Market Volatility Research Articles

Does high volatility increase connectedness? A study of Asian equity markets

Financial market integration metrics based upon variance decompositions from vector autoregression (VAR) models have become quite popular, and prior literature shows how these metrics change over time. However, only a few studies explain what is driving connectedness to change. Using stock market volatilities from eight Asian equity markets, we postulate that volatility may be a driving force behind increased connectedness. We use a two-step VAR estimation procedure to analyze the dynamics between equity market volatility and connectedness. The first step estimates the VAR model over rolling windows; this yields a sequence of connectedness-measuring spillover indices. The second step re-estimates the VAR with the spillover index sequence as an endogenous variable. Impulse response analysis from the two-step procedure confirms that volatility shocks increase connectedness. The two-step procedure indicates that volatility shocks from Singapore, Hong Kong, and China affect connectedness the most, whereas Malaysia affects it the least. China’s direct effect on the volatilities of the other markets is small. However, since China has a considerable impact on connectedness, China may have an important indirect effect on the other markets by increasing the speed and ease with which volatility spreads from market to market.

Uncertain Regulations, Definite Impacts: The Impact of the U.S. Securities and Exchange Commission's Regulatory Interventions on Crypto Assets

This study employs an event study methodology to investigate the market impact of the U.S. Securities and Exchange Commission's (SEC) classification of crypto assets as securities. It explores how SEC interventions influence asset returns and trading volumes, focusing on explicitly named crypto assets. The empirical analysis highlights significant adverse market reactions, notably returns plummeting 12% over one week post-announcement, persisting for a month. We demonstrate that the severity of market reaction depends on sentiment and asset characteristics such as market size, age, volatility, and illiquidity. Further, we identify significant ex-ante trading volume effects indicative of pre-announcement informed trading.

Study on AI-Assisted Statistical Approach for Improving Stock Price Prediction Accuracy

Stock price (SP) prediction is crucial for financial decision-making, yet achieving excessive accuracy remains challenging due to market volatility. Current models frequently struggle with capturing the complexities of SP fluctuations, leading to significant prediction errors. This study aims to improve SP prediction accuracy through a unique technique that uses AI-assisted statistical techniques with the Redefined Spotted Hyena great-tuned Dynamic Gated Recurrent Unit (RSHDGRU). The dataset includes the closing costs of numerous stocks influenced through market demand, corporate performance, and economic situations. Pre-processing using Z-score normalization to standardize the statistics. The proposed RSH-DGRU model significantly outperforms traditional techniques, achieving a R-squared (R²) value of 0.9852, a Mean Absolute Error (MAE) of 15.624 and Root Mean Square Error (RMSE) of 20.321. These results reveal the effectiveness of the RSH-DGRU in minimizing prediction errors and accurately capturing the complexities of SP fluctuations. By evaluating its overall performance with present fashions, the RSH-DGRU technique showcases stronger predictive capabilities. Financial analysts and investors that have access to a strong instrument for more precise market projections make better-informed investment selections.

The Impact of ESG Ratings on Investment Decisions in the Energy Sector During Uncertain Times

With increasing global awareness of sustainability, the financial sector must inevitably adapt. Our research investigates the role of Environmental, Social, and Governance (ESG) ratings in investment decision-making, particularly in the energy sector, by examining their impact on portfolio performance. The study aims to assess the impact of ESG integration on portfolio risk and returns. Using data from the SP500 index, econometric models and Monte Carlo simulations are employed to evaluate how ESG considerations influence portfolio optimization and risk management. The study reveals that integrating ESG criteria into investment strategies, especially during market volatility, may not always directly affect returns, they provide significant benefits in terms of risk mitigation and ethical investment alignment.

Optimization of Long Line Management in Seaweed Farming: Strategies for Increasing Farmers' Group Income in Pinrang Regency, Indonesia

This research aims to explore the role of the long-line method in increasing the income of seaweed farmers in Pinrang Regency, South Sulawesi, Indonesia, while examining the challenges they face and the strategies they employ to overcome them. The study employs a qualitative case study approach, involving 15 seaweed farmers from various coastal villages within Pinrang Regency, representing diverse demographic backgrounds and experiences. Data collection was conducted through semi-structured interviews, allowing for an in-depth understanding of the participants' farming practices and challenges. The findings indicate that seaweed farmers increase their income by optimizing resources, collaborating within farmer groups, and adapting to environmental and market changes. Strategies such as space optimization, material reuse, and knowledge sharing among farming communities significantly enhance productivity and sustainability. Furthermore, farmers employ adaptive techniques to mitigate environmental risks and market volatility, including using durable materials and processing seaweed into higher-value products. The implications of these findings are crucial for policymakers and development organizations, as they highlight the potential of sustainable farming practices in improving the livelihoods of coastal communities. Policymakers are encouraged to develop support programs that promote resource optimization, foster community-driven farming networks, and train farmers in adaptive strategies. However, the study's limitations, including the small sample size and reliance on self-reported data, may affect the generalizability of the results. Future research should incorporate larger, more diverse samples and objective measures to enhance the robustness of the findings, and explore the long-term impacts of the long-line method on income and sustainability.

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.

Integrating Twitter Sentiment with ML Models for Enhanced Stock Forecasting

This research investigates the integration of Twitter sentiment analysis with machine learning models, specifically Long Short-Term Memory (LSTM), Facebook Prophet, and Neural Prophet, to enhance the accuracy of stock market predictions. While these models are accurate in capturing long-term trends and seasonality, they often fail when exploring the short-term volatility in stock prices. By using real-time social media sentiments, we aim to improve short-term prediction accuracy. Sentiment data from Twitter, extracted and classified using NLP techniques, was integrated with the models to predict stock prices for Apple Inc. (AAPL), Paramount Global (PARA), and BP plc (BP) over the period 2016-2024. The study compares the performance of the models using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that Neural Prophet stood the best in terms of its accuracy and efficiency in stock predictions when compared to LSTM and Facebook Prophet, reducing RMSE by 25% and MAE by 20%. While the integration of sentiment analysis improved prediction accuracy, limitations remain in predicting the scale of sudden market changes. The findings highlight the potential of sentiment analysis in stock prediction but also call for further model enhancements to fully address short-term market volatility.

Analysis of the Effectiveness of Multi-Armed Bandit Algorithms in Crop Pricing

This study explores the application of multi-armed bandit (MAB) algorithms in dynamic pricing of crops, with a focus on evaluating the adaptive upper confidence bound (asUCB) and Thompson Sampling (TS) algorithms. Through simulation experiments on historical data, the study analyzed the performance of these algorithms in fitting actual market price trends and responding to future price fluctuations. The results indicate that the asUCB algorithm performed particularly well in both the training dataset and simulation tests, demonstrating low mean squared error (MSE) and minimal cumulative regret, reflecting its rapid convergence and stable pricing capabilities. In contrast, although the TS algorithm initially performed slightly less effectively, it demonstrated unique advantages in dealing with market volatility due to its strong adaptability. This study demonstrates the potential application of MAB algorithms in dynamic pricing, providing valuable insights for pricing strategies in the agricultural product market.

Adoption of cryptocurrencies in small and medium-sized enterprises: A strategic approach to enhancing financial inclusion and innovation

This review paper explores cryptocurrency adoption's strategic benefits and challenges for small and medium-sized enterprises (SMEs), emphasizing its potential to enhance financial inclusion and drive innovation. The paper overviews cryptocurrencies' history, key characteristics, and current adoption trends in various sectors. The paper examines the importance of financial inclusion and innovation for SMEs through a comprehensive literature review, highlighting how cryptocurrencies can reduce transaction costs, increase access to global markets, and improve financial transparency and security. The analysis also addresses the significant challenges SMEs face, such as regulatory and legal issues, technological and operational barriers, and market volatility. Practical recommendations for SMEs considering cryptocurrency adoption are provided, including steps for compliance, technological integration, risk management, and leveraging new business models. This paper aims to offer a balanced perspective, guiding SMEs in navigating the complexities of cryptocurrency adoption while maximizing its benefits.

Forecasting stock index closing points using ARIMA-GARCH with a rolling data window

This research develops and examines the efficacy of a hybrid ARIMA-GARCH model, augmented by a rolling data window approach, to enhance the accuracy of stock index prediction, specifically focusing on the NEPSE index. Accurate predictions of stock indices are of paramount importance to investors, analysts and policy makers to navigate and circumvent the market uncertainties. The AutoRegressive Integrated Moving Average (ARIMA) model captures linear trends and temporal dependencies in time series data, while the General AutoRegressive Conditional Heteroskedasticity (GARCH) model addresses volatility clustering—ubiquitous character of financial time series—thereby providing a comprehensive framework for prediction. Utilizing a dataset comprised of daily closing points of NEPSE index approximately three years and nine months, the study identifies the ARIMA (5,1,0)-GARCH (1,1) model as optimal fit, upon integrating 180-day rolling data window. This model achieved a Mean Percentage Error of -0.0058% and a correlation of 0.995, which is indicative of superior fit to the underlying time series data. These findings underscore the hybrid model’s capacity to adaptively respond to dynamic market conditions and acclimatize prediction parallel to most recent market trends and volatility. This research is useful for optimizing investment strategies for those invested in Nepalese stocks. Also, this research lays a foundational framework for future investigations into application of this advanced forecasting method in other emerging markets, financial instruments and indices.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

Modeling and Analyzing Carbon Emission Market Volatility and Impact: Evidence from Guangdong Province, China

This research investigates the volatility of carbon prices in Guangdong’s emission trading market, a critical element of China’s broader climate strategy aimed at reducing greenhouse gas emissions and promoting sustainable development. This study applies ensemble empirical mode decomposition (EEMD) to analyze the complex interactions between carbon price fluctuations and various economic factors, including energy prices and environmental regulations. By decomposing the data, we identify key trends and cycles within the market, providing a clearer understanding of both short-term volatility and long-term market trends. Our findings reveal that regulatory policies play a pivotal role in shaping carbon market dynamics, with shifts in regulations leading to significant price volatility. Additionally, fluctuations in global energy prices, especially oil and coal, are found to have a considerable impact on carbon price movements, further complicating the market’s stability. This underscores the interconnected nature of the carbon trading market with broader economic and environmental factors, both domestic and international. The findings provide valuable insights for policymakers and market participants, underscoring the importance of stable carbon markets for promoting the transition to a low-carbon economy and achieving broader sustainability goals.

Monetary Policy Instruments and Stock Market Returns Volatility in Nigeria

The study examines monetary policies instruments impact on stock market volatility in Nigeria for the period of 1993-2022. The study identified the independent variables, namely; reserve requirement (RR), cash reserve ratio (CRR), discount rate (DR) and money supply (MS) which were analyzed in relation to stock market volatility proxied with all share index volatility (ASIV). The data was analyzed with descriptive statistics, correlation matrix several diagnostics tests (VIF, validity test, ADF and Johansen tests) and the multiple regression analysis. The findings revealed that RR, CRR and MS have significant effect on ASIV while DR has insignificant effect on ASIV in Nigeria. It was concluded that reserve requirement, discount rate and money supply exert significant effect on Nigerian economy. Thus, it thereby recommended that management of CBN should put modalities in place to aggressively control ASIV in Nigeria. CBN should put measures in place to increase CRR. This will provide banks opportunity to fund economic transaction in the Nigerian economy, thereby reducing ASIV.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Market Volatility from a linear Perspective: Trend Forecasting for Business and stock movements Author’s Information

lives. From the simplest transactions such as buying groceries to the complex mechanisms of global markets, finance touches almost every aspect of our economic interactions. The term “finance” emerged at the end of the nineteenth century and became increasingly important as economies developed and expanded. A key element of finance is the technical analysis of stocks, an indispensable practice for financial experts and investors. The method uses historical data and statistical techniques to forecast market trends. By scrutinizing past price movements and trading volumes, technical analysts can uncover patterns and signals that guide strategic decisions, risk management, and portfolio optimization. This analytical approach helps not only in identifying market trends, but also in identifying key support and resistance levels, which are essential for effective trading timing. Therefore, technical analysis is a powerful tool that enables investors to deal with the complexity of financial markets with greater confidence and accuracy.

Analysis of changes in HSBC’s income statement between 2022-2023

The banking industry is a cornerstone of the world wide economy, primarily due to the unique nature of its primary product—money. This sector has long held a central role within society and continues to attract considerable interest from potential investors. However, investing in the banking industry is not without its challenges. Market volatility, regulatory compliance costs, and geopolitical uncertainties can profoundly impact the industry’s profitability and stability. Being among the biggest banks in the world, HSBC provides financial services such as personal banking, wealth management, and corporate finance in over 60 countries. This is an excellent example of an analysis of the banking sector. The essay lists changes in HSBC’s income statement between 2022 and 2023 and analyses them using vertical and horizontal analysis, ratio analysis, segment reporting analysis, and benchmarking. Through these analyses, the essay aims to see HSBC’s capacity to face banking crises and provide a few suggestions for investors.

Using the SPY Index for Linear Regression Analysis of AAPL Stock Returns

This study investigates the predictive relationship between Apple’s (AAPL) stock returns and the SPY index, representing the S&P 500. Utilizing a linear regression model, we aim to quantify the extent to which changes in the SPY index can explain variations in AAPL returns. Our findings show that the model parameters—an intercept (Beta0) of 0.001 and a slope (Beta1) of 1.067—indicate a strong positive correlation between these variables. The model’s predictive accuracy is validated using Root Mean Square Error (RMSE), with an in-sample RMSE of 0.000123 and an out-of-sample RMSE of 0.000234. We also introduce a hedging strategy to mitigate risks associated with market volatility. This research contributes to the field by providing a robust, data-driven framework for predicting stock returns and managing risk.

Analysis of Portfolio Optimization Performance: Markowitz Model and Index Model in Capital Markets

In the post-pandemic era, marked by economic uncertainty and stock market volatility, investors are turning to Modern Portfolio Theory (MPT) to guide their investment decisions. This study aims to develop a robust mathematical framework for portfolio construction using the Markowitz Model (MM) and the Index Model (IM) with added constraints. The objectives are threefold: (1) to create a portfolio framework that reflects investor preferences using MM and IM with additional constraints, (2) to compare these models against the Gaussian Distribution using Python and Excel, (3) to compare the statistical data and correlation tests of daily logarithmic returns in Python with monthly excess returns in Excel, and (4) to evaluate their performance relative to the traditional Markowitz approach through Monte Carlo simulations.The methodology involves incorporating five additional constraints into the MM and IM models. The analysis uses 20 years of historical daily return data for ten stocks from various sectors, one equity index (S&P 500) as a risk-free rate proxy (1-month Fed Funds rate). Daily logarithmic returns are analyzed using Python, while Excel Solver and Solver Table are employed for monthly excess return data.

Integrating Fintech-Driven Technical Analysis into Value Investing: A Study of Moving Averages in Cryptocurrency Market Dynamics

This research explores the application of fintech-driven technical analysis, specifically focusing on moving averages within value investing. Using Coinbase as a case study, the study examines the effectiveness of moving averages in volatile market conditions where traditional metrics are less applicable. Historical stock data was exported from Yahoo Finance and analyzed using Excel, emphasizing the 30 vs. 10 moving average combination. This combination was chosen to balance short-term and long-term market perspectives. The analysis revealed that while moving averages effectively highlight trends and momentum shifts, they are lagging indicators and require supplementary analysis to account for external factors such as news and regulatory changes. Despite not being a precise predictive tool, moving averages provided valuable insights into market behavior, supporting the argument that fintech tools can complement traditional value investing strategies by offering real-time analysis and dynamic approaches to market assessment.

استراتيجيات إدارة الأعمال الفعالة وتطبيقها في التخفيف من مخاطر مشاريع التطوير العقاري في المملكة العربية السعودية

The research intends to investigate efficient business management methods and their application in minimizing risks associated to real estate development projects in the Kingdom of Saudi Arabia. The questionnaires were delivered to a sample of 163 participants working in sectors connected to the study topic, and the data was examined using statistical analysis, which confirmed the reliability of the responses and the correctness of the findings. The findings highlight the essential significance that competent management methods play in decreasing the financial risks connected with real estate development projects. By implementing effective management practices, financial risks coming from market volatility, project delays, or cost overruns may be mitigated. The findings also revealed the need of good management methods in activating sustainable practices within these projects, since this contributes to the viability of projects in the long run by caring for the environment and resources. In addition, good management methods play a vital role in promoting transparency within real estate development projects, as they assist explain financial and administrative procedures, which adds to developing confidence among all parties engaged in the project. In conclusion, the research indicated that competent management is not only a tool for lowering risks, but also a vital aspect in assuring the sustainability and transparency of real estate developments in the Kingdom.