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Dear Readers,

I'm excited to share that our blog is transitioning to Substack! 

This move allows us to offer you enhanced content on portfolio management, market analysis, and financial education in a more interactive and engaging format.

Join Us on Substack

Where: Quaint Insights on Substack

What: Just like here, you'll continue to receive regular posts on portfolio management, insightful market analysis, and valuable educational content.

How: Simply visit Quaint and click 'Subscribe' to stay updated.

Your support has been invaluable here, and I look forward to continuing our journey on Substack. 

See you there!

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

Harnessing Machine Learning in Algorithmic Trading

Algorithmic trading's landscape has evolved with the integration of machine learning techniques, propelling the exploration of reinforcement learning (RL) models for financial decision-making.  Empirical findings reveal the model potential to adeptly navigate trading dynamics, achieving an impressive 263.38% return over the tested period, significantly outpacing the benchmark return of 43.22%.

Machine Learning: The New Frontier

Enter machine learning—a field that enables algorithms to learn from data and improve over time. In the context of trading, machine learning models, particularly those under the reinforcement learning umbrella, are game changers. They don't just react; they adapt, learning continuously from market feedback.

Data: The Fuel for Machine Learning

Our approach is deeply rooted in data. From stock prices and trading volumes to intricate financial metrics, our models ingest vast amounts of information. This data undergoes meticulous preprocessing, from computing daily returns to integrating expert financial insights, ensuring that the algorithms receive the richest possible input.

A Dynamic Trading Sandbox

To put our strategies to the test, we developed a custom trading environment. Think of it as a real-time market simulator where our machine learning-driven algorithm plays the role of a trader, navigating market shifts, making investment decisions, and continually refining its strategy.

Results

The empirical analysis underscored the model's robustness. The portfolio realized a remarkable 263.38% return over 981 trading days, with positive returns on 545 days. This performance, when juxtaposed against the benchmark's 43.22% return, illustrates the model's potential. Calmar Ratio at 0.8317 - this metric emphasizes the system's return efficiency relative to its maximum drawdown. A value nearing 1 showcases the system's capability to maximize returns while keeping potential losses in check.
    

However, real-world trading presents challenges like transaction costs, market slippages, and liquidity issues. Neglecting these factors could impact realized profitability.

Conclusion

The synergy between machine learning and finance is revolutionizing algorithmic trading. As we've seen, not only does it promise robust returns, but it also offers a dynamic risk management framework. As we continue to fine-tune our models and factor in real-world trading nuances, the horizon looks promising, filled with innovation and potential.

Optimizing Portfolio Performance with EVA Momentum

 Hello, dear readers! We are back with another insightful blog post, and today we're diving into an exciting financial performance measurement tool – the Economic Value Added Momentum, commonly known as EVA Momentum.

EVA Momentum, an evolution of the Economic Value Added (EVA) concept, was introduced as a modern measure of financial performance. This revolutionary tool was developed with the goal of providing a more accurate analysis and potentially improving firm performance.

But what is EVA, you ask? Economic Value Added is a measure of a company's financial performance based on the residual wealth calculated by deducting the cost of capital from its operating profit. In simpler terms, it calculates the value a company adds to its shareholders' investment.

Building upon the EVA concept, EVA Momentum goes a step further. It not only evaluates a company's current financial performance but also considers the change in EVA relative to the company's sales in the previous period. By calculating the EVA Momentum, investors can assess the rate at which a company's EVA is changing relative to its sales, providing insight into a company's operational efficiency and future performance potential. This measure can be particularly useful in identifying companies that are not only adding economic value but also doing so at an increasing rate.

One of the key highlights of EVA Momentum is its ability to improve and explain financial performance. It serves as an effective economic measure, providing a comprehensive overview of a firm's financial health. It is considered as one of the best measures for firm financial performance, as asserted by Stewart in 2009.

So, how can EVA Momentum benefit our portfolio?

EVA Momentum's focus on the rate of change in EVA relative to sales allows us to spot companies that are not just performing well now, but also show promise for future growth. This forward-looking aspect makes it an invaluable tool for constructing and managing our investment portfolio.

It enables us to identify potential winners early and adjust our portfolio, accordingly, potentially leading to higher returns. Furthermore, it provides a deeper understanding of a company's financial performance, helping us make more informed investment decisions.

In conclusion, EVA Momentum represents a significant advancement in financial performance measurement. Its potential to analyse and improve firm performance makes it a promising tool for portfolio management.

Source: Omneya, A. K., Ashraf, S., & Eldin, B. B. (2021). Is Economic Value Added Momentum (EVA Momentum) a Better Performance Measurement Tool? Evidence from Egyptian Listed Firms.

2023 Portfolio Performance: A Mid-Year Analysis

In the dynamic world of investing, a periodic review of your portfolio is crucial to assess performance, adjust strategies, and better plan for the future. As we stand at the midpoint of 2023, it's the perfect time to evaluate the performance of our investor's diversified portfolio, scrutinize its returns from a percentage perspective, and delve into risk management using metrics like the Sharpe Ratio.

At the start of 2023, we wisely diversified the portfolio across risk-free assets, Microsoft, Tesla, and Costco. The expected return was set ambitiously at 22.16%, while the risk, measured by standard deviation, was 25.62%. The initial risk-free rate was 3.79%.

Now, let's evaluate the mid-year performance of each asset. The risk-free assets, constituting 40% of the portfolio, are currently showing a slight negative return of -0.24%. This may be due to a minor increase in the risk-free rate to 3.82%.

Microsoft, which forms 13.2% of the portfolio, has performed well with an impressive return of 49.37%. The considerable rise in the asset price from 227.78 to 340.54 significantly bolstered this position.

Tesla, although holding only 8.4% of the portfolio, emerged as the star performer, boasting a remarkable return of 118.45%. This exceptional performance can be attributed to Tesla's share price jump from 119.77 to 261.77.

Costco, representing the largest weight in the portfolio at 38.4%, returned a respectable 12.16%. Despite its more moderate growth compared to Tesla, the significant allocation helped maintain a healthy overall portfolio return.

At this mid-year point, the portfolio value has grown by 21.04%, which would equate to an impressive 42.08% annualized return if the portfolio continues to perform at the same rate for the rest of the year. 

But returns are not the only key to a successful portfolio; risk management also plays a pivotal role. The Sharpe ratio of this portfolio stands at 1.68, a solid number that indicates the average return earned above the risk-free rate per unit of volatility or total risk. A Sharpe ratio of 1 or above is generally seen as favorable among investors.

Despite the promising returns, the portfolio is trailing the benchmark return of 28.32% at this mid-year review. Also, the portfolio has a lower standard deviation of 10.24% compared to the benchmark's 18.91%, indicating lower volatility but also lower returns.

In conclusion, the portfolio has demonstrated substantial percentage growth midway through 2023. The performance highlights the importance of strategic diversification, risk tolerance, and risk-adjusted returns. While some assets have not grown as anticipated, others like Tesla and Microsoft have shone, resulting in a positive overall portfolio performance. Moreover, the favorable Sharpe ratio indicates that the returns have been well-earned for the level of risk taken, affirming the efficacy of this investment strategy. We look forward to the end of the year with optimism, hoping for continued growth and success.