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Book part
Publication date: 4 April 2024

Chih-Chen Hsu, Kai-Chieh Chia and Yu-Chieh Chang

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed…

Abstract

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.

Open Access
Article
Publication date: 31 May 2023

Xiaojie Xu and Yun Zhang

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction…

Abstract

Purpose

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

Design/methodology/approach

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

Findings

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

Originality/value

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Details

Asian Journal of Economics and Banking, vol. 8 no. 1
Type: Research Article
ISSN: 2615-9821

Keywords

Article
Publication date: 26 September 2023

Mohammed Ayoub Ledhem and Warda Moussaoui

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric…

Abstract

Purpose

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

Design/methodology/approach

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

Findings

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

Practical implications

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

Originality/value

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Details

Journal of Modelling in Management, vol. 19 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 20 March 2024

Priyanka Goyal and Pooja Soni

The present research study aims to explore the impact of the most recent Israeli–Palestinian conflict, which unfolded in October 2023, on global equity markets, including a wide…

Abstract

Purpose

The present research study aims to explore the impact of the most recent Israeli–Palestinian conflict, which unfolded in October 2023, on global equity markets, including a wide range of both emerging and developed markets (as per the Morgan Stanley Capital Investment country classification).

Design/methodology/approach

The market model of event study methodology, with an estimation window of 200 days and 28-day event window (including event day, i.e. October 7, 2023), has been employed to investigate the event’s impact on the stock markets of different countries, with 24 emerging countries and 23 developed countries. The daily closing prices of the prominent indices of all 47 countries have been analyzed to examine the impact of the conflict on emerging markets, developed markets and overall global equity markets. Additionally, cross-sectional regression analysis has been performed to investigate the possible explanations for abnormal returns.

Findings

The findings of the study suggest the heterogeneous impact of the selected event on different markets. Notably, emerging markets and the overall global equity landscape exhibited substantial negative responses on the event day, as reflected in average abnormal returns of −0.47% and −0.397%, respectively. In contrast, developed markets displayed resilience, with no significant negative impact observed on the day of the event. A closer examination of individual countries revealed diverse reactions, with Poland, Egypt, Greece, Denmark and Portugal standing out for their positive or resilient market responses. Poland, in particular, demonstrated significantly positive cumulative abnormal returns (CARs) of 7.16% in the short-term and 8.59% in the long-term event windows (−7, +7 and −7, +20, respectively), emphasizing its robust performance amid the geopolitical turmoil. The study also found that, during various event windows, specific variables had a significant impact on the CARs.

Practical implications

The study suggests diversification and monitoring of geopolitical risks are key strategies for investors to enhance portfolio resilience during the Israeli–Palestinian conflict. This study identifies countries such as Poland, Egypt, Greece, Denmark and Portugal with positive or resilient market reactions, providing practical insights for strategic investment decisions. Key takeaways include identifying resilient markets, leveraging opportunistic strategies and navigating market dynamics during geopolitical uncertainties.

Originality/value

As per the authors’ thorough investigation and review of the literature, the present study is the earliest attempt to explore the short-term and long-term impact of the 2023 Israeli–Palestinian conflict on equity markets worldwide using the event study approach and cross-sectional regression analysis.

Details

Journal of Economic Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-3585

Keywords

Open Access
Article
Publication date: 28 November 2022

Ruchi Kejriwal, Monika Garg and Gaurav Sarin

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both…

Abstract

Purpose

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

Design/methodology/approach

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Findings

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

Originality/value

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Details

Vilakshan - XIMB Journal of Management, vol. 21 no. 1
Type: Research Article
ISSN: 0973-1954

Keywords

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