Stock Index Changes Research Articles

Navigating Inflation Challenges: AI-Based Portfolio Management Insights

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.

Stock market fluctuation and stroke incidence: A time series study in Eastern China

ObjectiveTo explore the association between daily change of Shanghai Stock Exchange (SSE) Composite Index and stroke incidence in Zhejiang, China. MethodsData on daily stock index change and stroke incidence during 2009–2016 were collected to form the time series. Data were analyzed using distributed lag non-linear model (DLNM) with quasi-Poisson as link function, controlling for long-term and seasonal trends, day of the week, public holiday, meteorological factors, and economic conditions. ResultsLarge changes in daily stock index were associated with increased risk of stroke. Rise of stock index was significantly related to higher stroke incidence without delay. The relative risks (RRs) of stroke on the lag0 day were 1.040 (95% CI 1.011–1.071) for 100 index increase, and 1.111 (95% CI 1.000–1.235) for 200 index increase. Additionally, fall of stock index was associated with higher stroke incidence at lag of 5 and 6 days. The relative risks (RRs) of stroke for −200 index decline on the lag5 and lag6 day were 1.058 (95% CI 1.025–1.093) and 1.061 (95% CI 1.019–1.104) respectively. The association was relatively consistent across subgroups stratified by the subtype of stroke, gender, and age groups. ConclusionBoth rise and fall of stock index were associated with an elevated occurrence of stroke.

Combination of transition probability distribution and stable Lorentz distribution in stock markets

It is well known that the stock price is a stochastic series of time. The probability distribution of the stock price changes should be stable. However, the empirically observed probability distribution of log-returns is not Gaussian and has power-law tails. We present a combination of Lorentz stable distribution and transition probability distribution description for the stock index changes. The Lorentz stable distribution describes the stochastic changes of stock prices and the transition distribution presents features of stock price transiting from S(t) to S(t+Δt) for small time interval. By making use of the Fokker–Planck equation, we give the explicit expression of the transition probability distribution for the stock price changes. The two stage formalism is in agreement with empirical observations on high frequency time series of the S&P 500 Index.

Monitoring and Training Stock Prediction System For Historical & Live Dataset using Lstm & Cnn

A country development and stability are directly associated with its economy and today’s economy is profoundly dependent on the Stock market. Stock market indexes are subject to continuous change with respect to time, a hype or fall in the stock market has a crucial role in deciding the investor’s profit. Due to the economical ups & down and rapid growth in profit from the stock market, there required a need of developing a software application which continuously monitor the stock index’s and a prediction algorithm which can predict the possible change in stock index as for where it can go in future. Prediction of stock market does not follow any rules or predefined guidelines, hence prediction of stock market is difficult to achieve and the data-set for stock market prediction is also non-linear in nature which requires an efficient approach to resolve the time-series dependency of non-linear data. In our proposed system we are using the LSTM (long short-term memory) for efficiently predicting the stock index on historical data and the sudden change in stock market due to number of un-controllable factors is analysed by CNN model. As per the noise in the data-set we are employing wavelet denoising technique. If any changes in stock index with more than 10% of its initial value is analysed by monitoring module, then the system will notify the user with the change and also aggregating the result of predicting algorithm on that specific stock. Using our model Moneypred the accuracy in stock prediction is more than 70%.

Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network

In order to establish an accurate effective stock forecasting model, the principal component analysis (PCA) was first used to analyze the main financial index data of some listed companies and the comprehensive score of evaluation index was obtained in this study. Then, the financial indicator data and the transaction indicator data were simultaneously used as the input variables of the stock price prediction research, three back propagation (BP) neural network algorithms were used for experiment, and its prediction situation was compared. Results show that the BP neural network based on Bayesian regularization algorithm has the highest prediction accuracy and can avoid over-fitting phenomenon in the training process of the model; the error between the predicted value and the actual value is small. Finally, this study constructed a stock price prediction study based on PCA and BP neural network algorithm as well as an investment stock selection strategy based on traditional stock selection analysis method. As a result, the proposed model is proved to be effective.

A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data

Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

Large Daily Stock Variation Is Associated with Cardiovascular Mortality in Two Cities of Guangdong, China

ObjectiveThe current study aimed to examine the effects of daily change of the Shenzhen Stock Exchange Index on cardiovascular mortality in Guangzhou and Taishan, China.MethodsDaily mortality and stock performance data during 2006–2010 were collected to construct the time series for the two cities. A distributed lag non-linear model was utilized to examine the effect of daily stock index changes on cardiovascular mortality after controlling for potential confounding factors.ResultsWe observed a delayed non-linear effect of the stock index change on cardiovascular mortality: both rising and declining of the stock index were associated with increased cardiovascular deaths. In Guangzhou, the 15–25 lag days cumulative relative risk of an 800 index drop was 2.08 (95% CI: 1.38–3.14), and 2.38 (95% CI: 1.31–4.31) for an 800 stock index increase on the cardiovascular mortality, respectively. In Taishan, the cumulative relative risk over 15–25 days lag was 1.65 (95% CI: 1.13–2.42) for an 800 index drop and 2.08 (95% CI: 1.26–3.42) for an 800 index rising, respectively.ConclusionsLarge ups and downs in daily stock index might be important predictor of cardiovascular mortality.

The causal nexus between oil prices and equity market in the U.S.: A regime switching model

The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S&P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S&P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S&P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S&P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns.

Examining the Role of Fiscal Policy in Malaysian Stock Market

This paper analyzes the relationship between stock index and macroeconomic policies (fiscal and monetary) using quarterly data for Malaysia for 1999:Q1–2011:Q4. The change in stock index was used as a proxy for financial performance. The co-integration test utilizes to detect the existence of long run relationship between stock index and macroeconomic variables and develops a vector error correction model (VECM) to test the existence of short run relationship (elasticities) among the variables. The findings indicate that there exists long run relationship among stock index, fiscal and monetary tools. This indicates that fiscal and monetary tools play an important role in accelerating financial performance in Malaysia. However, monetary tools can work faster than fiscal tools. This result would give a signal to investors to strategize their investment decision in the short and long run.

How 'Point Blindness' Dilutes the Value of Stock Market Reports

The stock index is a focal component of financial news reports. Though many reports draw attention to changes in major indices, few people realize that the value of a stock index changes frequently. We call this perceptual phenomenon point blindness. We examine causes of blindness and then propose alternate ways of reporting stock market information to counter it. The alternatives are easy to implement and can help citizens draw important inferences about stock values. An experiment shows that alternate modes of presentation have significant effects on public perceptions of the stock market.

Efficiency Tests in the Iberian Stock Markets

This paper investigates the efficiency of the two major stock indexes of the Iberian Peninsula, the Portuguese Stock Index (PSI-20) and the Spanish Stock Index (IBEX-35). We used daily data from January 1993 to September 2001 for the Portuguese stock index and daily data from October 1990 to September 2001 for the Spanish stock index. Serial correlations, unit root tests and variance ratio tests are used to test the efficiency of these two stock indexes. Although the complementary of these tests, we used all of them to get a higher robustness of the conclusions. We examined serial correlation coefficients for successive stock index changes to test whether they are statistically equal to zero to establish the random walk nature of stock indexes. The augmented Dickey-Fuller (ADF) test are used to test the null hypothesis that the series has a unit root and the variance ratio tests are used to examine the random walk hypothesis for the series of these two stock indexes. The results of the serial correlations, unit root tests and variance ratio tests provide ambiguous evidence for the random walk hypothesis. The empirical evidence from the unit root tests do not reject the efficient market.

AN EMPIRICAL STUDY ON THE STATISTICAL PROPERTIES OF ROMANIAN EMERGING STOCK MARKET RASDAQ

An empirical analysis of the Romanian emerging stock market RASDAQ based on the statistical study of the composite index RASDAQ-C reveals the leptokurtic profile of the probability density function (p.d.f.) of the stock index changes, the power law asymptotic behaviour of the p.d.f., the breakdown of scaling at long time scales, the absence of linear correlation in the stock index changes but existence of long-range correlation in nonlinear function such as the absolute value or the square of index changes (implicitly the long-range correlation in the index volatility). These results, consistent with the similar referring to the more liquid markets, suggest the presence of several universal features, in addition to several particularities related to the quickness of assimilation of the new information and its impact over the investors.

Forecasting the direction of change in sector stock indexes: An application of neural networks

In this study, neural network modelling procedures are used to forecast the direction of change in 19 sector stock indexes. Publicly available historical data on 19 economic indexes are used to perform the forecasts. Two separate sets of forecasts for each of the sector stock indexes are performed for the months January–May 2001 and for the months January–March 2003. The results are evaluated both on the basis of ability to forecast the direction of change in the stock index and on the basis of total gains in terms of index points. The results indicate that the models have some ability to forecast stock index changes.

Threshold effect in international linkage of stock prices

This paper examines whether the international linkage of the stock price indexes found in previous studies is confirmed for the case where the stock index changes only slightly, utilizing the daily stock price index data from 1975 to 1995 for the US, UK, Germany, and Japan. Using dummy variables in the regressions, it is shown that small changes in the stock price index of any country do not affect the other country's index. In contrast, large changes have a significant effect in most cases. Thus, there is a threshold effect in international linkage of stock prices. It is also shown that negative large changes have a clearer effect than positive ones.