International Journal of Accounting, Business and Finance

Advancing Financial Knowledge: Exploring Information Dynamics, Market Reactions, and Valuation Theories: Insights from the December 2022 Issue

Dharen Kumar Pandey — 2022-12-31

With the collaborative support of the editorial board members, authors, reviewers, section editors, technical editors, and production editor, we successfully launched the December 2022 issue of the International Journal of Accounting, Business and Finance (IJABF). The IJABF Volume 2 Issue 1 contains four articles dealing with contemporary issues. The authors try to unlock the research questions by providing empirical results and the scope for future studies. I thank all the contributors to this issue.

The informational variables impact on firm’s liquidity in the French market

Faten Moussa — 2022-12-31

This paper investigates the informational variables impact on stock liquidity in the French market. We use two types of informational variables: Google search volume from Google Trends database as a proxy of information demand and news headlines for each stock as a proxy for information supply. Concerning the liquidity proxies, we use these measures: the quoted spread, the turnover price impact and the Amihud illiquidity ratio. The results indicate that information variables have an influence on stock liquidity.

An event study analysis of the impact of bonus share announcements on Nifty 100 and Nifty Midcap 100 companies

Pankaj Kumar Mahato — 2022-12-31

This study examines how the large-cap and mid-cap firms listed on the National Stock Exchange responded to bonus share announcements between January 1, 2006, to September 30, 2022. The conventional event study approach has been utilized, along with the commonly used market model assessment of predicted returns to analyze 45 pure events during this period consisting of 20 events of large-cap and 25 events of midcap stocks. According to the analysis, stock values significantly changed around the time of occurrence. Announcements of stock dividends typically increase stock prices. The mean of average abnormal return around the event for Nifty Midcap 100 indexed companies (0.2087) is higher than that for Nifty 100 indexed companies (0.1446), although the difference is insignificant. The study also shows that the cumulative average abnormal return for Nifty Midcap 100 indexed companies (6.472) is higher than that for Nifty 100 indexed companies (4.483) during the event period.

Predicting Long Term Prices of Nifty Index using Linear Regression and ARIMA: A comparative study

Mohit Beniwal — 2022-12-31

Stocks are traded continuously in the financial market, creating huge time series data. Stock market time series is very volatile and highly complex to model. There are many methods to forecast a time series. This study predicts and compares the performance of two statical methods, linear regression, and ARIMA also referred to as the Box Jenkins method, which stands for Autoregressive Integrated Moving Average, is a robust time series forecasting technique used frequently by researchers. Simple linear regression is generally assumed unsuitable for non-linear stock time series data. However, a literature gap exists in comparing linear regression and the ARIMA method for forecasting stock prices. Hence, this study compares the ARIMA and linear regression methods to forecast stock prices using daily and weekly NIFTY data. Further, this study also experiments using a different length of time series, namely 1-year and 2-year data. The result shows that ARIMA outperformed regression on daily and weekly data when the test for 1 year of data. When 2-year data is taken, linear regression outperformed ARIMA on both daily and weekly data. Hence, the linear regression model and ARIMA are sensitive to input parameters such as the number of days for training and forecasting. An automated method or algorithm could improve the robustness of the model. Further, as stock price data is non-linear, machine learning algorithms such as neural networks and support vector regression can be more suitable for prediction.

Information Entropy Theory and Asset Valuation: A Literature Survey

Sana Gaied Chortane — 2022-12-31

The purpose of this study is to review the empirical work applied to market efficiency, portfolio selection and asset valuation, focusing on the presentation of the comprehensive theoretical framework of Information Entropy Theory (IET). In addition, we examine how entropy addresses the shortcomings of traditional models for valuing financial assets, including the market efficiency hypothesis, the capital asset pricing model (CAPM), and the Black and Scholes option pricing model. We thoroughly reviewed the literature from 1948 to 2022 to achieve our objectives, including well-known asset pricing models and prominent research on information entropy theory. Our results show that portfolio managers are particularly attracted to valuations and strive to achieve maximum returns with minimal risk. The entropy-based portfolio selection model outperforms the standard model when return distributions are non-Gaussian, providing more comprehensive information about asset and distribution probabilities while emphasising the diversification principle. This distribution is then linked to the entropic interpretation of the no-arbitrage principle, especially when extreme fluctuations are considered, making it preferable to the Gaussian distribution for asset valuation. This study draws important conclusions from its extensive analysis. First, entropy better captures diversification effects than variance, as entropy measures diversification effects more generically than variance. Second, mutual information and conditional entropy provide reasonable estimates of systematic and specific risk in the linear equilibrium model. Third, entropy can be used to model non-linear dependencies in stock return time series, outperforming beta in predictability. Finally, information entropy theory is strengthened by empirical validation and alignment with financial views. Our findings enhance the understanding of market efficiency, portfolio selection and asset pricing for investors and decision-makers. Using Information Entropy Theory as a theoretical framework, this study sheds new light on its effectiveness in resolving some of the limitations in traditional asset valuation models, generating valuable insights into the theoretical framework of the theory.