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on Financial Markets |
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Issue of 2018‒10‒22
five papers chosen by |
| By: | Rahul Roy (Pondicherry University); Santhakumar Shijin (Pondicherry University) |
| Abstract: | The present study introduce the human capital component to the Fama and French five-factor model proposing an equilibrium six-factor asset pricing model. The study employs an aggregate of four sets of portfolios mimicking size and industry with varying dimensions. The first set consists of three set of six portfolios each sorted on size to B/M, size to investment, and size to momentum. The second set comprises of five index portfolios, third, a four-set of twenty-five portfolios each sorted on size to B/M, size to investment, size to profitability, and size to momentum, and the final set constitute thirty industry portfolios. To estimate the parameters of six-factor asset pricing model for the four sets of variant portfolios, we use OLS and Generalized method of moments based robust instrumental variables technique (IVGMM). The results obtained from the relevance, endogeneity, overidentifying restrictions, and the Hausman's specification, tests indicate that the parameter estimates of the six-factor model using IVGMM are robust and performs better than the OLS approach. The human capital component shares equally the predictive power alongside the factors in the framework in explaining the variations in return on portfolios. Furthermore, we assess the t-ratio of the human capital component of each IVGMM estimates of the six-factor asset pricing model for the four sets of variant portfolios. The t-ratio of the human capital of the eighty-three IVGMM estimates are more than 3.00 with reference to the standard proposed by Harvey et al. (2016). This indicates the empirical success of the six-factor asset-pricing model in explaining the variation in asset returns. |
| Keywords: | Six-factor asset pricing model,FF portfolio,Human capital,IVGMM approach,Return predictability |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-01878923&r=fmk |
| By: | Jiangze Bian; Zhiguo He; Kelly Shue; Hao Zhou |
| Abstract: | We provide direct evidence of leverage-induced fire sales contributing to a market crash using account-level trading data for brokerage- and shadow-financed margin accounts during the Chinese stock market crash of 2015. Margin investors heavily sell their holdings when their account-level leverage edges toward their maximum leverage limits, controlling for stock-date and account fixed effects. Stocks that are disproportionately held by accounts close to leverage limits experience high selling pressure and abnormal price declines which subsequently reverse. Unregulated shadow-financed margin accounts, facilitated by FinTech lending platforms, contributed more to the crash despite their smaller asset holdings relative to regulated brokerage accounts. |
| JEL: | G01 G11 G18 G23 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25040&r=fmk |
| By: | Amy Lorenc; Jeffery Y. Zhang |
| Abstract: | We examine whether financial stress at larger banks has a different impact on the real economy than financial stress at smaller banks. Our empirical results show that stress experienced by banks in the top 1 percent of the size distribution leads to a statistically significant and negative impact on the real economy. This impact increases with the size of the bank. The negative impact on quarterly real GDP growth caused by stress at banks in the top 0.15 percent of the size distribution is more than twice as large as the impact caused by stress at banks in the top 0.75 percent, and more than three times as large as the impact caused by stress at banks in the top 1 percent. These results are broadly informative as to how the stringency of regulatory standards should vary with bank size, and support the idea that the largest banks should be subject to the most stringent requirements while smaller banks should be subject to successively less stringent requirements. |
| Keywords: | Bank failures ; Bank size ; Financial regulation ; Systemic risk ; Tailoring |
| JEL: | G21 G28 |
| Date: | 2018–09–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2018-66&r=fmk |
| By: | Sébastien Duchêne; Eric Guerci; Nobuyuki Hanaki; Charles N. Noussair |
| Abstract: | This paper studies the influence of allowing borrowing and short selling on market prices and traders’ forecasts in an experimental asset market. We verify, although not statistically significantly so, that borrowing tends to increase asset overvaluation and price orecasts, while short selling tends to reduce these measures. We also show that a number of results on beliefs, traders’ types, cognitive sophistication, and earnings obtained in earlier experimental studies in which borrowing and short selling are not possible, generalize to markets with borrowing and short sales. |
| Keywords: | experimental asset market, bubble, short sales, margin buying |
| JEL: | C9 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:lam:wpceem:18-18&r=fmk |
| By: | Jamal Bouoiyour (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour); Refk Selmi (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour); Aviral Kumar Tiwari (ICFAI University Tripura - ICFAI University Tripura); Olaolu Richard Olayeni |
| Abstract: | Despite its great popularity and gradual worldwide acceptance, most people are still confused as to what a Bitcoin actually is. This paper tries to reach clearer knowledge about what determines the Bitcoin's value. . Due to the intrinsic complexity of crypto market, standard approaches often fail to capture the non-stationary and nonlinear properties and properly depict the moving tendencies. This problem can be solved by an objective data analysis method, i.e. Empirical Mode Decomposition. By decomposing Bitcoin price into intrinsic modes based on scale separation, we will be able to explain its generation from a novel perspective. Specifically, the intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend. By doing so, the short-term fluctuations appear the major contributor of this new crypto-currency, without overlooking the power of long term trend. The first outcome suggests that Bitcoin is backed up by nothing other than the expectation of people' willingness to accept it and thus displays characteristics of purely speculative bubble. The second one implies that if traders appreciate risky investments, a trend can be identified and serious disappointments may await the unwary Bitcoiners. |
| Keywords: | Crypto market,Bitcoin price,Empirical mode decomposition |
| Date: | 2018–09–24 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01880330&r=fmk |