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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]

Copyright of Journal of Administrative Sciences / Yonetim Bilimleri Dergisi is the property of Canakkale Onsekiz Mart Universitesi, Terzioglu Kampusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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.

The main intention of this study is to use the accounting data using CAPM to determine the stock prices/returns for the Nigerian capital market. In this study, the independent variable is the prediction of the stock prices and the dependent variable is stock prices in the market. The proxy that is used in this study to measure the dependent variable is CAPM in Nigerian Market. The most important and interesting phenomenon to investors is the analysis from financial market pertaining to stock returns. The research method employed is quantitative which is unlike qualitative as a way of assessing the stock price. The study mainly aims at assessing the correlation of beta factors and the predictability of stock returns from Nigerian firms listed on the stock exchange. In order to boost the beta estimates and mitigate statistical problems resulted from incorrect measurement, the securities were combined into portfolios. In conclusion the study employs ordinary least squares (OLS) regression technique and obtained beta value which is positive and found conclusive evidence for using CAPM and is thus consistent with Nigerian stock market prices. The CAPM has implications for asset pricing since it shows how to calculate the requisite rate of return to assess the value of the stock prices with any given amount of systematic risk (beta) and since the beta is positive hence the policy makers and investors in the Nigerian stock market would make better informed decisions.

Using the capital asset pricing model, this article critically assesses the relative importance of computing 'realized' betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the 'pure' systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include 'realized' versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails. [ABSTRACT FROM AUTHOR]

AbstractThis study expands previous research by adding intellectual capital to the capital asset pricing model and deepening the measurement of intellectual capital using more comprehensive proxies. This study is novel in that it is related to evaluation according to market developments using tests on potential factors in the asset pricing model by adding intellectual capital variables. Intangible assets have the explanatory and predictive power of size and value strategies in predicting returns. This study uses a sample of all industries from all companies listed on the Indonesia Stock Exchange during 2012–2022; the sample totals 5767 firm-years. The results show a significant relationship between the six-factor capital asset pricing model, intellectual capital, and excess stock returns. The robustness test supports these findings, suggesting that intellectual capital performs better financially as an intangible asset. This study contributes to the literature regarding the role of intellectual capital in the capital asset pricing model, which can be helpful when making investment decisions in emerging stock markets.

This study expands previous research by adding intellectual capital to the capital asset pricing model and deepening the measurement of intellectual capital using more comprehensive proxies. This study is novel in that it is related to evaluation according to market developments using tests on potential factors in the asset pricing model by adding intellectual capital variables. Intangible assets have the explanatory and predictive power of size and value strategies in predicting returns. This study uses a sample of all industries from all companies listed on the Indonesia Stock Exchange during 2012–2022; the sample totals 5767 firm-years. The results show a significant relationship between the six-factor capital asset pricing model, intellectual capital, and excess stock returns. The robustness test supports these findings, suggesting that intellectual capital performs better financially as an intangible asset. This study contributes to the literature regarding the role of intellectual capital in the capital asset pricing model, which can be helpful when making investment decisions in emerging stock markets. [ABSTRACT FROM AUTHOR]

In the conventional capital asset pricing model (CAPM), standard deviation as a measure of risk penalizes both upward and downward movements. However, while downward movements in investments are risky, upward movements are favourable. Therefore, standard semideviation that treats only those negative differences in returns over the benchmark as risky was proposed as a measure of downside risk. Attempts have been made to obtain the formulation of beta in this context, called downside beta. In this work, CAPM in the mean‐semivariance framework (D‐CAPM), with semivariance measured in terms of negative deviation around expected return, is derived and established that the model proposed earlier with a version of downside beta with respect to this definition of semivaraince is invalid. The appropriate version of downside beta is obtained, and under the rectified formulation, the validity of the true D‐CAPM is empirically tested during July 2013–July 2018. The solution to the tangent point is also obtained. The proportion of investment in the stocks in the tangent portfolio and the downside beta for the stocks in the tangent portfolio are determined. We find that there is no evidence to disprove that D‐CAPM holds true considering either Nifty 50 or S&P BSE Sensex as market portfolio. [ABSTRACT FROM AUTHOR]

The present study aimed to assess the relationship between corporate governance factors and stock returns based on the capital asset pricing model and the Fama and French three-factor model. The statistical population included companies acting in the Istanbul Stock Exchange from 2010 to 2020. Using a systematic elimination sampling method, 70 companies were selected. The data panel method was used along with two-stage regression of Fama and Macbeth [16] and EViews11 software to estimate the models. The results represented a significant and positive relationship between the level of transparency in information disclosure, the structure of the directors' board, and the structure of ownership and shareholders' rights with stock returns based on the capital asset pricing equation. Moreover, there was a significant and positive relationship between the general index of corporate governance and the stock returns of companies based on the three-factor equation of Fama and French. This index is comprised of three sub-indices. [ABSTRACT FROM AUTHOR]

This study investigated the risk-return relationship using the Capital Asset Pricing Model (CAPM), Carhart four-factor Model (C4FM) and Fama and French Multifactor Models (FFMMs): F3FM and F5FM. This study analyzed the JSE ALSI returns of the South African market, in relation to the risk factors constructed by the data of twenty-six emerging markets, over the sample period from October 2000 to October 2021. The methodology employed was a comparison of the conventional regression analysis and novel Bayesian approach. Regression analysis estimated by Ordinary Least Squares (OLS) is one of the foremost methods used in South Africa. However, such an approach does not take into account the properties of the price data, namely, the asymmetric, volatile and random nature. While the Newey-West adjustment is one way to assist in capturing autocorrelation and volatility, it fails to consider asymmetry. The Bayesian approach accounts for all the aforementioned properties, overcoming the fundamental flaws of regression analysis. The additional model diagnostics highlighted in this study improved the Bayesian approach' robustness. The risk factors of the FFMMs estimated by regression analysis, with and without the Newey-West adjustment, were insignificant, whereas the Bayesian test results were significant. This finding clearly highlighted that model choice impacts the significance of parameter estimation and the financial decisions of investors, firms and policymakers. Jensen's alpha revealed CAPM to be optimal but none of the asset pricing models fully captured the risk premia. Thus, returns should be investigated with different risk measures for future research purposes.

The blueberry industry is growing significantly in Colombia. For investors, cost analysis plays a key role in project planning for any agribusiness. This paper is an exploratory study, with a descriptive scope to identify components of establishment and production costs of growing blueberries in Colombia. Data were collected and analyzed from a project aimed at establishing a 1 ha blueberry crop in Zipaquirá and 18 blueberry small farms in the Cundiboyacense highlands. Components were applied to estimate the capital investment and to identify the establishment, production, and maintenance costs of this project. Decision investment analysis, including discount rates for the capital asset pricing model (CAPM), were used to compute the net present value (NPV), internal rate of return (IRR), and the return on investment (ROI). Production costs are still expensive due to the Colombian peso (COP) exchange rate compared to the US dollar (USD). Results show advantages for promoting the establishment of blueberry crops because of suitable climatic conditions for continuous production throughout the year in Colombia. Labor costs and utilities are cheaper compared to other countries and represent a competitive advantage in the investment. Intermediation suppliers affect establishment costs for commercial reasons. Blueberry production cost analysis in Colombia may facilitate agribusinesses to promote projects in Colombian agriculture.

The main intention of this study is to use the accounting data using CAPM to determine the stock prices/returns for the Nigerian capital market. In this study, the independent variable is the prediction of the stock prices and the dependent variable is stock prices in the market. The proxy that is used in this study to measure the dependent variable is CAPM in Nigerian Market. The most important and interesting phenomenon to investors is the analysis from financial market pertaining to stock returns. The research method employed is quantitative which is unlike qualitative as a way of assessing the stock price. The study mainly aims at assessing the correlation of beta factors and the predictability of stock returns from Nigerian firms listed on the stock exchange. In order to boost the beta estimates and mitigate statistical problems resulted from incorrect measurement, the securities were combined into portfolios. In conclusion the study employs ordinary least squares (OLS) regression technique and obtained beta value which is positive and found conclusive evidence for using CAPM and is thus consistent with Nigerian stock market prices. The CAPM has implications for asset pricing since it shows how to calculate the requisite rate of return to assess the value of the stock prices with any given amount of systematic risk (beta) and since the beta is positive hence the policy makers and investors in the Nigerian stock market would make better informed decisions. [ABSTRACT FROM AUTHOR]

In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan's stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth's regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the role of a consumer satisfaction index (CSI) for financial investments in the Hong Kong market. Using yearly data for Hong Kong consumer satisfaction index (HKCSI) to compile a CSI at company level, the effect of consumer satisfaction on company market value is identified. A hypothesized investment portfolio based only on CSI at company level is created, and its return compares with a widely used index measuring stock market performance in Hong Kong. A formal statistical test on the outperformance of portfolios that load on consumer satisfaction is conducted. Using the Capital Asset Pricing Model (CAPM), the beta risk of the entire time period is evaluated, and shows that the portfolio risk based on company level CSI is not significantly different than the market risk. This paper concludes therefore that consumer satisfaction can be incorporated into financial models and applied for formulating investment portfolios with better performance than the market rate in Hong Kong. [ABSTRACT FROM AUTHOR]

Understanding why and how asset prices rise is a major concern for industry, policymakers and investors. This has attracted the attention of researchers to the issue of asset pricing, especially high-risk assets such as stocks. The single-factor capital asset pricing model (CAPM) and the Fama and French (1993) three-factor model are some of the well-known models that seek to explain investors' expectations for high-risk asset returns, and various multifactorial extensions of these models have been proposed. One of the latest versions of these models is the seven-factor model, which is expected to have a higher explanatory power than the CAPM model and the Fama and French three-factor model. The purpose of this study is to evaluate the seven-factor model in Tehran Stock Exchange and compare it with CAPM model and Fama and French model in order to predict the expected returns of companies operating in the financial sector of Tehran Stock Exchange during the period 2016: 1 to 2019: 12. In this study, the estimation method is the Auto Regressive Distributed Lag (ARDL). The results of the study show that the seven-factor model gives a better estimate of the expected return than the CAPM and Fama and French models. Also in the seven-factor model, the variables of book value to market value ratio, momentum, liquidity turnover ratio and commodity index have a negative and significant effect on expected returns. But market factor, government bond index and company size factors do not have a significant effect on expected returns.

When the cost of hedging is nil, the conditional capital asset pricing model (CAPM) holds. We empirically test the conditional CAPM by regressing asset returns onto the product of their conditional betas and market returns. Estimated intercepts are not statistically different from zero, implying that the conditional CAPM successfully explains the conditional level of asset returns. Yet, unconditional betas do not explain the cross section of average asset returns; the unconditional CAPM fails. We show why and how the success of the conditional CAPM actually explains the failure of the unconditional CAPM, thereby rationalizing the coexistence of these two intriguing results. This paper was accepted by Gustavo Manso, finance. Funding: The University of Texas at Dallas and the University of Toronto provided financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4381. [ABSTRACT FROM AUTHOR]

In this study, the reliability of the Fama–French Three-Factor model (FF3F) and the Carhart Four-Factor model (C4F) is examined thoroughly. In order to determine which of the asset pricing models is the best to explain portfolio returns on the Moroccan share market, these two models are indeed evaluated in the Moroccan market. Additionally, it is worth mentioning that five years of monthly data from the firms that listed on the Casablanca Stock Exchange are used in this research, as well over the period of nine years. The results of this inquiry show that these models barely have a partial hold on the Casablanca Stock Exchange (CSE), which limits their ability to predict the cross-sections of returns. In accordance with this, the C4F model has somewhat greater explanatory power than the FF3F Model. Moreover, our research adds to the body of knowledge by inserting two learned material asset pricing theories to the proof in the market, which is still evolving, and where distinctive anomalistic traits still exist (the CSE). [ABSTRACT FROM AUTHOR]

In this paper, we mainly use recurrence plot, recurrence quantification analysis and cross recurrence plot to investigate the potential nonlinear dynamical characteristics of the stock market. In addition, we introduce and analyze the capital asset pricing model (CAPM), which can analyze the correlation between returns and risks of stock price data. In particular, we selected the large-cap stocks represented by SSE 50 index and the small-cap stocks represented by CSI 500 index as our research objects. Through the recurrence plot analysis of the time series of β factor of CAPM and the time series of the stock market, we can find the advantages of large-cap stocks, such as stronger stationarity and long-term certainty, the disadvantages of small-cap stocks, such as greater risk and information, and nonlinear cross recurrence with the stock market. [ABSTRACT FROM AUTHOR]

The Arbitrage Pricing Theory (APT), based on arbitrage theory, emphasizes that a market can rebalance itself After the occurrence of an arbitrage opportunity. This capability of financial markets confirms the Arbitrage Pricing Theory. This study tests the validity of APT on the Istanbul Stock Exchange between the period of January 2009 and March 2020 on BIST100. Purposing to determine the relationship between security returns and other macroeconomic factor, it will serve as a compass for other emerging countries. With stock return factor as independent variable, this study uses a Vector Error Correction Model (from the VAR family model) with five macro-economic factors: GDP, interest rate, inflation rate, exchange rate and the countries' production indexes. The resulting model depicts a negative Error Correction Term (ECT) which indicates the validity of the model in the Turkish stock exchange. [ABSTRACT FROM AUTHOR]

Copyright of BES Journal: International Journal of Business & Economic Studies is the property of BES Journal - International Journal of Business & Economic Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of this study is to examine the moderation effect of the audit committee on the linkages between audit firm size, industry specialization, and the cost of equity capital with firm size and financial leverage as a control variable. This study is one of few that compared the accuracy of Ohlson and Capital Asset Pricing Model in explaining the cost of equity capital and the moderation role of the audit committee. A sample of 123 manufacturing firm-year observations was drawn from Indonesian-listed companies for five years period (2016-2020). The results show that while the audit committees significantly moderate the negative relationship between firm size on the cost of equity capital, there is no significant evidence of the moderation role of the audit committee on the relationship between industry specialization and the cost of equity capital using the Capital Asset Pricing Model. On the contrary, the moderation role of the audit committees on the effect of industry specialization toward the cost of equity capital under Ohlson was supported in the study. Further, it found that the Ohlson has a better performance than the Capital Asset Pricing Model in explaining the cost of equity capital as the Ohlson measurement expressed the role of earnings per share which represent a more real rate of return.

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]

Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.

Abstract In this paper, we examine the presence of short-term and long-term momentum returns in Indian stock market. The study also tries to shed light on the power of asset pricing models and select macroeconomic variables in explaining momentum returns. The results confirm the presence of short-term and long-term momentum returns in Indian stock market. It is also found that Carhart four-factor model’s performance is relatively superior to other factor models such as one factor capital asset pricing model and Fama–French three-factor model in terms of capturing momentum returns. Finally, macroeconomic variables which are considered for analysis do not have any power to explain momentum returns.