wealth definition

MongoDB (MDB): Handling TMI – Too Much Information


Business on Wall Street in Manhattan

Pgiam/iStock via Getty Images

Investment Thesis

Any intelligent stock investment is a bet about what the future holds. It requires a forecast of that future.

The further out in time we probe, the greater is the risk we may turn out to be in error. Time so spent is extremely costly because it cannot be replaced. “Conservative” long-term errors once apparent may become defeaters of major-objective goals.

The truly protective investment strategies are repeatable evaluative shorter-term ones. They can be identified more readily as failing and replaced by pursuits with better odds for desired achievements.

But they take more effort and information inputs, including realistic price target forecasts.

For most investors those inputs and forecasts are better obtained from market professionals making them day by day without biases likely to be counter to the investor’s objectives.

We obtain those inputs from direct work-experience knowledge of how market-makers must work to provide trading liquidity for major institutions in their management of multi-billion-$ portfolios.

We use MongoDB, Inc. (NASDAQ:MDB) here as an illustration in the pursuit of knowledgeable shorter-term investment guidance in selecting wealth-building stocks.

Company Description

MongoDB, Inc. provides general purpose database platform worldwide. The company offers MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premise, or in a hybrid environment; MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB. It also provides professional services, such as consulting and training. The company was formerly known as 10gen, Inc. and changed its name to MongoDB, Inc. in August 2013. MongoDB, Inc. was incorporated in 2007 and is headquartered in New York, New York.”

Source: Yahoo Finance

Street analysts estimate of subject growth

Yahoo Finance

The above are “street” analyst estimates “guided” by corporate officials designated as investor interfaces. While it should seem that corporate and investor objectives are parallel, there often are times when they are not. Investor judgment error-incurring times, particularly in regard to shares’ likely coming market prices.

Long-standing stock market mechanics intensified by advances in information technology require 21st-century equity transactions to be either highly-automated small volume/value trades with/among individual investors or big value/volume trades among “institutional” investors managing Billion-$+ portfolios.

The automated individual small trades are present on a fairly consistent basis, while the larger institutional trades tend to be irregular in their timing and urgency. Knowledge of impending big trades tend to move prices in anticipation, reducing their effectiveness for the trade initiator, so they seek quick, private accomplishment. That is aided by “Market-Maker” [MM] firms like GS, MS and a dozen or so others.

The MMs seek to balance buyer and seller share volumes by, where necessary (usually 90+% of the time) acting as buyers or short sellers of borrowed shares. Their risks thus taken are hedged by “insurance” deals in open public markets for derivative securities.

The costs and structures of the hedges define the price expectations of knowledgeable participants in those markets, often “prop” trade desks of MM firms and other institutions acting for their own accounts.

Here In Figure 1 is what the daily ranges of expected prices have been over the past 6 months for MDB:

Please do not jump to conclusions about what these pictures show

Figure 1 is NOT a conventional backward-in-time-looking “technical price chart.” Instead, it is a recent history of daily forward-looking price range forecasts made by well-informed, experienced market professionals.

Figure 1

Hedging-implied coming price ranges


(used with permission)

The vertical lines of Figure 1 span the range of price implied to be likely by the actions of Market-Makers [MMs] as they hedge the firm’s capital required to be put at risk. Their commitments are needed to balance buyers and sellers when “filling” client block trade orders from big-money-fund portfolio managers.

The implications of these actions have been known to sometimes vary significantly from forecast statements made by the “research” departments of the same firms.

The vertical forecast lines are split into upside and downside prospects by the heavy-dot end-of-day market quote for the issue on the day of the forecast. A measure of the imbalance between up and down implications is the Range Index [RI], which tells what percent of the whole forecast range lies to the downside. Here for MDB, the RI is 24, indicating about three times as much upside in prospect as downside.

The “thumbnail” picture at the bottom of Figure 1 displays where today’s RI relates to the RI experiences of the subject over the past 5 years. Positions to the left of the distribution’s peak imply additional ones likely may be higher and so are favorable. Positions to the right of the distributions mean may be not so.

The row of data between the two pictures of Figure 1 tells of the prior experiences of forecasts like the one seen at this point in time. We use the RI to see how well the MMs’ prior forecasts have worked out when a simple, practical portfolio management discipline is uniformly applied to all investment candidates at all times.

The acronym for that Time-Efficient Risk-Management Discipline is TERMD. It sets as a price sell-target the top of a price range forecast held likely to occur within a time horizon that can be credibly forecast. When the target is reached, the position is closed and the realized proceeds are reinvested, in their entirety into the then current best available candidate. For our purposes, the forecasts used come from the MM hedging actions, with position costs of the price at the end of the market day following the forecast. The forecast horizon used is 3 months (91 calendar days or 63 market days), when a still-open position is closed, it is all to be reinvested, regardless of gain or loss.

For MDB there have been 39 prior instances of RIs at 24 out of the 1086 market days in the past 4+ years. Profits were there to be earned in all of those experiences. The average return on all 39 was +21.8%.

Since many reached targets before 63 full market days, the average holding period on all 39 was 24 market days, only 3 more than in a typical month. That compounds to an annual [CAGR] rate of +727%. There can be no guarantee of a MDB position taken now producing profit at a +727% CAGR, but the proportion of RI forecasts at an adequate sample size of 39 market days, is impressive.

How do MDB’s competitors compare to these prospects under similar measurements? We can begin by looking at their Risk vs. Reward Tradeoffs.

With MDB as a guideline, let’s look in Figure 2 at how its trade-off between an upside forecast prospect of +20.2% and a typical worst-case price drawdown (from a position entry cost on the day after the forecast) of -5.4% compares with other software providers.

Risk-Reward tradeoff comparisons

Figure 2

visual comparison map of risks and rewards


(used with permission)

Upside price rewards are from the behavioral analysis (of what to do right, not of errors) by Market-Makers [MMs] as they protect their at-risk capital from possible damaging future price moves. Their potential reward forecasts are measured by the green horizontal scale.

The risk dimension is of actual price drawdowns at their most extreme point while being held in previous pursuit of upside rewards similar to the ones currently being seen. They are measured on the red vertical scale.

Both scales are of percent change from zero to 25%. Any stock or ETF whose present risk exposure exceeds its reward prospect will be above the dotted diagonal line.


Source link