Our BioTechQuant System Recommends a Model Portfolio with
a Strong Track Record of Returns, Far Outpacing Benchmarks

Jim Simons is a legendary hedge fund manager, and his Renaissance Technologies hedge fund has amongst the best track records on the Street, while charging the highest fees. His personal fortune is estimated by Forbes to be above $14 billion. Renaissance is a 100% quantitative-driven shop, which means they use systematic investing and models to achieve their returns. Unfortunately, they don't invest for individuals but only huge amounts of money for institutions. 

Before I explain about the BiotechQuantSystem, lets understand why systematic model investing has become such a powerful strategy which has driven and continues to drive multi-billion dollar fundamental funds like Ray Dalio's Bridgewater Associates, Steve Cohen's Point 72 to shift towards quantitative model investing. 

One of my academic areas of specialization was Systems Analysis and Design. It took time to design a system for based on the function there were many possibilities to consider and address. But the beauty of a system is that once it's done, it usually performs consistently based on the parameters entered. A complex system would require adaptive learning and ongoing modification. But there is so much analysis, experience and intelligence that goes into designing a complex system, that once it passes the tests you have to begin to trust it. Thereafter, the Discipline to follow a system with unwavering focus is a prerequisite to achieving the objective for which the system was designed. Most failures occur due to lack of this discipline.

Extensive research has been conducted on the efficacy of Systematic Investing compared to Expert or Discretionary Investing. Empirical evidence has come out consistently in favor of Model Investing. Paul Teehl, an American psychologist who is considered the founding father of the science of the predictive importance of quantitative models over human judgement, studied the outcomes of predictions in many different settings. He discovered a preponderance of evidence that predictions based on mechanical [algorithmic, quantitative] methods of data combination outperformed clinical [e.g., subjective, informal, "in the head"] methods based on expert judgement. Decades ago in his seminal work, Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence, Meehl noted on the efficacy of the quant models, saying:

In a 2000 paper by William Grove, et al, titled ‘Clinical versus Mechanical Predictions,’ the authors noted,

Why does systematic decision making through the use of even uncomplicated and limited variable quant models comes out ahead of expert opinions, which are nothing else but discretionary decisions made by experts?
The answer lies within us. Our emotional makeup or the behavioral tendencies, which are embedded in our thinking.

I would say that the human mind is very capable of consistently and systematically misjudging the world around us.

We are prone to overestimating or underestimating; being overconfident in our analysis while doubting data contrary to our opinions, using our perceptions to create or fill-up what doesn't exist. Psychologically, our opinions are filtered through our biases.
It’s natural for humans to be illogical. It’s in our genes, and the problem is exacerbated when the decisions involve investing, money, emotion, and time constraints.

And we've witnessed these tendencies in our own investments.

How many times are we so sure about a stock, and continue to hold it even though there may be ample signs that the sentiment and story may have changed?
We convince ourselves that our information is better.

How many times you need to walk away, but you stay-on and make just one more trade because you feel very confident that this one will make up for all the rest that have been going wrong?

How many times you have a gut-feel that this is an absolute bottom in the stock and I can’t go wrong buying it here or averaging-down?
Most of the time, the “gut-feel” decision eventually leaves you poorer and feeling exhausted, disappointed, stressed, and frustrated.

I know these feelings, for I too have experienced them.

A legitimate question is why do experts perform poorly even when they’re given the model’s output before making their decisions?

Once again it’s our propensity to overestimate our abilities, and the feeling that we possess special insight that can allow us to enhance the model’s decision. Something as mundane as a rough commute or a missed train can affect the way you perceive things once you arrive at the trading desk. Furthermore, the decision-making can be inconsistent from person-to-person, even when they look at the same data. All these factors reduce our “enhanced” performance compared to diligently following a cold, emotionless, analytical, quantitative model.

Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman, who was inspired by Meehl’s work, notes in his book, Thinking, Fast and Slow:

What the studies and empirical evidence tell us, you probably have already observed or experienced it?

How many times have you as an investor encountered analysts and economists making wrong calls? It happens all the time. If these professionals who have access to all kinds of data streams, data analysis tools, and support teams continue to be frequently wrong in their predictions, then it’s a fairly tall order to expect an individual investor to be consistently right making judgment calls. We used to often say on Wall Street that an economist can be usually right when they offer a forecast or a time frame, but not when they offer both at the same time.

The bottom-line is that if you’re going to trade using system-driven, quantitatve models, just don’t enhance the model's output with your own judgment calls. You may be thinking of pushing the performance ceiling higher, but studies of experts point to the incontrovertible fact that the performance ceiling is lowered. 

Trust Jim Simons to know something about quantitative model investing when he noted,

I started working on Wall Street in the early nineties as an Analyst.

I first worked as a Buyside Analyst (for an asset management firm), and thereafter as a Sellside Analyst (for broker-dealer investment banks). Over the years, the coverage included companies in various sectors including Software, Telcos, Pharmaceuticals, Semiconductors, IT Services and Medical Devices.

Things were going well, when the first blow-up happened. A software company that missed its numbers. Out-of-the-blue, Microsoft incorporated some of this company’s key product features into their next release of Windows, and this company was in a way, busted. A hard lesson learnt. I was perhaps too close watching the trees, and overlooked the forest.

There is a saying in Wall Street research departments, that if you haven’t had an earnings miss, then you haven’t been an Analyst long enough. In fact, at Goldman Sach they used to pass around a helmet in the Research department to the Analysts who suffered a blow-up. The idea was to hit the helmet with a hammer every time a blow-up occurred. This helmet was truly beaten-up.

I got better at picking stocks, but also had my stumbles. Some of them were unavoidable, but there were others where I hanged on to an opinion too long or looked at things differently, or misinterpreted data, consequently over-or-under-estimating. And I was not alone. Research departments at most firms have similar stories. Analysts and strategists who have scaled greater heights, were also riding these cycles of success and failure.

As I saw it, Consistency was sought hard, but hardly achieved. Some of the best succumbed to the human reversion to inconsistency over time.

Remember, Elaine Garzarelli making a 1987 prescient call of an impending market crash, or Goldman Sachs market strategist Abby Joseph Cohen from the nineties calling the bull market, Meredith Whitney from Oppenheimer from the late 2000s calling for a Citigroup dividend cut, which happened, and then a municipal bonds sector debacle, which never did. All of them had their flashes of brilliance, and then lost that consistency. The list of people goes on-and-on, and includes everyone – from market strategists to economists to analysts to portfolio managers.

Even if you haven’t heard of any such high-profile stories, just check out any company that missed earnings today, or for that matter the Top Losers list for stocks that declined sharply. You’ll observe that the stock cratered, and there are plentiful price and rating revisions after the meltdown. If super-sharp analysts with all the resources at their disposal and a 24/7 focus have found it hard to make prescient calls consistently, then what are the chances for an individual investor to make such calls.

Majority of times, the ‘Sell’ rating change is Forced; and it's forced post eventum.

Missed calls happen frequently.

Eventually, 1-year or 2-years down the road even if the analyst call or your conviction is proven right, do you’ve the resources like those of an institution with deeper pockets to weather a substantial decline.

How many such declines can you weather in a relatively smaller portfolio compared to an institution’s high-volume portfolio?

I left Wall Street, bitten by an entrepreneurial bug, and joined one of our investment banking clients.

But from the first bust-up, I continued to search for ways to create greater predictability in my research; to diminish the unexpected; to read signs of trouble; to survive an earnings season with more accurate forecasts.

Very early on, I developed a penchant for using market data, and analyzing it in various tools to create models that assist in building an Ideas Funnel. This was the Systems Analyst side of me. I did it with Professors as their graduate assistant, modeling econometric series with the idea of predicting interest rates. I brought that mindset to my Wall Street job as well. I was looking for certain tendencies for stocks that perform well. But at the same time, I was also growing my confidence in the various systems I tried. I looked at some of the foundational work in simulating and forecasting stock returns performed by Roger Ibbotson and Rex Sinquefield. Their book Stocks, Bonds, Bills and Inflation, illustrated the returns of major financial asset classes and the ability to predict based on historical returns.

While working as an Analyst, one day I saw a fund that was an institutional holder in all the stocks I was covering.
I called them up and I was told there are no Portfolio Managers there who will be interested in learning more about these specific companies. This intrigued me for they were investors in the names I covered. Persistently, I tried again a few times and was eventually put through to a person with a Portfolio Manager title, who laughed out loud when he heard why I was calling. He told me that the reason they were invested in the names I covered was because they existed on the stock market. “We buy them if they exist,” he told me as he went on to explain the idea. That’s when the concept of index investing and buying the entire stock market dawned on me. These guys were the index funds and the ETF industry before these industries were born. The name of the fund was Dimensional Funds Advisors, and they now manage ~$400 billion in quantitative strategies customized for institutions. The co-founders were none other than Rex Sinquefield and David Booth, both graduates of the University of Chicago business school, which is now referred to as the Booth School of Business.

Once I winded out of the Wall Street and Main Street roles, I decided to continue developing models for recommending portfolios. I focused on the Small Caps, Large Caps, S&P 500 stocks and ETFs. I created baskets of stocks with the intent to outperform certain indexes. I found the results of my models consistently outperforming the benchmark indexes. I started a service offering subscribers these model portfolios. Over time I focused mostly on small caps, since as an asset class no other class outperformed more, although the risk or standard deviation was higher as well. Well, that’s what I have been doing ever since - working with various models to enhance market returns and manage risk.

I’ve made many mistakes as an analyst and investor, many of them cited here, and I’ve tried to learn from them.
Through the years, one thing has become clear to me, and I repeat.

Consistently outperforming the market returns, year-in and year-out, is very hard to achieve for investors without the discipline of rules-based investing.

There are rare smart folks, who achieve it. But most investors are smart but still not consistently successful. Neither as individual investors do we have access to genius money managers for they are focused on managing billion dollar portfolios of institutional money.

Consistent returns require discipline and rules-based investing, unclouded by impulsive judgments. 
Many individual investors dream and aim for doubles-and-triples, when they invest. They may occasionally even get them. But the gains there will be offset to a significant extent by many other positions which are closed for losses or are held perennially hoping for a turnaround.

Model driven investing guides you to capture whatever returns the market can reasonably offer, and to outperform that level.
The model helps you get to the next-down or the next-base. And that’s how the returns accumulate. Incrementally, you accumulate your downs to get to the endzone or collect your bases to complete a run. That’s how you get to your double/triple in model investing. If the doubles/triples don’t happen, you still want to collect a solid return, and build on it with the next idea. But when an investor embeds the notion in their mind for a double or triple, emotions will eventually cloud the judgment and solder you to that position. It’s hard to let go. A terrible thing is to not let go and take a round trip or perhaps even worse. That situation takes a lot out of you. You know, the market will surprise – remember, the slew of downgrades and sharp price declines that occur every day.

Chasing returns gets you mired in mediocrity. Your Plus positions end up being offset by the Minus ones. You're running hard, but not making progress.
Instead, position your portfolio well, and let the returns accrue to the portfolio. Don’t be wedded to the ideas.
Think of positions as a vehicle or a bus to move you forward – to the next stop, or even further. But be prepared to get off if the situation warrants. For there is another bus coming behind. When I started off as an Analyst and used to show annoyance at missing an idea, the Director of Research used to tell me:

The message was that opportunities are plentiful, and never cease.

Emotionless investing is an important prerequisite for successful investment outcomes, and model driven investing provides that discipline. Just like hedge funds benefit from their quantitative investing strategies, I believe we too can grow our investment portfolio through the discipline of systematic model investing. This enhances the probability of achieving superior returns compared to market benchmarks over a period of time with relatively more consistent performance than individual and expert investing.

I’ve provided you with my experience, and empirical evidence from studies to bolster the argument for quantitative model investing. I’m not asking you to believe me. Do your own research. But what I do feel strongly about is that you must not ignore the consistent successes achieved by model investing driven funds run by stalwarts like Jim Simons, and Ray Dalio.

Investors only diminish their investment potential by not considering systematic strategies seriously.

We decided to match the potential of the Biotech sector with a systematic, model-driven approach. The result was our BioTechQuant model. 

Reflecting our experience over the years, the BioTechQuant model goes through various stages, crunching the multiple quantitative parameters and rules, processing information on both the biotech sector listings in our database and the broader market variables. The eventual output is a recommended list of the highest potential stocks within our parameters. This is the 8-stock model portfolio which we share with you through our monthly Prudent Biotech newsletter.

We know from the chart and table above, the extent of the Biotech outperformance over the broader market.  Below, we have overlaid the same chart with the Prudent Biotech Portfolio.

That's right! A $10,000 investment potentially growing to $3 million.

Now keep in mind that the exceptional back-tested track record for the model was based on hypothetical trades, and not actual trades. Generally speaking, Biotech stocks have more variable returns and substantially higher risk then the broader market indexes, and the Prudent Biotech portfolio has higher volatility, ~2x, than IBB. Furthermore, historical results do not guarantee future performance.

Nonetheless, the performance gap is stunning. What is equally noteworthy is the consistency with which the Prudent Biotech portfolio has outperformed.

Another thing to point out.

You may have astutely observed from the chart above that the performance spans an economic cycle, including the treacherous 2008 Bear.

During the brutal bear market of 2008-09, when the S&P500 index sank 55% in a matter of months, the Prudent Biotech Portfolio hardly suffered a material decline through the entire Bear market. The reason was straightforward. Our BioTechQuant model recognized the market risk to be abnormally elevated and along with input from other market directional overlay models, the Prudent Biotech portfolio simply exited to the safety of cash. When the models determined the risk/reward to be relatively safer, the Prudent Biotech Portfolio began buying stocks again in mid-2009. The ability to exit the market is relatively much easier for individual investors, compared to institutional investors, and we should leverage that into an advantage during times of acute market stress or a downward trend.

Lets be clear about what this product is not. It is not a get-rich quick scheme. We don't shoot from the hips, but are deliberate and long-term. You must have at least a 2-3 year horizon to benefit from such a portfolio product. We cannot avoid blow-ups, since the are part of biotech landscape, but we can manage them with a portfolio approach. You must recognize that the most important thing is to have reasonable and realistic expectations from the stock market, which is a risk-reward market and not a risk-free market. There is a 10% return expectation over the long-term from the stock market. There are years, when the markets climb 20% or higher, particularly after a bear market, but such years are less frequent. In addition, bear markets or corrections can take away 10% to 30%, and sometimes more, from an index. We aim to meaningfully outperform the benchmarks, and we try to be consistent. This is how a portfolio is built over the years.

The Prudent Biotech portfolio uses algorithms to guide its stock selection and trading each month.

What is an algorithm? It's a set of clear instructions or rules. These rules are programmed and then executed in a computer-driven quantitative model. Each month the mathematical investment model scours through the entire database to search, analyze and offer its best ideas. Any weak stocks are then dropped from the portfolio and replaced by the strong ones. By performing this rotation, the investment model positions the portfolio to hold the most promising stocks at that time... those which it determines to have the strongest potential of delivering market beating returns. Since we have a portfolio limited to 8-holdings, there are other names that are promising but not added simply because of our portfolio size.

A model Prudent Biotech portfolio may underperform initially or immediately after new positions are initiated. In addition, since there is exposure to a single sector instead of a broader set of industries, there is concentrated sector risk just like the Biotech IBB would have, when compared to broader market indexes like S&P 500. In addition, IBB is diversified over ~145 holdings, as compared to our 8-holdings portfolio, which significantly contributes to the Prudent Biotech portfolio's historical volatility of about 2 to 2.5 times that of IBB. But, as can be observed from the results, historically a Prudent Biotech portfolio has outperformed over longer, medium and even a shorter time frame. The methodology has allowed the Prudent Biotech model to deliver market-beating performance each year since inception, even though it must be noted that past performance cannot be a guarantee of future performance.

Many investors get caught up in Hope Investing. The Prudent Biotech portfolio never relies on Hope.

It just deals with the numbers as they exist and interprets them. No hoping or swearing.

That’s what prudent investing requires. An emotionless state or a highly-controlled emotional state of decision-making. As humans, our mind feeds on emotions. Over the long-run, emotions create turmoil with the investment performance. Recall, the underperformance of experts who couldn’t outperform systems even after possessing the recommended output of the systems.

Emotion was the variable Present in the Experts, and Absent in the System.

Operating within a rules-based system increases the probability of avoiding deep and total losses, and enhancing returns.
It’s great to be a person of conviction. But sticking to your guns stubbornly may have a real downside when it comes to investing.

The reason - because markets can be irrational.

You may be right in the end.
But will you be there in the end?

As noted British economist, and an active investor, John Maynard Keynes so very eloquently noted,

What you get is our Prudent Biotech model portfolio with a complete investing game plan behind it. A plan which has performed admirably well through an entire economic cycle.

Each month when the Prudent Biotech newsletter is available to you, the portfolio you access is not just names of 8-stocks. This portfolio of 8-stocks represents an investment game plan underpinned by quantitative model investing research. Based on your determination of the relevance of this information to your situation, you would now have the ability to use a systematic investing approach to invest in the market.

How powerful is this old English proverb. You've to start somewhere, sometime.
There is no guarantee of future performance. Nonetheless, it should still be noted that various studies and scholars have provided empirical evidence that bolsters the thesis that systems outsmart experts consistently and regularly.

If you feel our system can help grow your portfolio over time, and build the wealth that you wish for, then don't wait!
The Decision is Yours Now!

I leave you with a quote I mentioned earlier,

Graycell Advisors, and its affiliates, officers, employees, families, and all other related parties, collectively referred to as ‘Graycell’ and/or ‘we,’ is a publisher of financial information, such as the Prudent Biotech newsletter. Historical performance figures provided are hypothetical, unaudited and prepared by us, based on our proprietary analysis and system performance, back-tested over an extended period of time. The performance results obtained are intended for illustrative purposes only. Past performance is not indicative of future results, which may vary. All stock and related investments have a degree of risk, which can result in significant or total loss. In addition, biotech sector is characterized by much higher risk and volatility than the general stock market. Information contained herein does not constitute a personal recommendation or takes into account the particular investment objectives, financial situations, or needs of individual investors. If you decide to invest in any of the stocks of the companies mentioned in the newsletters, samples, alerts, etc., sent to you or available on our websites, you can and may lose some or all of your investment. You alone are responsible for your own investment decisions. We are not liable nor do we assume any responsibility for losses incurred as a result of any information provided or not provided or not made available in a timely manner, herein or on our website or using any other medium.  We also cannot guarantee the accuracy and completeness of any information furnished by us. Graycell is not a registered investment advisor and nothing contained in any materials should be construed as a recommendation to buy or sell securities. We may or may not already have existing positions in the stocks mentioned in our reports. Our models are proprietary and/or licensed, and can be changed or revised based on our discretion at any time without any notification. Subscribers and investors should always conduct their own due diligence with any potential investment, and consider obtaining professional advice before making an investment decision.