Capital Asset Pricing Model Research Articles

A new approach to assessing a company’s financial indicators: taking into account business and financial risks

There are two main methods for estimating the value of assets. The first method is the well-known CAPM (Capital Asset Pricing Model), which uses the risk-free rate as the initial return and takes into account only the business risk associated with investing in a specific asset, and not in the market as a whole. The second method is associated with the use of one of two main theories of capital structure (Brusov–Filatova–Orekhova (BFO) theory and Modigliani–Miller (MM) theory), which take into account only the financial risk associated with the use of debt financing and allow the calculation of all financial indicators of a company of arbitrary age (BFO theory) or perpetuity one (MM theory). The article develops a new approach related to the use of the company’s current profitability, taken from the annual report, as a seed profitability of an asset (company). As part of the new approach, a methodology has been developed that makes it possible to calculate all the main financial indicators of a company within the framework of capital structure theories, taking into account both financial and business risks. Transition from CAPM to a new methodology significantly improves the accuracy of the estimate. The new approach, unlike CAPM, has forecasting capabilities.

Best Value Investing Strategy: Analysis of Graham, Greenblatt, and Piotroski Methods for Smart Investment Decisions

This study aims to analyze the value investment portfolio strategy based on prominent investors Benjamin Graham, Joel Greenblatt and Joseph Piotroski. The three portfolio models are built according to the methodology developed by each author, using the last five years of financial accounting data and through stocks listed on the Kompas 100 Index listed on the Indonesia Stock Exchange for the 2022 period. This study uses a three-factor model from the Fama and French method which is an extension of the Capital Asset Pricing Model methodology by adding Small Minus Big and High Minus Low. The regression results show that all three strategies produce positive and statistically significant coefficients in the three-factor model setting. After backtesting the three portfolios, Graham's portfolio has a higher return compared to the other two portfolios. The managerial implications of this study include the implementation of strategies based on strengthening financial fundamentals and operational efficiency to increase the company's attractiveness in the eyes of value-investing investors.

The Economic Shockwaves of the Russia-Ukraine Conflict and Energy Sources Prices – The Impact on International Energy Markets

The Russia-Ukraine conflict has raised concerns regarding the stability of the global energy sector, considering that Russia is a big player in the industry, producing and exporting crude oil and natural gas globally. This study aimed to empirically investigate the impact of the conflict on the prices of energy sources in international markets. The analysis was conducted using secondary data. The data was for prices of various commodity products, including diesel, petroleum, natural gas, and crude oil. The data was collected on multiple international markets for periods from January 1, 2021 to November 30, 2023. The analysis was conducted using trend analysis and event study. The capital asset pricing model (CAPM) was adopted to run the event study analysis. The results indicated that immediately after the Russian invasion of Ukraine, major energy products such as US crude oil, European crude oil, and the global energy index experienced a sharp rise in price, followed by significant price volatility over the following up to seven months. The review of OPEC energy prices also indicated an increase in prices, and volatility was experienced in countries producing and exporting oil products. The event study further revealed that there was a sharp increasing trend immediately after the event date and an upward trend for more than ten months after the event, resulting in the highest prices recorded in the event window. The study suggested three policy implications - conflict should serve as a wake-up call on diversification of energy sources, investment in alternative, sustainable and green sources of energy to reduce the dependence on oil, and promotion of international cooperation on energy security.

An Empirical Study on Asset Pricing Models Based on The Chinese A-Share Market

Based on the analysis of the A-share markets in Shanghai and Shenzhen Stock Exchange from 2000 to 2023, this paper systematically evaluates the applicability and explanatory power of the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model (FF3) and the Fama-French Five-Factor Model (FF5) in the Chinese market. The study finds that although CAPM has some explanatory power in the early stage, the three-factor and five-factor models can better explain the cross-sectional changes in stock returns as the market develops. In the descriptive statistics, each factor's volatility and correlation reveal the Chinese market's unique characteristics, such as the relative strength of small-cap and growth stocks. In the regression analysis, the results of the Fama-MacBeth regression showed that the significance and explanatory power of the market factor, size factor and value factor were different in different models. Further robustness analysis reveals the COVID-19's impact on the model's performance through the sub-period analysis. This paper provides a new perspective on understanding the asset pricing of China's A-share market and a theoretical basis for investors' investment decisions. It also points out the limitations of existing models in explaining emerging market equity returns. It suggests that future research can deeply explore the performance of more factors in different market environments.

Putting the Price in Asset Pricing

ABSTRACTWe propose a novel way to estimate a portfolio's abnormal price, the percentage gap between price and the present value of dividends computed with a chosen asset pricing model. Our method, based on a novel identity, resembles the time‐series estimator of abnormal returns, avoids the issues in alternative approaches, and clarifies the role of risk and mispricing in long‐horizon returns. We apply our techniques to study the cross‐section of price levels relative to the capital asset pricing model (CAPM) and find that a single characteristic, adjusted value, provides a parsimonious model of CAPM‐implied abnormal price.

Decommissioning of a fuel oil-fired thermoelectric power plant in Brazil - Economic feasibility under certain and risk conditions

The strategic analysis for the decommissioning of thermoelectric plants is seen as a trend for the coming years. The search for renewable alternatives should stimulate investment in other energy sources, reducing the number of thermoelectric plants in the energy generation system. However, few studies have dealt with the decommissioning of oil-fired thermoelectric plants. In this work, the methodology was applied under deterministic and stochastic conditions using methods of net present value (NPV), internal rate of return (IRR), discounted payback, capital asset pricing model (CAPM) and weighted average cost of capital (WACC). The results showed that the deterministic NPV was positive, ranging from R$101.30 million (pessimistic scenario) to R$109.73 million (optimistic scenario). The IRR was higher than the WACC of 10.55 %, ranging from 13.50 % to 13.68 % per year. For the Monte Carlo simulation, NPV was observed with 100 % certainty of viability and the probabilities of occurrence allowed more analysis of the risks involved than those obtained by deterministic methods. this study contributes to future decommissioning projects in Brazil, considering the scenario of stimulating renewable sources and the energy transition, helping managers make decisions about their projects. In addition, it helps guide public policies that can optimize the decommissioning process and strengthen the national energy sector.

Stock market responses to economic sanctions: Evaluating the roles of national reserves and financial market access

Economic sanctions significantly influence stock market capitalization and have critical implications for the national economy. This study investigates the impact of economic sanctions on stock market capitalization, focusing on the roles of national reserves and financial market access in mitigating these effects, utilizing a sample of 87 countries from 2000 to 2021 and employing a two-step system generalized method of moments (GMM) approach to analyze panel data. The findings reveal that economic sanctions, including trade and financial sanctions, significantly reduce stock market capitalization. However, this negative impact diminishes over time as markets adapt. Moreover, higher national reserves and enhanced financial market accessibility can mitigate the adverse effects of sanctions. Case studies in Russia, Iran, China, and Ukraine further validate these results. This study refines economic models, such as the capital asset pricing model (CAPM) and country risk model (CRM), by including sanction intensity, national reserves, and financial market accessibility, offering valuable insights for policymakers and investors dealing with international sanctions.

Capital Asset Pricing Model and Ordered Weighted Average Operator for Selecting Investment Portfolios

The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPMOWAand its extensions, called CAPM-induced OWA (CAPMIOWA), CAPM Bonferroni OWA (CAPMBon-OWA), and CAPM Bonferroni-induced OWA CAPMBon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers.

Comparative Analysis of Portfolio Management Models: Markowitz, CAPM, and Multi-Factor Models

This paper presents a comparative analysis of three foundational portfolio management models: the Markowitz Efficient Frontier, the Capital Asset Pricing Model (CAPM), and Multi-Factor Models. It aims to explore their theoretical foundations, empirical applicability, and practical usage to assess their strengths and limitations in financial portfolio management. The Markowitz model is appreciated for its detailed risk-return optimization framework but criticized for its reliance on historical data and computational demands. The CAPM simplifies risk assessment through market beta, yet its assumptions of market efficiency and a risk-free rate are viewed as impractical under current market conditions. Multi-Factor Models address some limitations of the CAPM by incorporating various risk factors, offering enhanced explanatory power but also adding complexity in factor selection.The study concludes that while no single model is universally superior, the choice of model should depend on the specific needs of the portfolio manager, including risk-return objectives and market environment. The paper suggests a blended or adaptive approach to model selection, highlighting the need for flexibility in response to evolving market dynamics and advances in computational finance.

Corporate Value Assessment based on the EVA Model and Relative Valuation

As a risk-averse commodity, gold has seen new development opportunities in retail due to factors (e.g., geopolitical risks and conflicts, economic uncertainties, and inflation). Consequently, more and more investors are focusing on the gold retail industry. Evaluating the value of gold retail enterprises using scientific and effective methods is of great significance to both managers and investors. Therefore, this paper uses Chow Tai Seng, a Chinese gold retail enterprise, as a case study. Based on the company's development characteristics, we employ the EVA absolute valuation method and relative valuation methods including P/E, P/B and P/S ratios for valuation. The study finds that although its EVA values show a general downward trend from 2019 to 2023, they remain positive, indicating that the company's overall development is still on track. The development of enterprise valuation theory has gone through the MM (Modigliani-Miller) theory, Free Cash Flow Valuation Model, and the Capital Asset Pricing Model, among others. These theoretical models have gradually improved, and the theoretical system is continuously evolving. By summarizing and reviewing valuation theories and methods, this paper employs the DDM for valuation and the P/E ratio method under relative valuation based on the company's development characteristics. Combining these two valuation methods with the company's actual financial data and relevant value theories, the company can attract investors and address undervaluation by increasing its exposure through modern multimedia channels and enhancing data transparency.

Bitcoin: a Ponzi scheme or an emerging inflation-fighting asset?

Under the dual impact of the COVID-19 pandemic and the Russian-Ukrainian conflict, the excessive stimulation of monetary policy continuously pushes up global inflation (INF). Therefore, this article explores whether Bitcoin can serve as a safe haven for INF. We apply the rolling-window Granger causality test to solve the issue of parameter instability in vector autoregression (VAR) systems and investigate the time-varying interaction between INF and Bitcoin price (BP). The negative influence of INF on BP means a high inflation shock causes BP to decline, indicating that Bitcoin cannot be a safe asset against INF. This is because investors have decreased their willingness to hold Bitcoin under the high INF expectations and cause BP to fall. This finding is not supported by the Intertemporal Capital Asset Pricing Model, emphasising that INF positively impacts BP. Conversely, BP has positive and negative impacts on INF. The positive effect highlights the effectiveness of Bitcoin in predicting INF fluctuations, but economic factors could undermine this effectiveness. In the context of economic stagnation and market turmoil, investors can adjust their portfolio investments based on Bitcoin. The government should utilise the trend of BP to regulate the dynamics of INF to reduce uncertainty in the financial system.

Application and Empirical Analysis of Asset Pricing Models in the Chinese Stock Market

This paper examines the application and performance of asset pricing models in the Chinese stock market. We analyze the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model, and the Carhart Four-Factor Model, assessing their effectiveness in explaining stock returns within the context of China's unique market environment. Using data from major Chinese stock exchanges, we employ regression analysis to evaluate the models. Our findings reveal that multifactor models, particularly the Carhart model, provide superior explanatory power compared to CAPM, highlighting the importance of incorporating additional risk factors. This study contributes to the understanding of asset pricing in emerging markets and offers practical insights for investors and policymakers.

ESG Performance and Systemic Risk Nexus: Role of Firm-Specific Factors in Indian Companies

This study investigates the ESG performance–systemic risk (SR) nexus among Indian companies. Using the beta coefficient from the Capital Asset Pricing Model (CAPM) and statistical analysis, it explores how ESG performance affects SR. The findings reveal that firms with higher ESG scores have lower SR sensitivity. Notably, there is a significant difference in risk sensitivity between high- and low-ESG-rated companies, with ESG effects being less pronounced in high-cap firms compared to low-cap firms. Conversely, large firms, older firms, and those with lower borrowing costs show a diminished effect of ESG ratings on their SR sensitivity. These results underscore the importance of firm-specific characteristics in determining the efficacy of ESG strategies in risk mitigation. This study reveals that ESG performance reduces SR, with market valuation affecting this relationship.

Assessment of professional perceptions in business evaluation in South Africa

This paper seeks to evaluate proficient perceptions within business assessments in South Africa, where such perceptions play a significant role in the evaluation process, encompassing the selection of standards and the establishment of evaluation frameworks. The evolving economy of South Africa is marked by fluctuating economic circumstances and potential growth, compounded by inadequate benchmarks and a scarcity of relevant market information. Forecasting growth rates are further complicated by macroeconomic uncertainties, leading to a reliance on proficient perceptions when industry-specific guidelines and easily comprehensible alternatives to the Capital Asset Pricing Model (CAPM) are lacking. A mixed-methods strategy is employed in this study, combining quantitative and qualitative data, along with surveys set benchmarks for business assessments. The outcomes reveal that modifications based on judgment are necessary for variables like organizational size due to the absence of CAPM alternatives. This study stresses the importance of translating scholarly discoveries into practical applications, emphasizing the challenges and uncertainties that call for proficient perceptions in business evaluations. It advances the comprehension of these obstacles and proposes that further exploring macroeconomic variables and industry-specific guidelines could offer valuable insights. The study suggests that biases in evaluations, influenced by client interests, present a promising area for future inquiry.

Techno-Economic Assessment of Industrial Vanadium Flow Batteries Based on Experimental Data

This work presents a techno-economic model based on experimental and market data to provide forecasts of the profitability of vanadium flow batteries (VFBs), which are emerging as a promising technology for specific stationary energy services [1‒2]. Each component affecting the capital and operative costs was analyzed and the impact of side phenomena on capacity losses was considered. Relevant economic parameters were taken from real market data: discount rate was calculated based on the Capital Asset Pricing Model (CAPM) to account for real market conditions; the electricity price follows an arbitrage strategy to profit of the daily price fluctuations taken from the historical data of the Italian energy market [3‒4]. Technical parameters are taken from large-area multi-cell stack, rather than from small single cell experiments, thus allow to characterize the behavior of real industrial reactors [5‒6]. The economic performance indicators obtained are the capital cost, the operative cost, the Levelized Cost of Storage (LCOS) and the Net Present Value (NPV). The resulting values are: a discount rate of 9.12% and electricity prices of 200 € MWh–1 in selling and of 100 € MWh–1 in buying. Capital costs and LCOS were calculated for different system power and energy (E/P) ratings. At E/P = 2 h, the values of capital costs and LCOS were in the range of 800 – 900 € kWh–1 and 0.50 – 0.55 € kWh–1, respectively, whereas at E/P = 10 h they reduced to 350 – 380 € kWh–1 and 0.29 – 0.32 € kWh–1, respectively. In addition, a NPV analysis was carried out in order to evaluate whether these capital costs could provide a profit. The analysis suggests that only at capital cost < 105 € kWh–1 the break-even point (NPV = 0) was reached. The result is that the assumed techno-economic scenario VFBs were not profitable for every considered E/P. A perspective analysis was developed to reveal when VFBs can become profitable. Different screenings were made on both technical and economic parameters. At a technical level, a power density of 0.25 W cm–2, a RTE = 85%, a SOC = 90%, and a reduced number of regenerating processes in the lifespan (Nreg = 4) were considered. Regarding the economic parameters, a discount rate of 7 % was assumed, an electricity selling price of 250 € MWh–1 and a purchasing price of 75 € MWh–1 were used, compatible with a more pronounced Duck Curve of the daily electricity price, induced by the expansion of renewable energy sources expected in the near future. Under this scenario, the system costs decreased considerably: at E/P = 2 h, the Capital Costs and LCOS ranged as 530 – 570 € kWh–1 and 0.25 – 0.27 € kWh–1, respectively, whereas at E/P = 10 h, the Capital Costs and LCOS ranges as 260 – 270 € kWh–1 and around 0.17 € kWh–1, respectively. The latter figures made VFBs profitable for E/P in the range of 4 – 10 hours, as shown in the figure below.

Analyzing the Risk-Return Tradeoff in Commercial Banks' Common Stock: A Case Study of Nepalese Banks

This study investigates the risk-return tradeoff in the common stock of Nepalese commercial banks over a five-year period from 2018/19 to 2022/23. By analyzing financial data from four prominent banks listed on the Nepal Stock Exchange (NEPSE) i.e. Global IME Bank Limited (GBIME), Nepal Investment Mega Bank (NIMB), Nabil Bank Limited (NABIL), and Kumari Bank Limited (KBL). The study depends on the closing prices of these four commercial banks, which were generously provided through the NEPSE Index and annual general meeting (AGM) reports of each respective bank. The research aims to provide a comprehensive understanding of their risk profiles and expected returns using the Capital Asset Pricing Model (CAPM). The study employs descriptive statistics like mean, standard deviation, coefficient of variation, and maximum/minimum returns. The findings reveal significant variations in performance and risk levels: GBIME and KBL show positive average returns with high volatility, whereas NIMB and NABIL exhibit negative average returns, indicating losses. GBIME’s stock is slightly less volatile than the market, while NIMB’s low beta suggests it is suitable for conservative investors. NABIL’s and KBL’s higher betas indicate more volatility, appealing to high-risk tolerant investors. The study underscores the importance of portfolio diversification and continuous monitoring of market conditions for informed investment decisions. These insights are valuable for investors, financial regulators, and policymakers in developing effective risk management strategies and enhancing financial stability in Nepal's banking sector.

Stock Making Investment Decisions Using the Capital Asset Pricing Model (CAPM) Analysis of the Business Index-27 on the Indonesian Stock Exchange

The purpose of this study is to measure the ability of the Capital Asset Princing Model (CPAM) in analyzing investment decision making by predicting the risk and return that will be obtained by investors and helping investors in choosing efficient and inefficient stocks. CAPM is a measuring tool that can be used to determine the level of risk and return obtained and evaluate the rate of return on investment. The purposive sampling technique is used in selecting samples to be used in the study, namely companies listed on the Indonesia Stock Exchange and their shares are consistently included in the Bisnis-27 stock index. The stock criteria used in selecting efficient stocks, namely when individual return results exceed the expected return. The results of this study indicate that there are 8 stocks that meet the efficiency criteria, namely ADRP, AKRA, AMRT, BBNI, BMRI, INKP, JSMR, and PGAS with individual returns results exceed the expected returns. In investment decisions, stocks that are included in these efficient stocks are the priority stocks that investors should buy. Based on the analysis, there is a non-linear relationship between systematic risk and expected stock returns, making an important contribution to investment decision making in the Indonesian stock market.

Valuation of Medical Innovation Handling with Uncertainty and Risk.

Background: The purpose of this paper is to address how to handle uncertainty when performing an economic valuation of a medical innovation R&D project in orphan diseases from the perspective of the investor. Methods: We describe the specific uncertainty related to cash flows and the cost of capital for innovation in orphan diseases. The uncertainty in cash flows relates to sales, manufacturing and R&D costs, and probabilities of failure for each phase in the clinical trial program. We consider different net present values (NPVs) and higher standard deviations for orphan drugs compared to non-orphan drugs. Results: Numerical case base examples showed the differences in trade-off by an investor for R&D projects with differences in NPV and level of uncertainty. The investor will transfer the additional uncertainty in cash flows in a higher cost of capital. An alternative approach is the application of an "acceptability curve" based on a probabilistic sensitivity analysis, which displays the cumulative probabilities at a range of different values for the NPV. Finally, we consider uncertainty in the cost of capital itself by applying the Capital Asset Pricing Model (CAPM). Conclusions: In this paper, we described various types of uncertainty and explored various approaches to how to handle uncertainty in the economic valuation of medical innovation in orphan diseases. The bridging of health economics with economic valuation theory in the healthcare market is to our knowledge a novel approach for the valuation of medical innovation by investors.

Harnessding the Power of Machine Learning to Superchange CAPM Forecasts

This paper investigates the improvement of the Capital Asset Pricing Model (CAPM) by incorporating machine learning techniques. The objective is to address the model's conventional constraints in accurately predicting stock returns. The conventional Capital Asset Pricing Model (CAPM), which heavily depends on the beta coefficient to explain returns, frequently proves inadequate in emerging markets and neglects to consider market irregularities such as size and value effects. Our methodology enhances the estimation of beta by refining the Capital Asset Pricing Model (CAPM) to exclude the intercept term and incorporating a dynamic rolling regression method. This approach captures the fluctuations of beta more effectively across different periods, resulting in a more precise estimation. This study utilises ten years of weekly closing price data from companies listed on the NSE Nifty 50 index. It employs rolling regression and two-stage regression analysis to improve the prediction of excess returns above the risk-free rate. The revised CAPM model, which omits the intercept, exhibits a superior level of accuracy, as evidenced by higher adjusted R-squared values and improved F-statistics. In addition, the paper analyses the Fama-French models within the Chinese stock market and explores the potential of advanced machine learning models, such as Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), to improve forecasting methods in unpredictable markets. Integrating LSTM-RNN models presents a promising approach to capturing intricate patterns in financial time series data, demonstrating substantial enhancement in forecasting accuracy compared to conventional methods such as OLS.

Excess return and tracking errors of Chinese ETFs

We adopt 72 passive equity exchange-traded funds (ETFs) from the Chinese A-share market to examine the size and time-varying characteristics of their tracking errors, identify their determinants, and gain insight into the persistent and positive excess returns. Net asset value traces index better than closing price does; surviving ETFs perform better than delisted ones. The common determinants are fund expenses, liquidity and volatility of the index. Many determinants asymmetrically influence the tracking errors in bull and bear markets. Further examination based on the capital asset pricing model indicates persistent positive excess return is a result of A-share market inefficiency.