Oppenheimer Fundamental Alternatives Fund – The Market Knows More Than Ms. Borré
I recently came across a video advertisement for the Oppenheimer Fundamental Alternatives Fund (MUTF: QVOPX ), which is currently headed up by the alpha seeking Michelle Borré. Her pitch revolves around understanding both empirical “hard” data and the human “soft” data to extrapolate information about the future prospects of the market, all while trying to minimize volatility and still produce a solid positive return. This all sounds very appealing, but history has shown that these ideas and, more importantly, being able to anticipate or control them, have largely led to subpar investment performances. In this article , the entire Oppenheimer fund family was examined. The results were not too promising to say the least. Let’s dive deeper and isolate the Fundamental Alternatives Fund to see if there is any merit to the statements being made in their video. What we will show, not only empirically but also theoretically, is that Oppenheimer’s idea of being able to evade volatility or provide a superior understanding of human nature that they can extrapolate any sort of “alpha” from it is built on a false foundation. Let’s start by looking at their past performance based on a comparison to their Morningstar assigned benchmark. The alpha chart below shows their annual alpha (relative performance to the benchmark) since inception in 1999. Click to enlarge What is noticeable is that, overall, the historical performance has not been great. In 13 out of the 17 years, the fund had a negative alpha. On average, it lost by 2.36% per year. If we look at the performance since Ms. Borré took the helm (Nov. 2011), we can also see that she has also not provided superior performance with only 1 out of 4 years showing a positive alpha. Past results are not indicative of the future, but there is nothing substantial here that gives us a promising outlook on this fund’s ability to do what they say they are going to do. This is where theory can provide a more robust explanation of why we would not expect this fund, or any actively managed fund, to turn around in terms of their performance. First, there is the whole idea of trying to minimize volatility while still providing above average returns. Within equities, this involves hedging particular risks associated with stocks, and trying to pick next winner. For example, in its more simplistic form, if we wanted to gain exposure to the financial sector but wanted to remove the risks associated with the financial sector (Beta), we can go long a particular financial stock that we thought was undervalued and short a particular financial stock that was overvalued. We can even go broader and go long a group of financial stocks and short a group of financial stocks. By being long and short within financials, we are removing the systematic risks associated with financials while having a positive exposure to what we think will go up and a negative exposure to what we think we will go down. We are essentially doubling down on our ability to forecast the future. The problem with this approach is that it is very unlikely that Ms. Borré, or anyone, knows any more about a particular stock than the combined information of the thousands, if not millions of investors watching that stock. Remember, the price of a particular stock at any given time represents millions of estimations about the particular value of a given company not only based on information that is currently public, but also future forecasts and even some insider knowledge as well. To be successful in outperforming a market consistently over time, you must have better estimation faculties, quicker access to information, or have inside information, and then exploit that information in a cost effective way. As Mark Hulbert said in his 2008 NY Times article , “the prescient are few”. The chart below summaries the study Hulbert discussed in his article. Click to enlarge This idea also applies to trying to understand human nature or behavior and extrapolate information that can successfully be exploited in investment strategies. It should go without saying that not all investors are trading based on rational expectations as traditional economic theory suggests that people do. We can all think of someone in our life that makes decisions based purely on emotion with no logic at all. We are humans, not robots. The questions is whether or not we can anticipate these events (such as the herding effect we see with large overall movements in markets) and be ahead of anyone else that is trying to take advantage of the same exact “animal spirits” within the markets. Once again, we fall victim to our own estimation limitations as well as not having all and complete information. It is always easy in hindsight to say, “well that looked like irrational exuberance,” but very few actually successfully capture that irrationality in an investment strategy. I published an article that highlighted investment strategies that are based on exploiting behavioral biases. There is no empirical evidence that these strategies have been successful in that endeavor. There is an entire support team at IFA that helps put together these articles. Oppenheimer has been successful in building up their investment assets over time. We have shown that this success is largely built on advertisements, such as the one with Ms. Borré explaining her strategy. While it all sounds very good, a successful implementation of these ideas is not supported by empirical research, has not worked in the past AND more importantly, is not expected to work in the future. Market prices are moved by news and news is random and unpredictable. Let go of the idea that you or a manager you select will have wisdom greater than the market. Just index and relax. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.