Stock analysis

Using a data analysis GPT, I uploaded 10 years of weekly stock gains and losses for MSFT (Microsoft Corp) and then asked the GPT a number of questions.

Prompt:Analyse the data in this spreadsheet and provide some insights

Response: The spreadsheet contains data on the weekly stock performance of Microsoft (MSFT) over several years. Each row in the data represents a particular week of a year, with the following columns:

1. Year: The year of the record.
2. Week: The week number within the year.
3. Change: The percentage change in MSFT stock for that week.

To provide some insights, I'll analyze trends such as the average yearly change, the most and least volatile years, and other relevant statistics. Let's start by conducting this analysis. ​​

Here are some key insights from the Microsoft stock data:

1. Average Yearly Change: The average weekly stock change varied each year. Here are a few notable years:
   • Highest Average Change: 2023 had the highest average weekly change.
   • Lowest Average Change: 2022 experienced the lowest average weekly change, which was negative, indicating an overall decline in stock value that year.
2. Overall Average Change: Across all years, the average weekly change in the Microsoft stock was approximately 0.56%.
3. Volatility Analysis:
   • Most Volatile Year: 2020 was the most volatile year, indicating significant weekly fluctuations in stock price.
   • Least Volatile Year: 2017 was the least volatile year, showing more stability in stock price changes week-to-week.

These insights reflect the dynamic nature of stock performance over time, with certain years showing more growth and others more stability or decline.

Prompt: What was the highest and lowest weekly variation

Response: The highest weekly variation in Microsoft's stock was an increase of approximately 14.56%, while the lowest weekly variation was a decrease of about 14.90%. These figures highlight the range of fluctuations that the stock experienced on a weekly basis.

Prompt: Which week of the year had the highest average return over 10 years.

Response: Over the 10-year period, the week with the highest average return was Week 43, with an average weekly return of approximately 3.43%. This indicates that, historically, Week 43 has been a particularly strong week for Microsoft's stock performance.

Prompt: Splitting the year into quarters, which is the quarter that has the highest average return.

Response: The quarter with the highest average return over the 10-year period is the second quarter (Q2), which encompasses weeks 14 to 26. During this quarter, the average weekly return was approximately 0.75%. This suggests that historically, Microsoft's stock has performed best on average during the second quarter of the year.

Prompt: what was the average quarterly returns over the 10 year period?

Response: Over the 10-year period, the average quarterly returns for Microsoft's stock were as follows:

1. First Quarter (Q1): The average weekly return was approximately 0.37%.
2. Second Quarter (Q2): The average weekly return was approximately 0.75%.
3. Third Quarter (Q3): The average weekly return was approximately 0.37%.
4. Fourth Quarter (Q4): The average weekly return was approximately 0.72%.

These averages indicate that the second and fourth quarters (Q2 and Q4) historically had higher average returns compared to the first and third quarters (Q1 and Q3)

Prompt: Repeat but provide the cumulative average return over the quarter.

Response: The average return in the week following a week with a loss in Microsoft's stock was approximately 0.89%. This suggests that, on average, the stock tended to rebound with a positive return in the week after experiencing a loss.

Prompt: How does this compare with the average return?

Response: The average weekly return across all weeks in the 10-year period was approximately 0.56%. In comparison, the average return in the week following a week with a loss was about 0.89%. This indicates that, on average, weeks following a loss tended to perform better than the overall average, suggesting a pattern of recovery or rebound after down weeks.

Prompt: What about after 2 consecutive loss weeks?

Response: The average return in the week following two consecutive weeks of loss in Microsoft's stock was approximately 1.61%. This figure is higher than the average return following a single week of loss (0.89%) and also higher than the overall average weekly return (0.56%). This suggests a stronger rebound tendency after two consecutive weeks of losses compared to a single week of loss.

Prompt: what is average gain based on the month of the year?

Response: The average stock gains for Microsoft, based on the month of the year, are approximately as follows:

1. January: 0.35%
2. February: 0.39%
3. March: -0.03% (a slight loss on average)
4. April: 1.56%
5. May: 0.27%
6. June: 0.56%
7. July: 0.94%
8. August: 0.59%
9. September: -0.46% (a loss on average)
10. October: 1.20%
11. November: 0.98%
12. December: 0.05%

Prompt: Can you explain this pattern?

Response: The pattern observed in the average stock gains of Microsoft based on the month of the year reveals several insights, which, while based on historical data, may reflect broader market trends, seasonality, and company-specific factors:

1. Higher Gains in April and October:
   • April: This month often marks the start of a new fiscal quarter and can coincide with the release of first-quarter results. Positive earnings reports can boost investor confidence. Additionally, the end of the tax season in the U.S. might influence investment behaviors.
   • October: It typically follows the end of the third quarter, when companies release their quarterly earnings reports. Positive performance or optimistic forecasts for the holiday season can drive stock prices up.
2. Slight Losses in March and September:
   • March: This month marks the end of the first quarter. Investors may be adjusting their portfolios in response to the quarter's performance or in anticipation of tax season implications in the U.S.
   • September: Historically, stock markets have shown weakness in September, a phenomenon sometimes referred to as the "September Effect." It could be influenced by seasonal behavioral factors, such as investors returning from summer vacations and making significant trades, or businesses gearing up for the end of the fiscal year.
3. Steady Performance in Other Months:
   • The remaining months show varying degrees of gains. This could be influenced by factors such as market conditions, overall economic climate, specific company events (like product launches, mergers, or acquisitions), and investor sentiment.

It's important to note that these are general observations and interpretations. Stock market performance is influenced by a complex mix of factors, including macroeconomic conditions, industry trends, company-specific news, and investor sentiment. Additionally, past performance is not necessarily indicative of future results, and individual investment decisions should always be made based on comprehensive analysis and personal financial goals.

Prompt: If the stock drops by more than 5% percent in one week, what are the average gains in the following week.

Response: The average gain in the week following a week where Microsoft's stock dropped by more than 5% was approximately 1.74%. This suggests that, on average, the stock tended to rebound to some extent after experiencing a significant weekly drop.

Prompt: Can you suggest any other trading strategies other than buy and hold based on this data

Response: Based on the data and analysis we've done, there are several potential trading strategies you might consider other than the traditional buy-and-hold approach. However, it's important to remember that all trading involves risk and these strategies should be considered in the context of your overall investment goals and risk tolerance. Here are a few ideas:

1. Rebound Trading: This strategy involves buying stock immediately after a significant drop, particularly following weeks where the stock fell by more than 5%. Our analysis showed that there's a slightly higher than average probability of a positive return in the week following such a drop.
2. Seasonal Trading: Based on the monthly and quarterly analysis, you might consider buying or increasing your position in Microsoft stock at the beginning of months or quarters that historically show higher average gains (like April and October) and selling or reducing your position before months or quarters with lower average gains or losses (like March and September).
3. Mean Reversion Trading: If the stock demonstrates mean-reverting behavior (which seems to be the case, especially after extreme drops or gains), you can develop a strategy where you buy the stock after extreme drops (more than a certain threshold, e.g., 5%) and sell it after significant rises.
4. Volatility-Based Trading: Given the variability in the stock's performance year over year, a strategy that adapts to the level of volatility might be effective. For example, in more volatile years (like 2020), a more active trading strategy might be warranted, while in less volatile years (like 2017), a more passive approach might be better.
5. Earnings Reports and Key Events: If the data aligns with Microsoft’s fiscal quarters and major product announcements, trading around these events might be profitable, especially if you have a good sense of how these events might turn out based on historical trends and current market conditions.
6. Diversification with Other Stocks or Assets: While not a Microsoft-specific strategy, diversifying your portfolio to include other stocks or assets can help mitigate the risks associated with the volatility observed in Microsoft’s stock.

Remember, past performance is not necessarily indicative of future results, and it's crucial to do thorough research and possibly consult with a financial advisor before implementing any trading strategy.

Prompt: What would you predict the gain to be in the year following the results in the data set?

Response: Predicting future stock performance, including the gain for Microsoft in the year following the dataset, involves a significant level of uncertainty and is influenced by a wide range of factors beyond historical performance. While past trends can offer some insights, they are not definitive predictors of future results. However, I can provide a speculative estimate based on historical data trends:

1. Using Historical Average Returns: One approach is to look at the average yearly return from the dataset. If Microsoft's stock has shown consistent growth over the years, using the average annual return as a baseline prediction could be reasonable.
2. Considering Recent Trends: If recent years in the dataset showed a particular trend (increasing, decreasing, or volatile returns), this trend could potentially continue into the following year, barring major market or company-specific changes.
3. External Factors: Consideration of current economic conditions, market trends, Microsoft's business outlook, industry performance, and global events is crucial. For example, if the tech industry is booming or if Microsoft has announced significant positive developments, this might indicate a potential gain.
4. Risk and Uncertainty: It's important to acknowledge the inherent risk and uncertainty in stock market investments. Factors such as changes in management, competitive landscape, regulatory changes, and macroeconomic shifts can all impact future performance.

Would you like me to calculate a speculative estimate based on the average annual return from the dataset? Keep in mind, this would be a very simplistic projection and not a reliable forecast.

Prompt: Yes

Response: Based on the speculative estimate of a 0.55% average weekly return, the approximate annual gain for the year following the dataset would be around 33.32%. This calculation assumes compound growth with the same average weekly return occurring each week for 52 weeks.

However, it's important to note that this is a theoretical calculation and actual stock market performance can vary significantly due to a multitude of factors. The compounded effect seen here might not realistically reflect market conditions, especially since stock returns can vary greatly from week to week.

Quite impressive results in terms of the Data GPT's ability to analyse results quickly. I was able to ask quite complex questions (such as performance in the following weeks and the average return per month - which was estimated even though only weeks of the year were provided). What do you think? Would you trust the analysis and predictions from a GPT? Leave a comment below.