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The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study's findings show that Investec's South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group's analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation. [ABSTRACT FROM AUTHOR]

This paper aims to comprehensively compare the risk-return profiles of Islamic and conventional equity markets, providing valuable insights into the financial performance of stock market indices in Islamic countries as compared to those in developed nations. The objective is to delve into the intricate dynamics of these markets, shedding light on how they respond to various economic and financial factors. By focusing on the performance metrics of stock market indices in both Islamic and developed countries, this study intends to analyze not only the overall returns generated but also the associated risks involved. In doing so, it seeks to unearth the nuances in investment opportunities, uncovering potential patterns of divergence or convergence between these two market segments. Furthermore, this research endeavors to discern how different economic and regulatory environments impact these markets, contributing to a more profound understanding of global investment. [ABSTRACT FROM AUTHOR]

Researchers traditionally compute isolated points for an efficient frontier and assume a line which passes through a risk-free asset r f and is tangent to the frontier. The tangency plays pivotal roles for the capital asset pricing model (CAPM). However, the assumption may not hold in the presence of kinks (as non-differentiable points) on efficient frontiers. Kinks are detected by parametric-quadratic programming only and not by ordinary portfolio optimization. Up until now, there has been no research to theoretically scrutinize kink properties (especially implications to CAPM) and systematically quantify the nonexistence of the tangency. In such an area, this paper contributes to the literature. In theorems and corollaries, we prove the nonexistence of the tangency and substantiate that expected-return axis is composed of piecewisely connected intervals for which the tangency does not exist and intervals for which the tangency exists. Computationally, we reveal universal existence of kinks (e.g., 0.2 to 8.0 kinks for 5-stock to 1800-stock portfolio selections) and the tangency-nonexistence ratios as about 0.066. [ABSTRACT FROM AUTHOR]

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Price markups and firms' Tobin's Q ratios are widely believed to have been increasing in the past several decades. Various models for the calculation of price markups have been developed, each relying on the historically held definition of the ratio of price to marginal cost; however, all of these have methodological drawbacks, and some of the results they have produced have been poorly reflective of the near past wider macroeconomic experience. This paper defines a new approach for the definition and measurement of markup pricing, and it also avoids some of the issues surrounding the marginal cost approaches by using the measure of economic rent and the capital asset pricing model. The results show limited markup pricing for the UK's FTSE 100 companies (2018–2023), but that certain real estate, technology/media and financial services/equity investment firms have enjoyed higher price markup levels. An analysis of the business models of these firms is used to qualitatively propose explanations for such markups. This work offers formal proof that that the expected price markup is equal to Tobin's Q and finds that the empiric market level of markup is near equivalent to the market Tobin's Q; the differences between the markup and Tobin's Q at the level of the firm are equally assessed. This work challenges the general consensus that price markups are above one and have been increasing; it may also aid policy makers with respect to taxation policy and regulatory measures, as well as the financial management of firms in decisions concerning capital deployment and portfolio management. The method merits expansion to wider data sets, as well as to those from outside of the UK. [ABSTRACT FROM AUTHOR]

This study explores the effectiveness of entropy as a proxy of aggregate market risk, in explaining the cross-section of excess returns in asset pricing model, after controlling for established factors like market excess returns, size, book to market and momentum. The analysis considers Indian firms, given that Indian capital markets are characterized by relatively thin trading and higher volatility compared to developed markets. Entropy is estimated using Shannon Entropy. Factor mimicking portfolio is constructed based on Shannon Entropy, whose returns are used as additional risk factor in Fama–French–Carhart four factor asset pricing model. Gibbons Ross Shanken-F statistic and Adjusted R2 are used to judge the efficacy of this factor in capital asset pricing model. All analysis is done using built in functions of python. Market beta, size and Book-to-Market are found to impact equity returns significantly. Entropy factor also impacts equity returns, but to a lesser extent. Explanatory power of asset pricing model is found to improve after inclusion of entropy factor, as indicated by GRS-F Statistic and Adjusted R2. Entropy augmented Capital Asset Pricing Models can be used by firms to decide hurdle rate for project evaluation and by asset managers for identifying over-valued/under-valued securities. This is the first study that investigates the role of entropy in explaining asset returns, in addition to other established priced factors. This study is limited to Shannon Entropy only. Other forms of entropy may improve results further, and should be explored in future research. [ABSTRACT FROM AUTHOR]

Considering the particularities of economic development, countries should cooperate and participate in joint projects within regional and international groupings. The paper aims to develop a scenario approach to the investment policy of countries in the context of globalization. Defines how important the investment component of a country's economic development is and justifies why countries are heterogeneous regarding investment attractiveness. To fulfil the main goal the author applies the capital asset pricing model, which is a basic concept in the field of finance. In addition to the main goal, the text of the article systematizes the stages of development of the organizational project scenario, justifies the role of foreign investments in economic development, and analyses world trends regarding changes in the attractiveness of investments. The paper estimates how attractive national economies are for foreign investments as well. [ABSTRACT FROM AUTHOR]

The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.

Accurate stock movement prediction is essential to profit from the stock market. However, this task is challenging due to the complexity and non-stationary nature of the market. Deep learning methods have obtained more attention and success in mining price movement patterns.However, some limitations affect their performances. In general, the stock market is ever-changing, and many factors affect stock movement, so capturing the stock movement patterns is hard without enough prior information. To tackle it, we consider employing economic facts to help improve the deep learning method. In this paper, we propose a novel Hierarchical Deep learning Model that fuses Economic Facts (HDMEF) to predict stock movement from the micro to the macro tiers: the individual, industry, and whole market tiers. Specifically, we present three well-designed modules to separately model them based on the Capital Asset Pricing Model (CAPM), the herding effects, and the holiday effects in the stock market. Experiments on the A-share CSI300 and CSI500 indexes demonstrate that our proposed method performs best on all test phases compared with previous competitive baselines, even an absolute improvement of 2%–3% on some test phases where all the baselines act poor, proving our method is more efficient and robust in different market conditions. In addition, we do an ablation study to analyze the role of various economic effects used in our model, and the results prove that each module is helpful for prediction. [ABSTRACT FROM AUTHOR]