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Early theoretical work on equity valuation suggests that equity prices are determined by variables such as dividends and growth in dividends. However, these “traditional” views have been challenged by recent studies that seem to indicate that equity prices are determined by book value and discounted future abnormal earnings. This paper employs panel data methodology and equity prices from Athens Stock Exchange empirically to compare the performance of the traditional and the more recent models of equity valuation. The results show that the performance of the Ohlosn (1995) model is quite similar to that of the traditional models even though in some cases Ohlson’s model performance appears to be superior.

The purpose of this paper is to examine the validity of the Ohlson model and to explore the influence of intellectual capital (IC) on corporate value (V) and value creation (VC) in order to develop a business valuation model served as the managerial criterion of IC.

Hypotheses are based on current research on the Ohlson model and IC. Descriptive statistics are used to find the data patterns. Information content and incremental information provided by various capital sources are validated through multiple and stepwise regression.

Corporate value is measured by both IC and financial capital (FC). The Ohlson model with FC reveals information that is significant in corporate value. Besides, FC and IC – mainly, innovation and human capital – contains a great deal of incremental information in terms of V and VC.

In addition to financial statement, IC must be taken into account when intending to do business valuation.

To create higher corporate value, corporations must actively place a high premium on their IC and manage it well, particularly for innovation and human capital.

This paper focuses on the information technology industry in Taiwan. It, respectively, uses the share price and price and book value models to represent V and VC. It cites the more complete four aspects of IC, which are referred to as “other information”, to combine IC and the Ohlson model.

The objective of this paper is to investigate the validity of stock valuation theories and their forecasting ability by conducting an empirical study. It employs four most commonly used theories which are then tested using 19-year banking-firm market data. The usefulness of these models demonstrates with promising results.

This paper conducts a multi-country study using the multi-model testing approach to evaluate validity of theories and forecast accuracy of banking firms. It employs four methodology models used in finance literature; (1) P/E multiples model, (2) accounting-information-based clean surplus model, (3) theoretical model based on Gordon and Shapiro (1956) method and (4) the Damodaran-Kottler Free Cash Flow or FCF theory based on discounting model.

The tests show that the four theories under tests have a significant fit with actual price formation. The explained variation ranges from 72 to 92%, so the explanatory power of the theories accounting for variations in bank prices over 19-year period is substantial. The models fit suggest that the P/E model has superior predictive power followed by the RIM, DDM and FCFE. These findings shed new lights on the relative performance of valuation models.

The study is limited in terms of the sample period size for 1999–2019. The availability of essential financial data prior to 2000 is very limited, so one can understand interpretation of statistical results under certain assumptions.

The paper suggests that one-factor model is better than the two-factor model.

The work done in this paper is unpublished and original contribution to banking and finance literature and also not under consideration for publication in any other journal.

The purpose of this paper is to investigate the relationship among intellectual capital (IC), financial capital (FC), firm value (V), and value creation (VC) in different business cycles (BC) for the conduct of strategic management that will maintain stable values and further increase V.

This research cites ICs as “other information” to combine ICs and the Ohlson model. Information provided by various capitals is validated by multiple regression analysis. Multi-group analysis is performed to test whether the coefficient is moderated by BC.

Results indicate the significant information of ICs and FC, and the contingency perspective of BC. The value relevance of ICs is moderated by BC. Prosperity has more explanatory capacities, and recession ICs yield more incremental information.

VC is influenced by both ICs and FC. Besides, the macroeconomic situation should also be considered in strategic management and VC management.

In addition to ICs and FC, the macroeconomic situation must be taken into account when conducting strategic management, valuation management, investment decision, or industrial policy.

Results indicate a contingency of BC, which can be a reference for enterprises to create higher V, for investors to make appropriate investment, as well as for governments to formulate sound industrial policies.

This paper applies BC to explore the value relevance of ICs and FC, leverages two models to represent V and VC, and cites complete four aspects of IC as “other information” to combine ICs and Ohlson model.

The purpose of this study is to systematically review and analyze the literature in the area of liquidity of financial markets. The study summarizes the key findings and approaches and highlights the research gaps in the extant literature.

A variety of reputed databases are utilized to select 100 research papers, from a large pool of nearly 3,000 research papers spanning between 1972 and 2018 using systematic literature review methodology. The selected research papers are organized to provide an in-depth analysis and an account of the ongoing research in the area of liquidity. The study uses bibliometric network visualization and word-cloud analyses to compile and analyze the literature.

The study summarizes the recent approaches in the liquidity research on aspects such as methodologies followed, variables applied, sub-areas covered, and the types of economies and markets covered. The article shows that the literature on liquidity in the emerging markets (e.g. China and India) is deficient. Overall, the following research areas related to liquidity need further exploration in the context of emerging markets: liquidity beyond the best bid-ask quotes, intraday return predictability using microstructure variables (e.g. order imbalances), impact of algorithmic-trading and volatility of liquidity.

To the best of authors’ knowledge, in the recent past, a detailed account of the literature on liquidity has not been published. It provides a comprehensive collection and classification of the literature on the liquidity of financial markets. This would be helpful to the future researchers, academics and practitioners in the area of financial markets.

The purpose of this paper is to investigate if the prevalence of corruption and other unwholesome financial practices in Nigeria contributed substantially to the stunted growth of the capital market in general, and the stock market in particular.

The paper employed Gregory–Hansen cointegration approach to test the long-run equilibrium relationship between the occurrence of predatory banking practices and stock market capitalization in Nigeria.

There exists a long-run equilibrium relationship between bank fraud and stock market capitalization but with a structural break in 2005.

There is an urgent need to overhaul and re-assess from time to time the existing systems of internal checks and controls in banks, as well as other financial institutions in Nigeria.

This paper is the first to empirically test the long-run equilibrium relationship between bank fraud and stock market capitalization in Nigeria.

The purpose of this paper is to use the Barberis et al. (1998)’s valuation model to calculate the fundamental value of a stock and examine whether the differences between predicted and realized stock prices are explained both by psychological factors (that affect investor reaction to information) and by key macroeconomic variables.

This paper adopts a time-series analysis, as well as a panel data approach, to examine whether the price deviations from fundamental values are because of macroeconomic and psychological factors, using data from the London Stock Exchange.

The results indicate that these differences are explained by important macroeconomic variables, as well as by the sentiment of investors (that is used as a proxy of the psychological factors).

Based on the above results, this paper suggests that the price deviations from fundamental values are not treated as model estimation errors as proposed by Penman and Sougiannis (1998) but rather as deviations that are because of psychological factors, as well as to macroeconomic conditions.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

While some countries have used debt to drive economic growth, the asymmetric effect on sub-Saharan African (SSA) countries has received little attention in the empirical literature. This paper therefore examines the asymmetric effect of external debts on economic growth.

The panel nonlinear autoregressive distributed lag (NARDL) approach was employed in the study for 29 sub-Saharan African countries from 1990 to 2021. The cross-sectional dependence test was used to determine the presence of cross-sectional dependence, while the second-generation panel unit root tests was used to examine the unit-root properties.

The empirical results show that external debt has an asymmetric effect on economic growth in both the short and long run. In the long run, a positive shock in external debts of 1% triggers an upturn in economic growth by 0.216% while a negative shock triggers 0.354% decline in economic growth. This implies that the negative shock of external debts has a much stronger impact on economic growth than the positive shock. In the short run, a positive shock in external debts by 1% triggers a decline in economic growth by 0.641%, while a negative shock of 1% triggers a fall in economic growth of 0.170%.

The paper used the NARDL model to examine the asymmetric impact of external debt on the economic growth of SSA countries, which has not been extensively studied. It is recommended that governments in the selected countries in sub-Saharan Africa should drive economic growth by promoting domestic revenue mobilization since external debts impede economic growth.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.