We are the first to set up a random search based model to describe the pre-IPO market searching and matching process between private firms with intent to sell equity in an IPO and investment banks (IB) that underwrite the issue. Due to the wide existence of the market search friction, the necessary time is required in order to form a strategic pair between a private firm and an investment bank for a successful IPO. We derive a closed-form formula for the investment bank's share of profit from an IPO transaction at the market equilibrium. The calibrated simulation result for this value is consistent with the "seven percent solution" initially identified by Chen and Ritter (2000). Our model suggests that IPO underpricing is not a deterministic phenomenon but an empirical observation, the existence of which largely originates from the market-wide co-movement between the total gross proceeds of each IPO and the total number of successful IPOs.

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A theoretic model based on the concepts of constrained arbitrage and capital mobility is proposed to interpret closed-end fund puzzles. Although a discount for a closed-end fund's price relative to its net asset value is more prevalent, our model never excludes the possibility of a premium, which depends on the relative magnitude of the key parameters for the closed-end fund and its component stocks. Since closed-end funds tend to be more owned by individual investors who are less likely to be active traders due to investor inertia, and investor enthusiasm is usually higher for stocks than for closed-end funds, the aggregated price of component stocks will be more likely higher than the price of the closed-end fund, thus leading to the discount. Our model further shows that a closed-end fund's discount is negatively related to its expected dividends and the interest rate. The above results are reproduced by simulation.

Purpose This paper aims to explore the allocation of the exit value of a start-up company in market equilibrium between an angel investor and an entrepreneur in the very early-stage financing market. Design/methodology/approach The theoretical model is established based on the two-sided random search theory and the model’s ability to match the empirical data is evaluated via simulation. Findings The model indicates that the allocation of the final investment outcome is not proportional to the initial investments by the angel investor and the entrepreneur. The simulation results show that the continued investment by the entrepreneur and the private benefit acquired by the angel investor have a more profoundly negative influence on the angel investor’s share of the exit value of the start-up company. Moreover, the market search structure represented by the matching probability of an angel investor to an entrepreneur has a more significant impact on the angel investor’s share than the other model parameters. Originality/value The importance of market search friction in the very early-stage financing market is emphasized. The concepts of continued investments and private benefits are introduced and quantified for the first time under the framework of angel investment. The impacts of such model parameters as the matching probability of an angel investor to an entrepreneur, the success rate of a start-up company, the bargaining power of an angel investor and the discount rate on the allocation of the exit value of the start-up company are investigated as well.

Purpose Academia and financial practitioners have mixed opinions about whether artificial intelligence (AI) can beat the stock market. The purpose of this paper is to investigate theoretically what would happen if AI has further evolved into a superior ability to predict the future more accurately than average investors. Design/methodology/approach A theoretical model in an endowment economy with two types of representative investors (traditional investors and AI investors) is proposed, and based on the model, a long-run survival analysis for both types of investors is implemented. Findings The model presented in this paper indicates that being equipped with a superior ability to predict the future more accurately than traditional investors cannot guarantee AI investors to always beat the stock market in the long run. Those investors may be extinct, all depending on the structure/parameters of the stock market. Originality/value To the best of the author’s knowledge, they are the first to set up a representative agent equilibrium model to explore the above question seriously.