Investment: Chance or Skill?


My friend Piotr and I wanted to do a CAS project together so we brainstormed areas we were interested in and wrote them down. Immediately we saw that economics and math were two core subject areas that we both liked and had in common. I suggested we do something to do with finance and trading, since I had started watching a series called ‘Million Dollar Traders’ from the BBC. I had gotten quite interested in this area and read novels by Micheal Lewis such as ‘The Big Short’ and ‘Liars Poker’. Piotr liked this idea, as he, like me, wants to study economics/finance at university. We decided to challenge ourselves with an experiment that would help us expand our knowledge of trading by using economics and maths. The basics of the experiment were as follows: Using a trading simulator called Investopedia, we traded securities from the NASDAQ 100 in two portfolios. One portfolio is a list of 10 randomly selected stocks. The other portfolio is a list of 10 stocks hand-picked by Piotr and myself collaboratively using quantitative and qualitative analysis, as well as our economic knowledge. After a period of time, we will see which portfolio has the best rate of return. At the end of the experiment, we will post our results, trying to make it as realistic as possible. We will have to consider the ethics of trading in a manner of ways, for example trading tobacco companies, arms companies or other firms that have negative externalities of production or consumption. At the end, this project will hopefully give us a taste of what it feels like to trade in an environment where the results matter.

As soon as we decided on our CAS project, we deliberated on how to split the roles. We discussed the best strategy to pick our 10 stocks. Would it be better if we picked 5 stocks each to create our 10 stock portfolio? Or should we choose 10 each and debate which ones to put in? Maybe it would be best to simply pick all 10 stocks together? We decided that we would pick all 10 stocks together using a mix of quantitative and qualitative analysis. This meant we had to create an excel spreadsheet listing the 104 components of the NASDAQ 100. We created two separate pages, one for the 10 random stocks and one for the 10 handpicked stocks. First, I numbered the stocks which were listed alphabetically (1 – 104) and created a random number generator using Excel. Next, we planned how to pick our stocks. We wanted to use quantitative analysis, which meant gathering data. In the spreadsheet, we created columns showing the market cap, the P/E (price-earning) ratio, EPS (earning per share), Beta (measure of volatility) and 52-week price range. We gathered all this data, as well as the current share price, from Yahoo Finance, a reliable and extensive tool. We also wanted to analyse the companies qualitatively, so we included the sector and the number of employees, as well as a link to their websites. This way, we could read about the management and history of the company. All of these things must be considered before selecting a stock. We set a timeline of two weeks during October break to finalize our stock picks. After the break we will put in the orders to trade the 10 random stocks and the 10 hand-picked stocks. Since there are 10 stocks in each portfolio, we will put 10% of our capital in each stock. The simulator gives us $100,000 per portfolio to start with, so we will invest $10,000 in each stock. We also have to research what a financial report looks like, which means reading reports form hedge funds and investment banks.

The results of this experiment are as follows.

The value-investing portfolio rose about 4% over the span of 2 months. This is a significantly better return than what we would have made if we had left our money in a savings account. Although buying financial assets such as stocks is more risky than parking money in a bank account, the reward is far greater since the average return from a young person’s savings account is just 0.08% over two months. The value-investing portfolio is made up completely of stocks that we handpicked ourselves. We looked for stocks that were undervalued, specifically ones that had a high revenue to market cap ratio. This is a very popular investment strategy, Warren Buffett being the champion of value investing. Compared to the random game, this portfolio was level for quite some time, but after the two months, which included the market correction, it surpassed the random game in rate of return.

Random Game:

Hand-picked portfolio:

Our second investment portfolio was concerned with growth investment. We thought this strategy was less risky
and would work best over the long run. We invested in companies with high growth in revenue. This was easy to
find as all companies have to publish their revenues in annual financial reports. Over a period of 3 months, this
portfolio has earned a return of 15% or $15,000 from our original stake of $100,000. If this growth continued for
the rest of the year, we would be looking at a 60% return over the year. The random game was again close to the
growth investment portfolio, but peaked at a return of 10%. This showed that in the short run, it is extremely
difficult to differentiate between a hand-picked and random portfolio in terms of growth due to the random walk of
the stocks and the efficient market hypothesis. The random walk is a mathematical concept that explains the
random probability of stocks going up or down while the efficient market hypothesis states that it is impossible to
beat the market because stock market efficiency causes existing stock prices to always incorporate and reflect all
relevant information.

Hand-picked portfolio:

Random game:

This CAS project has taught me a lot about the practicalities of investing and about the investment strategies
used by wealth managers around the world. I learned about the different indicators and their role in displaying
information about a stock. We focused specifically on the EPS (earnings per share) and the P/E ratio (market
price/EPS) which both helped us evaluate the relative attractiveness of a company’s stock price compared to the
current earnings of the company. We used classic indicators such as the market cap (the market value of the
company) and the number of employees to gauge our investments. Using an Excel spreadsheet, we used these
indicators to pick undervalued stocks and find growth opportunities. I want to study politics, philosophy and
economics at university and work in finance, so this was a valuable learning experience for me personally.

At the end of this CAS project, we discussed the performance of our different portfolios, why they performed in
that way and what we learnt through this experience. This involved analyzing the efficient market hypothesis and
its effect on our stock picking strategies. The stock markets went through a bit of a rough patch as well due to the
market correction, which helped us understand what it is like to own assets during a small market crash and the
uncertainty that comes with it.


Written by Ilkka Cheema (’18) and Piotr Wojtaszewski (’18)


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