Stock Market Indices Research Articles

IMPLEMENTASI METODE SINGULAR SPECTRUM ANALYSIS (SSA) PADA PERAMALAN INDEKS LQ45

Stock market index forecasting is one of the research studies to obtain stock market index prediction using statistical and computational techniques, one of the techniques that can be used is Singular Spectrum Analysis (SSA). SSA aims to decompose a time series into a set of independent components which have nonstatic tendencies such as trends, oscillations, and noise. SSA can be applied without any prior assumptions such as stationarity, linearity, and normality which seem impossible to fulfill by the fluctuation of stock market index data. One of the stock indices listed in Indonesia Stock Exchange (IDX) is LQ45. The LQ45 index contains 45 selected stocks that have great performance in liquidity and fundamental aspects. LQ45 Index data has a nonstatic structure so it is suitable to be predicted using the SSA method. The forecasting uses the daily data of the LQ45 Index for the period of July 25, 2022 – November 14, 2022 with a window length (L) of 30. From the forecasting process, 4 groups were acquired and Mean Absolute Percentage Error (MAPE) value was 1.69% to forecast the next 20 days. These results indicate that the forecasting process using SSA for LQ45 Index of the period is accurate.

Analyzing the Impact of COVID-19 on Indian Stock Market Efficiency: Sectoral Behavior

The study examines the impact of the COVID-19 pandemic on the Indian stock market, providing insights for informed investment decisions. It analyses efficiency and behavior across eight crucial sectors during two distinct periods: prepandemic (Period 1: February 15th, 2018 to February 14th, 2020) and pandemic (Period 2: February 17th, 2020 to February 15th, 2022). Addressing a literature gap on sector-wise pandemic effects, the study contributes valuable knowledge to investors and policymakers. Utilizing the variance-ratio methodology enhances understanding of market efficiency. The findings reveal distinct behavior in the Indian stock market between Period 1, characterized by stable, normally distributed returns, and Period 2, marked by increased volatility and extreme events, likely influenced by the pandemic.

Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Testing in GARCH‐X models: boundary, correlations and bootstrap theory

In this article, we consider the so‐called Fixed Shrinkage (FS) bootstrap for the class of GARCH models with explanatory variables (GARCH‐X). Under the assumption of stationary covariates, the proposed FS bootstrap does not require modeling the covariates, as these are kept fixed in the bootstrap generating process. Our main focus is on testing whether one or more of the covariates can be excluded in the GARCH‐X model. As is well‐known the limiting distribution of the likelihood‐ratio (LR) statistic in this setting is non‐standard and depends in particular on whether nuisance parameters are on the boundary or in the interior of the parameter space. In particular, and as detailed here, the non‐standard limiting distribution depends on correlations, or dependence, between the explanatory variables. The FS bootstrap takes the presence of nuisance parameters into account by implementing shrinking as proposed in Cavaliere et al. (2022) for pure ARCH models. We establish asymptotic validity of the FS bootstrap for GARCH‐X models, and demonstrate by simulations that the bootstrap‐based test performs extremely well even when nuisance parameters lie on the boundary of the parameter space. The empirical illustration amplifies that the presence of nuisance parameters (especially whether or not on the boundary) are vital for interpreting the dynamics of conditional volatility in financial stock market indices.

Assessing the Performance of Initial Public Offerings (IPOs) in Indian Stock Markets: A Longitudinal Study from 2014-2023

This research investigates the performance of Initial Public Offerings (IPOs) in the Indian stock market over a decade (2014-2023). The study aims to assess both short-term and long-term IPO returns and the factors influencing their performance. Using a quantitative, longitudinal approach, data was collected from the National Stock Exchange (NSE) of India, covering 500 IPOs. The analysis examines short-term listing day returns and long-term performance up to five years post-IPO, focusing on variables like firm size, market sentiment, and sector-specific dynamics. The findings reveal substantial variations in IPO performance, with average listing day returns ranging from 18.98% to 26.35%, particularly peaking during market volatility in 2020. However, long-term returns exhibit a decline, with many IPOs underperforming five years after listing. Regression analysis shows that firm size and market sentiment significantly affect IPO performance, with larger firms and favorable market conditions driving better outcomes. The technology and pharmaceutical sectors outperformed, while real estate IPOs lagged. The study highlights the importance of due diligence for investors and the need for continued regulatory reforms to improve market transparency and efficiency. This research fills a gap in the literature by providing a comprehensive, decade-long perspective on IPO performance in India.

Asymmetric connectedness among BSE SENSEX, INR–USD exchange rate, gold price and crude oil price: fresh evidence from nonlinear ARDL

PurposeThis paper investigates the asymmetric connectedness among the Indian stock market, crude oil price, gold price and the USD–INR exchange rate.Design/methodology/approachWe construct a nonlinear autoregressive distributed lag model that contains the four aforementioned variables. We further used the pairwise Granger causality test to identify the direction of causality.FindingsThe results verify an asymmetrical long-run co-movement between the Indian stock market index, exchange rate, international crude oil prices and gold prices. In the long run, the performance of the Indian stock market is mainly affected by both positive as well as negative shocks in the INR–USD exchange rate, positive shocks in gold prices and positive shocks in crude oil prices. Further, a feedback mechanism is observed between positive shocks in the INR–USD exchange rate and Indian stock market performance.Originality/valueThe research paper revisited the linkage among the variables using a novel methodology proposed by Shin et al. (2014).

A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos.

Does index options trading destabilize Indian stock market volatility: an application of ARCH and GARCH models

Does index options trading destabilize Indian stock market volatility: an application of ARCH and GARCH models

Decoding Market Efficiency: A Multifractal Approach to Financial Market Dynamics

Financial markets were traditionally analyzed using the Efficient Market Hypothesis (EMH), which argued that asset prices fully reflected all available information. This led to the belief that price movements were random, making it impossible to consistently outperform the market. However, emerging research had highlighted that markets could exhibit multifractality, where inefficiencies existed across different time scales, challenging the traditional EMH framework. Our study built on these insights by investigating the market efficiency of several asset classes, including stock market indices, cryptocurrencies (Bitcoin, Ethereum, USDT, and Binance), commodities (gold and crude oil), fixed income (U.S. 10-Year & 30-Year Treasury Bonds), and foreign exchange (EUR/USD & GBP/USD). Utilizing the Multifractal Detrended Fluctuation Analysis (MFDFA) method, we analyzed the multifractal properties and efficiency of each asset class. The results revealed that traditional assets exhibited monofractality, adhering more closely to the EMH. In contrast, cryptocurrencies displayed significant inefficiencies, likely due to their volatility, market structure, and susceptibility to external shocks. The findings provided valuable insights into portfolio diversification and risk management, suggesting that inefficiencies in certain asset classes might present both opportunities and risks for investors.

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.

Investment Horizon and Portfolio Performance: A Study on Indian Stock Market

Investment Horizon and Portfolio Performance: A Study on Indian Stock Market

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

Superiority of six factor model in Indian stock market

Superiority of six factor model in Indian stock market

Implementation of machine learning in $$\ell _{\infty }$$-based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market

Implementation of machine learning in $$\ell _{\infty }$$-based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market

Breathless Returns: Assessing Air Pollution's Influence on Dividend Yields in Pakistan and the USA

Enterprises and companies are also impacted by air pollution in addition to people and the environment. Air pollution can result in decreased output and sharp rises in healthcare costs. Publicly traded firms' profitability and stock prices are significantly impacted by air pollution. Dividend yield, which has significant importance to investors, is a critical component of stock market growth. The relationship between air pollution and dividend yield can give investors relevant information on the financial success of businesses. Pakistan has been chosen as a case study for research on how air pollution affects dividend yield. In order to better understand the possible effects of air pollution on financial performance in emerging nations, it is fascinating to look at Pakistan, which has a booming economy and a rising stock market. Pakistan stock exchange (PSX) and S&P 500 stock exchanges' dividend yields are compared to see how air pollution affects them. In their respective regions, they are two of the most well-known and recognized stock market indices. This study employed regression analysis to look at the relationship between dividend yield and air pollution. Finding relationships between the variables and examining how air pollution impacts dividend yield were the two goals of the investigation. The results of the investigation depict that dividend yield is significantly impacted by air pollution.

Does Twitter happiness predict bullish markets? A study of the US stock markets

The evolving literature on using Twitter to capture investor sentiment suggests that stock market performance is linearly associated with Twitter's public mood. We investigate whether investor sentiment determined by the Twitter sentiment index (TSI) has a nonlinear predictive power for stock market behaviour. Using a dataset of seven US stock markets, we apply the cointegration techniques to show the relationship between Twitter sentiment and stock market behaviour. The results suggest a dynamic nonlinear cointegrating relationship. During the short run, Twitter happiness has a substantial effect on stock market indices, while in the long run, it has a moderating effect. Twitter happiness has a dominant effect on the US stock market indices than unhappy Twitter. These results highlight Twitter's growing role in predicting the stock market behaviour and show that the TSI acts as a better predictor of investor sentiment.

Nexus Between Stock Return and Market Volatility in Indian Perspective

This research seeks to explore the volatility patterns for the SENSEX index to figure out the characteristics of volatility in Indian stock trading and to investigate the relationship between returns and volatility of the Indian market for the last ten years. The research adds to the existing knowledge about stock market fluctuations, their implications for investors, and their impact on developing the country's economy. The study involves using daily returns data from BSE SENSEX from 01 Jan 2014 to 31 Dec 2023. Daily closing prices are obtained from the official website of BSE, and returns are calculated based on these prices. The Dickey-Fuller and Phillips-perron are taken to make the time series static. ARCH, GARCH, and GARCH-M tests are utilized to capture volatility clustering, return and volatility relationship. The results reveal that Fluctuations in the Indian stock market, particularly shown in the SENSEX index of the BSE, were highest in the year 2020. Findings suggest that GARCH-M does not show any relation between expected returns and market volatility. The risk-premium parameter is positive but statistically insignificant. If you want to hedge against risk, the risk premium is not very high. It means that taking risks does not give you more returns. Durbin Watson's value of 1.981401 further supports the model's suitability.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Assessing stochastic dominance of downside and upside financial risk profiles using the block maxima method in extreme value theory

This paper aims to assess the stochastic dominance of the extreme downside (negative return) and upside (positive return) risk profiles of three US stock market indices, namely NASDAQ Composite, S&P 500 and Dow Jones Industrial Average (DJIA) based on the block maxima method in extreme value theory. The extreme downside and upside risk profiles were developed using two datasets of 360 monthly minimum and maximum daily log returns respectively (from January 1992 to December 2021). Extreme losses beyond the 80th percentile (corresponding to a tail probability of less than 0.2) of the theoretical extreme risk profiles were adopted to investigate stochastic dominance. Pairwise comparisons show that the DJIA stochastically dominates the other two indices in both extreme negative and positive returns. Moreover, the extreme upside risk profile of the DJIA stochastically dominates its extreme downside risk profile. The paper finds that investment in short positions (encountering upside risk) provides the least extreme risk compared with long positions (encountering downside risk) for the DJIA, as well as both short and long positions for the S&P 500 and NASDAQ Composite.

Identifying extreme events in the stock market: A topological data analysis.

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that L1, L2 norms and Wasserstein distance (WD) of the world leading indices rise abruptly during the crashes, surpassing a threshold of μ+4∗σ, where μ and σ are the mean and the standard deviation of norm or WD, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing μ+2∗σ for an extended period for the banking, automobile, IT, realty, energy, and metal sectors. While for the pharmaceutical and FMCG sectors, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.

Echoes of Conflict: Unveiling the Interconnected Tapestry of Russia-Ukraine Warfare, Oil Price Ballet, and the Asian Stock Symphony

The purpose of this research is to look into the impact of the Russia-Ukraine war on the relationship between oil prices and the Asian stock market. While earlier studies have investigated the impact of oil prices on stock markets, there has been little research into the impact of crude oil prices on the Asian stock market in the context of the Russia-Ukraine war. For this purpose, the data is collected from NSE and Bloomberg database the study's findings imply that the Russia-Ukraine war has had a major impact on the relationship between crude oil prices and stock market indices in numerous Asia-Pacific countries. The study suffers from a few limitations such as it only examines the relationship between crude oil prices and stock market indices but there are other macroeconomic factors, such as interest rates, inflation, and political instability which also affect the market.