투자자 예상가격 기반 자산가격 시뮬레이션

Predicting the price of assets is an important subject of investment business, and a typical approach to this is to predict future asset prices by discovering specific patterns from historical data or by discovering predictor variables that affect returns. However, there have been rebuttals to these forecasting efforts based on the EMH. Also, studies have emerged by considering asset prices as geometric Brownian motion based on random walks. As an alternative to the existing research, this study proposed a new approach to asset price volatility and stochastic processes by developing a pricing mechanism that artificially creates an asset price time series through simulation. Based on the intrinsic asset price determination mechanism of supply and demand for assets, the price of period t+1 is determined by the difference between the total purchase and total sales according to the difference between the investor's expected asset price and the current market price. The experiments with Monte Carlo simulations generate price movements similar to geometric Brownian motion, which can be seen in real asset markets. As a result of the experiment, when it was assumed that the expected prices of all investors did not change, the asset price exhibited a characteristic of a anti-persistent average regression process that moved up and down around the average, and a certain proportion of investor’s expected prices was changed following the market price, the volatility and persistent trend of the asset price gradually become stronger. We showed that an investor's expected price and market price-follower’s proportion are direct and important factors in asset price volatility. The randomness of the market means that the distribution of expected prices of investors participating in the market are random, and the trend of the market can be interpreted as the proportion of market price-following investors is actually quite high.