Changes In Stock Market Research Articles

Empirical Analysis in The Concept of Indian Financial Market Using Machine Learning

This study aims to examine the relationships among the constituents of the Indian stock market, specifically, to examine the relationship between the S&P BSE Oil & Gas index and four other sectors, such as, S&P BSE IT, S&P BSE energy, S&P BSE auto, and S&P BSE power are being specifically explored. The aim of this study is to understand the potential effects of variations in the BSE Oil & Gas index on these industries. For a variety of stakeholders, including investors, politicians, and market analysts, this study is vital because it offers critical insights into the economies that form the basis of India. With the help of various methodologies, this study examines historical stock price fluctuations by using statistical methods and data analysis, with a focus on time series modelling. In order to assess and comprehend the possible effects of changes in the Indian stock market, this study carefully looks at important financial indicators and pertinent market trends. Examining any asymmetries in these relationships will help determine if the oil and gas business experiences positive or negative shocks, and which ones have distinct effects on the various sectors under consideration. This study is one of various contributions into the global discourse on market interdependence that provides useful guidelines for the Indian stakeholder in handling their vital segments of the markets in India.

Impact of government interventions on the stock market during Covid-19: A case study in Vietnam

The study aims to analyze the reaction of the stock market to the events of government intervention during the Covid-19 pandemic. The author used data from 341 companies listed on the Ho Chi Minh City Stock Exchange, which were divided into 10 sector groups in the period 2019–2021. Using the event research method, the author analyzed the change in stock market returns around the event date. The research brought many interesting results. Firstly, policy distancing, blockdown, restricting exposure, and restricting travel have a negative effect on the stock market. Secondly, the three times the state bank has reduced interest rates, it shows a positive reaction in the stock market. The findings help investors, regulators, and governments understand the short-term impact of interventions.

Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components

The main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of BFTSC and GFTSC based on linear trend less volatile data. They are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. Both techniques detects 99 % of the entire components in the time series data in a linear trend data.

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

Research on Investor Sentiment and Its Impact on the Market for Stocks

Both individual and institutional investors are affected by investor sentiment to varying degrees. Based on this, the research theme for this paper is the interaction between investor sentiment and the market for stocks. It begins with defining and quantifying the idea of investor sentiment and then analyzes how investor sentiment and the market for stocks are related, including stock market yield, stock price crash risk, and stock market bubble aspects. It is found that the definition of investor sentiment mostly revolves around the perspectives of "expectation" and "deviation", and the quantitative indicators of investor sentiment are mainly divided into three parts: objective, subjective, and network big data. Investor sentiment is highly predictive of changes in stock market yields in the short term, and high investor sentiment is more likely to lead to stock price crash risk or stock market bubbles. Finally, this paper points out the shortcomings of existing research, and puts forward the future prospects of relevant research conclusions, so as to provide a reference for subsequent scholars to conduct research on investor sentiment and the stock market.

Topological tail dependence: Evidence from forecasting realized volatility

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation of this study provide evidence that the predictions drawn from the Topological Tail Dependence Theory are correct and indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a turbulent period.

A Comparative Study of Chinese and American Stock Markets in the Context of the COVID-19- A Comparative Analysis of Several Crises

In the context of COVID-19, this paper compares the stock market performance and financial policies between China and the United States, as well as the similarities and differences of SARS in 2002, the U.S. subprime mortgage crisis in 2008, the U.S.-China trade frictions in 2018 and the COVID-19 in terms of these events impact on stock markets. By reviewing literature and event studies, this paper conducts a cross-sectional comparison and a longitudinal comparison of the Chinese and U.S. stock markets respectively. The study shows that the impact of COVID-19 on China is far less than that of the U.S. China adopted a more accommodative and longer-lasting financial policy which increased the stock markets independence significantly through sudden crisis events. The findings will help scholars to understand the performance of the U.S. and Chinese stock markets under several crisis events and the changes in the Chinese stock market over time.

Research on the Influence of Monetary Policy on Stock Market Return

These changes in China's stock market are influenced by factors such as the share-trading reform and the international financial crisis. During this period, a series of monetary policies implemented in China also had some profound effects on China's stock market. Capital market has always been the main transmission channel of monetary policy. In recent years, China's interest rate system, share-trading system and other reforms have been gradually implemented, and the reform of interest rate marketization has achieved certain results, but it has also brought more uncertainty to the transmission of stock market and monetary policy. With the continuous expansion of the stock market, it has played an important role in the transmission process of monetary policy. At the same time, monetary policy can affect the price of financial assets, thus changing people's behavioral decisions and achieving monetary policy goals. When the actual monetary policy changes beyond or below expectations, it will cause the change of stock price. The existence of expectation effect makes the relationship between monetary policy and stock price change more complicated: taking loose monetary policy as an example, when the actual degree of easing is greater than investors' expectations, stock prices will be positively stimulated by easing; When the degree of easing is less than investors' expectations, the stock price will turn downward. Therefore, it is worth studying to analyze the influence mechanism among monetary policy, stock market and real economy.

Stock Price Prediction with Traditional Statistics and Machine Learning

Stock forecasting is a very difficult and challenging task. There are so many subjective factors that are difficult to quantify and it is almost impossible to take all of them into consideration. The stock market changes so fast that forecasting models need to have forecast results in a relatively short period of time. For ordinary investors, it is necessary to predict relatively accurate results with less data. This paper compares the traditional statistical model with the machine learning forecasting models, linear regression, Backpropagation neural networks, time series ARIMA and LSTM. After predicting and comparing the four stocks with significantly different changes, it is found that the models with better prediction effects are different in different situations. In view of the fact that the neural network method requires a larger amount of data as a training set and needs to be retrained each time, so linear regression may be more suitable for ordinary investors.

한국주식시장에서 가용성 어림짐작과 닻내림 어림짐작의 유효 성에 관한 실증연구

Purpose - The purpose of this paper is to compare and review behavioral economics models that explain stock price changes after large-scale price shocks in the Korean stock market and to find a suitable model. In this paper, among the theories reviewed, it was confirmed that the anchoring heuristics theory has high explanatory power for stock prices after large-scale stock price fluctuations.
 Design/methodology/approach - This paper conducts an event study on stock price shocks in which the individual stocks that make up the KOSPI200 index show more than 10% fluctuation on a daily basis. In order to materialize the abstract predictions of heuristics theories in a varifiable form, this paper uses the daily stock price index change as a reference point for availability heuristics, and uses the 52-week highest and lowest price as reference point for anchoring heuristics.
 Research implications or Originality - As a result of the empirical analysis, the stock price reversals did not consistently appear for changes in the daily index. On the other hand, the stock price drifts consistently appeared around the 52-week highest and the 52-week lowest price. And in the multiple regression analysis that controlled for company-specific and event-specific variables, the results that supported the anchoring heuristics were more evident. These results suggest that it is possible to establish an investment strategy using large-scale price change in Korean stock market.

Impact of the war in Ukraine on the global stock market

This article focuses on and analyzes how the full-scale invasion of Russia on the territory of Ukraine on February 24, 2022, affected and continues to affect the part of the global capital market where securities are issued, bought and sold, namely the stock market. It is determined what any price changes are based on here; how stock markets can reflect the expectations of a certain group of people, in this case investors around the world, during a period of war; sanctions and pressure on the stock market. Also, the average response of twenty companies included in the Top-500 best stocks in the Center for Research in Security Prices database to percentage changes in the weighted commodity price index was evaluated. That is, we estimated how much a 1% change in the commodity price index would change the share prices of each of the 20 companies. It is determined separately how the military conflict affected the securities of American companies operating in various industries; for whom the war helped to increase capitalization, and for whom it contributed to the deterioration of the general financial condition. How the ranking of the best-performing S&P 500 stocks has changed in 2022. Why on September 13, the share prices of many global companies decreased, and the three main stock indexes (Dow Jones Industrial Average, Nasdaq Composite and S&P 500) had their worst day since June 2020. Forecast of their further changes. Separately, in addition to the American one, the changes that took place in the stock markets of Eastern countries, using the example of China, Thailand, India and Indonesia, are highlighted. Expected changes in the Russian stock market and the possibility of its complete disappearance due to sanctions imposed by various countries of the world are described. The situation on the market of Ukraine itself and the options of what exactly to invest in for domestic investors during the period of active military operations were considered. In addition to all this, the solution to problems related to the volatility of the stock market is highlighted. Thus, the question of the influence of the war between Ukraine and Russia on the state of the world stock markets, in modern realities, is paid a lot of attention not only by Ukrainian scientists, but also by foreign ones. This is not surprising, since it is impossible to ignore that the recent events have caused a chain effect, affecting firstly the prices of goods, the volume of exports, and subsequently the capitalization and growth or decline of the prices of securities of companies around the world.

Trade Conflict, Capital Flows and Dynamic Changes in Chinese Stock Market

In Mar, 22nd 2018, the U.S. government announced the list of tariff-imposed commodities, which will impose 25% tariffs on 1,333 items worth US$50billion of goods exports from China to the U.S. The trade war between China and US has been lasted for 4 years and 5 months, the US government has increased the tariffs 6 times, and specifically, this brings a huge impact on capital flows and dynamic changes in Chinese stock market. This paper demonstrates how increase the tariff bring huge impacts on Chinese stock market by analyzing different models, such as ADF test, VAR model, and ARMA-GARCH model, and by studying the results to better understand how Chinese stock market influenced by the tariff increase, and to provide an accurate prediction for the future trend of the Chinese stock market and make recommendation for Chinese investors and Chinese government. The trade war severe impact Chinese economy, and the stock price is falling which lead to investor to hold their money instead of investing in the market, and investors rushed to the safety of low-yielding government debt. The ARMA-GARCH model analyse and present the data and represents the impacts by analysing the stock returns and the volatility. Lastly, there are some recommendations based on current economic situation.

Analyzing the data from beverage companies to evaluate the validity of the Fama-French three-factor model

The Fama-Fench 3-factor model (FF3) is one of the most commonly used models for valuing companies. It is curious to see whether this model can reasonably capture the changes in the treacherous stock market and accurately predict the expected returns of a company in practice. In this article, machine learning is adopted to analyze data from the past five years in terms of the 10-year t-bill return as the risk free rate and the S&P 500 rate of return as the market rate of return to compare and analyses the rate of return of Nestle, Danone, Unilever, Coca-Cola and PepsiCo over the past five years. In the comparative analysis, this essay randomly selected the rate of return for 80% of the days in the past five years as the training set and used the rate of return for the remaining 20% of the days as the test set. Besides, this study uses regression analysis to obtain a theoretical model of FF3, which is then brought to the test set data for testing. In this article, the null hypothesis is that the error is equal to 0 and the alternative hypothesis is that the error is not equal to 0. According to the analysis, hypothesis validation is adopted to check the accuracy of FF3 by analyzing the results of the hypothesis test and using the FF3 derived from the training set to predict whether the error between the resulting data and the actual data is 0.

Metaheuristic enabled intelligent model for stock market prediction via integrating volatility spillover: India and its Asian and European counterparts

Recently, the price of a stock market changes often owing to a variety of factors. As a result, making an accurate stock price prediction is a difficult process. Hence, this research work proposes a novel intellectual stock market prediction model that incorporates the volatility spillover over Indian and its Asian countries. This intellectual model mainly involves two phases like data library construction and stock market prediction. For stock market prediction, a Neural Network (NN) model is employed and this model intake the data of calculated indicators in the data library and makes the prediction of Indian market. To attain more precise prediction, the NN weight is optimally chosen via novel hybrid algorithm namely Fly Updated Whale Optimization Algorithm (FU-WOA) that is the hybridization of WOA and Firefly Algorithm (FF). At last, the suggested model performance is exploited by comparing other conventional models in the view of various metrics. Especially, the computational cost of the proposed hybrid FU-WOA–NN model is 38.12%, 15.96%, 15.52%, 41.22%, 16.07% and 16.33% better than existing LM-NN, FF-NN, GWO-NN, WOA-NN, PCA as well as ARIMA methods respectively.

The role of the past long-run oil price changes in stock market

This paper examines the ability of the past long-run changes in oil price to predict the stock returns in the U.S. market. We find this long-lag model performs much better than the one-lag model. The past long-run changes in oil price contain useful information about future real stock returns and excess returns over a Treasury bill rate. This variable alone can capture more than 1% variations of next horizon (month) excess returns, and the predictive power are increasingly strong for long-horizon stock return. These findings are robust when considering other popular predictors into the model, these results are also maintained when considering various subsamples. For out-of-sample examination, the results of McCraken’s (2007) Ros2 and Clark and West’s (2007) MSPE-adjusted statistic explore that this variable contains useful information of future stock returns. More interestingly, the past long-run oil price changes also perform strong predictive power on excess returns for non-US countries.

The Russian-Ukraine Conflict, Crude Oil Price Fluctuation and Dynamic Changes in Stock Market: Evidence from the U.S and China

Political struggle is an important factor affecting the pattern of the international oil trade. The escalation of collisions between Russia and Ukraine, and the economic sanctions that the United States and European countries implemented against Russia had a significant impact on Russian oil exports. In this context, it is crucial to study how Chinese and American stock markets react to the burnt of international crude oil prices, in order to understand the phenomenon of risk contagion between markets and prevent risks. This paper selects the settlement prices of the Dow Jones index, S&P 500 index, Nasdaq index, Shanghai index, Shenzhen index, and international crude oil futures prices from three months before the Russian-Ukraine Conflict to the end of May 2022 to illustrate the tolerance to the volatility of two markets, using VAR and ARMA-GARCH model. The empirical results indicated that crude oil price fluctuation has a stronger impact on the US stock market than on the Chinese stock market, and specific stocks act differently during this “black swan incident”.

Tahvil ve Hisse Senedi Piyasaları Arasında Zamanla Değişen Nedensellik İlişkileri: Hatemi-J Dinamik Nedensellik Testi Bulguları

İktisadi gelişmeler ve risk faktörlerindeki belirsizlik durumu varlık sınıfları arasındaki ilişkileri zamana bağlı olarak değiştirebilmektedir. Finans teorisinde merkezi bir konumda olan tahvil ve hisse senedi piyasaları arasındaki nedensellik ilişkilerine odaklanan bu çalışma, tahvil piyasasından hisse senedi piyasasına doğru simetrik ve asimetrik nedensellik ilişkisinin olup olmadığını ve eğer varsa bu ilişkinin zamana bağlı olarak değişip değişmediğini ortaya koymayı amaçlamaktadır. Analiz yöntemi olarak Hatemi-J (2021) tarafından geliştirilen dinamik simetrik ve asimetrik nedensellik testleri benimsenmiştir. Analiz Ocak 1980 ile Nisan 2022 tarihleri arasında gerçekleştirilmiş ve ABD (Amerika Birleşik Devletleri) tahvil ve hisse senedi piyasası incelenmiştir. Dinamik simetrik ve asimetrik nedensellik testi bulguları tahvil piyasasından hisse senedi piyasasına doğru olan nedensellik ilişkisinin zamana bağlı olarak değiştiğini göstermektedir. Yani tahvil piyasası ile hisse senedi piyasası arasındaki nedensellik ilişkilerinde yapısal değişimler ve zaman bağımlılığı mevcuttur. Bu durumda statik modellerin riskten korunma, çeşitlendirme ve varlık dağılımı gibi kararlarda taraflı yönlendirme yapabileceği ortaya konulmuş olmaktadır.

Empirical Analyses of OLMAR Method for Financial Portfolio Selection in Stock Markets

The OLMAR method, which stands for the on-line moving average reversion method, is reported to be one of the most powerful among portfolio selection algorithms in the stock markets. In this research, we use intensive statistical and simulation analyses of long-term data on stock market changes to uncover the secrets of why and when the superiority appears. We find that there have been long-lasting fluctuations in the stock markets and that the OLMAR method actively makes use of such characteristics. In this paper, we analyze long-term stock data from Japan and the United States. The analyses confirm the following points. 1) The OLMAR method yields superior returns. 2) By using the moving average divergence rate provided by the OLMAR method, it is possible to detect specific fluctuation characteristics in long-term stock data from Japan and the United States. 3) Superior returns cannot be obtained from data in which specific fluctuation characteristics have been corrected.

A Comparative Study of Data Storage in Retail Management with Traditional Databases V/S Real Time Database

A real-time database is a database system which uses real-time processing to handle workloads whose state is constantly changing. This differs from traditional data containing persistent data, mostly unaffected by time. The graphs of the different markets appear to be very unstable and yet a database has to keep track of current values for all of the markets of the New York Stock Exchange. Real-time processing means that a transaction is processed fast enough for the result to come back and be acted on right away. Real-time databases are useful for accounting, banking, law, medical records, multi-media, process control, reservation systems, and scientific data analysis. For example, a stock market changes very rapidly and is dynamic. Traditional data is structured, relational data organizations have been storing and processing for decades. Traditional data still accounts for the majority of the world’s data.

Deep learning in the Chinese stock market: The role of technical indicators

A convolutional neural network (CNN) is applied to forecast stock price changes in the Chinese stock market. We use 27 technical indicators and 5 original price series as benchmark model setting and further explore the model forecasting performance with social media sentiment. Our results show that our model could obtain 70% forecasting accuracy on average. Moreover, social media sentiment could increase the forecasting performance for both indexes and individual stocks.