Systematic Investing

Driven by quantitative models and algorithms to achieve greater objectivity & reduce behavioral biases

"In 1988 I decided it’s going to be 100% models, and it has been ever since"
Jim Simons, Mathematician and $65 billion Quantitative Hedge Fund Manager

A System-driven Approach to Investing and Building Wealth

It's Time to Capture the Potential of Stocks, Leveraging Quantitative Techniques Like Hedge Funds Do ...

shadow-ornament

Our Quantitative System Recommends Model Portfolios with
a Strong Track Record of Returns, Far Outpacing Benchmarks

The Editor's Desk

Dear Fellow Investor,

You've arrived on this page today for you're looking to discover more about a better way to invest and capture the potential that the stock market offers. A way which is more efficient, powerful, and consistent. Fortunes have been made through disciplined investing. You've most likely heard of many such instances. And there's no reason why we can't do that, just as well. My name is Tarun Chandra, and let me explain more about quantitative investing, our system, as well as its track record over an economic cycle. That's the only way to empower you to make a decision if this system is one that can get you closer towards achieving your desired investing goals by setting you on a course of building wealth.

Quantitative Model Investing...
A System-Driven Unemotional Approach,
Embraced by Some of the Best Hedge Funds

Jim Simons

If you do fundamental trading, one morning you come in and feel like a genius, your positions are all your way… then the next day they’ve gone against you and you feel like an idiot… so in 1988 I decided it’s going to be 100% models, and it has been ever since... and it turned out to be a great business.

Jim Simons, Mathematician & Founder, Renaissance Technologies, $40 billion hedge fund

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 - over 40% of profits. His personal fortune is estimated by Forbes to be above $15 billion. Renaissance is a 100% quantitative-driven shop, which means they use systematic investing incorporating algorithms and models to achieve their returns. Unfortunately, they don't invest for individuals but only huge amounts of money for institutions. In fact, in its top performing Medallion Fund, the firm returned all outside money in 2005 and only invests in-house employee money. Let us understand why systematic model investing has become such a powerful strategy which continues to drive multi-billion dollar active management funds, like those of Ray Dalio's Bridgewater Associates, and 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 has been developed, it usually performs consistently based on the parameters entered, although optimization may be required to adapt to market conditions. There is a great deal of analysis and experience that goes into designing a system, that once it passes the tests one has to develop sufficient confidence to trust it. The discipline to follow the 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 Meehl, an American psychologist who is considered the founding father of the science of the predictive importance of quantitative models over human judgment, studied the outcomes of predictions in many different settings. He discovered a preponderance of the 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 judgment. 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 quantitative models, saying:

Paul Meehl

There is no controversy in social science that shows such a large body of qualitatively diverse studies coming out so uniformly in the same direction as this one … predicting everything from outcomes of football games to diagnosis of liver disease.

Paul Meehl, eminent psychologist

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

Superiority for mechanical-prediction techniques was consistent [over humans/experts], regardless of the judgment task, type of judges [experts], judges' amounts of experience, or the types of data being combined.”

Grove, William; Zald, David; Lebow, Boyd; et al, Psychological Assessment, Mar 2000

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.

Research reveals the human mind is very capable of consistently misjudging the world.

We are prone to over-or-under-estimating, being overconfident in our analysis while doubting data contrary to our opinions, using perceptions to create or fill-up what doesn't exist. Psychologically, our opinions are filtered through our natural biases.

This is not always a bad thing, e.g., in everyday mundane decision-making or when it's a question of survival. But the biases may create a tougher situation for sound decision making when such decisions involve investing, money, time constraints, and emotion.

Charlie Munger, Vice Chairman of Berkshire Hathway and long-term investment partner of Warren Buffett, refers to these emotions, multiple biases and tendencies acting at the same time in the same direction guiding us towards an irrational action as the "Lollapalooza effect." In his book, Poor Charlie's Almanack, Munger talks about a number of cognitive biases that we should be aware of in order to make better investment and business decisions.

Many of us may have already witnessed these irrational tendencies in our own investments.

How many times are we so sure about a stock that we continue to hold it even though there may be ample signs that the sentiment and story may have changed?

We convince ourselves to hold-on, even though an objective analysis would determine otherwise.

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

How many times we have a gut-feel that this is an absolute bottom in the stock and we can’t go wrong buying it here or averaging-down?

Most of the time, the “gut-feel” investing decision eventually leaves one poorer and feeling exhausted, disappointed, stressed, and frustrated.

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

Computer algorithms simply don't feel that way.

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. So the experts when given the same model's output end up modifying it, and making the results poorer. Something as mundane as a rough commute or a missed train can affect the way we perceive things. 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.

In October 2017, Richard Thaler, a behavioral economist from the University of Chicago, won the Nobel Prize for his work on exploring the biases that affect how people absorb information and arrive at decisions. In other words, how people are not rational as is often assumed in econometric models, but instead have mental quirks or biases, limited self-control and rationality, and social preferences that lead them towards irrational behavior, and shape market outcomes. He was not the first behavioral economist to receive the coveted Nobel Prize for work in the field of behavioral impact on decision-making. Other Nobel Prize winners were Daniel Kahneman and Vernon Smith in 2002, and Robert Shiller in 2013.

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:

Daniel Kahneman

Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula! They feel that they can overrule the formula [model] because they have additional information about the case, but they are wrong more often than not."

Daniel Kahneman, Nobel Prize Winner and author of 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 economists 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, quantitative 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,

Jim Simons

...if you’re going to trade using models, just slavishly use the models. You do whatever the hell it says no matter how you feel about it in the moment.

Jim Simons, mathematician and renowned quantitative hedge fund manager
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My Journey to Quantitative Modeling ...

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 learned. 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 Sachs, a helmet used to be passed around 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. Needless to say, this helmet was truly battered.

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.

Mark Twain

It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so.

Mark Twain, American writer and humorist (attributed but unverified quote)

As I saw it, Consistency was sought hard, but hardly achieved. Even the best succumbed to the human reversion to average performance over time.

Remember, Elaine Garzarelli making a 1987 prescient call for an impending market crash, or Goldman Sachs market strategist Abby Joseph Cohen from the nineties calling the bull market, or Meredith Whitney from Oppenheimer in 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, but could not maintain consistency. The list of people goes on-and-on with notables – from market strategists to economists to analysts to portfolio managers - and not so notables, like me.

Even if you haven’t heard of experts getting it wrong, just check out a company that missed earnings today or the list of stocks that declined sharply. You’ll discover the stock has cratered, and there are now plentiful price and rating revisions after the meltdown. If super-sharp analysts and asset managers 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 getting it right.

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

Eventually, 1 or 2 years down the road after the debacle, even if the analyst call or your conviction is proven right, do you’ve the resources to weather a substantial decline. Even institutions with deeper pockets find it hard.

How many such declines can you weather in your portfolio?

Leo Tolstoy

The most difficult subjects can be explained to the most slow-witted man if he has not formed any idea of them already; but the simplest thing cannot be made clear to the most intelligent man if he is firmly persuaded that he knows already, without a shadow of doubt, what is laid before him.

Leo Tolstoy, Russian writer regarded as one of the greatest authors of all time

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 idea 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 short-term interest rates and the timing of Federal Reserve's next move. 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.

Once I winded out of the Wall Street and Main Street roles, I decided to continue developing model portfolios focusing on the Small Caps, Large Caps, S&P 500 stocks, ETFs, etc. I created portfolios with the intent to outperform benchmark indexes. I found the results of my model-driven quantitative portfolios consistently and materially outperforming the benchmarks. I started offering subscribers these model portfolios, and that’s what I have been doing ever since - using quantitative analysis to develop model portfolios with the objective of outperforming benchmarks and managing 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 clearer to me.

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

There are rare smart folks, who achieve it by creating their own investing system and pursuing it with discipline. But many investors are smart but still not consistently successful. Neither as individual investors do we have access to exceptional money managers with excellent investing temperament who are focused on managing billion dollar portfolios of institutional and high networth money.

Warren Buffett

Success in investing doesn’t correlate to IQ. Once you’ve ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.

Warren Buffett, Legendary Investor

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 on a position, 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 very well know the market will surprise – remember the sharp price declines that occur daily.

Think of a portfolio, and the positions therein, 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:

Stock ideas are like the New York subway. There is one every few minutes."

Director of Research, New York Investment Bank

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 the discipline to invest without emotions. 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 and enhance the probability of achieving superior returns to benchmarks consistently.

I’ve provided you with my experience, and empirical evidence from studies to support 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 the potential of their investment portfolios by not considering systematic strategies seriously.

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How Systematic Investing Works?

The Graycell model portfolios use quantitative algorithms to guide the stock selection.

These algorithms, which are instructions or rules in a computer-driven quantitative model, are used to create the monthly model portfolios. Each month the investment model reviews the stock database to search, analyze and offer the best ideas for the strategy being pursued. At the beginning of the month the portfolio is reconstituted, to the extent required. This allows for weaker stocks to be closed out, replacing them with stronger ones. By performing this rotation where required, the investment model positions the portfolio to hold a promising group of stocks - those which the system determines to have a strong potential of delivering market-beating returns.

Based on the focus of the model portfolio, there may be exposure to a single sector instead of a broader set of industries, which creates concentrated sector risk and higher volatility. In addition, the model portfolios have a limit on the total number of positions, which can create higher volatility when compared to benchmarks holding hundreds of stocks. However, the concentration in the model portfolios has its benefits, as well-performing positions can have a stronger impact on the portfolio when compared to a broadly diversified one.

As will be observed from the results, the model portfolios have materially outperformed over longer, medium and even a shorter time frame. The methodology has allowed Graycell model portfolios to deliver market-beating performance even though it must be noted that past results cannot be a guarantee of future performance.

What The Systematic Investing Product Is Not...

Let's be clear about what our systematic investing product is not. It is not a get-rich-quick scheme.

We are deliberate and long-term. You must have at least a 2-3 year time horizon to benefit from a systematic portfolio.

We cannot avoid individual stock losses since they are part of stock investing landscape. But we can manage them with a portfolio approach.

As investors, we 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% annual return expectation over the long-term from the stock market. There are years when the market climbs 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.

Keeping all this in perspective, we aim to meaningfully outperform the benchmarks, and we aim to be consistent.

This is how a portfolio is built over the years and creates investment wealth.

The Future is About Hope!
The Market is Not about Hope, But Results!

Many investors get caught up in Hope Investing. Our quantitative model portfolios do not rely on Hope.

They just deal with the numbers as they exist and interpret them. No hoping or swearing.

That’s what prudent investing requires. An emotionless or a highly-controlled emotional state of decision-making. As humans, our mind feeds on emotions. Over the long-run, emotions and biases 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.

Emotions and biases were the variables Present in the experts, and Absent in a 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,

John Maynard Keynes

The market can stay irrational longer than you can stay solvent.

John Maynard Keynes, Economist
The Prudent Biotech Newsletter

Since I'm quite active in the markets, I was unsure about a hands-off approach. But I've turned around as I see the results from this discipline. Thanks for helping me out!

Marty H., Texas

This is awesome. It required patience at first but then it comes together. It took getting used to. Up and away. I should become an affiliate for you 🙂

Richard E., Florida

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.