Braided and Knotted Stocks in the Stock Market: Anticipating the Flash Crashes

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A surprising image of the stock market arises if the price time series of all Dow Jones Industrial Average stock components are represented in one chart at once. The chart evolves into a braid representation of the stock market by taking into account only the crossing of stocks and fixing a convention defining overcrossings and undercrossings. The braid of stocks prices has a remarkable connection with the topological quantum computer. Using pairs of quasi-particles, called non-abelian anyons, having their trajectories braided in time, topological quantum computer can effectively simulate the stock market behavior encoded in the braiding of stocks. In a typically topological quantum computation process the trajectories of non-abelian anyons are manipulated according to the braiding of stocks and the outcome reflects the probability of the future state of stock market. The probability depends only on the Jones polynomial of the knot formed by plat closing the quantum computation. The Jones polynomial of the knotted stock market acts, making a parallel with the common financial literature, in a topological quantum computation as a counterpart of a classical technical indicator in trading the stock market. The type of knot stock market formed is an indicator of its future tendencies.

“…Details of stocks braids topological properties can be find in [1] and the interested reader is encouraged to explore it in details.…”

Section: Crossing Stocks and Braid Of Stocks Diagramsmentioning

confidence: 99%

Section: Final Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Decoding Stock Market Behavior with the Topological Quantum Computer

Racorean

2014

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“…50 Some of the proposed solutions for managing flash crashes include using statistical models for phase transitions and Jones polynomials (which model the stock market with a knot and braid topology). 51 The idea would be to take these methods and apply field-theoretic principles such as attempting to avoid local minima in a random energy landscape, to likewise avoid the analogous singularity in a smart network system, behavior such as double-pendulum chaoticity that could result in flash crashes. 1,2 Collective Intelligence as an Emergent System Property Collective intelligence might be harnessed as an emergent system property, and provisioned across the network.…”

Section: Systemic Risk Managementmentioning

confidence: 99%

Smart Network Field Theory: The Technophysics of Blockchain and Deep Learning

Swan

Santos

2018

The aim of this paper is to propose a theoretical construct, smart network field theory, for the characterization, monitoring, and control of smart network systems. Smart network systems are intelligent autonomously-operating networks, a new form of global computational infrastructure that includes blockchains, deep learning, and autonomous-strike UAVs. These kinds of largescale networks are a contemporary reality with thousands, millions, and billions of constituent elements, and entail a foundational and theoretically-robust model for their design and operation. Hence this work proposes smart network field theory, drawing from statistical physics, effective field theories, and model systems, for criticality detection and fleet-many item orchestration in smart network systems. Smart network field theory falls within the broader concern of technophysics (the application of physics to the study of technology), in which a key objective is deriving standardized methods for assessing system criticality and phase transition, and defining interim system structure between the levels of microscopic noise and macroscopic labels. The farther implications of this work include the possibility of recasting the P/NP computational complexity schema as one no longer based on traditional time (concurrency) and space constraints, due to the availability of smart network computational resources. Deep Learning ChainsThe diverse smart network technologies are emblematic of an emerging class of global computational infrastructure that increasingly acts autonomously based on intelligence built directly into the network operating software. Smart network technologies may be used in convergence. Some species of smart network technologies, such as blockchain and deep learning, are in a special class in that they are both smart network technologies themselves, and can serve as control technologies for other smart network systems. The functionality of blockchains and deep learning converges in the concept of deep learning chains. Deep learning chains are a smart network control technology with properties of both blockchain and deep learning: the secure automation, audit-log tracking, remunerability, and validated transaction execution of blockchains, and the object identification (IDtech a ), pattern recognition, and optimization technology of deep learning.Deep learning chains might be used to control other fleet-many internet-connected smart network technologies such as UAVs, autonomous driving fleets, medical nanorobots, and spacebased asteroid mining rigs. For example, deep learning chains could be important in autonomous driving fleets for tracking what the vehicle does (blockchain), and for identifying objects in its driving field (deep learning). Deep learning chains could likewise apply to the body, as a smart network control technology for medical nanorobots, identifying pathogens (deep learning) and tracking and expunging them (blockchain). Likewise in supply chain automated receiving, deep learning chains could provide the integra...

“…A series of recent papers [15], [16], [17] formally introduced and developed the braid diagrams of stocks prices time series in a portfolio and sketched their connection with the topological methods of quantum computation.…”

Section: Introductionmentioning

confidence: 99%

Quantum Gates and Quantum Circuits of Stock Portfolio

Racorean

2015

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In quantum computation, series of quantum gates have to be arranged in a predefined sequence that led to a quantum circuit in order to solve a particular problem. What if the sequence of quantum gates is known but both the problem to be solved and the outcome of the so defined quantum circuit remain in the shadow? This is the situation of the stock market. The price time series of a portfolio of stocks are organized in braids that effectively simulate quantum gates in the hypothesis of Ising anyons quantum computational model. Following the prescriptions of Ising anyons model, 1-qubit quantum gates are constructed for portfolio composed of four stocks. Adding two additional stocks at the initial portfolio result in 2-qubit quantum gates and circuits. Hadamard gate, Pauli gates or controlled-Z gate are some of the elementary quantum gates that are identified in the stock market structure. Addition of other pairs of stocks, that eventually represent a market index, like Dow Jones industrial Average, it results in a sequence of n-qubit quantum gates that form a quantum code. Deciphering this mysterious quantum code of the stock market is an issue for future investigations.