Asset Pricing 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.

Market concentration of the Brazilian hospital medical supplementary health system

BackgroundThe Brazilian supplementary health market has undergone transformations in recent years due to constant mergers and acquisitions of by large corporations, contributing to the increase in market concentration, especially in the poorest and least developed regions of the country. Thus, given the care it provides and its economic relevance, understanding the fundamentals of these movements, the likely consequences and trends for the health market are relevant, important, and strategic.ObjectiveTo understand the general and specific context of Brazilian supplementary health, its scenarios, and trends, with emphasis on the analysis of market concentration and recent mergers and acquisitions.MethodologyThe research is applied, descriptive and exploratory and uses secondary data from various sources, submitted to quantitative data analysis methods. The data are organized into three groups: historical and regulatory documents; industry data; and market.ResultsThe results show the growing concentration of the market promoted by large publicly traded corporations, the growing relevance of tech startups on the healthcare landscape, the predominant use of the relative valuation model, with implicit multiples for asset pricing and the prevalence of corporate health plans.ConclusionThe growing concentration of the system projects a market with fewer options and less competitiveness, since the growth of large operators is evident, in addition to the relevant increase in the number of complaints from users of the system, which signals the growing gap between the expectations of users and the levels of quality care offered. The study also highlights the predominance of corporate health plans, revealing the direct relationship between access to supplementary health services and employability rates. The analysis of M&A operations, in addition to the increase in market concentration, reveals the prevalence of the use of the relative valuation model and implicit multiples for the pricing of traded assets. This denotes the future expectation of wealth generation, at least equivalent to the historical series of the sector, on the part of investors, whose frustration may signal the decreasing attractiveness of resources and M&A operations in the sector in the coming years.

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.

Preface: 3rd International Conference on Portfolios, Global Marketing and Economic Environment (PGMEE 2024)

2024 3rd International Conference on Portfolios, Global Marketing and Economic Environment (PGMEE 2024) was held in Sao Paulo, Brazil during September 07-08, 2024. This conference aims to provide an academic sharing and exchanging platform for researchers, practitioners, and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of portfolios, financial security, risk management, global marketing, asset pricing, investment policy and economic environment. The success of PGMEE 2024 is due to the high scientific level of all the contributions as well as to the enthusiasm of all the attendees. The member of the technical committee did a precious job in selecting the papers that were accepted for presentation and for inclusion in this book of proceedings among those that were initially submitted. Beside the authors, participants, we would like to thank the members of the organizing committee, reviewers and volunteers, whose work was fundamental for assuring the smooth running of the conference. We hope PGMEE 2024 can develop into persistent success in future years, involving participants and speakers from all corners of the world. And we believe that with the strong support of all participants, professors, scholars, this conference will be more distinctive. PGMEE Organizing Committees Sao Paulo, Brazil

The Effectiveness of Asset Pricing Models Under Special Conditions: Evidence from the COVID-19 Pandemic, Emerging Markets, and Financial Crises

Asset pricing models are important tools in finance. However, special market conditions can make them highly ineffective. This paper evaluates the performance of classical asset pricing models (that is, CAPM and Fama-French models) under three specific scenarios: the COVID-19 pandemic, emerging markets, and financial crises. Recent research and market data are reviewed to conclude that these models have very low efficiency in times of extreme occurrences for capturing proper asset valuations. Our findings reveal that standard models lose a substantial part of their explanatory power in periods of crises while new context-specific risk factors come into existence in other exceptional conditions. This work argues for the inclusion of liquidity and tail risk considerations, especially during market stress, as we also observe a greater need for more dynamic and adaptive modeling approaches that capture time-varying risk premia and factor loadings. The findings drive home the point that standard asset pricing models cannot be applied across the board in all market conditions and stress the need for developing more flexible and situation-specific valuation tools. Therefore, this research lies in its dual contribution, both in academic terms and practical finance, by providing an understanding of the behavior of asset pricing models in extreme markets and giving indications for future model development.

Forecasting the equity premium: can machine learning beat the historical average?

We empirically predict the equity premium with the selected machine learning methods in Gu et al. (Empirical asset pricing via machine learning. Rev. Financ. Stud., 2020, 33(5), 2223–2273). We also consider four additional popular machine learning methods (ridge regression, support vector regression, k-nearest neighbors, and extreme gradient boosted trees) and their combination method. Using a dataset of both macroeconomic and technical predictors, we find that despite showcasing strong in-sample forecasting abilities, particularly with tree-based models, the out-of-sample results support Welch and Goyal (A comprehensive look at the empirical performance of equity premium prediction. Rev. Financ. Stud., 2008, 21(4), 1455–1508) that the competing forecasting models generally fail to outperform the historical average benchmark. We attribute this failure to the small dataset size and the low signal-to-noise ratio inherent in equity premium prediction. Our variable importance analysis further identifies three bond interest rate-related variables as the most dominant predictors for the equity premium. The economic value from a market timing perspective highlights that the historical average benchmark strategy generates the highest average return of 25.63% and the best Sharpe ratio of 0.8. Finally, our findings are robust across a variety of settings.

The impact of pinning points on memorylessness in Lévy random bridges

Abstract Random bridges have gained significant attention in recent years due to their potential applications in various areas, particularly in information-based asset pricing models. This paper aims to explore the potential influence of the pinning point’s distribution on the memorylessness and stochastic dynamics of the bridge process. We introduce Lévy bridges with random length and random pinning points, and analyze their Markov property. Our study demonstrates that the Markov property of Lévy bridges depends on the nature of the distribution of their pinning points. The law of any random variables can be decomposed into singular continuous, discrete, and absolutely continuous parts with respect to the Lebesgue measure (Lebesgue’s decomposition theorem). We show that the Markov property holds when the pinning points’ law does not have an absolutely continuous part. Conversely, the Lévy bridge fails to exhibit Markovian behavior when the pinning point has an absolutely continuous part.

Social Status, Portfolio Externalities, and International Risk Sharing

We show that a model of “the spirit of capitalism”, or the concern for social status, can generate a high degree of international risk sharing as measured by asset prices, even when consumption and portfolio holdings exhibit “home bias”. We also show how portfolio externalities can arise in the model and highlight the caution that one needs in interpreting asset-price-based measures of international risk sharing: in the presence of portfolio externalities, even when the measured degree of risk sharing is perfect, it is still possible for government policies to induce investors to hold better-diversified portfolios and attain higher welfare.

The Effect Of Credit Derivatives On Hedging Risk Of Non-Payment: Applied Research

The purpose of the current research is to reveal the impact of credit derivatives by (purchased credit derivatives, the value of credit derivatives sold) in hedging the risk of non-payment at a sample of commercial banks in the Iraq Stock Exchange by (Middle East Bank, Gulf Commercial Bank, Trade Bank of Iraq, Bank of Baghdad, Credit Bank) for the financial period from (2016-2020), and for the purpose of analyzing the results, the Office package was adopted (Excel.V.2016) in order to extract the level of credit derivatives, And the risks of non-payment at the commercial banks surveyed, has been using a set of statistical methods to determine these variables, represented in (credit derivatives, arithmetic mean, correlation coefficient, and regression coefficient), as a result, the research came out with a set of results, foremost of which was that there is an effect of credit derivatives in hedging the risks of non-payment, and this shows that credit derivatives contribute to improving the ability to hedge against non-payment risks, allowing investors to reduce financial risks. The focus of the surveyed commercial banks on the use of credit derivatives to protect investors from fluctuations in interest rates and currency rates, which works to reduce the impact of non-payment risks on their profits, which means that these contracts allow the determination of a future price for financial assets.

How does informed trading affect credit bond spreads: Evidence from China's interbank bond market

China's interbank bond market is an institutional investor market, and transactions are more information-based. Whether and how informed trading can affect credit bond pricing is an urgent problem to be solved in the study of China's bond asset pricing. This paper uses credit bond issuance and trading data and stock high-frequency trading data from May 2014 to December 2020 to construct informed trading probability and bond liquidity indicators. Using regression analysis and adjustment and mediation effect test methods, this paper studies the impact of informed trading on China's interbank credit bond pricing and the channel role of liquidity. The results show that the probability of informed trading has a significant positive impact on credit bond spreads. Informed trading leads to an increase in credit bond spreads by amplifying bond illiquidity. The amplifying effect of informed trading on credit bond spreads is stronger for bonds with high illiquidity. The amplifying effect of informed trading on credit bond spreads will be stronger in private enterprises, enterprises with low information transparency, and high debt ratios. This study reveals the risk nature of informed trading in China's interbank bond market, which helps to deepen the understanding and management of the bond market.

Deep Learning Algorithms for Cryptocurrency Price Prediction: a Comparative Analysis

Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such price is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely: Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as \(R^{2}\) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.

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.

External Shocks, Asset Prices, and Credit Dynamics in Morocco: Insights from Disequilibrium Models

We investigate external shocks and asset price’s impact on the slowdown of business and household credit in Morocco using disequilibrium models. The results show that banks’ fly to quality, driven by a simultaneous decline in interest margins and borrower creditworthiness, is a key factor behind the slowdown of credit supply. On the demand side, slower growth and saturated housing demand have contributed to reduced borrowing and repayment capacity of borrowers. Furthermore, external shocks are transmitted to credit supply through foreign deposits and households’ credit demand through remittances. Additionally, stocks and residential real estate asset prices are closely tied to credit demand. These findings suggest that addressing bank credit barriers could stimulate economic growth. To do so, policymakers may consider employing unconventional monetary policy tools to effectively manage the transmission channels of external shocks and asset prices to bank credit dynamics.

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.

Heterogeneous beliefs and short selling taxes: A note

Short selling is widespread in financial markets but regulators can ban short positions. The intermediate policy of taxing short sellers has been studied in an asset pricing model with evolutionary competition of two belief types (Anufriev and Tuinstra, 2013). We extend this approach to an arbitrary number of belief types H, giving 3H−2H cases to check each period in the worst-case scenario. We provide analytic expressions for asset prices along with conditions on beliefs (optimism) that determine which types take long, short or zero asset positions at the market-clearing price. We use these results to construct a fast solution algorithm (quadratic in H) which can solve models with hundreds or thousands of types in a matter of seconds. A numerical example with a short-selling tax and many heterogeneous beliefs in evolutionary competition shows that price dynamics can differ substantially relative to the benchmark of few types.

New control variates for pricing basket options

Abstract Accepted by: Giorgio Consigli Accurate pricing of basket options, which are financial derivatives on multiple underlying assets, is a challenging and practically important task for financial institutions. We propose several new control variates for accurate, fast and efficient pricing of basket options. The first approach to deriving new control variates is the use of Hermite polynomial approximation of appropriate function of the underlying asset prices, which leads to a Black–Scholes-like analytic solution. This approach is new in the option pricing context and opens up new possibilities in derivative pricing. Further control variates are analytically derived using Jensen’s inequality in one case, and distributional properties of multivariate Wiener processes in other cases. All the newly proposed control variates are shown to lead to excellent variance reduction in numerical experiments based on realistic data. The proposed methods are novel, computationally simple and have a strong potential to replace more conventional methods, such as the geometric lower bound in simulation-based pricing of basket options and similar products used in financial risk management.

Professor Efthymios (Mike) Tsionas’ asset pricing model groundbreaking contributions

Professor Efthymios (Mike) Tsionas’ asset pricing model groundbreaking contributions

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

European vulnerable options pricing under sub-mixed fractional jump-diffusion model with stochastic interest rate

With the development of financial markets, the trading of financial derivatives has become more flexible. To meet the demands of investors, over-the-counter (OTC) trading of options has also become increasingly frequent, raising concerns about accurately pricing vulnerable options. This study investigates the pricing problem of European vulnerable options under the sub-mixed fractional jump-diffusion (SMFJ) model. Firstly, the underlying asset price model and the counterparty company value model are established under the SMFJ model with a sub-mixed fractional Vasicek stochastic interest rate. Then, analytical solutions for the European vulnerable options are obtained using the quasi-martingale measure transformation and the risk-neutral pricing method. Additionally, the accuracy of the pricing formula is verified through the Monte Carlo simulation method. Finally, the rationality and validity of the established model are confirmed by numerical simulations, and the influence of key parameters on the pricing models is analyzed.

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.