The Impact of Infrequent Rebalancing on Asset Return Predictability and Correlation

2013 ◽  
Author(s):  
Vincent Bogousslavsky
Keyword(s):  
Return Predictability ◽  
Asset Return ◽  
The Impact

scholarly journals A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

2021 ◽  
Vol 14 (7) ◽  
pp. 308
Author(s):  
Usha Rekha Chinthapalli
Keyword(s):  
Investment Decision ◽  
Theoretical Work ◽  
Financial Asset ◽  
Dynamic Features ◽  
Asset Return ◽  
Financial Variables ◽  
Ann Models ◽  
Hidden Layer ◽  
Association Structure ◽  
The Impact

In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

scholarly journals Is Human Capital the Sixth Factor? Evidence from US Data

2019 ◽  
Vol 8 (1) ◽  
pp. 21-55 ◽  
Author(s):  
Rahul Roy ◽  
Santhakumar Shijin
Keyword(s):  
Human Capital ◽  
Asset Pricing ◽  
Factor Model ◽  
Generalized Method Of Moments ◽  
Common Factors ◽  
Return Predictability ◽  
Pricing Model ◽  
Gmm Estimation ◽  
Asset Return ◽  
Asset Pricing Model

Problem/Relevance: Measuring the risk of an asset and the economic forces driving the price of the risk is a challengingtask 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. Implications: 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.

scholarly journals CAPITAL STRUCTURE AS DETERMINANT OF FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN ETHIOPIA (A CASE STUDY ON PRIVATE BANKS IN ETHIOPIA)

2021 ◽  
Vol 9 (03) ◽  
pp. 216-231
Author(s):  
Taddesse Shiferaw Deneke ◽  
◽  
Tripti Gujral ◽  
Keyword(s):  
Capital Structure ◽  
Financial Performance ◽  
Commercial Banks ◽  
Empirical Relationship ◽  
Asset Return ◽  
Return On Equity ◽  
Net Profit ◽  
Return On Capital ◽  
The Impact ◽  
The Capital Structure

A lot of studies have actually been done by numerous researchers both in developed and developing countries such as Ethiopia to ascertain the empirical relationship existing between capital structure and firm performance with varying samples and period as well as application of several and divergent statistical estimation. This study is based on the identification of the impact that capital structure have on the financial performance of commercial banks in Ethiopia. In this regard, secondary data is collected from varied sources especially annual reports of the private commercial banks in Ethiopia. The literature review is done in the report, and it is identified operating, and the capital structure heavily affects net profit. Apart from this, return on equity, asset and capitals employed also affected by the capital structure of the banks. Regression analysis and descriptive analysis tools are used to analyse the data that is related to the sixteenprivate commercial banks in Ethiopia. On analysis of data, it is identified that operating and net profit is heavily affected by the capital structure. However, in the case of return on asset, return on equity, and return on capital employed, such kind of relationship is not observed. Thus, it is concluded on the basis of entire work that capital structure have the huge impact on the operating and net profit, but it does not put any large impact on the return on asset, return on equity and return on capital employed. The study recommended that banks follow a specific policy, in order to maintain a balance in the capital structure. It is also recommended that managers must keep a keen eye on the changes that are taking place in the capital structure.

scholarly journals Does internal corporate governance mechanism control firm risk? Evidence from Indonesia’s three high-risk sectors

2019 ◽  
Vol 19 (6) ◽  
pp. 1362-1376
Author(s):  
Saarce Elsye Hatane ◽  
Stellania Supangat ◽  
Josua Tarigan ◽  
Ferry Jie
Keyword(s):  
Corporate Governance ◽  
Idiosyncratic Risk ◽  
Board Independence ◽  
Total Risk ◽  
Firm Risk ◽  
Board Size ◽  
Content Type ◽  
Asset Return ◽  
The Impact ◽  
Board Ownership

Purpose This study aims to examine the control of corporate governance towards firm risks for a sample of Indonesian firms in agriculture, mining and property industries. This study highlights the impact of four indicators of internal mechanism of corporate governance, i.e. board size, board independence, board gender and board ownership, on three measurements of firm risks, i.e. total risk, asset return risk and idiosyncratic risk. Design/methodology/approach Panel data analysis is conducted using a sample of 62 companies of agriculture, mining and property industries listed in Indonesia Stock Exchange from 2013 to 2017. Pooled ordinary least square with hetero-corrected is the statistical approach conducted to test the hypotheses. Findings The result indicates that board size and board gender insignificantly influence firm risks. While board independence gives varied impacts towards firm risks, it gives positive influence towards total asset return risk, insignificant towards idiosyncratic risk and negative towards total risk. Other interesting results are found in board ownership that has insignificant influence on asset return risk and negative influence on idiosyncratic and total risk. Research limitations/implications Firms should incorporate corporate governance, especially the impactful roles of board independence and board ownership as they serve as tools in reducing firm risk. Moreover, investors may have a better understanding of corporate governance and factors that are influencing firm risks. Therefore, this study can assist them to make the right investment decision. Originality/value This study is notably the first to use comprehensively three measurements of firm risks in Indonesia. Risks can come from internal and external, thus the company should understand the various types of risks facing the company. Total risk measures both the internal and external risks, while asset return risk gives another perspective using overall market perception about the equity and assets of the company. Finally, this study also measures internal risk, which is the only risk that can be controlled and minimised by the board of the company.

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