Geometry Of Markets - Vol 1 UPD

Life itself evolves around cycles of the universe and the planetary aspects. The Moon creates a pattern of rising tides and feeding habits for marine life. Night becomes day and day becomes night, the oceans rise and fall, economic conditions expand and contract, the path of life continues to unfold in some strict relationship with the past. Since most of the natural occurrences in nature revolve and evolve around cycles and vibrations they can be accurately measured. The principles that apply in all forms of nature also apply to the markets, as these are merely a reflection of human nature and the ingenuity of man himself.

Geometry of Markets - vol 1

Without the input of a dedicated few, those who understood that markets work to a structure dictatea by their past activity, we would ever have been able to advance our knowledge in such a relatively short period of time. It is my aim here to demonstrate the validitv for the use of TIME, PRICE, PATTERN & TREND analysis of markets as a LEADING TECHNICAL INDICATOR FOR PREDICTING CHANGE OF TREND.

In doing so, I will endeavor to illustrate and explain the uses of these lesser known technical techniques, their pitfalls and wars in which they can be enhanced to give better results. To succeed in tlie volatile tradmg environment we are experiencing from todays world of rapid communication requires a most sophisticated approach. The origins of the mathematical ratios and the numbers I hold out to be important to the time and price analysis of markets are explained in the appendices. If one can grasp the significance of the origins of these ratios and numbers it will provide a base for their successful use in the future.

In mathematical finance, the SABR model is a stochastic volatility model, which attempts to capture the volatility smile in derivatives markets. The name stands for "stochastic alpha, beta, rho", referring to the parameters of the model. The SABR model is widely used by practitioners in the financial industry, especially in the interest rate derivative markets. It was developed by Patrick S. Hagan, Deep Kumar, Andrew Lesniewski, and Diana Woodward.[1]

Abstract:In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or k-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.Keywords: econophysics; financial markets; correlation filtering; minimal spanning tree; planar maximally filtered graph; topological data analysis; SGX; TAIEX

This book develops some of the extraordinary richness, beauty, and power of geometry in two and three dimensions, and the strong connection of geometry with topology. Hyperbolic geometry is the star. A strong effort has been made to convey not just denatured formal reasoning (definitions, theorems, and proofs), but a living feeling for the subject. There are many figures, examples, and exercises of varying difficulty. This book was the origin of a grand scheme developed by Thurston that is now coming to fruition. In the 1920s and 1930s the mathematics of two-dimensional spaces was formalized. It was Thurston's goal to do the same for three-dimensional spaces. To do this, he had to establish the strong connection of geometry to topology--the study of qualitative questions about geometrical structures. The author created a new set of concepts, and the expression "Thurston-type geometry" has become a commonplace. Three-Dimensional Geometry and Topology had its origins in the form of notes for a graduate course the author taught at Princeton University between 1978 and 1980. Thurston shared his notes, duplicating and sending them to whoever requested them. Eventually, the mailing list grew to more than one thousand names. The book is the culmination of two decades of research and has become the most important and influential text in the field. Its content also provided the methods needed to solve one of mathematics' oldest unsolved problems--the Poincaré Conjecture. In 2005 Thurston won the first AMS Book Prize, for Three-dimensional Geometry and Topology. The prize recognizes an outstanding research book that makes a seminal contribution to the research literature. Thurston received the Fields Medal, the mathematical equivalent of the Nobel Prize, in 1982 for the depth and originality of his contributions to mathematics. In 1979 he was awarded the Alan T. Waterman Award, which recognizes an outstanding young researcher in any field of science or engineering supported by the National Science Foundation.

A"random walk down Wall Street": The factthat stock prices behave at least approximately like a (geometric) random walkis the most striking empirical fact about financial markets. But is it orisn't it a true random walk? If it is, then stock prices are inherentlyunpredictable except in terms of long-run-average risk and return. Thebest you can hope to do is to correctly estimate the average returns andvolatilities of stocks, along with their correlations, and use these statisticsto determine efficient portfolios that achieve a desired risk-returntradeoff. You can't hope to beat the market by microanalyzing patterns instock price movements--you might as well buy-and-hold an efficient portfolio.

The debatebetween "technicians" and "random-walkers" has taken a newturn with the emergence of behavioral finance as a lively field of study. Behavioral finance studies markets from the perspective that investors are notrational in the expected-utility-maximizing sense of classical finance theory,as laboratory experiments have convincingly shown, which suggests thatpsychological theories might be helpful in explaining some of the puzzles thatare observed in markets. On this view, reading stock charts might help toanticipate the patterns that other people are likely to read into stockcharts. (The idea that markets are better predicted by psychologicalanalysis than by rational economic calculations, analogous to guessing thewinner of a beauty contest, actually dates back to John Maynard Keynes.) But on the other hand, behavioral research has also convincingly demonstratedthat people tend to misperceive random sequences as non-random--the so-called"hot hand in basketball" phenomenon. For more discussion of therandom walk hypothesis and its implications for investing, see A RandomWalk Down Wall Street by Burton Malkiel. (The latestedition was published in 2012.) (Return to top ofpage.)

Currently, manufacturers are hoping to boost revenues by introducing siliconsensor-enabled cell phones in the North American and European markets, after thehuge success of similar cell phones in the Asia-Pacific region. Overall, keymarket participants' investments in the technological development of high-endsilicon fingerprint sensors will determine the market's future growth.

World Silicon Chip Fingerprint Markets is part of the AutomaticIdentification & Security Growth Partnership Service, which includesresearch services in the following markets: World Biometrics Markets, NorthAerican Criminal AFIS markets, and World Hand Geometry Markets. All researchservices included in subscriptions provide detailed market opportunities andindustry trends evaluated following extensive interviews with marketparticipants.

The COVID-19 pandemic is one of the most severe infectious diseases in recent decades, and has had a significant impact on the global economy, and the stock market. Most existing studies on stock market volatility during the pandemic have been conducted from a data science perspective, with statistical analysis and mathematical models often revealing the superficial relationship between Covid and the stock market at the data level. In contrast, few studies have explored the relationship between more specialised aspects of the pandemic. Specifically, the relationship found between major social events and the stock market. In this work, a multi-source, data-based relationship analysis method is proposed, that collects historical data on significant social events and related stock data in China and the USA, to further explore the potential correlation between stock market index fluctuations and the impact of social events by analysing cross-timeline data. The results suggest and offer more evidence that social events do indeed impact equity markets, and that the indices in both China and the USA were also affected more by the epidemic in 2020 than in 2021, and these indices became less affected by the epidemic as it became the world adapted. Moreover, these relationships may also be influenced by a variety of other factors not covered in this study. This research, so far, is in its initial stage, and the methodology is not rigorous and cannot be applied as an individual tool for decision; however, it could potentially serve as a supplementary tool and provide a multi-dimensional basis for stock investors and policymakers to make decisions. 041b061a72