Understanding Stock Forecasting

Investing in the purchase of shares is an alternate source of income and a way to meet one’s financial goals. In order to receive the highest returns on investment, many investors attempt to make informed decisions with respect to their investment. Investors do this by looking at political and economic trends, taking advice from professionals, analysing companies’ index performances etc. However, even with these efforts, investments are not always successful, and one cannot always expect high profit gains. Thus, is it possible to predict stock movement?

The random walk model

Share movements are random. This is the fundamental proposition of the random walk model. The idea put forth by Andrew W. Lo and A. Craig Mac Kinlay in 2002, said that in an efficient market where there is complete information, price changes in shares must be unforecastable if they were to completely incorporate all expectations and information of all market participants. This further means that historic trends and prices of stocks are of limited use and are not needed to be an area of focus.

The model is denoted with the following equation:

Pt = µ + Pt-1 + ε t (1)

where Pt is the natural logarithm of a stock-price index Pt at time t, Pt-1 is the natural logarithm of a stock-price index Pt-1 at time t-1, µ is the expected price change or drift or y intercept, and εt is a strict white noise error term, there is constant variance and the mean is zero. The error term counts for any fluctuations in the model.

There have been many papers that support the random walk model/ hypothesis. One seminal literature supporting this model is ‘A Random Walk Down Wall Street’ written by Malkiel in 2007. He states in his literature that it is fundamentally impossible to predict short term changes in stock prices. He supported his words with the use of a simple experiment, whereby he asked his students to create a chart comprising of assets with an initial selling price of $50. The movements of these assets in terms of changes in prices would be determined by the flip of a coin. Thus, he was able to say that while stock prices may seem like they are following a predictable cycle, this cycle at the end of the day is just luck. Hence, the random walk model supports the idea of the non-forecastable nature of stock price movements.

The efficient market hypothesis

The random walk model is just one idea about stock price movements. The other is the efficient market hypothesis by Lo and Mackinlay (2012). This hypothesis considers the random walk hypothesis to be a case of the efficient market hypothesis and not a stand-alone explanation. Also, this idea says that the random walk hypothesis explains only a portion pertaining to stock movements and cannot be universally applied.

The efficient market hypothesis has three forms: weak form, semi strong form and strong form. The weak form is also known as the random walk hypothesis. Thus the idea, that the random walk hypothesis explains only a small aspect of stock movements. To reiterate, the weak form of the efficient market hypothesis says that, the current stock price reflects all information pertaining to the stock and the historical trends of that stock price are not relevant. This idea mitigates the importance of technical analysis, which believes that it is possible to figure out patterns in price movements by observing past behaviour in accordance with accumulated information to predict future movements. This aspect of the weak form of the efficient market hypothesis utilises support from various tests. Some of these tests include serial correlation tests, run tests, and simulation tests.

The semi strong form of the efficient market hypothesis says that stock prices also include the informational content of the price, plus the publicly available knowledge. This hypothesis believes that the share price will absorb any information that becomes publicly available. Fama, Fisher, Jensen and Roll investigated the speed of the market reaction to a firm’s announcement of a stock split and the accompanied announcement with respect to the change in dividend policy. The investigators observed that the market did respond to the information on the stock split and was efficient with respect to consequent changes in dividend policy.

Lastly, there is the strong form of the efficient market hypothesis. This predicts that all information is useless and that no information can result in better returns. The price of the stock contains both public and private information. Practitioners of the strong form of efficiency believe that even insider information does not give any advantage as that information is already present in the price of the stock.

The strong form of efficiency can be seen with respect to mutual funds. Mutual funds perform well in the market as they are large pools of investment and involve high degree of information. The aim of the experiment was to observe if mutual funds earned above average returns, when these were defined as returns surpassing those that can be earned in a simple buy and hold strategy. The results showed that mutual funds did not seem to be able to earn greater net returns than those that can be earned by investing randomly in a large amount of securities and holding them. This evidence gives further support to the mutual fund efficient market hypothesis.

Implications of the efficient market hypothesis

The efficient market hypothesis is used to identify market inefficiency. If everyone has the same information, it would be more advantageous to invest in small companies with a niche market as there is a higher proportion of returns.

Furthermore, efficient market hypothesis highlights certain observations about the behaviour of the stick market. The efficient market states that stock prices are neither over nor undervalued. Thus, stocks trade at a fair value and as a result it is not feasible to beat the market. If one wants to have high returns on investment, one must invest in high-risk, high-reward stocks that could have a large payoff. But on the other hand, it is equally likely that one may lose his investment due to the high-risk factor.

Investing in mutual funds or index funds can be quite advantageous as this aim is to work alongside the market and not beat it. Thus, in a completely efficient market, it is better to trach the market than beat it which would result in the maximisation of returns at lowest risk.

Can we predict stock movements?

Stock forecasting is not an easy task as can be seen with these two theories delineating the nature of stock price movement and information. The efficient market hypothesis is a comprehensive idea, that attempts to explain the information pertaining to the stock prices, and how well the prices reflect all information surrounding stocks. The more efficient a market is, the greater is the information present or reflected in the price of the stock.

Thus, if one tries to predict stock movement, one must be aware of all information that could affect the stock. The more efficient a market is, lesser effort would be required to gather all information as this would be revealed in the stock price. The weaker the efficiency of the market, the greater the effort required.

Stocks are quite sensitive to world affairs and since it is feasibly not possible for many to predict all world events; the same logic goes for stock price movement. This does not mean that the stock market is completely devoid of analysis. We can make a few assumptions; the world economy will grow over time and it is reasonable to assume expansion in technology and global competition allowing for increased gains. At the end of the day, one must be kept abreast of stock price movements and should diversify investments for the highest returns with the given constraints.

 Picture Courtesy- Medium

Most Popular

To Top
Please check the Pop-up.