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Determination of Stock Prices

Whether done over short term or long term, investing in stocks can earn one very
handsome profits. It can also lead to major losses. That is why investors and traders are always
looking for a formula or strategy that maximizes their chances of making profits and minimizes
possibilities of making losses. The task is, however, as shown by this analysis, is not always
easy.

In the short term, movement of stocks is often determined by external factors such as
economic news. Conventional wisdom has it that good economic news has a positive effect on
stock prices. This is, however, not always the case. In fact, good economic news can, at least in
the short term, be bad news for stock and other financial markets (Friesen, 2017).

Factors such as market positioning, market expectations, significance of the data, and
economic phase as well as interest rate cycle can lead to stock prices falling even when there is a
release of positive economic news (Friesen, 2017). For instance, when the economy is
performing well, release of strong positive economic data may lead to fall in stock prices as
investors and traders alike may anticipate the Federal Reserve to raise interest rates (Friesen,
2017). Other data, despite being good economic news, may just be too outdated to cause any
market reaction. For instance, release of amended GDP figures for a previous quarter may not
reflect the current state of the economy and, therefore, may not be very useful to investors
(Friesen, 2017). These and other factors explain why seemingly good economic news sometimes
leads to a fall in stock prices

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One of the reasons why using data, either economic or financial, sometimes does not lead
to accurate prediction of stock price movement is because stock prices move randomly. This
randomness forms the basis of random walk theory (Hubbard & O’Brien, 2017). At the heart of
this theory is that prices of stocks take a random walk or are unpredictable. Thus, on a given day
the probability of them rising or falling is the same (Hubbard & O’Brien, 2017). There are many
implications for this random stock movement. One of them is that it is impossible for an investor
or trader to perform better than the market unless they assume extra risk.

Random walk theory is closely connected to efficient market hypothesis. Proposed by
William Sharp and Burton Malkiel, efficient market hypothesis argues that prices of stocks
reflect all the information available that is related to the stocks as well as expectations of the
stock’s movement (Hubbard & O’Brien, 2017). Therefore, current stock prices are a fairly
accurate representation of the intrinsic or true value of the stock.

The validity of efficient market hypothesis remains debatable. Its supporters argue that its
explanation of the working of the stock market closely reflects reality. Empirical work by
various economists has shown that stock price movements are often random (Hubbard &
O’Brien, 2017). There is also evidence that investors who pick their stocks randomly perform
just as good as professional investors who pick their stocks carefully (Degutis & Novickytė,
2014). Such performance lends credence to the hypothesis that price moments of stocks are
random and, therefore, attempts to predict their movements are futile.

There are, however, many investors and economists who argue that efficient market
hypothesis’ explanation of stock price movements is weak. One of its weakness is that although
investors and traders may possess similar information related to a given stock, their perception of

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that information may not be the same as the efficient market hypothesis suggests (Urquhart &
McGroarty, 2016). Their different perception of the same information available to them leads to
different assessments of the value of the stock. For instance, if two investors have access to the
same information related to a given stock but one is using the information to analyze the
potential for growth of the stock while the other one is analysing signs that the stock is
undervalued, they will reach different conclusions about the value of the stock. Thus, given the
large number of methods for determining the value of stocks, it is impossible to determine the
worth of a stock under efficient market hypothesis.

Another weakness of efficient market hypothesis is that the theory suggests that it is
impossible to perform better than average annual returns of investors or the market. The reality,
however, is that there have been many investors have regularly beaten the market. An example
of such investor is Warren Buffet who for decades has recorded higher returns than the market
average (Hubbard & O’Brien, 2017).

Lastly, some events in the stock market history have undermined some of the basic tenets
of efficient market hypothesis. For instance, the Dow Jones Industrial Average dropped by more
than a fifth in a single day during the stock market crash of 1987 (Urquhart & McGroarty, 2016).
Such a huge drop shows that stock prices do not always represent fair value of the stock.

Despite these weaknesses, there is still a general agreement that stock prices are relatively
random. Given this randomness it may seem that there is no rationale in buying stocks since one
cannot predict when prices will fall or rise. Profit making, therefore, becomes a matter of luck.
There are, however, anomalies in the efficient market hypothesis that allow investors to make
above average returns from their investment in stocks (Hubbard & O’Brien, 2017). One of these

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anomalies is small firm anomaly. Data has shown that investment in small firms consistently
produce higher returns than investment in big firms (Hubbard & O’Brien, 2017). Thus, despite
randomness of movement of stock prices, exploitation of these anomalies through various
strategies can lead to relatively handsome returns for investors.

With $100,000 the best strategy is to invest in an index fund. The value of index fund
rises and falls in reflection of overall profits or losses of corporate firms listed in the stock
market. Its relatively inexpensive compared to actively managed funds. Moreover, they have
been shown to perform just as good or even better than actively managed funds. For instance, in
2011 index funds performed better than 84% of actively managed funds (Atanasov, Pirinsky &
Wang, 2018).

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References

Atanasov, V., Pirinsky, C., & Wang, Q. (2018). Did the Efficient Market Hypothesis Affect
Investment Practice? Evidence from Mutual Funds.

Degutis, A., & Novickytė, L. (2014). The efficient market hypothesis: a critical review of
literature and methodology. Ekonomika, 93(2).

Friesen, G. (2017). When Good News is Bad News. Forbes. Retrieved on 9th December, 2018
from
https://www.google.com/amp/s/www.forbes.com/sites/garthfriesen/2017/10/27/when-
good-news-is-bad-for-stocks/amp/.

Hubbard, R. G., & O’Brien A. P. (2017). Money, banking, and the financial system (3rd ed.).
Boston: Pearson.

Urquhart, A., & McGroarty, F. (2016). Are stock markets really efficient? Evidence of the
adaptive market hypothesis. International Review of Financial Analysis, 47, 39-49.