Introduction
The purpose of this six-week investment simulation was to select several stocks, observe how their prices changed, and determine whether the combined portfolio generated a positive return. The holdings were Anheuser-Busch InBev SA/NV (BUD), 175 shares; Tesla, Inc. (TSLA), 130 shares; Nike, Inc. (NKE), 105 shares; Cannabis Wheaton Income Corp. (CBWTF), 15,000 shares; and The Scotts Miracle-Gro Company (SMG), 125 shares. These selections were influenced by product familiarity, family experiences, expectations about emerging industries, and interest in company brands. The exercise is valuable precisely because it shows the limitations of those motivations. A company can make a popular product and still be an unattractive stock at a particular price, while a fast-growing industry can contain weak businesses or highly speculative valuations. Because the project lasts only six weeks, its results should be interpreted as a lesson in price movement, recordkeeping, diversification, and behavioral bias rather than evidence that any of the five companies is a sound or unsound long-term investment.
Why the Five Companies Were Selected
BUD was selected because of familiarity with its beer products and family preference for the brand. That familiarity can help an investor understand product recognition and consumer loyalty, but it cannot establish valuation, debt capacity, margins, or future growth. Tesla was chosen partly because a family friend had reported a substantial profit and because the company’s vehicles represented innovation and personal aspiration. That motivation illustrates social proof and availability bias: another person’s successful trade becomes memorable even though the entry price, holding period, risk tolerance, and unsuccessful trades may be unknown. Nike was selected because its clothing and footwear were familiar through personal use, providing some insight into brand strength but not into inventory, wholesale relationships, margins, foreign exchange, or valuation. In each case, consumer experience generated a reasonable research question; the mistake would be treating familiarity as sufficient evidence for an investment decision. A disciplined process would connect brand knowledge with financial statements, competitive analysis, valuation, and risk.
CBWTF was selected because of expectations that the cannabis industry would expand as legalization developed and because medical cannabis had personal relevance through a grandmother’s cancer treatment and family interest in opening a dispensary. These experiences made the industry meaningful, but medical benefit in one case does not demonstrate that one publicly traded company will be profitable. Emerging industries often involve regulatory uncertainty, high capital requirements, dilution, business-model changes, competition, and speculative pricing. Fifteen thousand shares also sound like a very large position, but share count does not measure portfolio weight without the market price. SMG was chosen because experience playing baseball created awareness of the importance of grass seed, fertilizer, irrigation, and field maintenance. That connection provides insight into one use of lawn and garden products, while company analysis would also need to examine seasonality, retailers, weather, debt, inventory, consumer demand, and other business segments. These two selections again show how personal knowledge can start research without replacing it.
Portfolio Results and Corrected Return Calculations
The first recorded weekly portfolio value was $100,038.10, which provides the clearest baseline available in the assignment. At the end of week two, the value was $99,986.55. The change was therefore $99,986.55 minus $100,038.10, or a loss of $51.55, equal to approximately 0.052 percent. At the end of week three, the portfolio had fallen to $90,131.35. Relative to week two, that was a loss of $9,855.20, or approximately 9.86 percent. Relative to week one, the cumulative loss was $9,906.75, or approximately 9.90 percent. These figures do not match a smaller week-three loss recorded elsewhere in the assignment, indicating that another baseline may have been used or that a calculation error occurred. Consistent recordkeeping matters because an investor can reach the wrong conclusion if the starting value changes without explanation. A spreadsheet should preserve each holding’s shares, weekly price, market value, cash flows, dividends, and any transaction costs.
By the end of week six, the portfolio value had recovered to $93,434.20. Compared with the first-week value of $100,038.10, the six-week change was a loss of $6,603.90. Dividing that loss by the week-one value gives an approximate cumulative return of negative 6.60 percent. The portfolio did recover by $3,302.85 from week three to week six, which is approximately 3.66 percent, illustrating why return always depends on the selected beginning and ending dates. A gain over one interval can occur while the portfolio remains below an earlier level. The exercise also shows why position weights matter more than the number of stocks that rise. A large percentage increase in a low-value position may not offset a modest decline in a higher-value holding. Without the individual weekly prices and beginning market values for every holding, exact contribution analysis is not possible, so the appropriate conclusion is limited to the total portfolio figures that were actually recorded.
Diversification, Benchmarking, and Risk
The portfolio included five companies from alcoholic beverages, electric vehicles and technology, athletic apparel, cannabis-related finance, and lawn and garden products. This creates some industry variety, but it is not broad diversification. A five-stock portfolio remains highly exposed to company-specific events, and all five positions are equities rather than a mix of asset classes. The U.S. Securities and Exchange Commission explains that diversification can reduce the impact of one poorly performing investment, although it cannot eliminate market-wide loss. Portfolio weights also matter: equal share counts would not produce equal exposure because the stocks trade at different prices. A better simulation would calculate each position’s percentage of total value and compare the portfolio with a broad market benchmark over the exact same dates. A 6.60 percent loss has different meaning if the relevant market lost more than that than if it gained substantially. Benchmarking helps separate general market movement from the effect of concentrated stock selection, while a clear time horizon helps determine whether short-term volatility is acceptable.
Investment Theory and Behavioral Lessons
The efficient market hypothesis proposes, in varying forms, that market prices incorporate available information, making consistent risk-adjusted outperformance difficult. It does not instruct an investor simply to find obviously undervalued securities, because if public information is rapidly reflected in prices, such opportunities may be difficult to identify reliably (Fama, 1970). The greater fool concept describes buying mainly because another purchaser may later pay more, even when fundamental value is questionable; it is better understood as a warning about speculation than as a responsible long-term strategy. Rational-expectations theory emphasizes that people use available information when forming forecasts but does not imply perfect judgment. The selections in this project reveal several behavioral biases more directly. Familiarity bias appears in BUD, Nike, and SMG; social proof and availability bias appear in the Tesla choice; and optimism about a growing sector appears in CBWTF. Recognizing these biases does not make the choices meaningless. It shows why personal intuition should generate hypotheses that are then tested through financial evidence rather than treated as conclusions.
How the Investment Project Could Be Improved
A stronger version of the simulation would begin with a written thesis for each company covering the business model, revenue sources, profitability, debt, competitive position, valuation, expected catalysts, and principal risks. Position sizes would be assigned according to portfolio objectives and risk rather than arbitrary share counts. A spreadsheet would record trade date, purchase price, quantity, weekly closing price, dividends, fees, market value, percentage return, contribution to total return, and benchmark performance. The initial investment value would be fixed and every subsequent calculation would use a clearly identified baseline. The project could also compare the concentrated five-stock portfolio with a diversified index fund to show whether additional company-specific risk generated a compensating return during the same period. Six weeks remains too short to judge long-term business quality, but disciplined records and benchmarking would make the short-term exercise analytically useful. The objective should be learning how investment decisions are evaluated rather than forcing the results to match the title’s expectation of positive returns.
Conclusion
The portfolio did not generate a positive six-week return when the first recorded value is used as the baseline. It declined from $100,038.10 in week one to $93,434.20 in week six, producing a loss of $6,603.90, or approximately 6.60 percent. The exercise remains valuable because it exposes the difference between liking a product and analyzing a stock, the importance of portfolio weights, the weakness of anecdotal success stories, and the need for consistent calculations. BUD, TSLA, NKE, CBWTF, and SMG were chosen for understandable personal reasons, but those reasons should have been followed by structured financial research. The largest lesson is not that the five companies were necessarily poor long-term investments. It is that a short simulation cannot establish long-term quality, and inaccurate baselines can distort even short-term conclusions. Better due diligence, diversification, benchmarking, position sizing, and recordkeeping would make future investment exercises more reliable and reduce the influence of familiarity, social proof, and speculative enthusiasm.
References
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
U.S. Securities and Exchange Commission. (2026). Beginners’ guide to asset allocation, diversification, and rebalancing.
U.S. Securities and Exchange Commission. (n.d.). Researching investments.
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