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Asset pricing, liquidity and synchronicity of stock prices

Grant number: 14/01653-0
Support Opportunities:Scholarships abroad - Research
Effective date (Start): August 01, 2014
Effective date (End): June 30, 2015
Field of knowledge:Applied Social Sciences - Administration - Business Administration
Principal Investigator:Francisco Henrique Figueiredo de Castro Junior
Grantee:Francisco Henrique Figueiredo de Castro Junior
Host Investigator: Andrew Ang
Host Institution: Faculdade de Economia, Administração e Contabilidade (FEA). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Research place: Columbia Business School, United States  


The main topic of research during the post-doctoral research stage is the synchronicity of stock prices and how this construct provides signals to financial markets in order to help the decision maker in resource allocation and investment decisions process. The concept of synchronicity adopted here is the one developed by Roll (1988) and Morck, Yeung and Yu (2000). According to Morck et al. (2000), stock rates of return reflect the new information available and these may be information in the market level or at the enterprise level. The findings of Morck et al. (2000) indicate that stock prices in economies with higher GDP per capita move in a non synchronized way. As for the economies of lower GDP per capita, stock prices tend to rise and fall in higher synchronicity. This suggests that less specific information on the company level is produced and that the rates of return over react to the movements of the economy as a whole. The reason for this finding, according to the authors, is that in emerging economies the level of protection of private property rights are generally smaller and more uncertain. Moreover, political events and rumors are responsible, in large part, by movements in stock prices. According to Chan and Hameed (2006), the lack of specific information on the company level in emerging markets can be attributed to several factors. The first would be the low amount of regulations (or the low level of enforcement of existing regulations) to ensure greater dissemination of information. Additionally, there is a low level of voluntary disclosure, resulting in a low transparency of information to the market. Another factor is that many of the companies in emerging markets are run by families and, in general, it is more difficult to obtain information from these companies. Jin and Myers (2006) analyze the relationship between corporate transparency and synchronicity of stock returns and find that in more transparent economies, where specific business information is more widespread for the investor market, the level of synchronicity is lower. Synchronicity is also related to the market liquidity of companies. Liquidity, however, is not a directly observable variable. One of the determinants of stock liquidity is volatility. Chan et al. (2013) argue that an increase in the volatility of stock returns implies that liquidity providers face greater risks of adverse selection due to a greater possibility of trading with informed investors. As a result, the higher volatility of asset prices leads to lower liquidity of assets. The synchronicity of stock prices measures the proportion of systematic volatility relative to the total volatility (also called idiosyncratic volatility). The first studies that related liquidity with volatility of shares made no distinction between systematic volatility and idiosyncratic volatility (Stoll, 1978, 2000; Ho & Stoll, 1981).In turn, liquidity is an important risk factor and asset pricing models seek to explain how liquidity risk affects asset prices (Amihud, 2002; Amihud & Mendelson, 1986; Pastor & Stambaugh, 2003; Bekaert, Harvey & Lundblad, 2007). The approach to be used in this research will be like that of Acharya and Pedersen (2005), in which the agents in the economy are risk averse and trade assets whose liquidity varies randomly over time. The model used is like a liquidity adjusted CAPM. In this model, the expected rate of return of an asset is positively related to illiquidity and its beta (now net from the effects of liquidity). The expectation is that this adjusted model fits the data better than the traditional CAPM. (AU)

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