FAQs

It’s the EDGE metric for Demand, and the core predictive EDGE metric for entries and exits in any stock or sector, and the whole market. Sentiment is an algorithm, a ten-point mathematical scale measuring how stocks move from Oversold to Overbought. As a general rule, stocks spending more time over 5.0 Demand levels outperform, and vice versa. And rising Demand coupled with stable or falling Supply lifts prices.

Entries are rising Demand or falling Supply. Exits are falling Demand or rising Supply. That’s the central tendency – the highest probability. It’s best to buy when stocks have a higher probability of rising, and sell before they have a higher probability of falling. Headlines and financial results cannot reliably be counted on as signals because most of the money in the stock market isn’t tracking those. It’s mathematical. See in Tutorials Five Easy Steps videos #3 and #4.

Create a Dynamic Portfolio using our unique market structure metrics. See video Tutorials, and especially Five Easy Steps #2 on creating portfolios. The best stocks in a strongly rising market are the ones with excess Demand. Say you query for Stocks with Demand between 5-10, with a downtrend in Supply, and 1.5 standard deviations between the two. And in volatile markets, screen for big and stable stocks spending lots of time at 5.0. See the Easy Step #2 video under Resources for more.

Stocks are Overbought when Demand, Market Structure Sentiment™, is 10.0. On our 10-point scale, levels above 5.0 signal more demand than supply, and vice versa. All stocks and the whole market is somewhere on that scale at all times. Stocks with more demand than supply do better, and vice versa. When stocks hit 10/10, they’re Overbought, 1/10 Oversold. The whole market trades between 4.0 and 6.0 most times, meaning it’s Overbought above 6.0 and Oversold below 4.0. Use that data to increase the probability of both capturing and keeping gains. Don’t overstay an Overbought stock or market.

No. Short Interest, the widely followed gauge of stocks borrowed, sold and not yet covered, dates to 1974 when the Federal Reserve established it to track money in margin accounts. The USA had abandoned the gold standard in 1971, and financial-market volatility exploded. More money began using hedges like options and futures, and borrowing. But there were no electronic markets, no index funds, no exchange-traded funds, no high-speeding trading, no instant-messaging, on it goes. It’s not a contemporary or predictive metric. Short VOLUME, our Supply measure, by contrast is a 2009 measure regulated by Finra, the equity markets watchdog for brokers, metering the amount of trading volume coming from borrowed or created stock. It’s an excellent supply/demand metric, and it’s one of two key predictive measures we use.

Realtime data is not predictive. And EDGE is about trading Sentiment, not Price. Prices are set by machines vastly faster than us retail traders, and will fool us. But they don’t predict further out than fractions of seconds. We thus gain an advantage over them by shifting from the prices they create, to the DEMAND (or SUPPLY) they create. Correlations at the tick-level – at the point where prices are set – reach zero. Of what use is that? We take a LONG step back so we can observe patterns of change over time, reflecting Supply/Demand fluctuations, which become reliable signals of near-term directional change. Our data is T+1 in most cases, meaning it’s yesterday’s data. We’re aiming for signals giving us several days’ advance notice about changing stock prices, market-direction.

First, three rules: Context, Broad Sentiment, Sector. Context: are options expiring, is it earnings season, month-end? Those change outcomes. Broad Sentiment: Is it rising, falling, peaked, bottomed? And is money buying the sector the stock belongs to? And Demand rising off 1.0 isn’t excess Demand yet, only improving Demand. If it stops at 3.0 (that’s a top) and falls, you won’t win. Backtest that data! And be very careful buying stocks rising off 1.0 unless ALL other factors are in your favor: Money is back in the market, and buying your sector, and no context issues challenge probabilities.

Backtest your data with Profiler. See video #4 of the Five Easy Steps for success with EDGE. In sum, sell when the data show Demand is falling, or Supply is rising. Or take your gains! Say a stock has been at 10.0. Then it goes to 9, 8, 7, 6, 5…. If it falls below 5.0, supply exceeds demand. It’s likely to stop rising or fall. And sometimes it happens a lot faster – as soon as Supply ticks up, or Demand ticks down. Say a stock is at 10.0 for days, then Short Volume rises above the stock’s trend, and even over 50%. Supply is rising faster to Demand. Take gains. Not chances.

Yes. Stocks with prices below about $8 have higher standard deviation in metrics, which reduces reliability. And stocks with low LIQUIDITY are much harder to trade. That’s $/Trade a metric in the portfolio view. And stocks with lots of VOLATILITY, another metric. The BEST probabilities are in liquid stocks with lower volatility and predictable prices.

Because ETFs track stocks but don’t behave like them. ETFs are the greatest modern financial-markets phenomenon. Never has so much money chased one instrument so ravenously. But ETFs are also derivatives dependent on an arbitrage mechanism – different prices for the same thing – for prices. And because ETF shares are constantly created and redeemed (Investment Company Institute Data show around $500 billion of creations and redemptions every single month), the supply/demand features EDGE applies are less reliable. Maybe that’ll change in the future. One thing you CAN do: Use our sector portfolios to know when to buy or sell sector ETFs. In fact, you can build a proxy of stocks for any ETF to know when to buy or sell it. For instance, Gold. Create a portfolio of gold stocks and use Supply and Demand to know when to buy or sell GLD.

The quantitative software and mathematical models underpinning EDGE are predicated on the rules of Regulation National Market Systems (Reg NMS) governing quotes, trades, data and access to all three. That rule doesn’t apply to what’s colloquially called “the pink sheets,” the Over-the-Counter market where penny stocks are traded.

We update our models, math and analytics to reflect changes. In fact, in Jan 2020, the SEC proposed a revamp of Reg NMS that traders call Reg NMS II. It’s even larger (595 pps as proposed, versus 524 pages for Reg NMS Final Rule) and effects some big changes, especially to how data plans are governed and what constitutes a “round lot.” The new SEC regime has thus far not chosen to implement it. If it becomes part of the regulations, we’ll adjust models.

Once a day, maybe not even that often. Generally, though, check your positions relative to your math each day. If you’re buying stocks ticking up from 4.0, buy the first uptick over that level. If you’re selling stocks reverting below 5.0, follow it. CAUTION: Don’t be afraid to take gains! EDGE is designed to help you generate a few percentage points of returns every week or two while avoiding losses. It’s not intended to have you off chasing rainbows. Disciplined use of data can produce those sorts of returns in any market. So if you’ve seen an 8% gain and your position has topped Demand, rising Supply, take your gains. But you don’t have to sit and watch trading screens all day.

No, it’s rebalancing portfolios away from holdings that are Overbought and toward holdings that are Oversold. It’s what index funds do except the triggers here for rebalancing are driven by market structure rather than weightings. Statistically, some portion won’t hew to the math, but the great majority will, and disciplined data-application will keep your portfolio weighted toward holdings mathematically likely to outperform the market. Think about it this way. Returns in the stock market are the NET of up days minus down days. YTD 2022 at the end of March, SPY is down 35%, tallying the down days, and up 28%, adding up the positive days. We want to avoid the one and keep the other, and EDGE is aimed at improving that probability.

Technical analysis turns on price and volume, which are not metrics but consequences of underlying behaviors. We measure underlying behaviors discretely in context of the rules that govern how trades execute today. All prices are not equal and neither is all volume. The behaviors driving volume have different purposes and time-horizons, and by separating them and measuring how they set prices, we can statistically predict when these behaviors will wax and wane, affecting price-performance. And thus, EDGE shifts the focus to Demand and Supply, and away from Price.

Regulation National Market System has been in place since 2007 and forcing conformity on prices and behaviors, and Market Structure Edge(TM) is the first quantitative market-structure application for portfolio-shaping. We have plenty of time.

Of course that can happen! But the rules still apply. But the risk of declines is always greater in stocks that are 10/10 Overbought and more than 50% short before results. No matter what the companies report. The lesson really is that earnings, like options-expirations, can distort outcomes, so be wary trading during them.

Factor for them and do the math, but beating the benchmark by shifting resources toward stocks with high probability of outperforming the market due only to market-structure factors is a better strategy than reducing trading costs but underperforming the benchmark.

Market Structure Sentiment(TM), our Demand algorithm, reflects the behaviors, motivations, and time-horizons of ALL THE MONEY in its discrete proportions behind price and volume. But fundamentals don’t set prices. Machines do. It’s better to know how machines set prices and factor that into your decision-making than to hope fundamentals will manifest in the way machines set prices.

All Active investors are trying to do that, yet data consistently show 75-95% of them, depending on timeframe and market-cap, underperform the broad measures. Billionaire Ron Baron of Baron Capital said in a 2017 CNBC interview that he has owned 2,500 stocks since founding his asset-management firm, and if one removes 15, he would be average. That means 99% of his stocks were average. Trying to find the 1% that will win is a far riskier strategy than incorporating market rules that demonstrably produce statistical outperformance a majority of the time.