Risk Management: How to optimize your portfolio (Part 1)

Understanding how portfolio weight plays a part in chasing your goals.

Before this, we’ve spoken about how to forecast your portfolio returns. The topic was separated into two parts, and in the first article, we’ve seen the two key variables in predicting portfolio returns: historical returns and the weight of stocks in the portfolio. In this article, we will learn the basics of portfolio optimization first, which is what it means to optimize your portfolio.

Portfolio optimization?

Portfolio optimization is a strategy used in stock investing to construct an investment portfolio that aims to achieve the best possible return for a given level of risk or the lowest possible risk for a given level of return. The key idea is to diversify investments across different assets to maximize returns while minimizing risk.

So, what optimization does is tweak the weight of stocks in your portfolio for you to get the best possible return given your risk strategy. Some people might have low risk tolerance while others might swallow the risks whole. All in all, everyone seeks the same thing — the best possible returns for the risk they take.

How does it work?

In our previous article, this is our portfolio construct:

What you’re seeing is the average daily returns for each stock and the weights of each stock in the portfolio. To clarify the concept of portfolio optimization, however, we’ll move away a bit from the stocks above and look at this simple scenario:

You only invest in 2 stocks: Apple (AAPL) and Nvidia (NVDA).
Expected stock annual return = average daily return * number of trading days in the year
The expected annual return for the two stocks is AAPL 17.5% and NVDA 137.5%.

Assuming that your portfolio is weighted equally for both stocks, then here’s what your annual expected returns might look like:

Expected returns = (Weight of Stock A * expected returns of Stock A) + (weight of Stock B * expected returns of Stock B)…

Expected returns = (50% * 17.5%) + (50% * 137.5%)

Expected returns = 77.5%

Now that seems good, doesn’t it? But what if you’d paid more attention to NVDA? Say, the weight is AAPL (30%) and NVDA (70%) — that would give you an expected annual return of 101.5%! From laypersons’ perspectives, why not just put your money where it pays more right?

But here’s what differentiates laypeople and real investors — real investors know that nothing is too sure, and they need to see how risky each stock is. Why? As we mentioned before, to get the best possible combo (highest return at lowest risk).

Is that it?

For now, that’s all. Along the way, we will explore how there are many different strategies catered to your risk tolerance.

There are ones that seek minimum risk, so they’d forgo a riskier approach as long as the portfolio as a whole carries the least amount of risk. Some people take more risks but still keep in mind that it’s still the least risky approach to get the returns they seek.

All in all, the concept of portfolio optimization relies on one core idea:

Why would you take more risk to make $10 when you can take a safer route to make the same amount?

Bottom Line

Portfolio optimization changes the weight of stocks in a portfolio by looking at the corresponding returns and risk rate.
The core idea is to get the best return with the least risk.
There are, however, many strategies based on investors’ level of risk tolerance.
For some, they can afford to risk it for maximum returns, while others might compromise on the returns if they can run on safer investments.
What you need to do is see where you stand on the risk tolerance level and adopt a method that works best for you.

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None of the material above or on our website is to be construed as a solicitation, recommendation or offer to buy or sell any security, financial product or instrument. Investors should carefully consider if the security and/or product is suitable for them in view of their entire investment portfolio. All investing involves risks, including the possible loss of money invested, and past performance does not guarantee future performance.