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This paper examines stock price and volume effects associated with a change in the composition of the Nikkei 225 index in Japan in April 2000. Our results include the following: first, we show that newly added firms experience significant positive excess returns of 19% in the five-day period after the announcement of the change; in contrast, deleted and remaining firms’ returns are negatively affected, −36 and −14%, respectively; second, volume tests show significant increase in trading activity after the announcement for both added and deleted firms; third, cross-sectional analysis provides evidence that higher arbitrage risk and demand shocks increase the absolute value of excess returns.

Purpose: Investors and portfolio managers can earn profitably when they correctly predict when stock prices will go up or down. For this reason, it is crucial to know the effect levels of the factors that affect stock prices. In addition to macroeconomic factors, the psychological behavior of investors also affects stock prices. Therefore, the study aims to reveal the different sensitivity levels of the stock index against macroeconomic and psychological factors.

Design/Methodology/Approach: In this study, dollar rate (USD), euro rate (EURO), time deposit interest rate (IR), gold price (GOLD), industrial production index (IPI), and consumer price index (CPI) (inflation (INF)) were used as macroeconomic factors, while Consumer Confidence Index (CCI) and VIX Fear Index (VIX) were used as psychological factors. In addition, the BIST-100 index, which is listed in Borsa Istanbul, was used as the stock index. The sensitivity of the stock index to macroeconomic and psychological factors was investigated using the Multivariate Adaptive Regression Spline (MARS) method using data from January 2012 to October 2020.

Findings: In the analyses performed using the MARS method, the coefficients of INF, USD, EURO, IR, CCI, and VIX Index were found to be statistically significant and effective on the stock index. Among these variables, INF has the highest effect on stocks. It is followed by USD, IR, EURO, CCI, and VIX. GOLD and IPI variables did not show statistical significance in the model. The most important difference of the MARS model from other regressions is that each factor’s effect on the stock index is analyzed by separating it according to the value of the factor. According to the results obtained from the MARS model: (1) it has been determined that USD, EURO, IR, and CPI have both positive and negative effects on the stock market index and (2) CCI and VIX have been found to have negative effects on stocks. These results provide essential information about how investors who plan to invest in the stock index should take into consideration different macroeconomic and psychological values.

Originality/value: This study contributes to the literature as it is one of the first studies to examine the effects of factors affecting the stock index by decomposing it according to the values it takes. Also, this study provides additional information by listing the factors affecting the stock index in order of importance. These results will help investors, portfolio managers, company executives, and policy-makers understand the stock markets.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

The purpose of this paper is to examine the price discovery role of the Korea Composite Stock Price Index 200 (KOSPI 200) stock index options market in contrast to other developed options markets.

The price discovery roles of the stock and options markets using the error‐correction model derived from the co‐integration relationship are examined. Various analyses are conducted. First, Heston's stochastic volatility option pricing model is employed to confirm its usefulness, and compare the results with the Black and Scholes (BS) model. Second, whether the out of the money (OTM) options purchased by individual investors have a stronger price discovery role than options with other moneyness is examined. Finally, whether options have a stronger price discovery role in bullish or bearish markets than in normal markets is tested.

It is found that stock index prices lead implied index prices estimated from option prices using both BS and Heston models. In regards to the OTM options, the lead‐effect of real stock index to implied index prices holds. Also it is shown that there is a weak rise in the lead effect of the options to the stock index, but the lead effect of stock index market rules over that of the options market.

The paper examines the price discovery role of the KOSPI 200 stock index options market in contrast to other developed options markets and the results indicate that the consensus on the Korean financial markets may be incorrect.

Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.

Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.

The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.

This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

The purpose of this paper is to investigate and compare the weak‐form efficiency of a set of 24 African continent‐wide stock price indices and those of eight individual African national stock price indices.

Variance‐ratio tests based on ranks and signs were used to examine the weak‐form efficiency of the 32 stock price indices investigated.

On average, it was found that irrespective of the test employed, the returns of all the 24 African continent‐wide stock price indices examined in the study are less non‐normally distributed compared to the eight individual national stock price indices examined. The authors also report evidence of the African continent‐wide stock price indices having significantly better weak‐form informational efficiency than their national counterparts.

The policy implication of this evidence is that the African equity price discovery process can be significantly improved if African stock markets integrate their operations. Economically, this may contribute to improved liquidity and more efficient allocation of capital, which in turn can be expected to have a positive impact on economic growth.

The paper makes two major contributions to the extant literature. First, it offers for the first time a comparative analysis of the informational efficiencies of a sample of national stock price indices as against African continent‐wide stock price indices. Second, there is no prior evidence as to whether African stock markets can improve their informational efficiencies by integrating their operations. The paper fills this gap by demonstrating that the African equity price formation process can be improved if African stock markets integrate their operations.

While the classic futures pricing tool works well for capital markets that are less affected by sentiment, it needs further modification in China's case as retail investors constitute a large portion of the Chinese stock market participants. Their expectations of the rate of return are prone to emotional swings. This paper, therefore, explores the role of investor sentiment in explaining futures basis changes via the channel of implied discount rates.

Using Chinese equity market data from 2010 to 2019, the authors augment the cost-of-carry model for pricing stock index futures by incorporating the investor sentiment factor. This design allows us to estimate the basis in a better way that reflects the relationship between the underlying index price and its futures price.

The authors find strong evidence that the measure of Chinese investor sentiment drives the abnormal fluctuations in the basis of China's stock index futures. Moreover, this driving force turns out to be much less prominent for large-cap stocks, liquid contracting frequencies, regulatory loosening periods and mature markets, further verifying the sentiment argument for basis mispricing.

This study contributes to the literature by relying on investor sentiment measures to explain the persistent discount anomaly of index futures basis in China. This finding is of great importance for Chinese investors with the intention to implement arbitrage, hedging and speculation strategies.

The purpose of this paper is to examine changes in stock returns, liquidity, institutional ownership, analyst following and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership.

This study uses the event-study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following and investor awareness in the 1990–2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses.

This study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929–2015 period. Focusing on the most recent period, 1990–2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis.

Limited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across subperiods of the 1929–2015 period.

This study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

This paper aims to identify various macroeconomic variables that affect the stock market performance of developed and emerging economies. It also investigates the effect of these factors on the stock markets of both economies. The impact of these variables on broad market indices and sectoral indices is investigated and compared too.

The publications for the study were retrieved from databases such as Emerald Insight, EBSCO, ScienceDirect and JSTOR using the keywords “Macroeconomic variables” and “Stock market” or “Stock market performance.” The result demonstrated a growing corpus of scholarly work in the domain of stock market. The study was carried out separately for each macroeconomic indicator. Given a large number of articles under consideration, the authors began by reading the titles and abstracts of all publications to identify those that were relevant. The papers are evaluated in Excel and the articles for review range from 1972 to 2021.

The authors found that gross domestic product (GDP), FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment) have a positive effect on both emerging and developed economies’ stock market while gold price has a negative effect. Interest rates had a negative impact on both economies except for a few developing countries. The relationship with oil prices was positive for oil exporting countries while negative for oil importing countries. Inflation, money supply and GDP are the macroeconomic variables that have the same effect on sectoral indices as they do on broad market indices. The impact was sector-specific for the remaining variables.

This paper gives an overview of relation and effect covering variety of macroeconomic variables and stock market indices. Still, there is a scope for further research to analyze the effect on thematic, strategy and sectoral indices. A longer time horizon with new variables, such as bank deposit growth rate, nonperforming assets of banks, consumer confidence index and investor sentiment, can be studied using high-frequency data. This research may help stakeholders adopt and manage their policies during a crisis or economic slump.

This study will assist investors, researchers and educators in the fields of economics and finance in understanding how macroeconomic factors affect the stock market. Furthermore, this study can guide in portfolio diversification strategy across multiple sectors by examining the impact of macroeconomic factors specific to sectoral indices. This paper provides insight into society and researchers since it integrates a number of macroeconomic variables and their interaction with the stock market. It may also help pension funds and mutual fund firms to hedge their funds and allocate equity portfolios.

With respect to India, this study looked at new macroeconomic variables and sectors. It contrasted the impact of these variables in developed and developing economies. The effect of broad and sectoral stock indexes was also investigated and compared. The authors examined how these variables responded during crisis and economic downturns by using articles from a longer time frame. This research also looked into how changing the frequency of data for the variables altered stock performance. This paper emphasized the need for more research into thematic, strategy and broad market indices, such as small-cap and mid-cap indices.

This paper aims to investigate the implementation of the short selling ban policy imposed by the Italian stock exchange on health-care stock prices, as a tool to mitigate COVID-19 price effects. Important contributions are in terms of assessing the effect of the temporary short selling ban on restricted health-care stocks; the effect of COVID-19 cases and crude oil price volatility onto health-care stocks; and whether COVID-19 resulted in a change in the risk and average stock price of health-care stocks.

The methodology involves impulse responses to capture the shock of the short selling ban onto health-care stocks, and Markov switching regimes to capture the effect of COVID-19 onto the risk and prices in the health-care industry. Daily data from 9 November 2018 till 23 December 2020 is used.

Findings suggest there were significant changes in average prices in health-care technology and health-care services stocks before, during and after the short selling ban. Shocks to the number of COVID-19 cases and crude oil price volatility impacted health-care stocks but lasted only for a few days. While daily changes in the number of COVID-19 cases impacted some health-care stocks in the presence of a two-state Markov regime, insignificant coefficients and relatively low duration suggest that the short selling policy did not significantly change the average price and risk in health-care stocks to explain a two-state regime in the health-care industry.

Insignificant coefficients in a two-state Markov regime reinforce that short-selling policies have a short-lasting effect onto health-care equity prices. The findings are limited by the duration of the short selling policy, the pandemic event and the health-care industry.

This is the first study to look at the impact of early COVID-19 and short selling ban policy on health-care stocks.