What’s On Tap?
Happy St Paddy’s Day
Every week we here at Braver enlist the support of our entire firm to create a blog post or newsletter to illustrate and add perspective to current pertinent events for that week or month. Today I want to answer 2 questions: What is tactical? Why use it?
Today on this St Paddy’s Day we are tipping a pint to you on these two questions. They are extremely simple and yet they usually never get answered in an understandable manner. One challenge in answering the questions is that the quantitative models and processes that drive these strategies require a certain level of mathematical acumen to make heads or tails out of the answer. I have asked Andrew Griesinger our Chief Quantitative Officer to help me in this aspect of the illustration.
The first reason for why investors may use tactical investment strategies is actually inherent in this Blog’s research and purpose. Not to over simplify it but there are two sides to the investing coin. What to invest in and how much to pay for it. Most investment strategies consist of both Art and Science to help answer these questions:
1) Art = Fundamental valuation of securities, sectors and regions. This analysis is typically based on fundamental factor inputs such as economic, political and earnings data. The strength and differentiation between investment strategies lies within the subjective interpretation and implementation of ideas on valuation and future growth potential. This subjective judgement call based on underlying fundamental factors often determines what an investor holds in their portfolio.
2) Science = Price: What is the market willing to bare? No matter what the investor fundamentally believes a security is worth, that analysis is rendered meaningless if no one is willing to buy it. Price is a live, unemotional data point that is factual and can be monitored and tracked inherently well with the help of technology.
Price is one data point that we believe encompasses all known information available to the investing public both rational and irrational. Regardless of any other criteria, the goal of most investment strategies is to purchase something that will increase in value. Buy low and sell high. Any price that an investor deems fair value is only as good as the expectations of another investor who would be willing to buy or sell that security and expect the same or opposite outcome. This could be based on both on rational valuations or even irrational exuberance. Tactical strategies can rely simply on price analysis and trend following and can offer diversification in investing philosophy from a fundamental investment strategy. This diversification may lower risk of a portfolio due to lower correlations offered by blending different investment strategies into one cohesive portfolio.
The second reason for why use tactical is to avoid emotional, subjective investing that often proves detrimental to investment returns. With the aid of technology, we can monitor and evaluate scenarios in price movement that can, in theory, completely eliminate any emotional involvement. Even the Dutch Tulip bubble in the early 1600’s provided a scenario which, if they had the technology at the time, could have potentially identified the extreme strength in price trends to give an investor the ability to take advantage of the phenomenon but identify when the trend has run its course. The Dutch Tulip mania of the 1600’s is still used as an example of irrational behavior but technology applied to price movement may have avoided emotions and signaled investors to sell when price trends eventually turned lower. Before price trends violently reversed, investors did make money in those trades the same as Technology investors made money in the late 1990’s but most lost their gains as emotions kept them invested and they didn’t recognize the reversal patterns. The key to many tactical strategies is to be able to identify weakness in a price trend and exploit it to one’s advantage. Using one factor which is price movement and applying quantitatively based tactical models to those trends is the key diversifier to any security, sector or even now regions.
We strongly believe that quantitative/tactical strategies can play an important role in investor’s portfolios. While there are many different quantitative strategies out there, the one common goal is that they follow strict rules on when and what to invest in. We believe that having an investment plan, whether it’s driven by fundamental or quantitative decisions, is crucial to investment success. Without a plan, investors tend to fall prey to their emotions. These emotional responses are what lead to behavioral biases that may create less than optimal investment decisions. For example, loss aversion refers to people’s tendency to strongly prefer avoiding losses to acquiring gains. This can lead to investors holding onto losing stocks because it would be so painful to realize those losses. A more rational approach would be to look at the prospects of that security compared to other investments to see if there is a better opportunity in another stock. Machines don’t fall victim to these behavioral biases. In fact, we program our models to take advantage of human emotional biases. For example, momentum investing looks to exploit the herding instincts so prevalent in the investing world. “Group think” is common, and people tend to pile into the same stocks or sectors. Momentum strategies try to identify the early stages of these trends and invest in them before investors create a buying frenzy.
Lower correlation to the markets or traditional fundamental based strategies is another benefit of quantitative strategies. We know from modern portfolio theory that combining assets or strategies that have low correlation to one another can lead to portfolios that have higher returns and less risk. Also, strategies focused on downside protection may preserve capital when the markets are declining. This is especially important for investors near or in retirement where recovery from such losses could be difficult.
How we develop models
Model development follows a very strict procedure but is fueled by creativity. Finding those ideas that yield profitable models is the most time consuming and difficult part of the process. We spend a lot of time thinking about ways that we can improve existing models as well as developing new ones. Sometimes we are struck with that eureka moment or an exhaustive search through academic papers, investment related blogs, and debates within the investment team yields that kernel of an idea that ultimately creates a profitable model. Armed with an idea we then clean and gather as much data as we possibly can to test that idea. We would like to have at least 20 years of data to make sure the model works over many different investment cycles. We split the data into non-overlapping periods called in-sample and out-of-sample data. We test and optimize the model in the in-sample data and once we are confident we have a model that exhibits a good risk return profile we then test it in the out-of-sample data. The reason why you want two separate periods is that it reduces the likelihood that you over-fit the data by optimizing over many variables. An over-fit model would not likely work once you started using it in real time as it has been tuned to random noise in the data and not true patterns that signal positive forward returns. If the model doesn’t show a similar risk-return profile in the out-of-sample data compared to the in-sample, we go back to the drawing board and try to figure out another profitable idea and continue testing in the in-sample data. On the other hand, if the model does show similar returns and risk, we are much more confident that we found a model that is exploiting a pattern and has consistently worked over time. We don’t expect individual models to work in every period, but we do want them to show steady returns and to work over extended periods of time. We also reject models that produce very volatile returns or make money in narrow market conditions.
Once we have finalized a model, we set up an account with firm capital or paper trade it to make sure we have the procedure down to flawlessly run the model on a daily basis and generate the appropriate trades. When we are confident in our procedure, we then begin to apply the model to client assets or within an investment strategy.
Tactical strategies are an important part of a client’s overall portfolio and simply bring lower correlation, diversification, and disciplines to an investor similar to how Modern Portfolio Theory (MPT) spoke to adding Small and Mid-cap, international, emerging markets and other broad asset classes to a portfolio. Tactical investing, from our viewpoint, is another diversifying asset class or investment strategy that enhances MPT through lower correlations resulting from a differing philosophy center around price reflecting all available data and price’s movement.
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