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In the last post the conceptual framework for seeing some bits of order in apparent disorder. This post will focus on the sources of disorder in the stock market. Many of the concepts will apply readily to financial markets of all types. What you should take home from this post is that there is no clear-cut way to determine the value of a stock, and even if there was, there are plenty of reasons why it can't be traded at that value consistently. However, a greater question is, can a stock trade at any price? This post will answer this question with a degree of certainty that seasoned traders and new traders alike may find surprising.
To start, let's imagine a market established solely to determine the value of shares while paying no attention to whether they can be bought or sold in volume at that price. Looking back at the relationship between fundamental analysis and technical analysis, since fundamental analysis is the only avenue for information to enter the market through trading while technical analysis is effectively blind without some fundamental analysis or an existing trading pattern, it's clear that fundamental analysis is the only technique that would be able to determine the value of shares in the absence of trading and market data. Therefore we need to look more closely at what fundamental analysis is and where it loses traction in order to find out why stocks seem to trade so erratically sometimes.
The goal of the fundamental analyst should be to determine the price of the stock based on equity per share and future earnings. Equity per share is just an acknowledgment that the corporation does indeed have assets, cash and non-cash, that if liquidated will be worth an approximate value per share, effectively establishing a certain amount of "hard" value to the share. That is, the share is representative to a certain extent of at least that amount of real assets.
The value of future earnings is much trickier. For one, the value one should pay for earnings that have yet to be booked isn't well agreed upon. Good measures are usually derived from other more stable investments such as CD's and bonds. The value of potential earnings can be benchmarked against relatively guaranteed earnings and using various approximations for risk, the price one is willing to pay for future earnings can be obtained.
However, analysts don't always agree about their earnings predictions, growth rates, and the equity in a corporation. Depending on their models, the data their using, how they interpret it, their instincts through experience and hunches, and even whether or not the investment bank they work for is trying to get a banking deal with the corporation, their earnings estimates and target prices will differ. Different analysts will apply different growth models and will make approximations using different methods. This alone is enough to tell us that the problem of establishing the price a stock should trade at is not straightforward.
Two issues are responsible for most of the difficulty in calculating future earnings, which is a big part of the value per share. The first is that earnings is the difference between revenue and expenses per share. If revenue and expenses are relatively large compared to their difference, then relatively minor fluctuations in either revenue or expenses can produce huge percentage differences in the earnings. This is a condition known as low-margin, and it just means that there is little separation between revenue and expenses. Wall street likes the sound of high-margin because it means that earnings are much less sensitive to minor fluctuations in revenue and costs.
The second challenge to accurately predicting future earnings is the growth rate of the market or markets the corporation does business in. Growth has the most dramatic effect on long-term profitability. If one analyst predicts 5% earnings growth year-on-year and another predicts 10% growth, at the end of ten years the first analyst will expect 63% higher earnings while the second will be looking for 159% higher earnings. Clearly, in any situation where growth is exponential on a long time-scale, minute differences in the estimation of growth can lead to very large divergences in the valuation of a stock by different analysts.
This situation can be exacerbated even further if the net worth of the corporation is small compared to the expected value of future earnings. In this case, the future earnings are the dominant component of the value of the stock, and differences in the predictions of future earnings will create a high degree of uncertainty in the price of the stock.
This illustration shows the assets and liabilities, both hard items on a corporation's balance sheet, as metal coins since the value is very real. The revenue and expenditures are represented by the paper bills. The change in value of the corporation is a result of all of these numbers, and is usually small in comparison. From this chart, it's easy to see how minor fluctuations in these large numbers will produce large percentage changes in profit, causing profit and growth forecasts to vary greatly.
The big picture is starting to take shape, and it's obvious that fundamental analysis has a lot of room for error, particularly in situations where there a corporation has a small amount of assets relative to the size of its expected earnings or where the profit margin is tiny and very susceptible to minor fluctuations in revenue or expenses. What this tells us is that even in an imaginary stock market where the price is set solely by fundamental analysis, there is no perfect price to trade at, leading to the conclusion that fundamentals cannot be the end-all be-all of stock valuations.
This moves us to a second question of interest: Can a stock even trade at a theoretical best price if it exists? To begin answering this, take not that equity markets were originally conceived to facilitate capitalization and to alleviate supply-demand imbalances. Corporations obtain capital through Initial Public Offerings and other secondary offerings, and the market is where they go to sell those shares. Also, traders occasionally wish to move their assets and the market is what provides the medium of liquidity so that they can buy and sell when they need to. Managing supply and demand is a fundamental role of any market.
First to answer the question, supply-demand imbalances exist because not everyone wants to buy or sell at the same time even if they do know what price they would buy or sell at. To make this very clear, let's imagine that Leeroy Jenkens is a day trader worth millions of dollars and also likes to play World of Warcraft. One day Leeroy gets distracted while playing and realizes he's missing out a move he expects from BEBE. He hurriedly enters in an order, but due to carpal tunnel and the rush of the moment, accidentally enters in a few extra zeroes on the number of shares of his market order. Because he has enough cash on hand, the order goes live and wipes out all of the sell orders on the market, eating into higher and higher priced orders up to a theoretical infinity until other computer trading systems quickly catch on and cash in on poor Leeroy. This wouldn't likely happen in real life due to market makers managing such orders to give liquidity a hand, but the point is clear. If someone is buying or selling and has unlimited funds, anything is possible. Clearly the potential exists for localized supply imbalances, whatever the cause, to drive the price far from any expected best price.
To begin illustrating the extent to which these imbalances can affect the trading price, let's consider a different example. A village is visited by two trucks every six months. One is full of bread and the other is full of sneakers. At the day of their arrival, there is simply too much of both bread and sneakers for all of it to be sold. Being the clever merchant, you would look at the cheap prices and realize the opportunity for profit in the future when the demand recovered. However, you wouldn't be willing to accept the same discount on both products. Bread is a staple. It's necessity is guaranteed, so there is a high likelihood that the price will recover. Sneakers are discretionary, so there is a chance that people won't be willing to spare enough income for the prices to fully recover. By the time the price would have recovered, they may be out of style. You would only accept a large discount on the sneakers in order to reduce your exposure to risk. With bread, the price difference quickly becomes like arbitrage, where you trade an asset at a better price on another market. In this case, it's more like time-arbitrage. You just wait for the market to recover. However, with sneakers, the price changes involve more risk, which means it takes a deeper discount for the discount to translate to a sure profit. It takes longer for sneakers to become arbitrage-like than bread.
Turning back to the inaccuracy of fundamental analysis, the role of certainty of fundamentals in determining the sensitivity of the supply-demand relationship to price fluctuations can be made clear. A corporation whose earnings have more potential to fluctuate wildly and has a small amount of hard assets per share will require deeper discounts to attract more dollars. Every buy or sell order will require a large relative price change to succeed in executing a trade. Corporations with lots of hard assets, consistent earnings, and high margins will become arbitrage-like very quickly. In short, the uncertainty of fundamental analysis has a dominant effect on the supply-demand relationship.
The big picture is nearly finished. Not only is there no exact best price, but even if there was a theoretical best price, due to supply-demand imbalances this price can't always be traded at. Furthermore, the potential for valuation to fluctuate due to uncertainty in earnings has a direct effect on how powerful supply-demand imbalances will be in determining what price shares ultimately change hands at.
The last question is, does the theoretical best price stay constant over time? To answer this, we can simply look at what drives fundamental analysis. Existing and emerging financial data, news breaks with relevant impact on expectations, and insider information etc are all used to create the fundamental analysts estimates. In the case of existing data, it exists and won't change over time. This would form a static picture of the stock's valuation. However, the fact that there is emerging financial news will produce changes in this picture, making it dynamic as new data or news emerges.
To integrate this all into a single model, turn back to the idea that all information enters the market via fundamental analysis of some sort, be it insider information or SEC filings becoming expressed in expectations, followed by trading activity. We would expect that due to variations in expectations, that there is in fact no best price, but instead a range of plausible prices, with fundamentals creating a containment effect whereby supply-demand imbalances are evened out whenever the trading price becomes a less plausible valuation, attracting more counteracting trades. Near the edges of the range of plausible prices, supply-demand imbalances will be corrected more quickly. In regions where prices changes all result in plausible valuations, there is little sensitivity to price changes and other mechanisms are dominant. As new information becomes available one way or another, the range of plausible valuations is changed, giving us new locations to expect resistance to supply-demand imbalances to exist.
To visualize this, recall the cigarette diagrams. While turbulent smoke can take many paths, the underlying physics always act to ensure that the path that any particular smoke plume takes is going to fall within a certain range of likelihood. Within this range, the particular shape is more dominated by chaotic effects. If the cigarette was inside a region of slowly churning air, the range the smoke would exist in would so migrate, taking the smoke with it. This should start to seem very familiar to any seasoned trader. The range of likelihoods of smoke is analogous to the range of plausible valuations. The turbulent path within this range is analogous to the trading price as it ultimately exists at any given time. The migration of the smoke plume caused by tiny winds is analogous to the changes in plausible valuations that occurs as new information becomes available.
Turning back to the million dollar question of whether a stock can trade at any price, it's already been made clear that extreme supply-demand imbalances can exist and can severely impact the stock price one way or another. Upon first inspection, this would seem to suggest that it is in fact possible for any price to exist at any time. Internet stocks and the recently failed bank stocks are two examples where valuations can seem to indeed demonstrate that any price is possible. However, lets look at what the model says about these two situations. With internet stocks, they had low assets, high expected earnings growth, and little or no current earnings, all factors which increased the uncertainty in valuations and thereby decreased the response to supply-demand imbalances. Thus we saw the stocks trading at very generous valuations even if they were to ultimately fail. The potential earnings were so high that there was little to rein in the price at the top end. In the case of failed banks, it could be said that the banks had low volatility in earnings predictions leading up to the collapse of the stock price, but they also were affected considerably by emerging news. Lehman Brother announcing bankruptcy was a major change to the set of existing data, which facilitated a huge move in the stock price. What we begin to see in these examples is that, while at first glance almost any stock price does seem possible, extreme valuations and changes to valuations will only occur in the presence of large uncertainty in fundamentals or when those fundamentals themselves have been greatly modified by new data. What this says is that, while any stock price can be possible over time, at any one given time, only certain stock prices are likely. In short, $60 Lehman would never have traded at $2. It was emerging news that facilitated this price. It can and did trade that way over time, but these valuations are not plausible for every stock at any time.
The keen scholar would say that the potential for supply-demand imbalances to move a stock to any price would negate the assertion that there is such a thing as a trading range. However, consider a different field of study that can and does tolerate such local variance. Quantum physics, in stark contrast to classical physics, only requires that things are conserved on the average. Essentially, there is a finite chance that particles can appear out of absolute total nothing, but the chance is small and, on average, the balance of particles just randomly appearing and disappearing out of thin nothing is zero. To get a better idea of why this doesn't break the model, consider the recent popular movie, The Watchmen, in which a main character, Dr. Manhattan, declares he longs to see oxygen turn into gold. While theoretically possible with a finite chance that is about as close to zero as it can get, Dr. Manhatten should be disappointed to find out that his one atom of gold, while worth nearly $1000 per ounce, is almost worthless. To rephrase this, likely events will happen with great effect and are very tradable while small variances are not tradable and will happen with fleeting effect on the larger market.
At this point we need to take a brief detour from our charge into the market to get some very helpful conceptual background. While the concepts are rooted in mathematical rigor, some nice drawings have been prepared to present them in a more elegant form. What you need to take home from this post is that chaos is anything but random. The limited determinism that exists for any chaotic system is a very meaningful and powerful tool, one we'll be using extensively and should be studied closely.
There are numerous analogies for chaotic systems. Paul Ormerod liked to use ant models in his book, Butterfly Economics. However, a more telling example in the context of chaos in the stock market is the cigarette smoke diagram. It is the canonical example given to undergraduate students of fluid dynamics to describe the change between smooth, predictable laminar flow to irregular, chaotic turbulent flow. Cigarette smoke is an analogy that is quite extensible into the study of the stock market and therefore will be used throughout this blog to describe chaotic phenomenon in hands-on language.
Click to Enlarge Image Cigarette smoke rising off of the burning tip will rise because it is hotter than the surrounding air. As it speeds up, it grows unstable and begins to break down into turbulent flow. The initial region of instability is periodic flow, meaning it just wobbles back and forth over and over in a very repetitive, predictable fashion. The actual point where the turbulent flow begins is distinct. You can try watching a few cigarettes or incense etc for yourself to gain firsthand familiarity. (Must observe in very still air)
The key things to note here are that both the laminar region and the periodic region are very easy to model and predict, and the model will be able to predict tiny features at any point in the smoke with a relatively high degree of accuracy. However, the turbulent region is different. It will begin with very subtle perturbations that suddenly cause the smoke to change remarkably in its flow characteristics and behavior. The resulting pattern is actually non-random. All of the physics of the fluid are still intact. Everything behaves exactly as we understand it, it's just that the solution is based on so much data that we can't possibly collect enough to model it.
To illustrate some other key aspects of turbulence, the diagram includes two other illustrative (and quite suppositious) features. Imagining we could identify the region where the turbulence would begin to develop two seconds from now, we would perplexingly find nothing of great interest. In fact, it would be probably impossible to distinguish the smoke that would cause turbulence from the smoke that wouldn't. This is because chaotic behavior is the culmination of tiny, tiny features getting conserved, propagated, and amplified so that when the tipping point is reached, it will be highly dependent on nearly everything. The second illustrative feature is the arrow that says "chaos starts here." While the cigarette undoubtedly plays a role in the shape of the final turbulent smoke swirling pattern, it's again impossible in practice to tell what exactly about the cigarette lead to the smoke transitioning to turbulent when and how it eventually did.
One of the easy mistakes to make here is to believe that the smoke turning to turbulence is the result of one tiny disturbance, such as a tiny perturbation in the ambiant air or an imperfection in the cigarette paper. The truth is that every single part of the picture contributes to the final result, and no single part is dominant. No matter which part of the picture you change, be it a tiny tear in the cigarette paper or a slight twitch in the surrounding air a foot away, the resulting shape of the smoke will be different and could even be unrecognizable.
If you're asking yourself what isn't random about this extreme sensitivity to initial conditions, think of observing a large number of cigarettes. Although Phillip Morris may have you believe that every single cigarette will produce an equally unique and interesting smoke pattern, the truth is that they will get less and less unique as you keep making observations. Each smoke plume will fall within roughly the same area, and if you could measure this over a large enough cigarettes, you could predict with a high degree of certainty the maximum growth rate of the turbulent smoke.
It turns out that this characteristic of turbulent smoke is a very well understood concept known as a boundary value problem. Although every cigarette will be unique, they will all tend to fall within the same range over time, and what this essentially says is that there is a lack of uniqueness with respect to the range that the turbulent smoke will occupy. Lack of uniqueness is another way of saying determinism, which is another word for a sure thing. Limited-determinism refers to the idea that each smoke plume will be in many ways unique and in at least one way very non-unique, with larger exceptions becoming more and more rare. It's not certain what particular shape the smoke will take. Only the range that it will occupy can be defined or measured with some certainty. The applicability of this concept to the stock market should be self-foreshadowing at this point. Welcome to the chaos highway.
This illustration is simply to make clear that boundaries only tell us where the chaos should go, not where it should go within the boundaries. We'll see in the next chapter that, for the stock market, there are reasons why these boundaries are soft, but that they exist and can be used to make relatively definite predictions about the price a stock will trade at in the future in certain situations. Stay tuned.
*note there is a typo in the first cigarette smoke diagram. will get corrected at an indefinite time in the future™.
In the last entry, we were introduced to the market at its most granular level. Moving one rung up the ladder, let's turn our attention to where these orders actually come from. Even without being overly simplistic, there is really only one source of all the action. Real people in offices and on the internet all over the world enter in orders or run computer systems that do the trading for them. We still haven't invented true artificial intelligence, so the software running on these systems is inevitably the result of the careful reasoning of a human being who designed an algorithm based on what he or she thought to be the most appropriate indicators for a computer to act on.With the understanding that the market price we observe is the result of orders and orders are the result of decisions made by people, then the origin or price movements is rooted in those decisions. Stock trading has traditionally been characterized under two broad headings.
- Fundamentals - The fundamentalist aims to accurately know the "value" of a stock and capitalize on deviations from this. The basis of a strictly fundamental investor's decisions are existing and emerging financial data and models used to mathematically quantify the implications of this data to come up with the value per share. A grossly simplified example of fundamental analysis is the simple value plus twenty times earnings rule. Real models will include lots of projections regarding future product demand and market growth, and they will also be designed for fast modification to obtain the new projections quickly as more data is made available. The methods employed are up to the analyst, but the common attribute of all fundamental analysis is that it's rooted in hard numbers and rigorous business forecasting.
- Technical Analysis - The technical trader tends to be a direct student of the stock market itself, believing that because the trading decisions are ultimately what drive the price, direct analysis of these trading decisions is in fact more telling than the fundamental analysts predictions. Different technical models and indicators are more varied than fundamental analysis techniques. There are probably dozens of simple techniques based on theories like fractal patterns or candlesticks etc, and most of them get refined to the point of tears to deal with inconsistencies with varying degrees of success. Furthermore there are huge statistical models that use data all the way back to the beginnings of the stock market. Technical indicators based on mathematical analysis of the current trading and volume pattern have entire books written about them. The list goes on. What all technical analysis has in common is the use of existing trading data and a focus on the implications of trading decisions. It's a bit like watching ants in order to find out where food is. Since the technical traders themselves become ants whenever they buy and sell, the presence of feedback is obvious.
This is a more traditional evaluation of the big schools of stock trading. Let's rewrite this a little bit to gain some new perspective:
- Primary Information Consumers - These traders and investors form their evaluation of a stock based on the emerging news and financial data. None of this information influences the market until this group of traders digests it.
- Recyclers - These traders are highly reactionary and introduce feedback into the system. By divining buy and sell signals from the result of current trading activity and the secondary reaction to the news, they both increase the overall sensitivity to the current trading pattern and introduce meaningless and purely inventive behavior to the muddy brown water.
- Cold-War Specialists - These traders seek to get farthest ahead in the feedback game, predicting and perhaps purposely triggering new cycles of feedback based on existing feedback behavior. In the process it's theorized that they will make money. This introduces a whole new layer of fantasy, as the process of making money is dependent upon buying and selling, which influences the market, which the computer models performing this kind of trading are even more highly dependent upon. At this level the market is so circular and feedback oriented that it's doubtful any of the resultant behavior has anything to do with the initial information trickling through to the final result.
The first classification is an academic one that shows the intellectual differentiation between two major schools of stock market analysis. The second classification scheme recognizes instead a food-chain of traders where each higher tier is trying to be a step ahead of the last, introducing more feedback and amplifying noise to the point that there is no meaningful signal left, a situation that believe it or not makes further technical analysis more productive.
This cartoon is a caricature of where we're at in this analysis.
Each step up the information food chain sees deeper than the last and employs more high-powered tools to analyze the information. Each layer tries to look over the shoulder of the last in order to get the jump. The take-home fact is that the only point where new information hits the market is at the financial data level. All other market movement results from the recycling of old news that has become expressed in the trading pattern, which will go on to create new cycles of feedback in the absence of any new news. These categorizations are non-exclusive. Many traders and organizations belong to several or all groups of traders. Indeed, if you read to the end of this book, you will likely belong to several groups yourself. The right tool for the right job is always the best tool.
In the illustration, we see a fundamental analyst sitting at his desk crunching away numbers and news articles to fit them into his model. The ninja looks over his shoulder while a pirate with a spyglass looks over his shoulder. A commando with binoculars looks over the pirate's shoulder and finally an astronaut in space uses the Hubble Space Telescope; everyone is looking at the same information.
Not everyone agrees to this relationship. The author of this particular article believes that "the technicals are the chicken and the fundamentals are the egg."
His argument is based on the idea of technical analysts perceiving subtle changes in the trading pattern in advance of the information becoming public knowledge. All this actually proves is that fundamentals sometimes hit the market in the form of insider information or perhaps just good detective work. In short, the distinction cannot be rightly made from fundamentals that are known to the trader and fundamentals that are known to at least one trader who then feeds the information into the market via his activities.There you have it. Everything that happens because of fundamentals is a primary information source to the market. As this information is reused over and over, it's highly susceptible to changes in initial conditions. Given the market price at a particular instant and all of the fundamental data available to all traders, it would still be impossible to predict the stock price since the market is in fact re-digesting the old decisions and the chart leading up to any particular instant may have taken a variety of shapes, leading to a variety of new feedback mechanisms.
One interesting thing to note here is that, without at least some fundamental traders present, the price will never incorporate the emerging financial news and data. Pure technical traders live in a cubicle and implicitly push the same numbers over and over again without any way to corroborate their analysis with actual financial data. It is only by the signals contributed by fundamental analysis that technical analysts can navigate. If a stock were purely dominated by technical analysis models, then it would be possible to trade at prices independent of any changes to underlying fundamentals. While fundamentals can only do so much to determine a viable stock price, take the fact that stocks don't regularly diverge from solid valuations (in situations where these can be confidently made) by orders of magnitude to indicate that there is at the very least some connection from trading price to fundamentals.
In conclusion there is a very telling and definite distinction between fundamental and technical analysis. One is the precursor to all other trading activity that may follow. The other is dominant whenever this influence is minor. Think of it like a library where only fundamental analysts bring in new books. As long as there are new books, there is plenty to keep the technical analysts busy. In the absence of new books, only knowledge in the library already will be circulated.
To establish the context of discussion let's start by looking towards the market as it exists at the most granular level, orders. Orders are entered and then executed, modified, or canceled. Every single order goes through this life cycle. Each individual active order has two essential characteristics, price and size. Price is straightforward. Size just indicates how many assets to trade. Buy orders and sell orders are all vying for shares and being adjusted to reflect the perceived supply-demand situation. If you've never watched a daily chart with level II volume in real-time, the easiest way to describe it is a rugby scrummage where all of the orders influence the supply-demand balance. Orders are made either with the goal of obtaining shares or simply pushing the pile by influencing perception, but in the end all orders will continue to move the front lines (the spread) one direction or the other. Whenever orders meet up in the middle, they are compatible and will execute. When we plot these executions against time, they show up as the spot price chart that we're familiar with. Orders continuously join and leave the pile. Some orders are bigger and, like a large rugby player, will influence the pile more than others. Orders meet in the middle and transactions occur. The whole pile shifts around as orders are adjusted or orders dry up on one side, temporarily giving way to a route until more orders are encountered at a new price. This is what trading looks like at the knife-edge of the market.
The scrummage analogy is only missing one important element. While orders do pile up at the middle and tend to push the flow of new orders and order modifications, there are always some traders that are actually looking to make transactions, and because they're always looking for the best price, they tend to act like self-conscious singles at a bar. Everyone wants to be first but nobody wants to be desperate. For this reason, the leading orders will often get canceled or shuffled to a price farther away from the action, hoping to get a better price on the shares the trader wants. The compliment to this effect is that when there are few orders on the other side, traders who need their orders to execute will often go chasing after orders, hoping to avoid the feared situation of having no trading partners. There you have it: self-conscious singles playing date rugby at a bar.
The big picture is composed by these orders tossing back and forth like grains of sand guided by shifting and turbulent wind. The grains strike the knife-edge and cause it to jitter around as it moves forward through time. We represent this on various kinds of charts, but arguably the most useful is the simple daily candle chart. Each day is a discrete unit of time, so the candles cleanly represent what has gone on for each trading day. Line charts are somewhat deceptive in that connecting the dots creates the illusion that the stock is at one price on any given time interval. This blog will go on to eventually describe many levels of information in the charts and will also analyze many of the big-picture mathematical significance, but for now study this chart briefly and take in the concept that the market is composed of granular trades. All further analysis will be based on identifying the characteristics of these trades and what that description tells us about any market.
This topic is honestly a little bit below the target level of the book, so if you need further elaboration, I suggest looking up the following terms: Level II Quotes, Depth of Market, Order Book. A simple site like investopedia should be sufficient. If not let me know and I'll develop a more thorough introduction. If you're already more than familiar with these concepts or find yourself wondering why I'm starting at such an elementary level, please bear with me as I work up to more advanced concepts.
First I'd like to welcome you to the blog and tell you a little bit about why I've decided to do it. Last weekend I had a long conversation with my friend, who is a graduate of physics at WPI, about chaos and the stock market. My experience in trading came before I had gained enough exposure to the rigorous mathematical concepts necessary to fully flesh out the peculiarities I had noticed. To make a long story short, at the end of several hours, we had assembled all of the right pieces of the puzzle so that my friend, who has a degree in physics, is now thoroughly convinced that there is in fact a level of determinism to the stock market. If you've never tried to prove something to a physics graduate, take my word for it that you can't do with smoke and mirrors.
In the end we concluded that there are a lot of important concepts that can be easily drawn from the model we developed, convincing me that I should go ahead and develop a book to explain the whole model, its limits, and how to apply it to best capitalize on the determinism that does exist, which is brings me to explaining the title. Looking at the market like a cow we'd like to convert to steak, but because of the chaotic aspects that shroud a lot of its behavior in impenetrable mystery, there are only certain cuts of its meat that can be reliably carved. Conventional wisdom says that the easiest profit opportunities are the least rewarding. Take it with a grain of salt for now, but I aim to explain in due course that the most profitable and reliable trades are in fact the easiest to identify and capitalize on.
Still, the cow we're after is highly erratic whenever these opportunities don't exist, so prudence is still necessary to avoid the more mystical cuts of the cow that, while potentially quite rewarding, cannot be rigorously described like the deterministic parts, giving us no real indication as to our likelihood of profit. In short, there are carvable cuts and uncarvable cuts of this chaotic cow. Traders, being profit driven, will inevitably attempt to carve up every piece of the cow, which often leads them into the mystical regions of the meat where determinism breaks down into randomness. Through this blog, I aim to hone the concepts and their delivery down so that the eventual book can both stand up to academic scrutiny and be accessible enough so that even retail investors can utilize the principles to identify the meat that is there for the taking without getting confused by the noise of a hungry stock market.
This illustration shows the assets and liabilities, both hard items on a corporation's balance sheet, as metal coins since the value is very real. The revenue and expenditures are represented by the paper bills. The change in value of the corporation is a result of all of these numbers, and is usually small in comparison. From this chart, it's easy to see how minor fluctuations in these large numbers will produce large percentage changes in profit, causing profit and growth forecasts to vary greatly.
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