Firms’ Geographic Location,informational Efficiency And Asset Pricing

Information plays a vital role in economic decision of individuals.Efficient Market Hypothesis argues that market price always fully reflects information related to the value of its underlying asset.However,in real markets,geographic location has an important impact on information flow.Building on this,the dissertation firstly introduces both firm’s headquarter location and its subsidiaries’ location to a stylized model that generates lead-lags and co-movement at the regional level.Then we empirically check the existence of stock return predictability and return co-movement among firms which locate at the same state.What’s more,we investigate the lead-lag effect of informational efficiency between states using vector autoregression model.Finally,we examine whether information efficiency is priced in stock returns and compare the informational efficiency between US and Chinese markets.Firstly,using a stylized model,the dissertation theoretically analyzes the impact geographic location has on asset pricing from the perspective of return predictability and co-movement.In the model,links between firms by geographic location are sorted into co-headquartered link(HH),link where the subsidiaries of focal firm and peer firms’ locate at the same state(SS),link where the headquarter of focal firm and one subsidiary of peer firms locate at the same state(HS),and link where one subsidiary of focal firm and the headquarter of peer firms locate at the same state(SH).We find that,for HH and SS,the return predictability and co-movement are determined by the variance of geographic shock and analyst cover.For SH and HS,they are also affected by the extent to which the geographic shocks on headquarter and that on subsidiary.Secondly,the dissertation empirically examines the return predictability based on firms’ geographic location.Using a data set to identify local peer firms located in the state in which the firms’ headquarters and firms’ material subsidiaries operate,we show that value-relevant local peer’s information is slowly diffused into the stock prices of the focal firms.This return predictability is robust to industry momentum effect and is more pronounced among firms that receive lower investor attention and that are more costly to arbitrage.The effect is hard to square with risk-based explanations but rather more consistent with slow incorporation of new information into stock prices.The results suggest that market price cannot absorb relevant information instantly.Then,the dissertation empirically investigates return co-movement between firms which locate in the same state by headquarter or subsidiaries.Based on the four basic links,we further develop three links by combining the basic ones.We find significant return co-movement among firms linked by these links excluding HH.The comovement cannot be explained by changes in fundamentals related firms’ operation performance,but largely driven by changes in regional economic condition.Changes in stock demand or supply caused by investors’ home bias also contributes to the return co-movement by a small part.What’s more,the dissertation investigates the lead-lag effect of regional informational efficiency between 50 states in US.Researches mentioned above mainly focus information flow within a state,while this chapter pays attention to information flow between states.Based on Maximum Entropy Principle(MEP),we use approximate entropy to measure stock informational efficiency and construct the state-level measure.Using vector autoregression model,we find that more than a quarter of state groups witness significant lead-lag effect,suggesting geographic location plays an important role in information flow among states.Lastly,the dissertation examines whether information efficiency is priced in stock returns.Using a sample of the US and Chinese stocks between July 1999 and June 2016,we investigate the role of informational inefficiency in stock pricing with factor models.We find that the relations between returns and IIF change from significantly positive,through insignificantly positive or negative,to significantly negative as informational efficiency increases.This finding provides a new insight on the widely held belief that emerging markets are less efficient than developed markets.The main contributions of this dissertation are as follows: First,using a novel data set,the dissertation introduces time-varying locations of firms’ material subsidiaries to better identify firms’ geographic information.Second,motivated by MEP,we propose a new proxy to measure stock-level informational efficiency.Third,to the best of our knowledge,no study in the existing literature investigates the lead-lag effect of informational efficiency among states,and we fill the gap.Finally,the dissertation contributes to the literature by introducing the informational inefficiency factor,and finding an inverse feedback mechanism in the U.S.and Chinese stock markets,which provides new insights into market efficiency.