THE ORIGINS OF 2018 GRAND SALAMI STOCK METHODOLOGY
As a young child growing up on the outskirts of New York City, I shared a room with my older brother -- who was the type of kid to have a Lamborghini poster on the wall, speak openly (starting in 5th grade) about his desire to work on Wall Street, and never stopped badgering my mother to buy Honda stock in the 1980's -- and then reminding her in the 1990’s of the fortune she would have made had she listened to her 11 year-old son.
I, on the other hand, had zero interest in business or investing and spent most of my time figuring out which pair of new high top sneakers I wanted to get next, playing blackjack in the school cafeteria using chocolate chip cookies in place of money, arguing with friends about whether Magic Johnson or a pre-champion Michael Jordan was the superior player, debating who was the hottest chick amongst the triumvirate of Nicole Eggert, Alyssa Milano and Christina Applegate, and pondering the probabilities of the existence of UFOs and Bigfoot.
After college I went on to graduate school, while my brother became a trader in perhaps the hottest sector in the history of the stock market, the late 1990’s internet stock bubble. Back then, I was a young man with little money in his pocket, submerged in a continual despondency about my underperforming facial follicles and the resultant inability to "connect" my goatee. While my capitalistic bro was undoubtedly making tons of coin, as a broke student I was taking the bus everywhere and making excuses to prospective dates about my car always being in the shop. I was working as much as possible while putting myself thru graduate school, but the money I earned from tutoring math, biology and Spanish mostly went towards paying off the interest on my undergraduate loans and the $5/hour job I had checking Library passes only covered my White Castle runs and Domino's deliveries -- don't get me wrong, it wasn't all bad -- it did position me to meet lots of women in a casual environment, hone my pickup game and score a couple numbers, so it was actually a pretty cool gig and I would have done it for no compensation.
After paying for rent, food and my social life, I was only able to save up about $500, which I figured might be enough to buy me something semi-respectable to get me around town. So I scanned the classifieds and went out hunting for used cars between 10-15 years old. After checking out a few rides, I quickly realized that my 500 bucks was just enough to get me a really good stereo system surrounded by a rusted-out box with a clown-like paint job and crappy tires in dire need of replacement. I was pretty bummed.
But sometimes in life, a great enlightenment can sprout from the depths of disappointment. So I typed “how to research stocks” into the Lycos search bar and out popped links to articles and messageboards. Always one who enjoyed learning on my own in lieu of “experts” writing for Forbes, Fortune and the like, I spent a few weeks reading about a slew of internet stocks on ragingbull, siliconinvestor and yahoo messageboards. Contrary to my expectations, I was actually quite entertained by the differing opinions, debates and wide assortment of strategies utilized in the selection of stocks deemed worthy of investment.
Due to my vast inexperience and the seemingly endless complexity of the stock market, I felt I didn't possess enough knowledge to feel confident in researching an entire sector of stocks and then choosing the eventual winners from that array. I thought about it for a few days and developed a strategy that I figured just might work. Inside of the New York Times financial section, a top % gainers list for both the Nasdaq and the NYSE was published daily. After seeing consistently higher % gains on the Nasdaq list, I decided to limit my universe to solely Nasdaq-traded internet stocks. Having narrowed the field down to one exchange, I sought to reverse-engineer and deconstruct these stocks in an attempt to uncover factors they possessed that may have predictive power for these large gains. I then planned on applying these criteria to the sector and selecting the stocks most likely to explode next.
Almost immediately, I noticed some chief characteristics shared amongst these huge daily gainers – they tended to trade below $10, were considered small caps due to their valuations, possessed low floats, and traded with higher volumes in relation to their float size than their peers. So these became some of the first criteria which most attracted my interest and I named these share-based criteria supply/demand factors. I also noticed that the majority of the stocks that appeared on this list tended to rally for the next few months and easily outperformed the sector as a whole during that timeframe. These big daily gainers even had a strong tendency to reappear on the top % gainers list within a few months. That wound up being a very important lesson I learned in my early years as a stockpicker: stocks that have achieved large % gains in the recent past have the potential to become epic runners in the near future.
As a result of this likelihood of big gainers repeating this feat within such a short timeframe, I figured I wouldn't need to canvass the entire sector, rating all of the Internet stocks – that seemed too time-consuming and inefficient. Instead, I could rate only those stocks that appeared on the top % gainers list and this would comprise my universe of potential internet stocks to purchase. In addition, I examined quarterly financial reports, read press releases, listened to conference calls and studied charts.
After a few weeks into my journey, I realized that traditional fundamental factors like revenue and projected revenue growth, margins, profitability, debt, etc. did not correlate to appearances on the top % gainers list. Moreover, these factors appeared to have zero predictive ability in regards to future stock price movement over the course of several months in the internet sector. In fact, the vast majority of the internet stocks that were skyrocketing were in very early stages of development and most of them were pre-revenue with a high debt load – therefore most investors considered them to have horrible fundamentals and “ridiculous” market caps. I found technical analysis significantly more correlative to future price movements but certainly not reliable enough to be used consistently as the sole predictor of future stock price movement.
It turned out that the predictive secret sauce was sentiment, most commonly termed hype. It seemed that the more excited people were about the prospects of a particular company, the better the stock performed. So I figured the end game for me was to study the hype factor around internet stocks, in an attempt to determine the subscales that comprised it, much like others studied the roots of fundamentals and specific technical indicators. But between academics, hanging out with buds and dating, things got very busy at school, so I never had enough time to carry out my research on the intricacies of sentiment. Instead, I took a shortcut by deciding to evaluate CEOs, press releases, conference calls, media mentions and message boards and I developed a composite sentiment score from those domains.
So now I had 3 different composite scores with which to rank the high % gainer stocks – supply/demand, technical and sentiment. On a daily basis I performed this same analysis on all top % gainer stocks and chose my highest rated 1-2 stocks for my larger watch list. I would wait patiently for their price corrections and typically select 2-3 stocks at a time to buy for my portfolio. And then I would watch them trade on Level 2. Since I invested a great deal of time and energy into stock selection, discovered that it took awhile to maximize my return on each ticker, and never developed any real interest in nor aptitude for daytrading, I would typically hold my positions for a few months before selling. I was fortunate enough to buy a new car with cash and pay off my undergraduate debt before the rigors of my academic work necessitated a cessation of trading activity.
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