Chaos theory has numerous application including helping explain phenomena or helping to predict the future. Chaos theory is applicable in various fields ranging from weather, business to medicine. Chaos theory explains the reason why it is practically improbable to predict the weather with the current technology as well as providing a way for people to find patterns in the chaotic system of stock exchange. It also helps with the running of organisation by showing what sort of condition is needed for a profitable business as well as helping doctors predict when heart failure may occur. Fractals which is a concept of chaos theory also is portrayed in the natural world in examples such as lightning and neurons in the brains. Chaos theory has …show more content…
However, the long-term future cannot be predicted due to the same reasons as weather can only be predicted only three weeks into the future. The stock market is a nonlinear dynamical system as it contains positive and negative feedback. Positive feedback such as when you make a profit after investing in the stock market causes people to again invest money into the stock market leading to more buying which raises price. Highly complex systems are not always chaotic instead they will behave predictably for a certain period and then seemingly randomly ill shift into chaotic behaviour. These types of systems can be mapped using simple chaotic systems which often exhibit patterns called strange attractors which demonstrate the system jumping into different modes of behaviour. The chaos in stock markets are caused due to the human psychology of trading which is never completely rational due to many outside factors. By analysing the statistical data, it is possible to find fractal which are infinitely complex patterns that are self-similar across different scales. These fractals are created by repeating straightforward process over and over in an ongoing loop and due to the simplicity of the fractals they can be used to predict the short-term future. The long-term prediction is practically impossible just like weather due to similar reasons as well. The butterfly effect means that variables that seemingly have a very minute effect on the overall outcome of the stock market slowly have an increased amount of effect in the outcome. Therefore, the short-term future of the stock market can be predicted using the Lorenz attractors and fractals however the lack of information causes long term predictions to be practically
The Scientific Context of the Word Chaos In a scientific context, the word chaos has a slightly different
Lord of the Flies is an intriguing novel about a group of English boys who are stranded on a remote island during World War II after their plane was shot down. The schoolboys quickly use the resources they find and create a temporary form of order. As they continue to stay on the island, their proper English ways quickly turn into savage like instincts. In William Golding’s, Lord of the Flies, Golding uses the conch, the Beast, leadership, murder, and fire to show that without rules there is chaos.
A complex adaptive system is entity of networks and connections. It can “learn and adapt to change over time” which can change the “structure of the system” (Clancy, Effken, Pesut, 2008). It contains twelve elements: autopoesis or self-regenerization, open exchange, participation in networks, fractals, phase transition between order and chaos, search for fitness peaks, nonlinear dynamics, sensitive dependence, attractors that limit growth, strange attractors of emergence...
Indirectly through out his novel, Camus compares people who rely too much on their logic and rationality, versus those who accept that our world is confusing and unpredictable. Similar to his thinking, in “Crickets, Bats, Cats and Chaos” Lewis Thomas suggests that chaos stimulates the brain and actually suggests that even crickets or cats have thoughts during chaotic or unpredictable situations. Even though I have always seen chaos as a total lack of order, a desperate situation in which an individual loses control, Thomas gave me a new concept for chaos. He says that it emerges when a system is altered by a small change or small uncertainty in its interior; chaos is then the
It is often said that perception outweighs reality and that is often the view of the stock market. News that a certain stock may be on the rise can set off a buying spree, while a tip that one may be on decline might entice people to sell. The fact that no one really knows what is going to happen one way or the other is inconsequential. John Kenneth Galbraith uses the concept of speculation as a major theme in his book The Great Crash 1929. Galbraith’s portrayal of the market before the crash focuses largely on massive speculation of overvalued stocks which were inevitably going to topple and take the wealth of the shareholders down with it. After all, the prices could not continue to go up forever. Widespread speculation was no doubt a major player in the crash, but many other factors were in play as well. While the speculation argument has some merit, the reasons for the collapse and its lasting effects had many moving parts that cannot be explained so simply.
According to Edward Lorenz (founder of the Chaos Theory) chaos is the science of surprises, of the nonlinear and the unpredictable. It teaches us to expect the unexpected. Chaos Theory deals with nonlinear things that are effectively impossible to predict or control, like turbulence, weather, the stock market, our brain states, and so on. (Lorenz 1995: 187)
The development of the Chaos began with a computer and mathematic problems of random data that can calculate and predict patterns that repeat themselves. For example, it picks up the pattern of a person’s heart beat and the pattern of snowflakes hitting the ground. Researchers have found that the patterns may be viewed as “unstable”, “random” and “disorderly” they tend to mimic zig-zags, lightning bolts or electrical currents. This theory has not only been used by physicist, but has also been used by astronomers, mathematicians, biologists, and computer scientists. The Chaos Theory can be applied to predict air turbulence, weather and other underlying parts of nature that is not easily understood (Fiero, p.
Even more difficult than defining art is coming to an agreement of what constitutes art. Along this extensive history of debate came the consideration of whether film is art. Films were not considered an art form and had not been seriously debated until film theorist Rudolf Arnheim challenged what art could be with his theory. Arnheim, who claims that the more a film differs from reality the more it should be considered art, would certainly argue that a film like Black Swan (Aronofsky, 2010) is art in that it significantly displaces the viewer from their lived reality. He rejects the “assertion that film is nothing but the feeble mechanical reproduction of real life” (“Film Theory and Criticism” 228), instead postulating that human perspective and choices should be involved in the process of making a film to meaningfully shape elements of our lived experience. In Black Swan, director Darren Aronofsky uses multiple tools and aspects of the medium of film to create a surreal narrative. The film Black Swan qualifies as art by Rudolf Arnheim’s standards because of the ways that the viewers’ experience of the film differs from our experience of reality.
In Heart of Darkness, by Joseph Conrad, the strongest conflict is an internal conflict that is most prominently shown in Marlow and Kurtz. This conflict is the struggle between their image of themselves as civilized human beings and the ease of abandoning their morality once they leave society. This inability has a close resemblance to the chaos theory. This is shown through the contrast of Kurtz as told by others and the actuality of him and through the progression of Marlow's character throughout Heart of Darkness.
Looking at the world where we live everything in someway is connected. Our world is not simple and in fact consists of multiple complex systems. Some everyday examples of complex systems are the brain, immune system, insect colonies, and even social networks such as Facebook and twitter. So what exactly do all these have in common in order to be a complex system? First is the fact that each one has a large amount of simple components that work together by communication through signals without being under leadership. But not all systems are exactly the same so we can break it down further into chaotic systems, complex adaptive systems, and nonlinear systems. Chaotic systems differ in that they are non-linear and are sensitive to initial conditions. Therefore any uncertainty in the system will not produce an outcome that can be predicted later on. A good example of a chaotic system would be the stock market because the prediction of its outcome is unknown due to its sensitivity to initial conditions. Complex adaptive systems are just like they sound. They are capable of adapting to the environment such as the immune system. It’s white blood cells work together to recognize foreign bodies and create antibodies for future encounters.
The efficient market hypothesis has been one of the main topics of academic finance research. The efficient market hypotheses also know as the joint hypothesis problem, asserts that financial markets lack solid hard information in making decisions. Efficient market hypothesis claims it is impossible to beat the market because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information . According to efficient market hypothesis stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments . In reality once cannot always achieve returns in excess of average market return on a risk-adjusted basis. They have been numerous arguments against the efficient market hypothesis. Some researches point out the fact financial theories are subjective, in other words they are ideas that try to explain how markets work and behave.
1. What is the difference between a. INTRODUCTION The efficient market, as one of the pillars of neoclassical finance, asserts that financial markets are efficient in information. The efficient market hypothesis suggests that there is no trading system based on currently available information that could be expected to generate excess risk-adjusted returns consistently, as this information is already reflected in current prices.
Wessels, R.D (2005) "Stock Market Predictability" [Online] Available On: http://www.indexinvestor.co.za/index_files/MyFiles/StockMarketPredictability.pdf [Accessed on 5 december, 2011].
Chapter 11 closes our discussion with several insights into the efficient market theory. There have been many attempts to discredit the random walk theory, but none of the theories hold against empirical evidence. Any pattern that is noticed by investors will disappear as investors try to exploit it and the valuation methods of growth rate are far too difficult to predict. As we said before the random walk concludes that no patterns exist in the market, pricing is accurate and all information available is already incorporated into the stock price. Therefore the market is efficient. Even if errors do occur in short-run pricing, they will correct themselves in the long run. The random walk suggest that short-term prices cannot be predicted and to buy stocks for the long run. Malkiel concludes the best way to consistently be profitable is to buy and hold a broad based market index fund. As the market rises so will the investors returns since historically the market continues to rise as a whole.
Fractals are a geometric pattern that are repeat over and over again to produce irregular shapes and surfaces that cannot be classical geometry. It is also, an innovative division of geometry and art. Conceivably, this is the grounds for why most people are familiar with fractals only as attractive pictures functional as backdrop on the PC screen or unique postcard design. But what are they really? Most physical structures of nature and lots of human artifacts are not normal geometric shapes of the typical geometry resulting from Euclid. Fractal geometry proposes almost limitless ways of depicting, evaluating, and predicting these natural occurrences. But is it possible to characterize the entire world using mathematical equations? This article describes how the two most well-known fractals were fashioned and explains the most significant fractal properties, which make fractals helpful for different domains of science. Fractals are self-similarity and non-integer dimension, which are two of the most significant properties. What does self-similarity imply? If you look methodically at a fern leaf, you will become aware that every small leaf has the identical shape as the whole fern leaf. You can conclude that the fern leaf is self-similar. The same is with fractals: you can magnetize then as many times as you like and after each time you will still see the same shape. The non-integer dimension is more complicated to explain. Classical geometry involves objects of integer dimensions: points, lines and curves, plane figures, solids. However, many natural occurrences are better explained using a dimension amid two whole numbers. So while a non-curving straight line has a component of one, a fractal curve will obtain a dimension between...