021 by applying time-varying parameters vector autoregressions (TVP-VAR). The results reported strong spillovers between clean energy markets and stocks, while dirty ones demonstrated disconnected spillovers. Asymmetry in the spillovers detected dominance of negative (bad) spillovers due to various uncertain economic and financial circumstances. The most significant negative (bad) events tracked in time-varying attributes are the global financial crisis, European Debt Crisis, Shale Oil Crisis, Brexit, US interest rate agenda, and the COVID-19 pandemic. Meanwhile, negative asymmetries prevailed in time-varying analysis, echoing the pre-eminence of negative crisis periods. We identified clean energy markets and stocks as net spillover emitters and dirty energy markets as net recipients. These findings have profound implications for a range of stakeholders, including investors, policymakers, and market participants, offering valuable insights for portfolio diversification and risk management strategies amidst the ongoing energy paradigm shift.