US Stock Indices Research Articles

Energy Prices and Their Impact on US Stock Indices: A Wavelet- based Quantile-on-Quantile Regression Approach

This study delves into the effects of crude oil and gas prices on the United States’ (US) conventional, Islamic, and environmental, social, and governance (ESG) stock indices from January 2013 to December 2022. Decomposing original time series data to minimise inherent fluctuations and using the Quantile-on-Quantile (QQ) regression approach presents a nuanced view of how these energy prices impact different stock indices. The findings reveal that crude oil prices have a variable impact on the indices: high prices negatively influence the indices, low prices have a positive effect, and moderate prices yield a moderate positive impact. After data decomposition, this positive influence diminishes in higher quantiles, indicating an emerging neutral effect in stabilised conditions. In contrast, gas prices show a limited impact, with high prices slightly benefiting conventional and ESG indices but less so for the Islamic index. This suggests a more pronounced influence of oil prices on the indices, likely due to the dependence of many listed companies on oil. The study emphasises the importance of considering oil-related risks in investment strategies and highlights the asymmetric impact of crude oil prices on the US stock indices. These findings have significant implications for investors and policymakers. They underscore the need for careful consideration of oil price dynamics in investment decisions and the importance of staying vigilant against shifts in oil prices that could lead to market instability.

Stock market prices and Dividends in the US: Bubbles or Long-run equilibria relationships?

This paper presents a novel approach to identifying potential bubbles in the US stock market by employing alternative time series methods based on long memory, including fractional integration and cointegration, as well as duration dependence non-parametric models. To test for duration dependence, the paper employs a unique non-parametric hazard function estimation method, using monotonic P-splines, which enables the estimation of a coherent and sophisticated Bayesian confidence interval for the function. By analyzing historical data of real US stock index from January 1871 to November 2022, the results reveal strong evidence of bubbles in the US stock market. It is worth noting that this is the first time a fractional integration approach and a novel non-parametric hazard function have been utilized to test for rational speculative bubbles in the US stock market, making this paper a significant contribution to the field.

Measuring the risk and return of Indonesia's and United States Stock Index

This research investigates the relationship between the returns of selected Indonesian and US stock market indexes and their risks so as to guide new investors on how to choose their investments wisely. A quantitative descriptive method was used using performance data from three Indonesian and three US stock indexes over ten years to calculate an average return. The Sharpe Index was used to measure each index's risk. The results show that the average stock return for each index in the US is higher than the Indonesia indexes, while the level of risk in the US, on average, is lower. Investors are advised to invest in index categories with higher returns and low risk to increase the chance of gaining better returns while managing their risk to be as low as possible.

Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach

This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.

Investigating the Determinants of Foreign Institutional Investor Inflows in Indian Equity Market: An Application of the Augmented Autoregressive Distributed Lag Bounds Testing Approach

The purpose of this study was to investigate the determinants of foreign institutional investors (FIIs) in the Indian equity market. For this purpose, the study has chosen country-specific push and pull factors for the period from January 2011 to December 2019. The beginning of the concerned period is marked by increased FII flows in the Indian market after the global financial crisis of 2008. The study has identified the stock market index of India, interest rate differential between India and the USA, inflation rate, and exchange rate as possible determinants of FII inflows in India and used the augmented autoregressive distributed lag (ARDL) approach based on the statistical properties of variables selected. The study found that in the long-run Indian stock market index, appreciating exchange rate, inflation, and interest rate differential are positively related to FII inflows in the Indian equity market whereas the US stock index is negatively related to FII inflows in India. JEL Codes: F30, G1

Volatility forecasting with Hybrid‐long short‐term memory models: Evidence from the COVID‐19 period

AbstractVolatility forecasting, a central issue in financial risk modelling and management, has attracted increasing attention after several major financial market crises. In this article, we draw upon the literature on volatility forecasting and hybrid models to construct the Hybrid‐long short‐term memory (LSTM) models to forecast the intraday realized volatility in three major US stock indexes. We construct the hybrid models by combining one or multiple traditional time series models with the LSTM model, and incorporating either the estimated parameters, or the predicted volatility, or both from the statistical models as additional input values into the LSTM model. We perform the out‐of‐sample test of our Hybrid‐LSTM models in volatility forecasting during the coronavirus disease 2019 (COVID‐19) period. Empirical results show that the Hybrid‐LSTM models can still significantly improve the volatility forecasting performance of the LSTM model during the COVID‐19 period. By analysing how the construction methods may influence the forecasting performance of the Hybrid‐LSTM models, we provide some suggestions on their design. Finally, we identify the optimal Hybrid‐LSTM model for each stock index and compare its performance with the LSTM model on each day during our sample period. We find that the Hybrid‐LSTM models' great capability of capturing market dynamics explains their good performance in forecasting.

Do SEC filings indicate any trends? Evidence from the sentiment distribution of forms 10-K and 10-Q with FinBERT

PurposeThis study quantified companies' views on the COVID-19 pandemic with sentiment analysis of US public companies' disclosures. The study aims to provide timely insights to shareholders, investors and consumers by exploring sentiment trends and changes in the industry and the relationship with stock price indices.Design/methodology/approachFrom more than 50,000 Form 10-K and Form 10-Q published between 2020 and 2021, over one million texts related to the COVID-19 pandemic were extracted. Applying the FinBERT fine-tuned for this study, the texts were classified into positive, negative and neutral sentiments. The correlations between sentiment trends, differences in sentiment distribution by industry and stock price indices were investigated by statistically testing the changes and distribution of quantified sentiments.FindingsFirst, there were quantitative changes in texts related to the COVID-19 pandemic in the US companies' disclosures. In addition, the changes in the trend of positive and negative sentiments were found. Second, industry patterns of positive and negative sentiment changes were similar, but no similarities were found in neutral sentiments. Third, in analyzing the relationship between the representative US stock indices and the sentiment trends, the results indicated a positive relationship with positive sentiments and a negative relationship with negative sentiments.Originality/valuePerforming sentiment analysis on formal documents like Securities and Exchange Commission (SEC) filings, this study was differentiated from previous studies by revealing the quantitative changes of sentiment implied in the documents and the trend over time. Moreover, an appropriate data preprocessing procedure and analysis method were presented for the time-series analysis of the SEC filings.

The Bitcoin Halving Cycle Volatility Dynamics and Safe Haven-Hedge Properties: A MSGARCH Approach

This paper introduces a unique perspective towards Bitcoin safe haven and hedge properties through the Bitcoin halving cycle. The Bitcoin halving cycle suggests that Bitcoin price movement follows specific sequences, and Bitcoin price movement is independent of other assets. This has significant implications for Bitcoin properties, encompassing its risk profile, volatility dynamics, safe haven properties, and hedge properties. Bitcoin’s institutional and industrial adoption gained traction in 2021, while recent studies suggest that gold lost its safe haven properties against the S&P500 in 2021 amid signs of funds flowing out of gold into Bitcoin. Amid multiple forces at play (COVID-19, halving cycle, institutional adoption), the potential existence of regime changes should be considered when examining volatility dynamics. Therefore, the objective of this study is twofold. The first objective is to examine gold and Bitcoin safe haven and hedge properties against three US stock indices before and after the stock market selloff in March 2020. The second objective is to examine the potential regime changes and the symmetric properties of the Bitcoin volatility profile during the halving cycle. The Markov Switching GARCH model was used in this study to elucidate regime changes in the GARCH volatility dynamics of Bitcoin and its halving cycle. Results show that gold did not exhibit safe haven and hedge properties against three US stock indices after the COVID-19 outbreak, while Bitcoin did not exhibit safe haven or hedge properties against the US stock market indices before or after the COVID-19 pandemic market crash. Furthermore, this study also found that the regime changes are associated with low and high volatility periods rather than specific stages of a Bitcoin halving cycle and are asymmetric. Bitcoin may yet exhibit safe haven and hedge properties as, at the time of writing, these properties may manifest through sustained adoption growth.

The Relationship Between CNN Money’s US Stock Market Fear and Greed Index and US Stock Indices

The Relationship Between CNN Money’s US Stock Market Fear and Greed Index and US Stock Indices

The Impact of the Ukrainian War on Stock and Energy Markets: A Wavelet Coherence Analysis

This study attempts to examine the existence of interdependencies between specific stock market indices, exchange rates and crude oil for the period January 2021 to July 2022 with daily data. In the period we have chosen, the post-vaccination phase against COVID-19, as well as the war in Ukraine, is covered. The variables selected for this study are RTSI, Eurostoxx, S&P 500, EUR/USD and RUB/USD exchange rates and crude oil prices. The selection of the specific variables was made because they are directly related to the pre-war period that coincides with the post-vaccine period of the pandemic, which allowed us to characterize it as the normal period and to characterize the period of the war in Ukraine that coincides with the energy crisis as the unstable period. In this way, the present study covers the markets of Russia and other developed economies. For empirical purposes, we applied a wavelet coherence approach in order to investigate the possible existence of simultaneous coherence between the variables at different times and scales for all the considered times. The findings of the study reveal the existence of strong correlations between all variables, during different time periods and for different frequencies during the period under review. Of particular interest is the finding that shows that during the crisis period, the RTSI significantly affects both the European and American stock markets, while also determining the evolution of the Russian currency. In addition, it appears that capital constraints in the Russian stock market, combined with increased demand for crude oil, determine the interdependence between RTSI and crude oil. Finally, an interesting finding of the study is the existence of a negative correlation between the US stock index and crude oil in low-frequency bands and the RTSI and Eurostoxx with crude oil for the post-vaccination and pre-war periods in the medium term. These findings can be used by both investors and portfolio managers to hedge risks and make more confident investment decisions. In addition, these findings can be used by policy makers in the planning of regulatory policies regarding the limitations of the systemic risks in capital markets.

International linkages of emerging market index futures, under the closure of underlying spot market – evidence from Indian Nifty futures

PurposeThis study examines the dynamic linkages between the Indian Nifty index futures traded on the offshore Singapore Exchange (SGX) and US stock indices (DJIA, NASDAQ and S&P 500) under the closure of the spot market for Nifty futures.Design/methodology/approachWith high-frequency 5-min overlapping price data, the authors employ the Johansen cointegration test to investigate long-run relationships, the Granger causality test to assess short-run dynamics and the BEKK-GARCH model for volatility spillover investigation.FindingsThe empirical findings reveal that the SGX Nifty futures market is cointegrated with the US DJIA market. The US DJIA stock index strongly influences the price discovery of SGX Nifty futures and past innovations in the US markets strongly impact the current volatility of SGX Nifty futures.Practical implicationsFindings from this study have significant implications for market participants, particularly foreign investors and portfolio managers. These findings might be helpful for market participants to improve the prediction power of expected SGX Nifty futures price and volatility, especially under the closure of the spot market. Also, SGX market participants can take the significant role of the US market into account when formulating hedging and trading strategies with Indian Nifty futures. Besides, our findings have significant implications for policymakers in evaluating market stability.Originality/valueThis article adds to the very limited research on offshore or international stock index futures; it is the first study that empirically examines the international linkages of offshore SGX Nifty futures under the closure of its underlying spot market and also the driving force behind the linkages.

Zero capital depreciation point for Ukrainian commercial organizations

The problem of capital shortage for Ukrainian commercial organizations has been clarified, which makes the theoretical and methodological provisions of its increase and preservation relevant. Insufficient capital for investment processes is a significant obstacle to the development of most business entities in Ukraine. The analysis of recent research and publications on the topic of the work showed that the issues of tools for estimating the point of zero depreciation of equity for Ukrainian commercial organizations still have room for improvement. The aim of the work is to substantiate the tools for estimating the point of zero depreciation of equity for Ukrainian commercial organizations. The main source of compensation for the depreciation of equity due to inflation and devaluation of the national currency is the profit of the organization. The return on equity is a relative indicator that characterizes the degree of appropriate compensation. The tools for estimating the point of zero depreciation of equity should be considered as such a value of return on equity, which compensates for its depreciation due to various factors. Due to the need to divide the factors of impairment of equity into two groups, the use of two types of zero depreciation points is justified: Cash and alternative. As a widely used alternative to the use of equity for the average investor, investing in shares of ETFs is justified, completely repeating the US stock index S and P-500 with a long-term efficiency of 9.5% per annum in US dollars. Approbation of the presented valuation tools allowed us to determine that for Ukrainian commercial organizations the alternative point of zero depreciation of equity is 26.9% and cash-17.6%. It is specified that the average enterprise in the country does not reach the level of even a cash point. There are a limited number of economic activities in the country where the cash point of zero depreciation of equity is exceeded. Reducing inflation to 7-6% per year along with the stabilization of the national currency will bring a significant number of economic activities in the country to the level of reaching the cash point of depreciation of equity.

Practical Applications of An Empirical Examination of Lifestyle Mutual Funds

In <b><i>An Empirical Examination of Lifestyle Mutual Funds</i></b>, from the Winter 2022 issue of <b><i>The Journal of Retirement</i></b>, author <b>Srinidhi Kanuri</b> of the <b>University of Southern Mississippi</b> analyzes the performance of lifestyle mutual funds. Unlike target-date funds—which reallocate to more conservative investments as the investor’s retirement date approaches—lifestyle funds maintain a static asset allocation that is aggressive (mostly equities), moderate, or conservative (mostly fixed income). Lifestyle funds are increasingly popular, so it is important to know how they perform relative to lower-cost investments like index funds. The study measured the performance of all lifestyle funds in the Morningstar database from January 1994 through August 2017. It found that lifestyle funds underperformed US stock indexes and comparable balanced portfolios of stock and bond index funds (net of fees). Lifestyle funds had lower risk-adjusted performance and negative net alpha, but their gross alpha was statistically no different from zero. Therefore, they selected well-performing investments but charged high fees that cut into performance. Financial advisors may wish to consider alternatives to lifestyle funds—such as allocating to a mix of low-cost index funds—when constructing balanced retirement portfolios for clients.

Feature ranking and network analysis of global financial indices.

The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. The feature ranking is the probability of influence of each index on the target. The feature ranking matrix is determined by using the returns of indices on a certain day to predict the price returns of the next day using Random Forest and Gradient Boosting. We find that the North American indices influence others significantly during the global financial crisis, while during the European sovereign debt crisis, the significant indices are American and European. The US stock indices dominate the world stock market in most periods. The indices of two Asian countries (India and China) influence remarkably in some periods, which occurred due to the unrest state of these markets. The networks based on feature ranking are constructed by assigning a threshold at the mean of the feature ranking matrix. The global reaching centrality of the threshold network is found to increase significantly during the global financial crisis. Finally, we determine Shannon entropy from the probabilities of influence of indices on the target. The sharp drops of entropy are observed during big crises, which are due to the dominance of a few indices in these periods that can be used as a measure of the overall distribution of influences. Through this technique, we identify the indices that are influential in comparison to others, especially during crises, which can be useful to study the contagions of the global stock market.

Return and Volatility Spillover Effects between Rupee–Dollar Exchange Rate and Asian Stock Indices

India is a major Asian economy that has attracted global investment due to its economic stability and relatively open financial markets. We investigate the return and volatility spillover effects between the Indian rupee against US dollar and stock index volatility in neighboring (Asian) nations. Consistent with the uncovered equity parity, we find that when the rupee depreciates against US dollar, the foreign institutional investors pull back their portfolio investments from other Asian markets and invest in Indian stock market, similarly if rupee appreciates they may divest their investments to gain profits. The unidirectional causality from the exchange rate to all stock indices is indicated by the causality in the mean approach. Our study shows a strong linkage between the currency value of a stable economy and the economies with which it has strong trade ties. Further, the results also show that investors adjust their position in the related markets during periods of currency appreciation and depreciation in an economy with significant growth prospects. JEL Codes: F31, D53, B23

Diversification benefits of Turkey-based investors: evidence from top trading partners based on a multivariate-GARCH approach

PurposeThe aim of this study is to measure portfolio diversification benefits of the Turkey-based equity investors into top trading partner countries. Portfolio diversification benefits are analyzed from the viewpoint of two types of investors in Turkey: conventional equities investors and Islamic equity investors.Design/methodology/approachIn order to evaluate the time-varying correlations of the trading partner country's stock index returns with the Turkish stock index returns, the multivariate-generalized autoregressive conditional heteroskedasticity–dynamic conditional correlation (GARCH-DCC) is applied based on daily data covering 13 years' period between January 22, 2008 and January 22, 2021.FindingsThe results revealed that the US stock indices provide the most diversified benefit for both conventional and Islamic Turkey-based equity investors. In general, Islamic indices exhibit relatively lower correlation with trading partners than conventional indices. Turkey and Russia are recorded as the most volatile indices.Originality/valueThe diversification potential in trading partners for Turkey-based Islamic equity investors has not been studied yet. This study is to fill in this gap in the literature and to give fruitful insights to both conventional and Islamic investors.

Determination of the world stock indices' co-movements by association rule mining

PurposeThis study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.Design/methodology/approachIn this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.FindingsIt is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.Originality/valueThe important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.

The Effect of Quantitative Easing through Google Metrics on US Stock Indices

The purpose of this study is to investigate the fluctuations that occur in stock returns of US stock indices when there is an increase in the volume of Google internet searches for the phrase “quantitative easing” in the US. The exponential generalized autoregressive conditional heteroscedasticity model (EGARCH) was applied based on weekly data of stock indices using the three-factor model of Fama and French for the period of 1 January 2006 to 30 October 2020. The existence of a statistically significant relationship between searches and financial variables, especially in the stock market, is evident. The result is strong in three of the four stock indices studied. Specifically, the SVI index was statistically significant, with a positive trend for the S&amp;P 500 and Dow Jones indices and a negative trend for the VIX index. Investor focus on quantitative easing (QE), as determined by Google metrics, seems to calm stock market volatility and increase stock returns. Although there is a large body of research using Google Trends as a crowdsourcing method of forecasting stock returns, this paper is the first to examine the relationship between the increase in internet searches of “quantitative easing” and stock market returns.

The Relationship between Crude Oil Futures Market and Chinese/US Stock Index Futures Market Based on Breakpoint Test.

Combined with the B-P (breakpoint) test and VAR–DCC–GARCH model, the relationship between WTI crude oil futures and S&P 500 index futures or CSI 300 index futures was investigated and compared. The results show that breakpoints exist in the relationship in the mean between WTI crude oil futures market and Chinese stock index futures market or US stock index futures market. The relationship in mean between WTI crude oil futures prices and S&P 500 stock index futures, or CSI 300 stock index futures is weakening. Meanwhile, there is a decreasing dynamic conditional correlation between the WTI crude oil futures market and Chinese stock index futures market or US stock index futures market after the breakpoint in the price series. The Chinese stock index futures are less affected by short-term fluctuations in crude oil futures returns than US stock index futures.

Is the effectiveness of government bonds as a diversifier of equity risk weakened after the Covid-19 crisis?†

In this paper, we examine how the relationship between the US stock index and the US Treasury market has changed after the start of the Covid-19 crisis. For that purpose, we compute upside and downside correlations between the index and interest rates of different maturities. Our main findings are as follows: first, we document the correlation asymmetry. The downside correlation is higher than the upside correlation before the crisis but the opposite is true after the start of the crisis for all maturities. The magnitude of the change is large for short-term maturities but small for long-term maturities. It indicates that the benefit of holding the short-term government bonds as a diversifier for stock investors is weakened after the start of the Covid-19 crisis. We also study the economic impact of ignoring the correlation asymmetry and report that the cost increased after the start of the crisis in general.