Performance 2023

Dear Readers,

At the beginning of the year, we introduced the optimized portfolio for 2023. The most efficient point on the curve is the one where, for a risk level of 51.87%, the return is 55.74%. However, given the assumed restrictions, the optimal portfolio would also achieve a expected return of 22.16% for a risk of 25.62%. 

The optimal portfolio in this case is composed of the following assets and their respective weights:

Microsoft - 22%
Tesla- 14%
Costco - 64%

Based on these considerations, you can find the year-by-year evaluation of this decision in terms of portfolio performance below. When we look at the portfolio, it is composed of 60% in the equity portfolio and 40% in Treasury bonds. Read more here: Optimized Portfolio 2023

Portfolio Composition and Performance

Risk-Free Asset: This makes up a significant portion of the portfolio at 40%.  Despite the slight increase in yield, the amount sold indicates a minor loss, resulting in a negative ROI of -0.72%.

Microsoft: With a 13% weight, the investment in Microsoft has shown a substantial increase in stock price from $237.47 to $376.04. This resulted in a high ROI of 58.27%, indicating strong performance.

Tesla: Representing 8% of the portfolio, Tesla has seen a remarkable price increase from $108.10 to $248.48. The ROI here is the highest among the assets at 129.73%, demonstrating an exceptional return.

Costco: Holding the largest share at 39%, Costco has also experienced significant growth in stock price from $439.87 to $660.08. The ROI is solid at 49.96%.

The total ROI pre-tax for the portfolio stands at 37.47%.

Risk Analysis

The portfolio's risk is measured by variance and standard deviation, with the risky portfolio showing a variance of 6.56% and a standard deviation of 25.62%. These figures suggest a moderate to high level of risk, which is corroborated by the substantial gains from high-volatility stocks like Tesla.
Benchmark Comparison:

Comparing the portfolio’s performance to benchmarks:

Standard Deviation: The portfolio's standard deviation of 25.62% is higher than the benchmark standard deviation of 17.34% from 2018. This indicates a higher risk taken by the portfolio compared to the benchmark.

Returns: The portfolio's return of 37.47% compares favorably to the benchmark return of 36.83% from 2023, suggesting the portfolio manager was successful in achieving returns above the benchmark, albeit with higher risk.
Conclusion:

The portfolio has performed well over the assessed period, outperforming the benchmark return in 2023 with substantial contributions from high-performing assets like Tesla and Microsoft. The higher standard deviation compared to the 2023 benchmark indicates a higher risk, but this has been rewarded with higher returns. The negative ROI on the risk-free asset, however, warrants a review of the assumptions regarding its stability and contribution to the portfolio.

Expect the optimized portfolio for 2024 in the next few days. 

Farfetch v Diversification & ML

 Farfetch & Diversification: how on earth do they converge you may ask?  

In my financial analysis postgraduate course, we scrutinized Farfetch’s financial state, focusing on the convertible bonds issued in April 2020. These bonds, totaling $350 million, were a strategic move to raise capital, coinciding with Farfetch’s stock surge of over 500% from 2020 to 2021 during the COVID-19 pandemic.

The significant price drop experienced by Farfetch stock was primarily attributed to the company not generating profits. Despite the increase in stock value following the convertible bonds issuance, the lack of profitability ultimately led to a decline in the stock price. This underscores the importance of sustainable financial health and profitability in maintaining investor confidence.

As an investor, I constantly seek stability in the capricious world of finance. I came across a research paper on Conditional Portfolio Optimization (CPO) using Machine Learning (ML) by Dr. Ernest Chan that adapts to markets regimes and it seems to be just the tip of the iceberg.

Imagine a tool that learns from market patterns and adapts your investment strategy accordingly. This is where ML in CPO comes in – it’s not just about data crunching but about understanding the nuances of market trends.

For me, the Farfetch case and this research intersect at a crucial point: the need for diversification and adaptability. It’s a lesson in not putting all your eggs in one basket and relying on smart, data-driven strategies to navigate market volatility. 

As I integrate these learnings into my investment approach where Machine Learning meets portfolio optimization, I see a path to a more secure financial future. I will publish a new post about the Portfolio Optimization Machine Learning Model I have been building this past year and the exciting news I have for you. 

Stay tuned & happy holidays! 

Incorporating AI and Active Portfolio Management: A Commentary on BlackRock's Market Outlook for 2024

BlackRock's Market Outlook for 2024 underscores the necessity, particularly under the theme "Steering Portfolio Outcomes", for an active approach to managing portfolios in the face of heightened volatility and market dispersion. This theme resonates deeply with my journey in portfolio management, which has evolved from leveraging Markowitz's theory to embracing AI models for optimized portfolio solutions.

BlackRock's emphasis on heightened volatility and dispersion in the current financial regime highlights the need for an active approach to managing portfolios. This perspective aligns with my belief in dynamic portfolio management, which has been a cornerstone of my strategy since the incorporation of AI models. The transition from static exposures to a more granular approach in portfolio allocations, as suggested by BlackRock, is a strategy I have advocated for, especially in my blog.

So, which approach is right for you?

The hypothetical scenario posed by BlackRock about accurately predicting U.S. equity sector returns highlights the potential of AI in investment strategy. My approach, using AI model, seeks to embody this hypothetical scenario in real-life applications. By processing and analyzing market data more efficiently and accurately, AI models provide a foundation for making informed decisions swiftly.

 

 

BlackRock'schart on the impact of rebalancing on U.S. equity returns illustrates the value of a dynamic investing approach. This is where I believe AI models play a crucial role. They enable a more frequent and “informed” rebalancing strategy, which, as per BlackRock's analysis, yields greater rewards compared to traditional buy-and-hold strategies.

Portfolio - AI model

In conclusion, BlackRock's Market Outlook for 2024 echoes many principles that I have incorporated into my investment strategy. A dynamic approach to portfolio management is not just a trend but a necessity in the current financial climate.

BlackRock Market Oulook 2024