Stock Market Price Fluctuations Research Articles

Research On the Impact of Stock Market Price Fluctuations on Corporate Financing Costs

The fluctuation of stock market prices is one of the research areas that has attracted much attention in the current financial market. As the core of the stock market, price fluctuations not only affect investors’ investment returns, but also increase the financing costs of enterprises. This article delves into the factors that affect stock market price fluctuations, analyzes the impact of stock market price fluctuations on corporate financing costs, and further proposes corresponding suggestions to promote sustainable development of enterprises and stable operation of financial markets.

Influence of RMB Internationalization on International Crude Oil Price Fluctuation

In recent years, China has been gradually improving its international status. As far as monetary policy is concerned, China has been gradually promoting the internationalization of the RMB, aiming to reduce its dependence on the international financial market dominated by the US dollar and reduce the impact of the Federal Reserve's political measures on the domestic economy. Under the influence of global economic and financial market factors such as COVID-19, the international financial system dominated by the US dollar is unstable and dangerous, and the international financial system has become more volatile. In contrast, the implementation of this measure will have an impact on some international financial transactions, such as stock market price fluctuations, commodity prices and foreign exchange transactions. For a long time, the oil price has been dominated by the US dollar due to the linkage between the US dollar and oil, and has increased with the appreciation of the US dollar. However, the process of people's internationalization affected the oil price by changing this traditional logic and impacted the global status of the US dollar, thus reducing the oil price by triggering the decline of the US dollar. This paper would like to focus on elaborating the influence of RMB internationalization on international crude oil price fluctuation.

Stock Prediction Analysis and Risk Conduction Path Research Based on EMD-LSTM Model

Stock market, government bond market and corporate bond market are important components of my country's financial market. It is of great significance to study stock market price fluctuations and their forecasting methods, and to reveal the risk transmission path between them. The first part of this paper mainly studies the construction of the index price prediction model, and makes a comprehensive comparison of the high-frequency fluctuation characteristics, long-term trend characteristics and average trend characteristics of the EMD decomposition results of the Shanghai Composite Index, the SSE Government Bond Index, and the SSE Corporate Bond Index. The LSTM prediction model and the EMD-LSTM prediction model were established for the three sequences respectively, and the prediction effects of the models were comprehensively compared by calculating the RMSE and MAPE of the predicted values of the test set. The results show that after the introduction of the EMD method, the prediction effects of the SSE Composite Index and the SSE Corporate Bond Index prediction model have been greatly improved, but the prediction effect of the SSE Government Bond Index prediction model is far inferior to that of the LSTM model. The second part of this paper mainly studies the financial market risk analysis and transmission path. First, the fluctuation risk analysis and stationarity analysis of the logarithmic return series of the three index price series are carried out. The yield series are stable. Then, the LSTM models based on different training data predict three index price series and compare the prediction results to study the similarity of the price fluctuation mechanism of the three financial markets. The results show that the price fluctuation mechanism of the stock market is similar to that of the corporate bond market, and the price fluctuation mechanism of the government bond market is more similar to the corporate bond market than the stock market. The paper conducts E-G cointegration test on three logarithmic return series that have been tested for stationarity to study the long-term equilibrium relationship between the three markets and finds that there is a cointegration relationship between the SSE Composite Index and the SSE Government Bond Index, as well as the SSE Government Bond Index and the SSE Corporate Bond Index. Finally, the Granger causality test is carried out between the sequences with cointegration relationship, and the result shows that the risk transmission mechanism between the national debt market and the corporate bond market is strong, while the risk transmission mechanism between the stock market and the bond market is weak.

Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach

As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments.

A Study of Stock Market Predictability Based on Financial Time Series Models

In today’s era of economic globalization and financial integration, the stock market is constantly complex, showing many deviations that cannot be explained by classical financial analysis, but at the same time, some classic financial statistical features have striking similarities. This suggests that although the stock market is intricate, there are universal laws that can be found through data mining to find its underlying operating rules. In this paper, we construct financial time series models such as ARIMA, ARCH, and GARCH to predict the stock market price fluctuations and trends. The ARIMA model is used to fit the linear financial time series, and the GARCH model is used to fit the nonlinear time series residuals. The results show that the integrated tree model based on the idea of weight voting has high accuracy in predicting stock market bulls and bears, with XGBoost prediction accuracy up to 96%, and the neural network model is also very effective, with an accuracy rate of over 90%.

A graph-based approach to multi-source heterogeneous information fusion in stock market

The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations.

A Stochastic Analysis of Stock Market Price Fluctuations for Capital Market

A Stochastic Analysis of Stock Market Price Fluctuations for Capital Market

Stock Prices in Nepal: Macroeconomic Determinants

The article attempts to identify the relationship between major determinants and its impact of stock market of Nepal. On employing, Regression Analysis on data set for the period of 1994-2020, it is found that there is significant relation between determinants of stock market and stock price. The findings imply that stock market price fluctuations are closely related to broad money supply, interest rate, inflation, and exchange rate in the long run. The fluctuation of stock market prices in relation to interest rates is in the opposite direction. In the short run, GDP, money supply, and exchange rate all have positive relationships, but only the money supply has a positive relationship in the long run. Stock market prices rise as the money supply expands. This implies that an increase in money supply leads to economic expansion through increasing cash flows, and that an expansionary monetary policy would help stock values. Capital market development strategies should be coordinated with macroeconomic fundamental policies.

Can Crude Oil Serve as a Hedging Asset for Underlying Securities?—Research on the Heterogenous Correlation between Crude Oil and Stock Index

In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China’s stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger.

Long-Range Correlations and Memory in the Dynamics of Internet Interdomain Routing.

Data transfer is one of the main functions of the Internet. The Internet consists of a large number of interconnected subnetworks or domains, known as Autonomous Systems (ASes). Due to privacy and other reasons the information about what route to use to reach devices within other ASes is not readily available to any given AS. The Border Gateway Protocol (BGP) is responsible for discovering and distributing this reachability information to all ASes. Since the topology of the Internet is highly dynamic, all ASes constantly exchange and update this reachability information in small chunks, known as routing control packets or BGP updates. In the view of the quick growth of the Internet there are significant concerns with the scalability of the BGP updates and the efficiency of the BGP routing in general. Motivated by these issues we conduct a systematic time series analysis of BGP update rates. We find that BGP update time series are extremely volatile, exhibit long-term correlations and memory effects, similar to seismic time series, or temperature and stock market price fluctuations. The presented statistical characterization of BGP update dynamics could serve as a basis for validation of existing and developing better models of Internet interdomain routing.

Causal Relationship between Macro-Economic Indicators and Stock Market in India

This paper investigated the market efficiency and causal relationship between selected Macroeconomic variables and the Indian stock market during the period January 2005 to February 2011 by using Ljung-Box Q test, Breusch-Godfrey LM test, Unit Root test, Granger Causality test.The study confirms the presence of autocorrelation in the Indian stock market and macro economic variables which implies that the market fell into form of Efficient Market Hypothesis. Further the Granger-causality test shows evidence of bidirectional relationship between interest rate and stock market, exchange rate and stock market, international stock market and BSE volume, exchange rate and BSE volume. So it suggests that any change of exchange rate, interest rate and international market significantly influencing the stock market in the economy and vice versa. The study also reported unidirectional causality running from international stock market to domestic stock market, interest rate, exchange rate and inflation rate indicating sizeable influence in the stock market movement in the considered period. The study points out that the Indian stock market is sensitive towards changing behavior of international market, exchange rate and interest rate in the economy and they can be used to predict stock market price fluctuations.

How much trust should risk managers place on “Brownian motions” of financial markets?

PurposeThe purpose of this paper is to investigate the recent global economic downturn. Particularly, the study explores the utilization of the concept of Brownian motion in financial risk management in organizations in the USA.Design/methodology/approachThe three assumptions, namely, independence, stationarity, and normal distribution that underlie the concept of Brownian motion are examined.FindingsIt is concluded that the widely used risk management strategies predicated on Brownian motion fail to provide a rational understanding of financial turmoil. Consequently, prescriptive insights are offered to aid the industry in developing an apposite mechanism for risk management.Research limitations/implicationsThis paper offers new and improved risk management strategies that need to be undertaken to augment our understanding and prediction of financial scenarios.Practical implicationsThe paper is useful for managers in all financial organizations, which employ computer models using Brownian motions. Specifically, this study contends that static models are unsuitable and dynamic models are more useful for risk assessment.Originality/valueThe paper reveals the weaknesses of the key assumptions of the risk management models used in financial organizations, namely, normal distribution of stock market price fluctuations, statistical stationarity, and efficient market assumption. Valuable guidelines are provided for financial managers who either do not have the inclination or time to sift through the voluminous literature related to the risk management models and computer software designed on these models.

Non-extensive behavior of a stock market index at microscopic time scales

This paper presents an empirical investigation of the intraday Brazilian stock market price fluctuations, considering q -Gaussian distributions that emerge from a non-extensive statistical mechanics. Our results show that, when price returns are measured over intervals less than one hour, the empirical distributions are well fitted by q -Gaussians with exponential damped tails. Scaling behavior is also observed for these microscopic time intervals. We find that the time evolution of the return distributions is according to a super-diffusive q -Gaussian stationary process within a nonlinear Fokker–Planck equation. This regime breaks down due to the exponential fall-off of the tails, which in turn, governs the transient dynamics to the long-term macroscopic Gaussian regime. This exponentially damped, non-extensive modeling provides a new framework to investigate the dynamics of other stock markets intraday price fluctuations.

"BEHAVIORAL ECONOMETRIC" INTERPRETATION OF DYNAMIC SUPPLY AND DEMAND FUNCTIONS IN A MARKET PRICING MODEL

I present a model of stock market price fluctuations incorporating effects of share supply as a history-dependent function of previous purchases and share demand as a function of price deviation from moving averages. I discuss interpretation of model parameters and of the form of dynamically evolving supply and demand functions. Parameters and dynamic supply and demand functions can be regarded as providing quantitative behavioral characterization of investors within markets. Price charts generated assuming different dynamic supply functions exhibit structure consistent with the hypothesis that dominant investor behavior is different on different time scales.

Generation of predictive price and trading volume patterns in a model of dynamically evolving free market supply and demand

I present a model of stock market price fluctuations incorporating effects of share supply as a history‐dependent function of previous purchases and share demand as a function of price deviation from moving averages. Price charts generated show intervals of oscillations switching amplitude and frequency suddenly in time, forming price and trading volume patterns well‐known in market technical analysis. Ultimate price trends agree with traditional predictions for specific patterns. The consideration of dynamically evolving supply and demand in this model resolves the apparent contradiction with the Efficient Market Hypothesis: perceptions of imprecise equity values by a world of investors evolve over non‐negligible periods of time, with dependence on price history.

Self-organized percolation model for stock market fluctuations

In the Cont–Bouchaud model [cond-mat /9712318] of stock markets, percolation clusters act as buying or selling investors and their statistics controls that of the price variations. Rather than fixing the concentration controlling each cluster connectivity artificially at or close to the critical value, we propose that clusters shatter and aggregate continuously as the concentration evolves randomly, reflecting the incessant time evolution of groups of opinions and market moods. By the mechanism of “sweeping of an instability” [Sornette, J. Phys. I 4, 209 (1994)], this market model spontaneously exhibits reasonable power-law statistics for the distribution of price changes and accounts for the other important stylized facts of stock market price fluctuations.

Modeling the stock market prior to large crashes

We propose that the minimal requirements for a model of stock market price fluctuations should comprise time asymmetry, robustness with respect to connectivity between agents, “bounded rationality” and a probabilistic description. We also compare extensively two previously proposed models of log-periodic behavior of the stock market index prior to a large crash. We find that the model which follows the above requirements outperforms the other with a high statistical significance.