Basic Models of Complex Systems

Crux of the Duplex Method
plus Case Study
of the Dow Stock Index

 

We live in a world full of complex and chaotic systems. A good example concerns the stock market that stymies all manner of investors ranging from casual amateurs to gung-ho professionals.

According to the Efficient Market Hypothesis, the current price always reflects the totality of information available to the investing public. As a byproduct, no one can detect any clues for predicting the market in a trusty fashion.

Instead, the market is deemed to move in an utterly erratic way. In particular, a popular myth known as the Random Walk shuffle contends that the price level shifts with equal likelihood and to similar extent in either direction, whether to the upside or downside.

At first glance, the image of pure randomness does ring true in practice. For instance, the average investor is unable to beat the market averages such as the Dow Jones index. While the lack of success may seem like a letdown, the truth is even worse. In actuality, the participants in the aggregate lag comfortably behind the benchmarks of the bourse.

If we look more closely, the lousy performance of the actors springs mostly from their frantic efforts to beat the competition. Amid the frenzy, the demons of greed and fear prod the antsy players into making impulsive moves that are not only groundless and futile but actually counterproductive and harmful to their cause.

On the bright side, though, the market displays a smattering of patterns that can be exploited by a sober person. An example concerns the seasonal cycle behind the monthly moves of the Dow benchmark.

To fathom the elusive waves in a stringent fashion, we turn to the duplex method of modeling shifty systems. The sturdy framework makes use of the binomial test: the simplest and strongest, as well as safest and surest, way to profile chancy events regardless of the domain.

To this end, we first transform the conceptual models of the stock market into a trio of precise templates. The formal blueprints are then converted into R code: the top choice of programming language and software platform for statistical workouts. The trenchant results serve to debunk the fable of efficiency and confirm the existence of hardy patterns in the marketplace.

In short, the benefits of the seasonal model lie in simplicity and potency in sundry forms. The drawcards include the ease of acquiring the information required, the leanness of the dataset employed, the ubiquity of the software deployed, the universality of the experimental setup, and the strength of the conclusions at high levels of statistical significance.  

NOTE:  The full report is titled, “Basic Models of Complex Systems”. The document may be downloaded in PDF form at Smashwords or ResearchGate. Moreover, a digest of the report is available as a video at YouTube.

 

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Duplex Models of Complex Systems

Binomial Framework and Case Study 

of 

Seasonal Waves in the Stock Market 

Duplex models can portray complex systems with the utmost of simplicity, clarity and efficacy. The drawcards range from the dearth of initial premises to the soundness of final conclusions. The mettle of the binomial approach shows up, for instance, in debunking the welter of myths and misconceptions that pervades the fields of finance and economics. According to the Efficient Market Hypothesis, the marketplace always reflects the totality of information available to the general public. Since every nub of know-what and know-how informs the latest prices, no single actor can improve on the valuation of assets ranging from stocks and bonds to commodities and realties. 

One consequence is the lack of trusty cues for forecasting the market: if every clue has been fully utilized, then any move henceforth has to come as a complete surprise. Another fallout lies in the Random Walk Model that pictures the path of the market as a form of Brownian motion whereby the price level is wont to shift in any direction with equal likelihood. 

Unfortunately, the Efficient credo abounds with flaws ranging from unreal assumptions and spurious concepts to inconsistent models and faulty conclusions. A counterpoint involves the wave motion of the stock market that belies the premise of utter randomness. As a recourse, a true science ought to build on hard data and staunch precepts, rigorous models and tenable results. To this end, the study at hand represents a small but fundamental step toward a coherent theory of the marketplace. 

To underscore the gulf between the mythos and reality, the work plan takes a minimalist approach. For starters, the inquest draws only on a minute fraction of the trove of information freely available at the most popular portal among the investing public. Moreover, the quantitative analysis relies solely on the simplest technique in statistical testing. From a computational stance, the attendant program invokes a skimpy subset of the built-in functions within the core module of the R system: the leading choice of programming language and software platform for data science in disparate domains. 

NOTE:  The ebook is available under the title of “Duplex Models of Complex Systems”. The document in PDF form may be downloaded from the Internet Archive or at ResearchGate. In addition, the title is distributed in EPUB format by Apple Books and other partners of Books2Read.

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A Lodestar for Complex Systems

Joint Program in Real and Financial Markets

by

Kenwave Research plus MintKit Institute

Following its debut in 2011, the MintKit Institute has pursued a wide-ranging program of research on vital issues in finance and economics. The studies have unveiled the true nature of the marketplace and refined basic strategies for ample growth at modest risk. The topics in focus range from beguiling myths and rampant mistakes in the financial forum to secular trends and public policies in the real economy.

In this endeavor, MintKit is now joined by a kindred spirit in the form of Kenwave Research. The mission of the latter is to develop supple tools for decision making in complex fields. For this purpose, the core techniques span the rainbow from multivariate models and robust statistics to neural networks and genetic algorithms. Moreover, an integrated approach that combines two or more methods can yield synergetic results that enhance the strengths of elemental schemes while bypassing their respective flaws.

Hybrid methods of this sort can excel in knotty domains rife with chaotic structures and erratic events. The applications of the methodology range from scientific discovery, medical diagnosis, and business strategy to financial forecasting, socioeconomic planning, and public policy.

The programs at MintKit and Kenwave display some similarities as well as differences. An example of a shared trait lies in the systematic approach to probing cryptic systems and making deft decisions at the frontiers of innovation and enterprise. 

A related hallmark resides in the dual strategy of plumbing the innate nature of murky domains while forging trenchant solutions to tricky problems. In particular, a descriptive portrait of a mazy system captures the pith of the subject despite the mantle of myths and misconceptions that confounds the world at large. Meanwhile, a prescriptive template corrals the findings and provides the groundwork for wholesome action. 

From the converse stance, MintKit and Kenwave differ in crucial ways. A key distinction concerns the objectives of the research agenda. To wit, MinKit pursues a spectrum of applications geared toward bracing growth in a global marketplace along with salient functions such as financial forecasting and public policy. By contrast, Kenwave assumes a technical slant keyed to pliant tools for complex tasks regardless of the domain. 

Despite their separate charters, however, the two parties share a common interest in the area of decision making in financial economics; that is, the crossroads of dicey markets and stringent methods. The intersection of ambits affords plenty of opportunities for collaboration. The promising projects may be classified into two broad types: factual knowledge to capture the marrow of the marketplace as well as mantic modeling to pave the way for cogent action. 

Moreover, the shared interest in finance and economics represents the mainstay of the research program at Kenwave in the early stages. The role of the newcomer centers on quantitative studies to assess the qualitative models developed at MintKit. To this end, Kenwave draws on a medley of extant and newborn techniques in data science in areas ranging from protean graphics and nonparametric statistics to causal modeling and machine learning.

To round up, the partnership between MintKit and Kenwave entails a series of creative projects dealing with the real and financial markets. The case studies make use of ductile tools to fathom abstruse systems abounding in chaos and complexity. The resulting harvest of insights and guidelines provides the fodder for passive frameworks as well as active templates to bolster decision making in diverse domains. 

The audience for the joint studies includes the readers of the reports prepared by MintKit Institute as well as the users of the software crafted by Kenwave Research. The interaction of the research hubs renders a bounty of rewards to both parties. The ultimate beneficiary is the global community of stakeholders that partakes of the results.

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