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Learning how to reduce investment risk helps protect your nest egg. This podcast and article offer two ways of using algorithms to reduce investment risk.

How to reduce investment risk is one of the most important issues an individual investor must consider. It’s a particularly tough one because knowing which kind of investment poses higher risk (or lower) depends on many unknown variables. This is especially true in our post-2008 world. Supposed risk knowledge and generally accepted risk principals based on pre-2008 ideals are misleading at best and destructive to a portfolio at worst.

In the past almost-decade the old tools that guided investment strategies and investing decisions no longer work as advertised. Likewise, the old tools of how to reduce investment risk are no longer reliable either. Asset allocation, portfolio diversification and dollar-cost averaging yield scant genuine value in a world so deeply affected by

  • central bank intervention
  • the influence of institutional algorithms

It’s time for a new approach to guide the way you as an individual investor reduce investment risk in your portfolio.

For further thoughts listen to the podcast below, and continue reading

The quest to protect your nest egg gets a boost from knowing how to reduce investment risk. Straight from the Rosenthal Capital Management trading desk, Head Trader, Principal, and Chief Algorithm Architect, Bret Rosenthal, illustrates two examples of how to use investment algorithms to reduce investment risk.

Have questions about what you hear on the podcast or read in the investment algorithm article? We love answering follow ups: Call or email us.


There’s one general problem we see everyone having when making investment decisions (and it applies whether it be equity portfolios or others). That problem is this:

Assessing the reward versus the risk in the investment.

To us, it seems that people don’t take enough time to thoroughly address that issue. Instead, we frequently see most people spend more time addressing less productive topics, including:

  • Fear of missing the market (which is greed).
  • Anxiety about the market’s implosion (which is fear).

All of this amounts to emotions controlling investment decisions.


A more equalized approach would be to behave like the most successful professional investors. This means striving to reduce the noise of the decision process plus focusing on addressing reward versus risk assessment.

In summary, the process looks like this:

When the reward is significant enough we take the risk (as long as said risk can be defined).

We know that investment risk exists in every investment opportunity and every investing strategy decision. The only way to subvert risk in an investment portfolio would be to only buy short-term CDs or Treasuries. In recent years, however, this approach has been “outlawed” by the Fed and other central banks with near zero to negative interest rates.

As a result, here’s the interesting world we live in post-2008:

  • Interest rates are so ridiculously low that they don’t even keep up with inflation.
  • This forces people to put money into equity or debt markets.
  • Such an action causes them to take on more portfolio risk than they might desire.

Prior to 2008 there was always some kind of short-term rate of return that was somewhat acceptable. Think back to interest rates in the 1970s

Short-term rates got up to 15%. You could take almost no investment risk and get 15% returns. That explains why the equity market suffered so much.

Furthermore, that scenario illustrates the flip side of the equation we have now. The 1970s was a world where interest rates were so high on short-term government paper that you would put money there and not experience any investment risk at all.

Today, however, we have a world in which some parts have negative interest rates. This includes not only the Western world but Europe and the Pacific Rim as well. This situation highlights that we live in a different dynamic now than 30+ years ago. The new dynamic requires a proactive alteration in how to assess (and then reduce) investment risk.


Institutional algorithms further complicate today’s stock market environment. You must always remember that some institutional algorithms (major creators of today’s markets) are designed to take advantage of human emotion.

Be aware: If you ever feel yourself making an investment strategy decision based on an emotion… Or, you get a phone call from your investment advisor and you can hear emotion in his voice and he’s saying you’ve “…got to do this because X is happening!” That’s your immediate cue to:

  • Pause.
  • Take a step back.
  • Recognize that being driven by emotion represents an example of being fleeced by these high frequency execution algorithms.

(As a matter of fact, these types of institutional algorithms are designed to create that response.)

You can insulate yourself from this type of institutional algorithmic behavior by employing an algorithm yourself, plus an algorithmic investing approach. To get yourself (back) on track focus on the goal of getting on the same playing fields as the institutions.

At its core, this means an algorithmic investing strategy based on statistics and probability; as often as possible you want to be on the right side of both metrics. You don’t want to find yourself making an investment where your chances of being successful are 100:1. Returns would be great if you get lucky. However, most of the time with such odds you’re obviously going to lose money.

Instead, you want to get on the other (right) side and say, “OK, if I continue to follow this strategy then 6 or 7 out of 10 times it should be successful.” We want to get on that side. Why? Because that’s what successful professional investors are doing. They’re getting on the right side of statistics and probabilities.


So, the day arrives when you realize, “I’m forced to put money into the equity markets.” The important question then becomes, “How do I handle the investment risk?”

What we see all too often:

People just close their eyes and invest in the equity market with the hope that some government body will bail them out when things get ugly.

We don’t do that. To us, this sounds downright dangerous and irresponsible.

We find it unacceptable to take our nest egg and put it into investments in which we have to hope that there’s a continuation of a move higher.

Likewise, we don’t want to hope that if such a rise doesn’t happen there will be some outside force to rescue us.

(In fact, as finance professionals all of our wealth management advice stems from a perspective deliberately developed against such old school “buy and hope” investment strategies.)

Professionals neither hope for rise nor rescue. Instead, they focus on how to reduce investment risk. As an individual you can (and should) do this too.


We rely on a technological solution to reduce investment risk. Our approach:

Employ algorithms specifically designed for different indexes in different groups specifically geared toward the volatility of each asset that we’re investing in.

Subsequently, the interplay of these algorithms is how we create stock market portfolio risk reduction. Moreover, that’s how we create portfolio protection.

What you’re about to read (and what you always hear in our algorithmic investing podcasts) represents the practical application of algorithms.

From our financial investment advisor perspective there are two ways to use algorithms to reduce investment risk in an individual portfolio.

The process begins by recognizing there are actually two types of investment risk. As an example, let’s take an equity portfolio where we’re long

  • individual stocks
  • exchange traded funds of indexes and groups.

Within this scenario two types of investment risk must be addressed:

  1. Overnight risk – There’s some dislocation around the world and the market craters.
  2. Intra-day risk – Volatility that happens within the boundaries of one trading day.

In today’s post-2008 world algorithms offer the most help with both overnight and intra-day investment risk.


Sound investing strategies are not built on guessing which form of investment has the least amount of risk involved. Similarly, they’re not made by guessing what’s going to happen next, or using fear as a guide to run out and buy X to help you overnight. Emotions cannot be used to make investment strategy decisions.

We prefer to use a method based on the objective data of statistics and probability. Such data offers a more reliable investment strategy “new tool” for today’s stock market environment. We believe this so much, in fact, that we have spent the past 5 years developing our own proprietary algorithms to guide our investment strategy decisions.

To see the algorithms in action let’s consider a scenario using our S&P algorithm:

  • We’re long the equity S&P ETF (SPY)
  • We’re up 3-5% on the investment
  • It’s been 15 days that we’ve been long the asset
  • Things are going well

Now, let’s weave in a second algorithm geared toward monitoring the 20+ Year US Treasury market (TLT is the symbol). All of a sudden this algo says the reward is now worth the risk to put on a position. When this signal occurs we’ll put capital into Treasuries.

The result: We’ll have a balanced portfolio.

It doesn’t matter that we don’t know why our algorithm says to go long 20+ Year US Treasuries. What we do know carries full weight:

  1. We know the algorithmic information is based on statistics and probabilities. In light of this we don’t need to know, for example, the latest political maelstrom or bit of world news.
  2. Rather, all we need to know is that something has developed where the potential reward is significant for us to take a position in 20+ Year US Treasuries.
  3. Then, overnight the S&P is down ½ – ¾% and Treasuries (often negatively correlated to the S&P) are up 1%. The portfolio is protected.

These three steps offer a core overview for how to deal with overnight investment risk.

Furthermore, let’s say that you do not want to buy 20+ Year US Treasuries. You can still use algorithmic investing information to determine a different investment strategy. One alternative would be to identify different assets that are negatively correlated. Then, build your investment portfolio based on these types of algorithms.

In practice that could look like this:

  • If you know that the algorithm for those Treasuries is saying it’s time to put money to work then you might choose to pull back on your equity investment.
  • You can use the investment algorithm signals as an impetus to protect capital.
  • Consequently, you would reduce exposure and book profits.

Monitoring and creating different algorithms for different assets firmly takes investment strategy decisions out of the realm of emotion and into the realm of data-driven science.


Then, of course, we have to deal with intra-day risk. That’s where portfolio risk management gets even more sophisticated. For this, you need to use algorithms for intra-day trading.

At the bottom line algorithmic investing on an intra-day level is not for everybody. From the get-go, managing intra-day risk is a difficult process. Very often it’s also a contrarian process.

Even so, if intra-day risk applies to your investment strategy and interests you, then imagine this scenario:

  • We’re long the market (i.e. the S&P) and
  • Our intra-day hedging tool tells us to get short the Small Cap Index

In response, then, for the day we might actually be long the S&P (which we’ve been long for the past 15 days in this scenario). Plus, now we’re also short the Small Cap Index intra-day to help mitigate any risk on our long position.


In conclusion, managing overnight and intra-day investment risk are two effective ways you can use an algorithm – and different algorithms – to protect capital.

In the discussion above we’re not talking about predicting the future; algorithms never know everything. And we’re not talking about High Frequency Trading or trying to day trade for 10 cents.

Instead, we’re talking about managing traditional equity portfolios through an investing strategy that shifts us from being a buy-and-hope investor to a buy-and-hedge investor. Such a transition results in reliable periodic methods to reduce investment risk whenever necessary. We achieve the majority of risk reduction through methods of capital protection.

At the same time, working with algorithms offers another premium capital protection benefit:

Over a period of time investment algorithms train the individual investor to modulate emotion. In this way, developing a new method to reduce investment risk further refines itself by encouraging a diversion from feelings of greed or fear.

Remember this final point more than anything:

Do not use fear and greed or hope as your guides to investment strategy decisions. You always want to be on the right side of probabilities and statistics.


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