This dissertation studies the effects of informational frictions and information acquisition on asset prices and investor decision-making.
Chapter 1 build models for capital market opening based on the noisy rational expectations equilibrium (NREE) framework. To reconcile the mixed empirical evidence on whether domestic investors have an advantage over foreign investors in domestic stock market tradings, I distinguish soft information, which is qualitative and cannot be transmitted if the receiver is not physically present, and hard information, which can be stored in the form of data, purchased, and transmitted regardless of the presence of the receiver. The models feature a mismatch of data processing technology level and availability of soft information.
Furthermore, the partial opening model also features a mismatch between the accuracy of hard information and foreign investability of shares. The equivalent data amount and the information spillover rate governs domestic and foreign institutional investors' data choices, respectively. Sufficiently, when the unit precision of hard information for foreign investable shares directly from processing data for them exceeds that indirectly acquired through processing data for foreign non-investable shares, foreign institutional investors would exhaust data processing capacity on foreign investable shares. The proportion of foreign institutional investors and their technology level both need to be large enough to overcome the dilution of soft information. Given that the signal-to-noise ratio increases, if the domestic investors learn more about foreign investable shares or the proportion of foreign investors is larger than a threshold, then the average precision of dividend innovation for foreign investable shares increases. In the full opening model, the average posterior precision of dividend innovation changes in the same direction as the signal-to-noise ratio for all shares.
The case of China is characterized by a very small proportion of foreign investors, a large proportion of domestic individual investors, a large disparity between domestic and foreign data processing technology, and high precision of soft information. Foreign institutional investors would allocate their data processing capacity in both type of shares, and the signal-to-noise ratio for foreign investable shares decreases. Domestic institutional investors are also induced to learn slightly less about those shares, and thus the average posterior precision of dividend innovation decreases. In the full opening counterfactual, the signal-to-noise raios for both type of shares increase.
Chapter 2 fills a gap in the theory literature of NREE models by studying the effects of learning about future demand shocks. Learning about future demand shocks makes price more informative in the future but less informative now. Ex-ante utility improves through what I term as the future uncertainty channel: on the aggregate level, fundamental information acquisition decreases, which makes dividends more risky and leads to higher risk premium; on the individual level, the investor is more certain about the asset payoff in the future due to the signal about future demand shocks she acquires today. As a result, her ex-ante utility improves.
Chapter 3 zooms in on the learning process and highlights a specific subprocess---the perception process. Agents take the same sensory information as inputs, direct them through different perceptual sets and thus form different perceptual information, which is then used to update posterior beliefs. Biased perceptual sets thus lead to persistent disagreement among agents, while set revision corrects any perceptual bias and allows agreement to be reached. Depending on the intensity of perceptual shocks, agents' sensitivity to perceptual shocks and their initial entrenchment levels, there could be overreaction or underreaction in the price to sensory information in the markets. When overreaction happens, the trading volume also increases
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