Volume 8 (2019): … free online access to latest research in Finance, Risk and Accounting!

ACRN Journal of Finance and Risk Perspectives
Vol. 8 ISSN 2305-7394


Volume 8 / Ongoing Publication

pdf Economic and Financial Transactions Govern Business Cycles

Victor Olkhov
TVEL, Moscow, Russia


Problem/Relevance - This paper presents new description of the business cycles that for decades remain as relevant and important economic problem.
Research Objective/Questions - We propose that econometrics can provide sufficient data for assessments of risk ratings for almost all economic agents. We use risk ratings as coordinates of agents and show that the business cycles are consequences of collective change of risk coordinates of agents and their financial variables.
Methodology - We aggregate similar financial variables of agents and define macro variables as functions on economic space. Economic and financial transactions between agents are the only tools that change their extensive variables. We aggregate similar transactions between agents with risk coordinates x and y and define macro transactions as functions of x and y. We derive economic equations that describe evolution of macro transactions and hence describe evolution of macro variables.
Major Findings - As example we study simple model that describes interactions between Credits transactions from Creditors at x to Borrowers at y and Loan-Repayment transactions that describe refunds from Borrowers at y to Creditors at x. We show that collective motions of Creditors and Borrowers from safer to risky area and back on economic space induce frequencies of macroeconomic Credit cycles.
Implications – Our model can improve forecasting of the business cycles and help increase economic sustainability and financial policy-making. That requires development of risk ratings methodologies and corporate accounting procedures that should correspond each other to enable risk assessments of economic agents.

Keywords: business cycle, economic transactions, risk assessment, economic space

pdf Is Human Capital the Sixth Factor? Evidence from US Data

1,2 Pondicherry University, India


Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challenging task that preoccupied the asset pricing literature for decades. However, there exists no consensus on the integrated asset pricing framework among the financial economists in the contemporaneous asset pricing literature. Thus, we consider and study this research problem that has greater relevance in pricing the risks of an asset. In this backdrop, we develop an integrated equilibrium asset pricing model in an intertemporal (ICAPM) framework.
Research Objective/Questions:
Broadly we have two research objectives. First, we examine the joint dynamics of the human capital component and common factors in approximating the variation in asset return predictability. Second, we test whether the human capital component is the unaccounted and the sixth pricing factor of FF five-factor asset pricing model. Additionally, we assess the economic and statistical significance of the equilibrium six-factor asset pricing model.
Methodology: The human capital component, market portfolio, size, value, profitability, and investment are the pricing factors of the equilibrium six-factor asset pricing model. We use Fama-French (FF) portfolios of 2 3, 5 5, 10 10 sorts, 2 4 4 sorts, and the Industry portfolios to examine the equilibrium six-factor asset pricing model. The Generalized method of moments (GMM) estimation is used to estimate the parameters of variant asset pricing models and Gibbons-Ross-Shanken test is employed to evaluate the performance of the variant asset pricing frameworks.
Major Findings: Our approaches led to three conclusions. First, the GMM estimation result infers that the human capital component of the six-factor asset pricing model significantly priced the variation in excess return on FF portfolios of variant sorts and the Industry portfolios. Further, the sensitivity to human capital component priced separately in the presence of the market portfolios and the common factors. Second, the six-factor asset pricing model outperforms the CAPM, FF three-factor model, and FF five-factor model, which indicates that the human capital component is a significant pricing factor in asset return predictability. Third, we argue that the human capital component is the unaccounted asset pricing factor and equally the sixth-factor of the FF five-factor asset pricing model. The additional robustness test result confirms that the parameter estimation of the six-factor asset pricing model is robust to the alternative definitions of the human capital component.
The empirical results and findings equally pose the more significant effects for the decision-making process of the rational investor, institutional managers, portfolio managers, and fund managers in formulating the better investment strategies, which can help in diversifying the aggregate risks.

Keywords: Asset pricing; FF five-factor model; human capital; return predictability; six-factor asset pricing model; sixth factor.


Francisca M. Beer1, Frank Lin2
1,2 California State University, San Bernardino, USA


Problem/Relevance: This study is motivated by psychological evidence of a strong connection between sporting event outcomes and mood. To evaluate this connection, we analyze the Indian stock market reaction to sudden changes in investors’ mood captured by India’s cricket results. By focusing on a rarely studied mood variable and a very infrequently studied stock exchange, this study adds to our understanding of the association between sporting event outcomes and mood.
Research Objective/Questions: In this study, we investigate the impact of cricket wins and losses on the Bombay Stock Exchange. We hypothesize that cricket wins or losses will drive investors’ mood substantially and unambiguously so that the game outcomes will be powerful enough to impact asset prices. We also evaluate the hypothesis that losses are psychologically more powerful than wins.
Methodology: We analyze the daily data from the Bombay Stock Exchange using the methodology of Edmonds et al. (2007). This methodology has the advantages of capturing the Bombay Stock Exchange stock returns time-varying volatility through a GARCH model.
Major Fundings: Our findings show that cricket wins and losses do not impact the Bombay Stock Exchange. On the exchange, stock prices reflect relevant information. Our results are thus consistent with the Efficient Market Hypothesis.
Implication(s): Our results imply that on the Bombay Stock Exchange, cricket wins and losses cannot be reliably used by investors and portfolio managers to achieve returns in excess of the average market returns on a risk-adjusted basis.
Keywords: Sentiment, Sports Sentiment, Investor mood, ARCH, GARCH, Bombay Stock Exchange