Daily Closing Values Research Articles

PERBANDINGAN RETURN PENGGUNAAN MOVING AVERAGE CONVERGENCE DIVERGENCE DAN RELATIVE STRANGE INDEX PADA INDEKS SAHAM SYARIAH INDONESIA

This study compares the return levels from the use of the technical indicators Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) applied to the simulation of the movement of the Indonesia Sharia Stock Index (ISSI). This research is a population study using panel data in the form of daily closing values over 12 years of the Indonesia Sharia Stock Index movement. The data was processed using a technical analysis approach with two momentum indicators, MACD and RSI, to obtain buy and sell signals based on the principles of each indicator, applied to the ISSI. The use of the MACD and RSI technical indicators on the ISSI shows that MACD yielded a profit of 207% with a success rate (win rate) of 72%, while RSI provided a profit of 125% with a win rate of 55%.

Impulsive Response Analysis of Financial Markets in QUAD Economies: A VAR Modelling Approach

Financial market integration remains a key focus in academic finance, especially given how crucial it is to portfolio diversification. The purpose of this research is to investigate a crucial yet underexplored aspect like the cointegration of economies with formal or unofficial military ties, focusing on countries within the Quad strategic forum—India, Japan, Australia, and the United States. The daily closing values of benchmark stock market indices of Quad economies viz. Nikkie 225 for Japan, S&P ASX 200 for Australia, NASDAQ composite for the United States, and S&P Sensex for India were considered. Unit root tests, Johansen Cointegration tests, VAR (Vector Autoregressive) model, and the Granger Causality statistics are computed for data analysis. Additionally, the Impulse response function and variance decomposition are computed for better financial market predictions. The result reveals intriguing dynamics: while the Indian stock market initially responds positively to shocks in the Australian market, this effect diminishes over time. Also, movements in the Australian and United States stock markets significantly contribute to predicting fluctuations in the Indian market, contrasting with the limited predictive power of the Japanese stock market. These findings carry significant practical implications for international investors, offering valuable guidance for optimizing portfolio diversification strategies amidst geopolitical and economic complexities. This paper contributes to academic literature by offering empirical insights into the short-term dynamics and causal relationships of Quad countries’ stock markets, setting a foundation for future studies on long-term trends and market responses to external factors.

Returns and Asymmetric Volatility Interdependencies between Sustainable and Conventional Stock Indices: Evidence from Developed and Emerging Countries

The study investigates the co-integration and asymmetric volatility interdependence between the sustainable and conventional stock indices of emerging and developed countries. Daily closing values are used from 1st January 2015 to 15th July 2022. The study employs ARDL and VAR-Granger causality model to examine long-run association and cause-effect relation, respectively. Moreover, the Bivariate EGARCH model is also used to analyse return and asymmetric volatility spillover between two indices. The analysis reveals no evidence of co-integration between sustainable and conventional indices. However, bidirectional causality exists in all countries except the United Kingdom (UK) and United Arab Emirates (UAE). The return spillover result exhibits that return of the conventional index of Japan and Brazil are negatively affected by changes in its sustainable index. The EGARCH results reveal the bidirectional volatility spillover in all the markets. The volatility shocks from the sustainable index of the United States of America (USA) and China have an asymmetric effect on the conventional indices of these countries. Further, positive asymmetric spillover was observed between the sustainable and conventional indices of Brazil and UK. The knowledge of linkage and asymmetric volatility spillover will assist the investors in portfolio management and formulating hedging strategies during financial and economic adversities.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

The Nexus of Stock Markets Among BRICS Nations : An Empirical Analysis Pre and Post Spread of the COVID-19 Pandemic

The present corroborations from the empirical literature on the interconnections of BRICS stock markets have been conflicted. Thus, the present paper attempted to re-examine both the long and short-run nexus among BRICS stock markets before and after the COVID-19 pandemic. The current study period was segregated into two sub-periods to compare long and short-run dynamics among BRICS stock indices pre and through the times of distress and the exorbitant volatility caused by the COVID-19 pandemic. It used daily closing values of the BRICS stock market indices from January 1, 2010 to November 30, 2019 as the pre-spread COVID-19 pandemic phase and from December 1, 2019 to April 30, 2021, the post-spread COVID-19 pandemic phase. Furthermore, this study undertook the Johansen cointegration test, vector error correction model (VECM), and the Granger causality tests to examine the long and short-term nexus of the BRICS stock indices. The study findings suggested that BRICS stock markets shared linkages in totally different manners pre- and post-spread of the COVID-19 pandemic. The study findings will benefit institutional investors, individual investors, high net worth individuals, and public investors for finalizing their investment and hedging strategies for availing the opportunities of diversifying investment risk and earning high returns due to existing relations among the BRICS stock markets.

AN ASSESSMENT OF COINTEGRATION AND CAUSALITY RELATIONSHIPS BETWEEN BIST 50 AND KATILIM 30 INDICES

Ülkemizin sermaye piyasası platformu Borsa İstanbul'da (BIST) işlem gören KATILIM endeksleri son yıllarda her profilden yatırımcının ilgi odağı olmuştur. Bu çalışmada, BIST 50 geleneksel endeksi ile İslami prensiplere uygun faaliyet gösterdiği belirlenen 30 şirketin hisselerinden oluşan KATILIM 30 endeksi getirileri arasındaki uzun dönemde denge ve kısa dönem ilişkileri araştırılmıştır. Her iki endekse ait veri seti 06.01.2011 tarihinden 30.09.2021 tarihine kadar olan dönemin günlük kapanış değerlerinden oluşmaktadır. Johansen eşbütünleşme analizi sonucunda endeks getirileri arasında uzun dönemde en az 2 eşbütünleşme bulgusu; çift yönlü Granger nedensellik analizi neticesinde ise yalnızca BIST 50 endeksinden KATILIM 30 endeksine doğru tek yönlü istatistiksel olarak anlamlı nedensellik bulgusu saptanmıştır. Buradan hareketle, kısa dönemde geleneksel endekslerin İslami endeks getirileri üzerinde belirleyici bir etkisinin olduğu ve iki farklı metodolojiye dayalı olarak işlem gören bu araçların eşbütünleşik olmaları sebebiyle birbirinden ayrışmadıkları, dolayısıyla Modern Portföy Teorisi çerçevesinde BIST 50 ve KATILIM 30 endeksleriyle yapılacak portföyün yatırımcı açısından faydalı olmayacağı öngörülmüştür.

Developing Financial Forecast Modeling With Deep Learning On Silver/Ons Parity

In this study, financial prediction models have been developed over the silver / ounce parity using deep learning architectures. LSTM and ARIMA architectures, which are deep learning algorithms, are used. By loading the train-ing and test data into the established algorithms, the system was learned and a graphical estimation was requested on the silver / ounce parity for the next 10 days. Written algorithms can produce different results each time they are run. However, in the graphs we have taken as an example, the graph created with the ARIMA architecture has produced a more realistic result by specifying a range and making an upward forecast. The prediction chart we obtained with the LSTM architecture did not create a much decrease or upward forecast. However, as a feature of the LSTM algorithm, it clearly predicted the daily closing values, and did not specify an estimation as a range and direction as in the study with the ARIMA architec-ture. It should not be forgotten that these algorithms are dynamic and can give different results in predictions even when they are run with the same data. According to the results obtained in the research, although the LSTM architecture clearly stated the daily closing values as numbers, the estimation study made with the ARIMA architecture produced a result closer to the graph in terms of both interval and direction.

A 2D Lévy-flight model for the complex dynamics of real-life financial markets.

We report on the emergence of scaling laws in the temporal evolution of the daily closing values of the S&P 500 index prices and its modeling based on the Lévy flights in two dimensions (2D). The efficacy of our proposed model is verified and validated by using the extreme value statistics in the random matrix theory. We find that the random evolution of each pair of stocks in a 2D price space is a scale-invariant complex trajectory whose tortuosity is governed by a 2/3 geometric law between the gyration radius Rg(t) and the total length ℓ(t) of the path, i.e., Rg(t)∼ℓ(t)2/3. We construct a Wishart matrix containing all stocks up to a specific variable period and look at its spectral properties for over 30 years. In contrast to the standard random matrix theory, we find that the distribution of eigenvalues has a power-law tail with a decreasing exponent over time-a quantitative indicator of the temporal correlations. We find that the time evolution of the distance of 2D Lévy flights with index α=3/2 from origin generates the same empirical spectral properties. The statistics of the largest eigenvalues of the model and the observations are in perfect agreement.

Comovement of stock markets after the first COVID wave: a study into five most affected countries

PurposeThe purpose of this paper is to look at the contemporaneous movement of the stock market indices of the five most COVID-infected countries, namely, the USA, Brazil, Russia, India and UK after the first wave along with market indices of the three least affected countries, namely, Hong Kong, South Korea and New Zealand during the first wave.Design/methodology/approachData have been collected from the website of Yahoo finance on daily closing values of five indices. Augmented Dickey–Fuller test with its three forms has been applied to check the stationarity of the select five indices at the level and at the first difference before the pandemic, during the pandemic and post-first wave of the pandemic. Johansen cointegration test is applied to find out that there is no cointegration among the select five indices.FindingsThe five countries do neither fall in the same economic and political zone nor do they have the same economic status. But during the period of pandemic and the new-normal period, the cointegration is very distinct. The developing and developed nations thus stood at an indifferentiable stage of the economic crisis which is well reflected in their stock markets. However, the least three COVID-affected countries do not show any cointegration during the pandemic time.Originality/valueThe comovement even seen during the normal time in the other studies is not compared to a similar period in earlier years. But, in this study to look into the exclusive effect of COVID pandemic, the period most affected with it is compared with the period after it and that in the immediate past year had no effect.

Measuring stock market volatility - a study in India's perspective

Volatility is an inherent characteristic of stock market. The presence of volatility provides an opportunity to earn unexpected profits by risk seeker investors and becomes a matter of diffidence among risk averse as high risk is anticipated with high return during the period of high volatility. Informed investors consider market volatility while making investment decision. Volatility is reflected due to uncertainty in stock return, price and unexpected events that can be measure through the GARCH family models. Analysis is performed on daily closing value of NSE and BSE from 1 April 2011 to 31 March 2019, extracted through the online-portal of BSE (bseindia.com) and NSE (nseindia.com) for the purpose of the present analysis. The output of the current study confers the acceptance of E-GARCH model for the purpose of predicting and forecasting the market volatility.

KREDİ DERECELENDİRME KURULUŞLARININ VE EKONOMİ İLE İLGİLİ HABERLERİN HİSSE SENEDİ PİYASASINDA YARATTIĞI VOLATİLİTE

The study aims to identify volatility in the banking sector and general market related to some different types of announcements. Totally 103 announcements are clustered considering their sentiment and source. Daily closing values of banking and market indices, XBANK and BIST100, are included in the dataset which spans from the beginning of January 2018 to the end of March 2019. Findings indicate that the banking sector is not affected by the announcements released by the government. Announcements of credit rating agencies carry an informative value and play an important role in volatility. Furthermore, the relationship between economic indicators and volatility exists for the national market. As an interesting finding, investors tend to react 2 days before to the announcements belonging to Standard & Poor’s and Fitch.

Cross-correlations between economic policy uncertainty and precious and industrial metals: A multifractal cross-correlation analysis

This study combines Seasonal-Trend decomposition using LOESS (STL) and multifractal cross-correlation analysis (MFCCA) to estimate the cross-correlations between the evolution of the economic policy uncertainty (EPU) of the USA and return of precious and industrial metals. Based on daily closing values of EPU and eight metals ranging from 01-Jan-2010 to 02-May-2021, the empirical findings confirm the presence of nonlinear dependencies, based on all EPU-Metal relationships, suggesting that EPU and metal markets are interlinked. Furthermore, there is evidence of multifractality, although with a varying degree, between EPU and metal markets. The strongest multifractality is found in EPU-Tin and EPU-Silver, while EPU-Platinum and EPU-Gold exhibit the weakest multifractality. Precious metals show persistent cross-correlations while industrial metals have anti-persistent cross-correlations with EPU, with that cross-correlation behavior being more persistent in the case of small fluctuations, which have different implications for the different market agents.

Currency Hedging Strategies Using Histogram-Valued Data: Bivariate Markov Switching GARCH Models

Previous studies aimed at determining hedging strategies commonly used daily closing spot and futures prices for the analysis and strategy building. However, the daily closing price might not be the appropriate for price in some or all trading days. This is because the intraday data at various minute intervals, in our view, are likely to better reflect the information about the concrete behavior of the market returns and reactions of the market participants. Therefore, in this study, we propose using high-frequency data along with daily data in an attempt to determine hedging strategies, using five major international currencies against the American dollar. Specifically, in our study we used the 5-min, 30-min, 60-min, and daily closing prices of the USD/CAD (Canadian Dollar), USD/CNY (Chinese Yuan), USD/EUR (Euro), USD/GBP (British Pound), and USD/JPY (Japanese Yen) pairs over the 2018–2019 period. Using data at 5-min, 30-min, and 60-min intervals or high-frequency data, however, means the use of a relatively large number of observations for information extractions in general and econometric model estimations, making data processing and analysis a rather time-consuming and complicated task. To deal with such drawbacks, this study collected the high-frequency data in the form of a histogram and selected the representative daily price, which does not have to be the daily closing value. Then, these histogram-valued data are used for investigating the linear and nonlinear relationships and the volatility of the interested variables by various single- and two-regime bivariate GARCH models. Our results indicate that the Markov Switching Dynamic Copula-Generalized autoregressive conditional heteroskedasticity (GARCH) model performs the best with the lowest BIC and gives the highest overall value of hedging effectiveness (HE) compared with the other models considered in the present endeavor. Consequently, we can conclude that the foreign exchange market for both spot and futures trading has a nonlinear structure. Furthermore, based on the HE results, the best derivatives instrument is CAD using one-day frequency data, while GBP using 30-min frequency data is the best considering the highest hedge ratio. We note that the derivative with the highest hedging effectiveness might not be the one with the highest hedge ratio.

Analysis of Bitcoin's Impact on the Efficiency of a Diversified Portfolio for Brazilian Investors

Purpose – This paper aims to investigate if the inclusion of Bitcoin among the assets available to retail investors in the Brazilian market has an impact on the efficient frontier and, therefore, on the optimal choices of investors. Design/methodology/approach – This study calculates the efficient frontier with and without the inclusion of Bitcoin and estimates optimal portfolios for different criteria and time intervals. The sample period runs from 07/01/2013 to 06/30/2018 and the daily closing values of the selected assets/indices were used. Findings – This study finds evidence that the inclusion of Bitcoin among the investment alternatives would cause a statistically significant positive displacement and an expansion of the efficient frontier of the Brazilian retail market. This would result in a significant increase in the return on the tangency portfolio. In addition to improving the indicators of optimization of the risk-return binomial, the cryptoasset would be included in many optimal portfolios in the 2013Q3-2018Q2 period. Originality/value – The results obtained show that, as reported for more developed markets, Bitcoin has caused an expansion of the efficient frontier of the Brazilian retail market.

A Comprehensive Framework for Uncovering Non-Linearity and Chaos in Financial Markets: Empirical Evidence for Four Major Stock Market Indices.

The presence of chaos in the financial markets has been the subject of a great number of studies, but the results have been contradictory and inconclusive. This research tests for the existence of nonlinear patterns and chaotic nature in four major stock market indices: namely Dow Jones Industrial Average, Ibex 35, Nasdaq-100 and Nikkei 225. To this end, a comprehensive framework has been adopted encompassing a wide range of techniques and the most suitable methods for the analysis of noisy time series. By using daily closing values from January 1992 to July 2013, this study employs twelve techniques and tools of which five are specific to detecting chaos. The findings show no clear evidence of chaos, suggesting that the behavior of financial markets is nonlinear and stochastic.

The Extended Holiday Effect on US Capital Market

Studies on the financial markets proved that not all calendar anomalies are persistent in time. Some of them experienced various types of changes, including passing from the classical form to an extended one, with an enlarged specific time interval. This paper approaches the Holiday Effect extended form on the United States capital market. In its classical form, the Holiday Effect refers to abnormal stock returns on a trading day before a public holiday and a trading day after. We study the behavior of stocks returns for a time interval that starts four trading days before a public holiday and it ends four trading days after. In this investigation we employ the daily closing values of four important indexes from the United States capital market: Dow Jones Industrial Average, Standard & Poor's 500, Russell 2000 and NASDAQ Composite. In order to capture the changes experienced in time by the Extended Holiday Effect we analyze the returns of these indexes for three periods: January 1990 - December 1999, January 2000 – December 2009 and January 2010 – April 2020. The investigation revealed, for some trading days from the enlarged specific time interval, returns that were, in average, significant larger or smaller than those of the days outside of this interval. We found especially high abnormal returns on four or three trading days before public holidays and low abnormal returns on one or two trading days after public holidays. The results also suggest that the Extended Holiday Effect was more visible in relative quiet periods than in the turbulent ones and it influences especially the stock returns of small cap companies.

Stock Market Anomaly: Day of the Week Effect in Bombay Stock Exchange with the Application of GARCH Model

It has observed from many stock markets around the world that index value used to vary due to fluctuation in stock prices. One of the most important factors of variation in the stock prices is the day of the week effect, which indicates calendar irregularities in stock markets. Investment in the stock market is the most uncertain; therefore investors get worried regarding the appropriate day to trade in the financial market. The main objective of the present study is to find out the appropriate day of the week effect of developing the stock market of an emergent nation like India from 1st January 2000 to 31st December 2018. For fulfilling the objectives of the study, the daily closing value of four major indices of the Bombay Stock Exchange has been taken into consideration. To test the equality between average returns to different days and to examine the distribution pattern of daily returns series that measure the day of the week analysis, the parametric tools alike Mean and Standard deviation have employed. Apart from the parametric test, t-test has also applied to the daily returns in order to test the hypothesis. In this study, descriptive statistics and the GARCH model has also used with the purpose of measuring the day of the week effect analysis. Conferring to the results, the coefficients express that the return among different days of the week are statistically significant,

Modeling Long-Term Relationships in Philippine Stock Market (PSE) Indices: A Cointegration Analysis

Over previous decades, market participants have been unsure about how to forecast the movement of the stock market. Coming up with a good forecasting model could lead stock market participants towards a less risky investment portfolio. This study attempts to explore the haphazard nature of Philippine Stock indices and the long-term relationship between Philippine Stock Exchange (PSE) sectoral indices and the PSE index. The data series used are the daily closing values of the PSE index and six sectoral indices between July 2010 to December 2019. This study employs an Augmented Dickey-Fuller (ADF) unit root test and a Johansen cointegration test. The results revealed that all indices are non-stationary at index level, but stationary at first difference. The results showed that all indices followed a random walk; however, no long-term relationships between the PSE index and five sectoral indices were found. Therefore, long-term investors are able to diversify their portfolio to reduce investment risk.

Emerging Stock Market Integration among Selected SAARC and Developed Countries: An Empirical Analysis of Short-Term and Long-Term Linkages

This paper has tried to explain the presence of integration among the stock markets of selected SAARC countries within themselves as well as with selected three Developed countries of the world. The data set consists of daily closing values of benchmark stock indices of selected SAARC and developed countries for more than 27 years period starting from November 5th, 1991 till December 31st, 2018. The data is analysed using Unit Root tests, Correlation Analysis, Vector Autoregression, Granger Causality Test, Impulse Response Analysis, Johansen's Co-integration Test and Vector Error Correction Model. The results indicate the strong influencing nature of USA economy which is found to significantly affect the stock market behaviour of selectedSAARC countries both in the short term as well as long term specially after the Asian and Global Financial Crisis. Not much integration has been found among the stock markets of SAARC countries. The extent of linkages among the stock markets has been found to intensify with the passage of time because of opening up of economies, capital market reforms and advancements in Technology. This study is unique because of its emphases on selected SAARC countries, for which there is dearth of literature. Moreover, the time period of study is substantial. Sub-period analysis has also been carried on to check for the impact of Asian and Global Financial Crisis. Therefore, its results can be of great help in formulating portfolio diversification strategies by potential investors. The findings also have noteworthy value for government, regulators, policy makers, academicians and researchers.

Stochastic Pattern of Major Indices of Bombay Stock Exchange

It is interesting to get inside and draw a meaningful inference by studying the movement of various stock indices. Portfolio managers, analysts, and investors are very keen to know about the technical pattern of indices. They consider the stock market is one of the economic barometers or market indicators of an economy. Indian financial market has undergone radical and vital change during the past few years. The purpose of this study is to check stochastic movements in selected indices and to signify nexus and interdependency among one another by the virtue of econometric analysis. The study comprises of daily closing value from 1st April 2014-1st April 2018, including major indices i.e. S&P-BSE 100; S&P-BSE-200, S&P BSE-500, S&P-BSE:Large cap, S&P-BSE:Mid-cap, S&P-BSE:small-cap, and BSE-SENSEX. Moreover, typical econometrics tool Augmented Dickey-Fuller Test, Granger Causality Test, and Johansen Co-integration Test were implemented to conclude the result. The study is one of its kinds to analyze the static and pair wise relationship among seven BSE indices along with the direction of their expected future movement that would help practitioners, policy makers and investors in anticipating the future movement of the indices. The Dickey-Fuller and Johanson test administered to analyze unit root and co-integration among the series in long run, followed by Granger causality test to observe the route of the short term relationship among various indices. The tests reveal uni-directional and in some cases bi-directional causality in selected indices. Further, it has been observed that due to co-integration, prices of different indices can’t move far away from one another [1]. This stochastic study delves volatility pattern of some major indices of Bombay stock exchange with the help of econometric tools. It clearly delineates nexus of all the indices and provided an explanation to appreciate concrete conduct of one series into a mutual relationship. Hence, investors or analyst may predict the movements, interdependency and their relationship in a significant manner.