Index Returns Research Articles

Effects of Conditional Oil Volatility on Exchange Rate and Stock Markets Returns

Saudi Arabia experienced extreme volatility in oil price booms during a short period due to COVID-19, which traded for 16 days. The oil price boom period has received significant attention as it is essential to a nation’s economic sector. The objective of this study was to examine how changes in oil volatility under specific conditions affect the stock market return indices and exchange rate of Saudi Arabia. The Saudi Arabia capital market was used as the case study, while the average daily exchange rate and stock market were employed as the primary variable. This study covers the period from March 21, 2014, to November 19, 2020. The wavelet methodology was used for data analysis. The study's findings revealed that oil is not a safe haven for the bond market in Saudi Arabia. Similarly, oil-primarily functions as a safe haven for a short period that last for 16 days. The findings revealed that oil price fluctuation has no impact and a safe haven effect on the various deals in the Saudi Arabian capital market. It also suggests that market participants focus on oil price trading as a significant tool for determining the volatility of stock market returns in underdeveloped countries.

Comparative Assessment of Nutrient Management Technologies for Yield, Quality, Nutrient Use Efficiencies and Economics of Cauliflower-Tomato Production System Under Subtropical Conditions in NW Himalayas

ABSTRACT Cauliflower and tomato are two most important cash crops of the North-Western part of Himalayas often grown in sequence and bring remunerative returns to the farmers of mid-hill region. The study was undertaken in order to evaluate the effect of application of various nutrient combinations through organic and inorganic inputs on the post harvest soil fertility status, crop quality parameters, nutrient use efficiencies, nutrient harvest indices, and economics of the cauliflower–tomato production system. The total of 11 treatment combinations, comprising sole organics, graded doses of inorganics and substitution of inorganics with organic inputs were studied. The study concluded that the application of 75% NPK + 100% FYM + 25% recommended N through FYM and VC on 50:50 N-equivalence basis significantly improved most of the quality and yield parameters in both the crops simultaneously giving highest N (197.8 kg ha−1) and K (167.4 kg ha−1) uptake, NUE (46.3%), AE (57.0 kg kg−1), P harvest index (50.7%) and net returns (5476 US$ ha−1) in cauliflower. This treatment was also accorded with significantly highest N (141.9 kg ha−1), P (30.6 kg ha−1), and K (185.0 kg ha−1) uptake, NUE (33.6%), AE (115.4 kg kg−1), and net returns (7858 US$ ha−1) in tomato. Hence, it is concluded that 75% NPK + 100% FYM + 25% recommended N through FYM and VC on 50:50 N-equivalence basis may be adopted as cost-effective nutrient module for farmers following cauliflower–tomato cropping sequence to achieve higher crop production on sustainable basis.

Private Equity Firm Performance: An Empirical Evidence From India Using Benchmark Index

The present study attempted to examine the comparative return performance of private and non-private equity firms in India. For this purpose, we have used deal-level data retrieved from venture intelligence for the period ranging from 2009 to 2019. To measure the return of private equity, we applied Return multiple, internal rate of return (IRR) and extended internal rate of return (XIRR). We have used the Nifty 50 benchmark index as a proxy for investigating the return generated by public market returns. Study findings revealed that private equity firms outperformed the benchmark Index return and open-up routes for global and domestic investors for further investment. We identified that most private equity deals exited via the M&A route; some also followed the IPO route to exit privately held firms. Results differ from prior studies advocating IPO as the prime mechanism private equity managers use for exiting. Further, returns measured under both routes outperformed the public market index Nifty 50. The study found that 84% (277 Deals) of deals provided a positive cash multiple in the last decade and outperformed the benchmark index. These findings are helpful for private equity firms and fund managers to make investment decisions in the Indian private equity market.

How competitive is SRI in developing financial markets: The case of Central and Eastern Europe

This study investigates the competitiveness of sustainable and responsible investment (SRI) in Central and Eastern European (CEE) financial markets. Specifically, we examined whether a statistically significant measurable difference in the return and volatility between an SRI index and two conventional benchmark indices in the CEE region exists. To test whether the market volatility may affect the results, we applied a Markov regime-switching model to examine the performance in high and low volatility environments. We also used the Fama-French three-factor model to analyse the potential sources of outperformance and verify the initial analysis results. The analysis covered an eleven-year period (January 2011–December 2021) and was based on monthly returns of indices available on the Vienna Stock Exchange: CECE SRI, CECE Composite and CECE MID. Our findings showed that the SRI phenomenon in developing financial markets of the CEE countries followed performance patterns similar to ones in developed financial markets. Sustainable and responsible investment is competitive with conventional investment in the CEE region. However, the differences in returns between the SRI index and conventional benchmarks were statistically insignificant. Although a statistically significant difference in volatility between the SRI index and the large-cap CECE Composite index was reported, we did not find any evidence of exposure to the SRI factor regarding the analysed returns of the CECE SRI index. Our analysis of SRI returns pointed to the statistical significance of the common risk factors, such as the market and the size, which is similar to the analysed conventional benchmarks, with alpha not being statistically significant.

Economic Policy Uncertainty and Government Bond Prices

Purpose : This paper investigated the impact of economic policy uncertainty (EPU) in the US, UK, Japan, Italy, India, Germany, France, China, Canada, and Brazil on Indian government bond prices using a new dataset of the Clearing Corporation of India Limited Broad Total Return Index (BTRI) and Liquid Total Return Index (LTRI). Methodology : We used the quantile regression approach and monthly dataset from January 2004 – December 2020 for the analysis. Findings : We found that the top 20 government bond prices decreased due to EPU in India, Japan, the US, and the UK. In contrast, the EPU of Canada, China, and the UK had a statistically significant positive impact on BTRI. Further, a negative relationship was found between the top five government bond prices and the EPU of three economies: India, Japan, and the US. Practical Implications : The analysis will help identify potential risks and vulnerabilities in government bonds. It assists regulators and policymakers in implementing effective risk management measures to safeguard financial stability. The findings will also be useful for investors and market participants to make informed investment decisions. Originality : From a data standpoint, this is the first study that used CCIL’s BTRI and LTRI data for the first time to canvass the impact of EPU on Indian government bonds as far as we know. Further, we took into account the unique characteristics of the EPU of the top 10 economies and directly compared the reaction of these economies’ EPU to the government bond price fluctuations.

Stock Return Synchronicity and Profitability: Evidence from India

The current research article studies the stock return synchronicity (SYNCH) and profitability for Indian firms. SYNCH is measured using the value of R2 calculated from the market model for the sample firms. The market model runs regressions of individual stock returns with the Nifty index return. The quantile regression model is run to study the SYNCH and profit after tax (PAT). In addition, the SYNCH and its relation with PAT for low-quantile firms are compared to the SYNCH of high-quantile firms. After controlling for relevant variables, the quantile regression results provide evidence that SYNCH is related to PAT. The reported results also provide evidence that the lower quantile firms differ from higher quantile firms in terms of the impact of PAT on SYNCH.

HEMODIALYSIS EFFECTS ON AUTONOMIC FUNCTION: RESULTS OF LINEAR AND NON-LINEAR ANALYSIS OF HEART-RATE-VARIABILITY AT REST AND IN RESPONSE TO PHYSICAL AND MENTAL STRESS TESTS

Objective: Cardiac arrhythmias are the most common cause of death in hemodialysis. Autonomic dysfunction plays a central role in this arrhythmogenic background. Previous studies on hemodialysis-related changes in heart-rate-variability(HRV) give contradictory results. This study investigated HRV indices both at rest and in response to physical and mental stimulation maneuvers at multiple time-points around and during the hemodialysis procedure. Design and method: Autonomic function was assessed by linear and non-linear HRV indices at pre-dialysis, during-dialysis(3 equal time-periods), post-dialysis, and on the non-dialysis day in 36 hemodialysis patients. Continuous measurement of beat-by-beat heart-rate was recorded with Finometer-PRO(The Netherlands) at rest and after orthostatic, sit-to-stand, handgrip and mental-arithmetic test. Results: The RMSSD, SD1, and SD2 indices significantly increased during dialysis(early-HD, mid-HD, late-HD periods) compared with the pre-dialysis levels(p<0.05) and returned to baseline post-dialysis(RMSSD: 54.39±83.73 vs 137.98±109.53* vs 119.85±97.34* vs 144.47±88.74* vs 85.82±121.43msec, *p<0.05 vs pre-dialysis and post-dialysis). No differences were detected in the above indices between the pre-dialysis and non-dialysis day. However, post-dialysis, the HRV responses to orthostatic and sit-to-stand tests were more exaggerated than in the pre-dialysis measurements (p<0.05). The HRV responses both at resting and physical tests in the non-dialysis day were similar to the pre-dialysis levels. HRV indices in mental-arithmetic test during hemodialysis were much higher than at the non-dialysis day(RMSSD: 77.05 (180.41) vs 19.75 (105.47)msec; p = 0.031) were detected. Conclusions: Hemodialysis causes marked changes in autonomic function. Resting HRV indices return to baseline post-dialysis, but HRV responses to physical stress remain exaggerated and return to baseline on the non-dialysis day. Detecting patients with significant autonomic dysfunction may help towards reduction of arrhythmia risk through individualized approaches.

Feasibility Analysis of The Investment Project of Additional High Frequency Machinery in Increasing Production Capacity

UD Berkah is a small business, that currently produces surface mats for adult slippers and children's slippers. Hot Embossing is one of a series of processes for making the surface of slippers. The Hot Embossing process requires a High Frequency machine to create a pattern on the printed slipper surface. Currently UD Berkah has 3 units of High Frequency machines to meet consumer demand. Due to the large number of requests, not all of them can be done by the company itself, so it requires the services of subcontractors to meet production shortages. Currently, the company plans to purchase High Frequency machines to overcome this problem. So it is necessary to analyze the feasibility of investment projects by considering several aspects with the Straight Line Depreciation method approach, Payback period, Net Present Value, Profitability Index and Internal Rate of Return. This is done to consider whether the investment in adding High Frequency machines is feasible or not for UD Berkah. Research results with an investment value of Rp. 55,000,000 with an economic life of 5 years, the results for a depreciation of Rp. 7,000,000 every year, NPV of Rp. 477,026,441, payback period results for 1 year and 8 months, Profitability Index value is 11.7, the IRR value shows the result of 37.54%, So it can be concluded that investing in high frequency machines is feasible.
 Keywords: Hot Embossing, High Frequency, Investment, Net Present Value, Internal Rate of Return.

Analisa Kelayakan Investasi Proyek Moda Transportasi Masal Light Rail Transit (LRT) Jabodebek

This journal aims to analyze the feasibility of investing in mass transportation development project in Indonesia, specifically LRT Jabodebek project using Capital Budgeting method. The author conducted an investment feasibility analysis using four Capital Budgeting methods, which consist of Payback Period (PP), Net Present Value (NPV), Profitability Index (PI), and Internal Rate of Return (IRR). In addition, there is also a sensitivity analysis to measure the impact of important variable changes on the analysis results. The analysis results show that the LRT Jabodebek project is a feasible project to continue, which indicated by PP duration of 22.59 years, NPV above 11 trillions rupiahs, PI of 1.42, and IRR of 8.09%. Sensitivity analysis shows that LRT Jabodebek project is still in the feasible category after being recalculated by applying several scenarios. This journal is expected to have significant implications for decision-makers and stakeholders in the public transportation investment sector in Indonesia.

The directional spillover effects and time-frequency nexus between stock markets, cryptocurrency, and investor sentiment during the COVID-19 pandemic

PurposeThis paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.Design/methodology/approachThe authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.FindingsThe results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.Practical implicationsThis study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.Originality/valueThis finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

What drives the performance and causality of green bond indices?

This paper empirically assesses the performance of green bond indices and the causality of that performance using a range of financial and commodity data. We present new insights from the novel application of datasets, neural networks and performance measurements. We find that green bond indices do not outperform the market when factors beyond market return are considered. We find that Brent crude oil has the most significant effect on certain indices, a finding that contrasts with other studies on green bonds. A greater sensitivity to oil prices and global green equities also evinces a negative impact on a green bond index’s ability to outperform the market. For the first time, a linear causal relationship is established between Title Transfer Facility (TTF) returns and green bond index returns. Additionally, a fundamental shift in causal relationships is observed over the COVID-19 period. In this way, we contribute to the literature on sustainable green bonds and the impact of COVID-19. These insights provide more clarity to market participants for navigating the uncertainties of both the global energy transition and the postpandemic period.

COVID-19 pandemic: measuring stock indices correlation between different countries

The study's goal is to assess cross-country stock correlation during the 2019 global corona-virus outbreak. The paper uses vector autoregression model (VAR) for analysis of correlation between 6 countries stock indices. This paper investigates international stock return correlations between 6 countries, China, the U.S., France, Germany, the U.K and Japan. Estimate correlations are modeled in EViews 9 to evaluate that based on Covid-19 whether the stock markets in different countries can affect each other. Results show that changes in one of the endogenous variables cause fluctuations in the other variables. COVID 19 produced some shocks to the representative index returns of the six countries mentioned above. Also after analysis using the impulse function, there is areas of strength for a relationship between's the list return instability of the six nations, i.e. stock market volatility in each country affects other countries to a greater or lesser extent during special events, providing an idea for improving the current situation of financial markets in each country. Therefore, governments need to consider the stock market situation in other countries in order to take effective action to prevent stock markets from being affected by Covid-19.

Inclusion of debt claims in asset pricing models: Evidence from the CDS Index

Asset pricing theory suggests that the correct proxy for the market portfolio should contain both the debt and equity claims of the economy, whereas prevailing empirical studies fail to include the debt claim. Motived by the discrepancy between the theoretical and empirical models and the difficulty in constructing proxies, the study uses the Credit Default Swaps (CDS) market index as a proxy for the debt market and empirically tests its explanatory power in explaining stock return variations. Employing panel regression and Fama-MacBeth regression of all publicly traded U.S. companies from 2005 to 2020, the study finds a negative relationship between CDS index returns and stock returns. On average, a one standard deviation increase in CDS index return is associated with a 0.02% decrease in daily stock returns. Results of two-stage regressions show that the estimated systematic credit risk is positively priced in stock returns with similar economic magnitude as the well-documented beta risk. These results support asset pricing theories in the inclusion of debt claim and the risk-return tradeoff, while contradicting the credit risk puzzle documented in prior studies.

Weather Sentiment Index and Stock Return Predictability: Evidence from China

ABSTRACT This paper introduces a weather-related sentiment (mood) index (WSI) for the Chinese stock market based on precipitation and temperature data with the PLS method. We find that the WSI is negatively correlated with the equity market and has strong predictive power that is far greater than that of other market and macroeconomic variables. The predictability also holds under the characteristic-mimicking portfolios. The driving force of the WSI’s predictive power appears to stem from its ability to predict future cash flow, which reflects investor preference.

THE RELATIONSHIP BETWEEN EXCHANGE RATE VOLATILITY AND STOCK INDEX RETURN: EVIDENCE FROM TURKEY

Exchange rate fluctuations do not affect only the companies and individuals that engage in foreign currency-based transactions. As economic exposure features, even entities with no foreign currency assets and obligations are affected from the movements in exchange rates. Exchange rates, in fact, are closely related with several macroeconomic variables including stock prices and returns. Investigations into the relationship between exchange rate fluctuations and stock returns are widely observed among financial participants and academic circles. This study aims at exploring the link between exchange rate volatility and stock returns by investigating the US Dollar/Turkish Lira (USD/TRY) exchange rate volatility and Borsa Istanbul 100 Index (BIST-100) returns. In the study, 406 days of data for the period 11.03.2020-28.10.2021 were included in the analysis. Augmented Dickey-Fuller (ADF) unit root test was used to analyze the stationarity of the variables. As a result of the test, it is seen that the series are stationary at the I(0) level. After performing the stationarity test, ARMA (2,2) from linear stationary stochastic models and EGARCH (2,2) from general autoregressive conditional variable variance models were estimated to model exchange rate volatility. Then, Granger causality test was used to see if there is a relationship between exchange rate volatility and stock returns. The findings put forward the existence of bidirectional causality relationship between the variables. As the time span of the data period overlaps with the pandemic period, it appears that causality relationship obtained by the study is like the ones that held in the studies in the pre-pandemic period.

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could be distinguished. Motivated by this idea, our study aims to distinguish between the long memory and regime switching of financial volatility. We firstly modeled the long memory and regime switching of volatility using the Fractionally Integrated Exponential GARCH (FIEGARCH) and Markov Regime-Switching EGARCH (MRS-EGARCH) frameworks, respectively, and performed a simulation study on their finite-sample properties when innovations followed a non-normal distribution. Subsequently, we demonstrated the confusion between the FIEGARCH and MRS-EGARCH processes using simulations. A recent study theoretically proved that the time-varying smoothing probability series can induce the presence of significant long memory in the regime-switching process. To control for its effect, the two-stage two-state FIEGARCH and MRS-FIEGARCH frameworks are proposed. The Monte Carlo studies showed that both frameworks can effectively distinguish between the pure FIEGARCH and pure MRS-EGARCH processes. When the MRS-FIEGARCH model was further employed to fit series generated with the MRS-FIEGARCH process, it outperformed the ordinary FIEGARCH model. Finally, an empirical study of NASDAQ index return was conducted to demonstrate that our MRS-FIEGARCH model can provide potentially more reliable long-memory estimates, identify the volatility states and outperform both the FIEGARCH and MRS-EGARCH models.

Trading Strategies Using Technical Indicators: An Active Portfolio Management Approach

The study aims to evaluate the effectiveness of active trading strategies developed using technical indicators against the buy-and-hold passive investment strategy. It uses the simple moving average (SMA), Bollinger band (BB), and moving average convergence divergence (MACD) as technical indicators to generate the buy and sell signals. The study uses the data of treasury bills as a proxy for a risk-free portfolio and the Nifty Total Return Index (TRI) as a proxy for a risky equity portfolio in daily frequency from June 30, 1999 to March 31, 2021. It creates the cumulative wealth index for the sample period using active portfolio management based on the buy and sell signals generated using various technical indicators. The findings suggest that SMA and MACD trading strategies provide better risk-adjusted returns than the buy-and-hold and Bollinger band trading strategies. Also, the shorter days moving average outperforms the longer days moving average trading strategy. The trading strategies perform better during the bear market than the bull market.

The Influence of Force Majeure of Non-government Factors on People's Willingness to Insurance in China

Except for investors, people usually regard insurance as a tool to transfer unknown risks. We believe that insurance can minimise financial losses when a risk occurs and reduce people's fears when disaster strikes. Therefore, some scholars put forward the hypothesis that people's willingness to buy insurance is related to some force majeure. From the perspective of behavioral finance, this paper selects indicators such as the rate of return of insurance stocks and insurance search index as proxy variables for insurance willingness. It also selects natural disasters and epidemics as proxy variables for force majeure other than government actions (referred to as force majeure in the text). Regression analysis was carried out on the above variables, and it was concluded that force majeure other than government actions was positively correlated with people's willingness to purchase insurance.

Impact of public news sentiment on stock market index return and volatility

Impact of public news sentiment on stock market index return and volatility

Geographic Investing: Stock Return Indexes Based on Company Operations

Portfolio allocations to firms of various geographic areas should be guided by underlying risks of operations. In most statistical studies of international stock returns, a firm is included in a country’s index if its headquarters is located in that country, a classification scheme that ignores the operations of the firm taking place in multiple geographic areas. In prior work, we have proposed a model of country factors that is based on the business activities of all firms operating in a country, be they domestic firms or multinationals. In the present paper, we compare the resulting indexes with the domestic revenue exposure indexes already available in the industry. We conclude that our new indexes allow a portfolio manager to track geographic risk much more accurately.