The Strong Market DAILY UPDATES includes our indicators for the DOW, S&P500 and Nasdaq and is available to all clients. It includes our current analysis, charts and indicators based on our data-driven approach to the major financial markets. Updated daily after the close. Click below to activate and rgister for your free subscription:
WHY JOIN US?
IF YOU CAN'T MEASURE IT, YOU CAN'T MANAGE IT
This statement applies to many types of businesses, including trading and investing. We've been able to measure buying and selling activity in the market unlike anything anyone has ever seen before. And that ability to precisely measure supply and demand has provided our clients an unparalleled edge and nearly prescient knowledge of market moves, or market clarity.
This is an invitation to evaluate our method for yourself. We analyze supply and demand and the behavior crowds as market participants on the NYSE and the NASDAQ. The analysis is then applied to analyze major market indexes: The DOW, S&P500 and Nasdaq Composite. But we can also apply it to individual stocks and ETF's. (Contact us below for more information).
You’ll find our analysis unique in the world of market tools and extremely accurate in predicting the behavior of major financial markets in the any time frame, short-, intermediate-, and long-term.
We started with a simple "what if" question: What if market participants are "herding"? In other words, small changes in market buying and selling by participants can trigger a herding effect which results in the market trends we see over time. We developed our own algorithms and quantitative analysis on fundamental market data to track market buy/sell orders and found that "herding" to be an accurate description of the behavior by financial market participants.
(See below for starter research topics on the subject)
The reason we created this new methodology or what we call "data-driven technical analysis" was to give ourselves and traders an edge in the market that did not previously exist.
Data-driven analysis avoids ‘blind spots’ in traditional chart-based technical analysis. 99.9% of traders analyze the markets for trading purposes using ‘price smoothing’ via MACD, stochastics, moving averages, Bollinger Bands, etc. but that’s not what moves markets – price tells you what happened, not what will happen.
The market moves as a result buying and selling activity. That demand (buying) and supply (selling) is driven by market participants who herd in the direction of what they perceive to be the ‘majority’. Humans participating in markets as economic agents as a group, feel ‘safe’ in crowds and follow crowds they have an affinity with. It’s basic human nature – we don’t want to be left out of the group.
As we measure and analyze market participants actions, we know what they are most likely going to do before they do it. We don’t predict price, we predict the behavior of the crowd that moves the market. We see market data anomalies, a look ‘behind the curtain’ if you will and position ourselves ahead of the winning crowd – bull or bear.
WHO CAN USE OUR DATA-DRIVEN APPROACH?
Our unique method of market analysis is for professional traders, investment advisors, hedge funds, institutional traders, family offices, prop traders and retail traders who want to dramatically improve their bottom line investment and trading results.
HOW CAN YOU BENEFIT?
We provide our data-driven technical analysis on the market at the end of each trading day to our clients for the major market indexes: DOW, S&P500 and Nasdaq. It is available after the market closes each day to provide actionable insights into herding activity (crowd behavior) in the market. The correlations, data relationships and formulas to extract the signals have been honed over the past 25 years, so this is not a 'new' tool, but the methods we use to analyze them have constantly improved as our understanding of market participants behavior evolved. The original developer of the method and the CEO of Strong Market LLC, discovered in 1992 that there was a high, positive correlation between certain market data and daily closing prices of the DOW Industrial Average and the S&P500. This correlations and the anomolies they create provide key insights into buying and selling activity in the market which were attributed to the behavior of the self-interest of crowds that are herding.
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STARTING RESEARCH ON HERDING IN FINANCIAL MARKETS
"Some followers of the technical analysis school of investing see the herding behavior of investors as an example of extreme market sentiment. The academic study of behavioral finance has identified herding in the collective irrationality of investors, particularly the work of Nobel laureates Vernon L. Smith, Amos Tversky, Daniel Kahneman, and Robert Shiller.[a] [emphasis added]. Hey and Morone (2004) analyzed a model of herd behavior in a market context. Their work is related to at least two important strands of literature. The first of these strands is that on herd behavior in a non-market context. The seminal references are Banerjee (1992) and Bikhchandani, Hirshleifer and Welch (1992), both of which showed that herd behavior may result from private information not publicly shared. More specifically, both of these papers showed that individuals, acting sequentially on the basis of private information and public knowledge about the behavior of others, may end up choosing the socially undesirable option. The second of the strands of literature motivating this paper is that of information aggregation in market contexts. A very early reference is the classic paper by Grossman and Stiglitz (1976) that showed that uninformed traders in a market context can become informed through the price in such a way that private information is aggregated correctly and efficiently. In this strand of the literature, the most commonly used empirical methodologies to test for herding toward the average, are the works of Christie and Huang (1995) and Chang, Cheng and Khorana (2000). Overall, it was shown that it is possible to observe herd-type behavior in a market context... this has important consequences for a whole range of real markets." [emphasis added] (SOURCE: https://en.wikipedia.org/wiki/Herd_behavior)
"The herding measure essentially tests whether the observed distribution of Pit is fat tailed relative to the expected distribution under the null hypothesis that trading decisions are independent and conditional on the overall observed level of buying... (1) The latter term in this measure... accounts for the fact that we expect to observe more variation in the proportion of buys in stocks with few trades (see Lakonishok et al., 1992, for details). If small trades are independent, the herding measure will have a mean of zero. We calculate the mean herding measure in each month from January 1983 through December 2000 for both large and small trades. For small trades [$5000 or less], the mean herding measure is 7% and is positive in 214 out of 216 months. This measure of herding is in the same ballpark as the monthly herding measures of 6.8% to 12.8% estimated for individual investors by Barber, Odean, and Zhu (forthcoming). For large trades [$50,000 or more], the mean herding measure we estimate is 10% and is positive in 196 out of 216 months.[emphasis added]" (SOURCE: https://faculty.haas.berkeley.edu/odean/Papers%20current%20versions/DoRetailTradesMoveMarkets_RFS_2009.pdf?abstract_id=869827)
"The term herd behavior as it applies to humans first appears in Dr. Wilfred Trotter's 1914 book Instincts of the Herd in Peace and War. It wasn't exactly a new idea, though Trotter can be credited with the phrase. Sigmund Freud, for instance, extensively discusses his ideas of crowd psychology, and Carl Jung suggests that such psychology is the result of universal or collective unconscious...
...You may see many examples of herd behavior in economics. For instance, if a few people begin to sell a certain type of stock, it may lead to a mass selling spree, panic, and leave the market open to crashing. [emphasis added] Similarly, you might look at the behavior in the retail environment on day after Thanksgiving sales (known as Black Friday). People have been injured in attempting to get to a special item offered at a very good price, when the doors of a store opens and the crowd stampedes in. Such stampedes have also occurred at rock concerts with open seating, where all people try to rush to get the closest seats to the front. Remember Tickle me Elmo or Beanie Babies, people were stealing, waiting in line and fighting over these items. These have occasionally had tragic results. Other areas that this plays in is Gangs, people join and belong to Gangs for a sense of being, belonging, safety in numbers, to have power, feel secure and safe in their area. Teenagers and school kids will smoke to be cool or accepted, they will join band or sports or some group that enable them to belong or to be with the same type of people, where they connect or feel safe. Look at sporting events where crowds of people yell and cheer for their teams, they get protective, where the same colors and dislike and opposite team. As the crowd cheers or boo's the fans join in and mod up. In riots, people get caught up in the violence and excitement and chaotic behavior and then that attitude continues to grow and progress and soon you have a stampede.One aspect of herd behavior that is often noted is that the herd is not completely interested in protection of the group. Instead self-interest (self-preservation) is a primary motivator. Herd animals, when they fear a predator, work to get into the center of the herd so they are less vulnerable and safer. Just as people have only self-interest in mind when they knock over others to get to a cheaply sold item, or the front seats of a rock concert; or even more so when they start selling or purchasing stocks to either make a profit or make an investment that will prove profitable in the very near future...
...Such things as housing prices can be determined by herd behavior and may be augmented by reports. In 2007, the Santa Rosa, California Press Democrat featured an angry letter to the editor asking them not to write anything else on the declines in the housing market. The writer was concerned that continued reports were driving the price of his own house down; in other words, he feared the herd instincts of others who would panic and try to sell before home prices dropped more, which would only lead to a drop in home prices and a flooded market. I say this a lot when I tell people that work with horses that when you try and prevent something from happening, you end up causing it to happen." (SOURCE: http://www.thinklikeahorse.org/index-33.html)
Read more about herding in financial markets (Google search - opens new window)
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