Over the years, the theory of random walk presented by Professor Fama has undergone public scrutiny. There are several research scholars who worked on this matter. Various scientific research studies are conducted by opening many trials which are made to demonstrate its importance. The idea of the efficient market can be divided into two parts. The first idea is that the return from the stock is random in an efficient stock market. Moreover, the second idea is that the participants of the stock market cannot earn a surplus profit (Dunham, 2013). Since the inception of the random walk theory and efficient market hypothesis, many scholars work on this theory and search the relevance of this theory in modern finance study and empirical field.
The research article written by Professor Fama is still relevant in the contemporary investment scenarios, and several academic scholars still work on the theories presented in the paper. The random walk theory is considered as the critical finance theory, and it provides significant insight of the market. It supports long-term investment in the security market instead of depending on the speculative daily trading in the stock market. The random walk theory does not rely on the fundamental analysis of the stock market as it states that the security price is immensely unruly and unmanageable. Thus, the prediction of the future price of security cannot be possible. Often the prediction can be true, but that is the factor of chance. Moreover, the investment decision cannot be taken based on probability. According to the random walk theory, the past performances of security have not any connection with its future price, and the price changes are independent and cannot be predicted by the past performance of the stocks. Several academic scholars have addressed the issues regarding the stock market efficiency and also conducted several research works on this particular topic. The random walk theory suggests that the stock price data seem to exist in a consistent way and a stock’s price level can be more efficiently predicted by the path of a range of collected random numbers.