scholarly journals Subjective/ Behavioural Factors Influence the PSI 20 and IBEX 35

2020 ◽  
Vol 11 (5) ◽  
pp. 13
Author(s):  
Stefan Abrantes Costa ◽  
Pedro Manuel Nogueira Reis ◽  
Antonio Pedro Soares Pinto
Keyword(s):  
Time Series Data ◽  
Statistical Significance ◽  
Investor Sentiment ◽  
Institutional Development ◽  
Bad News ◽  
Series Data ◽  
Asymmetric Effect ◽  
Key Factor ◽  
Behavioural Factors ◽  
The Impact

This study assesses the impact of investor sentiment on the volatility of the PSI 20 and IBEX 35 from time series data from January 1988 to May 2019. The impact of investor sentiment on market and portfolio selection has aroused great interest in the literature, however the results obtained are not consensual, considering the different methodologies used to build sentiment indices, as well as the various levels of institutional development in the market.Asymmetric volatility behaviours according to good or bad news were evaluated using the TGARCH model. The results indicate that there is an asymmetric effect of good versus bad news on the volatility of IBEX 35. It was also noted that for Portugal and Spain investor sentiment presents statistical significance with a negative sign, suggesting that market volatility is more sensitive to negative shocks in the conditional variance. In Portugal, contrary to Spain, sentiment has no relevance on return. The study reveals that investor sentiment is a key factor in selecting investment in the market. The relationship that this establishes with volatility, can help to implement policies that allow to minimize future shocks’ impact on return. The study reveals for the first time that investor sentiment is a key factor in selecting investment in the market for Portugal.

The Impact of The Covid-19 Pandemic on Investor Risk Tendency: The Case of Turkey

2021 ◽  
Vol 15 (9) ◽  
pp. 3046-3049
Author(s):  
Abdulkadir Kaya
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Statistical Significance ◽  
Series Data ◽  
Causality Test ◽  
Significance Level ◽  
Risk Appetite ◽  
Central Registry ◽  
The Face ◽  
The Impact

Introduction and Aim: It is an important issue that what kind of changes occur in the risks that people face in the face of emerging problems and the role of people in possible pandemics in the last twenty years and in the future. The solution of the problems that arise in the control and management of these risks attracts the attention of many researchers. In this study, the causality effect of the COVID-19 pandemic on risk appetites representing the attitudes and behaviors of securities investors. Materials and Methods: In the study; To represent the pandemic, weekly time series data of the number of COVID-19 cases (COVID) and the Risk Appetite index (RISK) announced by the Central Registry Agency for the period 30.03.2019-30.08.2021 were used. In order to determine the causality relationship, the Hatemi-J Causality test was performed. Results: It was determined that the negative shocks of the COVID variable were a cause of the positive shocks of the RISK variable at a statistical significance level of 1%. Conclusion and Suggestions: The effect of the pandemic process on the investment decisions of the investors is reduced, with the expectation that the economy and financial markets will improve, positively affecting the behavior and risk perceptions of the investors, and this expectation causes the investment behavior and risk appetite to increase. can be expressed. Keywords: COVID-19, Risk appetite, Pandemic, Hatemi-J

scholarly journals Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Simon L. Turner ◽  
Amalia Karahalios ◽  
Andrew B. Forbes ◽  
Monica Taljaard ◽  
Jeremy M. Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Time Series Data ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. Methods A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. Results From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. Conclusions The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

The Impact of The Covid-19 Pandemic on Investor Risk Tendency: The Case of Turkey

2021 ◽  
Vol 15 (10) ◽  
pp. 2941-2944
Author(s):  
Abdulkadir kaya
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Statistical Significance ◽  
Series Data ◽  
Causality Test ◽  
Significance Level ◽  
Risk Appetite ◽  
Central Registry ◽  
The Face ◽  
The Impact

Introduction and Aim: It is an important issue that what kind of changes occur in the risks that people face in the face of emerging problems and the role of people in possible pandemics in the last twenty years and in the future. The solution of the problems that arise in the control and management of these risks attracts the attention of many researchers. In this study, the causality effect of the COVID-19 pandemic on risk appetites representing the attitudes and behaviors of securities investors. Materials and Methods: In the study; To represent the pandemic, weekly time series data of the number of COVID-19 cases (COVID) and the Risk Appetite index (RISK) announced by the Central Registry Agency for the period 30.03.2019-30.08.2021 were used. In order to determine the causality relationship, the Hatemi-J Causality test was performed. Results: It was determined that the negative shocks of the COVID variable were a cause of the positive shocks of the RISK variable at a statistical significance level of 1%. Conclusion and Suggestions: The effect of the pandemic process on the investment decisions of the investors is reduced, with the expectation that the economy and financial markets will improve, positively affecting the behavior and risk perceptions of the investors, and this expectation causes the investment behavior and risk appetite to increase. can be expressed. Keywords: COVID-19, Risk appetite, Pandemic, Hatemi-J

Impact of exchange rate and oil price on the yield of sovereign bond and sukuk: Evidence from Malaysian capital market

2017 ◽  
Vol 5 (4) ◽  
pp. 27
Author(s):  
Huda Arshad ◽  
Ruhaini Muda ◽  
Ismah Osman
Keyword(s):  
Exchange Rate ◽  
Capital Market ◽  
Time Series Data ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Sovereign Bond ◽  
Long Run ◽  
Short Run ◽  
The Impact

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.

Transport infrastructure of the Rostov Oblast: Problems and prospects for development in the context of national projects implementation

2020 ◽  
Vol 19 (6) ◽  
pp. 1015-1034
Author(s):  
O.Yu. Patrakeeva
Keyword(s):  
Time Series Data ◽  
Mutual Influence ◽  
Rostov Oblast ◽  
Transport Infrastructure ◽  
Series Data ◽  
Cointegration Tests ◽  
Level Of Economic Development ◽  
Short Run ◽  
The Government ◽  
The Impact

Subject. The paper considers national projects in the field of transport infrastructure, i.e. Safe and High-quality Roads and Comprehensive Plan for Modernization and Expansion of Trunk Infrastructure, and the specifics of their implementation in the Rostov Oblast. Objectives. The aim is to conduct a statistical assessment of the impact of transport infrastructure on the region’s economic performance and define prospects for and risks of the implementation of national infrastructure projects in conditions of a shrinking economy. Methods. I use available statistics and apply methods and approaches with time-series data, namely stationarity and cointegration tests, vector autoregression models. Results. The level of economic development has an impact on transport infrastructure in the short run. However, the mutual influence has not been statistically confirmed. The paper revealed that investments in the sphere of transport reduce risk of accidents on the roads of the Rostov Oblast. Improving the quality of roads with high traffic flow by reducing investments in the maintenance of subsidiary roads enables to decrease accident rate on the whole. Conclusions. In conditions of economy shrinking caused by the complex epidemiological situation and measures aimed at minimizing the spread of coronavirus, it is crucial to create a solid foundation for further economic recovery. At the government level, it is decided to continue implementing national projects as significant tools for recovery growth.

A Macroeconomic Analysis of Agricultural Sector in Nigeria

2019 ◽  
Vol 5 (1) ◽  
pp. 18-25
Author(s):  
Isah Funtua Abubakar ◽  
Umar Bambale Ibrahim
Keyword(s):  
Value Chain ◽  
Crop Production ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Size Analysis ◽  
The Real ◽  
Real Gdp ◽  
The Impact

This paper attempts to study the Nigerian agriculture industry as a panacea to growth as well as an anchor to the diversification agenda of the present government. To do this, the time series data of the four agriculture subsectors of crop production, livestock, forestry and fishery were analysed as stimulus to the Real GDP from 1981-2016 in order to explicate the individual contributions of the subsectors to the RGDP in order to guide the policy thrust on diversification. Using the Johansen approach to cointegration, all the variables were found to be cointegrated. With the exception of the forestry subsector, all the three subsectors were seen to have impacted on the real GDP at varying degrees during the time under review. The crop production subsector has the highest impact, however, taking size-by-size analysis, the livestock subsector could be of much importance due to its ability to retain its value chain and high investment returns particularly in poultry. Therefore, it is recommended that, the government should intensify efforts to retain the value chain in the crop production subsector, in order to harness its potentials optimally through the encouragement of the establishment of agriculture cottage industries. Secondly, the livestock subsector is found to be the most rapidly growing and commercialized subsector. Therefore, it should be the prime subsector to hinge the diversification agenda naturally. Lastly, the tourism industry which is a source through which the impact of the subsector is channeled to the GDP should be developed, in order to improve the impact of such channel to GDP with the sole objective to resuscitate the forestry subsector.

The Effects of Oil Prices Changes on Output Growth and Inflation: Evidence from Turkey

2013 ◽  
Vol 5 (11) ◽  
pp. 730-739 ◽  
Author(s):  
Pelin ÖGE GÜNEY
Keyword(s):  
Time Series Data ◽  
Output Growth ◽  
Oil Price ◽  
Series Data ◽  
Negative Effects ◽  
Price Changes ◽  
Asymmetric Effects ◽  
Price Increases ◽  
Causality Tests ◽  
The Impact

This paper investigates the effects of oil price changes on output and inflation for the case of Turkey using monthly time series data for the period 1990:1–2012:3. Recent studies suggest that oil price changes may have asymmetric effects on the macroeconomic variables. To account for asymmetric effects, we decompose oil price changes into positive and negative parts following Hamilton (1996). Our results show that while oil price increases have clear negative effects on output growth, the impact of oil price decline is insignificant. Similarly, oil price increases have positive and significant effects on inflation. However, oil price declines have not a significant effect on inflation. The Granger causality tests also support these results.

Does Tax Increment Financing Pass the “But-for” Test in Missouri?

2019 ◽  
Vol 33 (3) ◽  
pp. 187-202
Author(s):  
Ahmed Rachid El-Khattabi ◽  
T. William Lester
Keyword(s):  
Economic Development ◽  
Kansas City ◽  
Time Series Data ◽  
Local Level ◽  
Conclusive Evidence ◽  
Series Data ◽  
Tax Increment Financing ◽  
Data Set ◽  
Development Indicators ◽  
The Impact

The use of tax increment financing (TIF) remains a popular, yet highly controversial, tool among policy makers in their efforts to promote economic development. This study conducts a comprehensive assessment of the effectiveness of Missouri’s TIF program, specifically in Kansas City and St. Louis, in creating economic opportunities. We build a time-series data set starting 1990 through 2012 of detailed employment levels, establishment counts, and sales at the census block-group level to run a set of difference-in-differences with matching estimates for the impact of TIF at the local level. Although we analyze the impact of TIF on a wide set of indicators and across various industry sectors, we find no conclusive evidence that the TIF program in either city has a causal impact on key economic development indicators.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

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