Financial Market Fluctuations Research Articles

Government decisions and macroeconomic stability: Fiscal policies and financial market fluctuations

Since the 2008 Financial crisis, macroeconomic and financial markets' stabilisation policies relied mainly on monetary actions, reducing the ECB's margin of manoeuvre and calling for a major role for fiscal authorities. We investigate the impact of government decisions on financial markets for 11 Eurozone economies (from 1995 to 2021), in the very short run, using an event study, finding a positive (negative) reaction to fiscal expansion (consolidation) announcements and in the short/medium-run, with a Bayesian TVP-FAVAR, identifying cross-country heterogeneities; stronger (weaker) public finances show a direct (indirect) relation between fiscal policies and stock prices and an indirect (direct) one with 10-year bond yield.

A Systematic Review on Impact of Political Crisis and Election on Capital Market Returns

The capital markets worldwide are vastly impacted by market sentiments and investors tend to react and rebalance their portfolios time to time due to such changes in market sentiments from political events. This systematic review examines the impact of political crisis and election on the capital market returns worldwide. It employs a systematic review research method and has gone through 4573 articles, out of which 62 articles published in reputed journals between 1970 and 2023 were considered for review. Of the methodology of the studies reviewed, 43.5% is quantitative, 27.4% is mixed, 17.7% is qualitative, and the rest is conceptual. Researches published in the context of Asian countries contributed 40% of this reviewed study, followed by Europe, Africa, and North America, each with 10%, South America with 3%, Australia with 1%, and allied countries within unions or agreements with 26%. The results show that political crises significantly impact capital market returns in Asia. Studies in North American context indicate that before elections and during political crises, the capital market volatility significantly rises with decreased capital market returns. In Australia, political events cause financial market fluctuation. In Africa, political instability hinders long-term capital accumulation. Additionally, informal political instability in South America directly affects economic growth, while formal instability indirectly affects economic vulnerability. The outcomes of such research hold practical implications for policymakers in formulating portfolio strategies and, therefore, delineate avenues for research exploring the integrated effects of political events on capital market returns by amalgamating trading information from stock exchanges worldwide.

Nonlinear relationships between the participation index and key financial assets: A quantile regression analysis

The Participation Index is essential for assessing market sentiment, investor confidence, and financial market direction. Gold, known for its historical role in protecting wealth and providing a stable asset during economic instability, plays a key role in investment portfolios as an alternative guarantee against financial market fluctuations. The USD and EUR are key current currencies in global economics, affecting international trade, financial transactions, and investment decisions. The purpose of this study is to analyze the complex relationships between the Participation Index (KAT) and three significant financial assets: gold, the US dollar (USD), and the Euro (EUR) using Quantile Regression analysis. Daily data between 16.11.2021-08.08.2023 were used in this study. According to the results; the impact of the euro exchange rate on the "KAT" variable exhibits a positive relationship, which increases as the quantile level rises. The impact of the exchange rate of the United States Dollar (USD) on the variable defined as "KAT" indicates a positive relationship, which intensifies as the quantile level rises. The relationship between gold prices and the "KAT" variable shows a positive strengthening impact as the quantile level increases, and these effects demonstrate a high level of statistical significance.

Sejarah Perkembangan Kebijakan Ekonomi Pada Masa Orde Baru dan Pengaruh Krisis Moneter terhadap Perekonomian Indonesia

The history of economic policy development during the New Order era in Indonesia reflects the political and economic dynamics that shaped the nation's development trajectory. Initially, the New Order government adopted a centralized and authoritarian economic development model, emphasizing industrialization and modernization. While these programs spurred economic growth, they also led to social inequality and political tensions. The late 1990s currency crisis, particularly the 1997 Asian financial crisis, significantly destabilized Indonesia's economy. Economic policies of the New Order era, characterized by dependence on foreign debt and imprudent monetary practices, rendered Indonesia susceptible to global financial market fluctuations. The impact of the crisis was profound, manifesting in a sharp decline in economic growth, a currency crisis, and a banking crisis, which compelled the government to implement urgent economic reforms. These reforms included financial sector restructuring, economic deregulation, and enhanced transparency in public financial management. This study provides an in-depth analysis of the New Order's economic policies and the subsequent impact of the currency crisis on Indonesia's economy.

Optimizing Banking Stock Price Prediction: Deep Learning Based Approach

Recently, the stock market has experienced significant instability, especially during the global pandemic which resulted in unprecedented economic impact. The main focus of this research is to develop an optimal prediction model using deep learning techniques for the projection of closing prices of bank stocks during the pandemic period. This study evaluates the performance of banking stocks, specifically ARTO, BRIS, BBNI, and BMRI in the Jakarta Composite Index (JCI). Data was extracted from Yahoo Finance and processed through LSTM and GRU algorithms, including data cleaning, normalisation, and descriptive statistical analysis. Scoring metrics such as MSE, RMSE, and MAPE are used to measure the effectiveness of the predictive models. The results show that the LSTM and GRU models can predict stock prices well. These findings provide a basis for trading strategies and improved decision-making in the stock market. Recent research confirms the importance of integrating deep learning methods such as LSTM and GRU in stock price prediction, helping to understand complex financial market fluctuations and improve prediction accuracy.

Classification of stochastic processes based on deep learning

Stochastic processes model the time evolution of fluctuation phenomena widely observed in physics, chemistry, biology, and even social science. Typical examples include the dynamics of molecular interactions, cellular signalling, animal feeding, disease transmission, financial market fluctuation, and climate change. We create three datasets based on the codes obtained from the published article; the first one is for 12 stochastic processes, the second one for the Markov and non-Markov processes, and the third one for the Gaussian and non-Gaussian processes. We do the stochastic process classification by employing a series of convolution neural networks (CNNs), i.e. VGG16, VGG19, AlexNet, and MobileNetV2, achieving the accuracy rates of ‘99%’, ‘98%’, ‘95%’, and ‘94%’ on the first dataset, respectively; in the second dataset, the test accuracy of VGG16 is ‘100%’, and for the rest of the models, it is ‘99%’; and in the third dataset, the test accuracy of all models is ‘100%’, except the VGG19, which is ‘99%’. According to the findings, CNNs have slightly higher accuracy than classic feature-based approaches in the majority of circumstances, but at the cost of much longer training periods.

The influence of trade war on the international economic system and its adjustment measures

This paper discusses the influence of trade war on the international economic system and the corresponding adjustment measures. First of all, the trade war has had a negative impact on global economic growth, investment flows, international trade patterns and financial market fluctuations. Secondly, the trade war has caused international investment problems, which has led to the adjustment of the supply chain of multinational companies and the increase of investment uncertainty. Then, the financial market fluctuated and the currency, stock and bond markets were disturbed. Finally, by strengthening international cooperation, promoting trade multilateralism, lowering trade barriers and paying attention to sustainable development, we can meet the challenges brought by trade wars and look forward to establishing a more stable and sustainable international economic system in the future.

Research on the Correlation between Financial News Text Analysis and Stock Market Fluctuations Using Artificial Intelligence Algorithm Models

Research on the Correlation between Financial News Text Analysis and Stock Market Fluctuations Using Artificial Intelligence Algorithm Models

Oil price volatility and gold prices volatility asymmetric links with natural resources via financial market fluctuations: Implications for green recovery

The impact of financial market volatility on the correlation between oil and gold prices is explored here to implicate the green recovery future. Our research aims to aid oil and gold market analysts, financiers, policymakers, and risk managers in making informed decisions. To do this, with a particular emphasis on the Chinese context, we use rigorous econometric methods to examine historical data from a chosen period. Our research reveals a critical relationship between oil price volatility and gold price volatility, where noticeable effects of volatility fluctuations on gold price volatility may be seen. Our research also reveals that these impacts are not always equal and may change depending on the state of the market, highlighting the need to consider the larger economic context when evaluating these commodities. The data shows that gold and oil prices are very volatile, as are the markets as a whole. The analysis found that the gold and oil markets are both very unpredictable. This result makes it abundantly evident that the connection between the stock and commodities markets is crucial to the selection method. The analysis found that the gold and oil markets are both very unpredictable. We may get a deeper comprehension of the connection and draw more applicable conclusions if we factor in the volatility of financial markets. Policymakers may include variations in oil price volatility in their decision-making on commodities markets and financial stability, and market analysts can improve their forecasting models.

Commodity Prices and the US Business Cycle

This article explores the relationship between commodity price cycles and the US business cycle. Commodity price cycles are known to foster capricious macroeconomic activity, and understanding their behaviour offers valuable economic insight. The US business cycle is a key indicator of the broader economic conditions, reflecting changes in economic activity, consumer spending, and overall market conditions. By examining the dynamics and interplay between these two cycles, this study provides insights into the potential synchronisation, lag, or lead between commodity price cycles and the US business cycle. The study employs a Fourier analysis of commodity price cycles and the US business cycle. In addition, the same empirical method will be used to analyse historical rainfall patterns in the US as a means of furthering the role of historical rainfall patterns in shaping agricultural productivity and subsequent price movements. Results show dominant cycles of 14.2 years throughout the commodity price dataset, 3.8 years within the US business cycle, and 14.2 years in US historical rainfall patterns. The study also identifies several factors that influence the relationship between these two cycles, including global demand, trade policies, and financial market fluctuations.

Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins

This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observations are used to assess the forecasting models. We compare statistical, machine learning, and deep learning forecasting models to evaluate the financial markets, such as the ARIMA, hybrid ETS-ANN, and kNN predictive models. The study results indicate that predicting financial market fluctuations is challenging, and the accuracy levels are generally low in several instances. ARIMA and hybrid ETS-ANN models perform better over extended periods compared to the kNN model, with ARIMA being the best-performing model in 2018–2021 and the hybrid ETS-ANN model being the best-performing model in most of the other subperiods. Still, the kNN model outperforms the others in several periods, depending on the observed accuracy measure. Researchers have advocated using parametric and non-parametric modeling combinations to generate better results. In this study, the results suggest that the hybrid ETS-ANN model is the best-performing model despite its moderate level of accuracy. Thus, the hybrid ETS-ANN model is a promising financial time series forecasting approach. The findings offer financial analysts an additional source that can provide valuable insights for investment decisions.

Ruang Lingkup Manajemen Keuangan Scope Of Financial Management

Effective financial management is an important factor in the success and sustainability of the company. One of the key aspects of financial management is managing company liquidity. This abstract aims to describe the importance of managing liquidity and its impact on the company's financial performance. The results of the analysis show that good liquidity management contributes positively to the company's financial performance. Companies that have a high liquidity ratio tend to have a better level of profitability and are able to generate stable growth. In addition, good liquidity management also provides benefits in facing external challenges, such as financial crises or market fluctuations. The company's financial managers need to find the right balance between sufficient liquidity to meet operational needs and profitable investments. In addition, this research can also contribute knowledge regarding best practices in financial management to academics and researchers in this field.

Application of discriminant models in predicting bankruptcy of energy sector companies in Poland

The following research focuses on the use of discriminatory models in predicting bankruptcy of energy companies in Poland. Over the past several years, this sector has faced unforeseen events, such as the COVID-19 pandemic or the conflict in Ukraine. Past studies have shown that foreign discriminatory models should not be used in the context of the Polish market. As a consequence, a T. Maślanka's model adapted for Polish companies was used. The aim of the article was to verify the financial condition (in the context of bankruptcy risk assessment) of companies in the energy sector using multi-criteria discriminant analysis. Statistica 13.3 package was used for calculations. The results of the research proved that discriminant models can be used to assess financial health and bankruptcy risk. Applying them in the empirical research presented in the article proved that the energy companies had varying financial health in 2019-2022, so in the period immediately before, and during the COVID-19 pandemic and the war in Ukraine. Most companies are not at risk of bankruptcy and have coped relatively well with the unstable situation in the global economy, including the commodity markets. Five companies (PGE, ENEA, PEP, ONDE and KOGENERA) obtained a T.Maślanka's model score above the cut-off point throughout the study period, indicating their good financial health. CEZ obtained results indicating a high risk of bankruptcy. The conclusions of the study are the first, known to the authors, researches about energy companies in Poland in the period of recent financial market fluctuations.

Early prediction of Ibex 35 movements

AbstractIn this paper, we examine the early predictability of the market's directional movement using intraday high‐frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high‐frequency time series of both price and certain technical indicators.The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.

Banking systemic risk estimating of China's banking industry during the COVID-19 pandemic—based on complex network theory

The spread of COVID-19 worldwide has led to significant fluctuations in the global financial market, and the banking industry has also been exposed to the dual impact of the real economy and the financial market. In this paper, Chinese bank networks in different COVID-19 periods were constructed using CoVaR model and least absolute shrinkage and selection operator (LASSO) regression, and the characteristics and risk changes of the proposed bank networks were analyzed based on complex network theory. Additionally, the index of complex network theory was combined with the non-performing loan ratio of banks to build a new index for evaluating and measuring the systemic risk of banks in different periods. It was found that the network density, clustering coefficient and average network strength of banking networks increased during the COVID-19 pandemic, while the level of inter-bank correlation, and the systemic risk of banking networks increased. It was suggested after using the new indicators that joint-stock commercial banks and rural commercial banks were the main risk spillovers in different periods, and the systemic risk of these banks had increased during COVID-19. Therefore, the supervision of joint-stock commercial and rural commercial banks should be strengthened to prevent the risks from spreading to these banks during the COVID-19 pandemic.

DYNAMIC RELATIONSHIP BETWEEN CAPITAL INFLOWS AND BANK LENDING: THE ROLE OF COMMODITY PRICE AND GLOBAL FINANCIAL MARKET SHOCKS

This paper investigates the dynamic relationship between capital inflows and bank lending in Indonesia. We use a Structural Vector AutoRegression (SVAR) model that allows exogenous international commodity prices and global financial market fluctuations to influence capital inflows. We find that commodity price shocks are more important as compared to global financial shocks in explaining the variance of capital inflows in the long run. Furthermore, shocks from capital inflows lead to a change in bank lending allocation across economic sectors.

A Blockchain and Metaheuristic-Enabled Distributed Architecture for Smart Agricultural Analysis and Ledger Preservation Solution: A Collaborative Approach

Distributed forecasting of agriculture commodity prices has an attractive research perspective that delivers active breakthrough analysis of the rapid fluctuations in pricing forecasts for participating stakeholders without being manually dispatched lists. The increased use of an efficient forecasting mechanism for the agriculture information management of generated records and processing creates emerging challenges and limitations. These include new government mandates and regulations, the price of land for expansion, forecasting the growing demand for commodities, fluctuations in the global financial market, food security, and bio-based fuels. Building and deploying distributed dynamic scheduling, management, and monitoring systems of agricultural activities for commodity price forecasting and supply chains require a significant secure and efficient approach. Thus, this paper discusses a collaborative approach where two different folds are demonstrated to cover distinct aspects with different objectives. A metaheuristic-enabled genetic algorithm is designed to receive day-to-day agricultural production details and process and analyze forecast pricing from the records by scheduling, managing, and monitoring them in real-time. The blockchain hyperledger sawtooth distributed modular technology provides a secure communication channel between stakeholders, a private network, protects the forecasting ledger, adds and updates commodity prices, and preserves agricultural information and node transactions in the immutable ledger (IPFS). To accomplish this, we design, develop, and deploy two distinct smart contracts to register the system’s actual stakeholders and allow for the addition of node transactions and exchanges. The second smart contract updates the forecasting commodity pricing ledger and distributes it to participating stakeholders while preserving detailed addresses in storage. The simulation results of the proposed collaborative approach deliver an efficient E-agriculture commodity price forecast with an accuracy of 95.3%. It also maintains ledger transparency, integrity, provenance, availability, and secure operational control and access of agricultural activities.

Analysis of Causal Relationship Between Different Cortical Regions Measured by Transfer Entropy

The behavior information of financial market plays a more and more important role in modern economic system. The behavior information reflected in INTERNET search data has already been used in short-term prediction for exchange rate, stock market return, house price and so on. However, the long-run relationship between behavior information and financial market fluctuation has not been studied systematically. Further, most traditional statistic methods and econometric models could not catch the dynamic and non-linear relationship. An attention index of CNY/USD exchange rate is constructed based on search data from 360 search engine of China in this paper. Then the DCCA and Thermal Optimal Path methods are used to explore the long-run dynamic relationship between CNY/USD exchange rate and the corresponding attention index. The results show that the significant interdependency exists and the change of exchange rate is 1–2 days lag behind the attention index.

Multistability Regions and Related Basins of Multiattractors in Piecewise Linear Discontinuous Map from Financial Markets

By adding trend followers, we extend the model given by Tramontana et al. from one-dimensional ([Formula: see text]D) piecewise linear discontinuous (PWLD) map to a new 2D PWLD map. Using this map in financial markets, we describe the bifurcation mechanisms associated with the appearance/disappearance of cycles, which may be related to several cases: border collision bifurcations; Poincaré equator collision bifurcations; degenerate flip bifurcations in both supercritical and subcritical cases. We investigate the multistability regions in the parameter plane and related basins of multiattractors to uncover the reason for the unpredictability of the internal law of price fluctuations in financial market.

Financial market volatility based on complex network and fuzzy logic theory

Volatility is an inherent attribute of the financial market, which is usually expressed as the degree of volatility of financial asset prices. The volatility of the financial market means that there is uncertainty or risk in the market. This paper mainly studies financial market fluctuations based on complex networks and fuzzy logic theory. This article first systematically organizes and summarizes the theoretical construction of complex networks and fuzzy logic. In terms of complex networks, the definition of complex networks, the theory of commonly used functions (classical models of complex networks) and the solving methods are sorted out. In the construction of fuzzy logic theory, starting with quantifiable financial market volatility indicators, the construction models of realized volatility and implied volatility are discussed, and complex network models of implied volatility and model-free models are discussed. The theoretical construction methods were compared and analyzed. Finally, it summarizes the theoretical construction methods of implied volatility index and points out the advantages of model-free implied volatility as a market volatility and risk measurement index, which contains more effective future risk information and is based on implied volatility. The empirical research on indexes and complex network models has laid a theoretical foundation. Experimental data shows that the bond market and the foreign exchange market have the largest fluctuations in the correlation coefficient, reaching 0.35; followed by the stock market and the bond market, which is about 0.17; the stock market and foreign exchange market with the smallest fluctuations are about 0.08. The experimental results show that the financial market volatility research data based on complex networks and fuzzy logic theory is more accurate.