Can Physics Beat the Market? A Quantum Financial Gravity Model Global Macro Shadow Traders www.gmshadowtraders.com *Please do not quote without permission* All personal views are the authors‟ own and may be subject to change December 5th 2014 © 2014 GLOBAL MACRO SHADOW TRADERS DISCLAIMER: THE INFORMATION CONTAINED IN THIS DOCUMENT DOES NOT CONSTITUTE A RECOMMENDATION TO BUY, TO SELL OR TO INVEST. PLEASE SEEK PROFESSIONAL ADVICE ON ALL MATTERS BEFORE DECIDING TO BUY, TO SELL OR TO INVEST IN SECURITIES. BE AWARE THAT ALL TRADING DECISIONS CARRY RISK AND LOSSES IN SOME CASES MAY EXCEED YOUR INITIAL DEPOSIT. GLOBAL MACRO SHADOW TRADERS LTD IS NOT RESPONSIBLE OR LIABLE IN ANY WAY FOR THE ACTIONS TAKEN BY THOSE WHO HAVE TRADED ON THE BASIS OF WHAT THEY SEE OR READ. MORE INFORMATION MAY BE FOUND ON THE COMPANY WEBSITE. Abstract: In this document we describe a model to forecast the future likely direction of prices for a financial asset using historical time series data and advanced analytics or Data Science. We use results from theoretical physics together with a novel way to depart from the efficient market hypothesis (EMH), the cornerstone of all of modern finance and economics. A new model called Quantum Financial Gravity (QFG) is presented. The market data are first observed in their raw form and are then digitally extracted into an algorithm designed to produce multiple realities from the underlying data itself. By looking at large samples of data we aim to reconstruct the quantum-ized versions of time series graphs which nevertheless obey a classical universe. Time series graphs subsequently output from the procedure are argued to capture the actual statistical distribution of how the price series itself is evolving over time. This theoretical framework it is claimed offers additional predictive power today, contrary to what a strict interpretation of the EMH says. Possible uses of the model include the creation of new superior tools for calculating more accurate risk profiles of securities in the global financial markets. Length: 6226 words, 14 pages. This document is made available so that readers may understand the material on the main site at www.gmshadowtraders.com. Please see the site for information and to register as a subscriber. 1 (i) What is the true mechanism behind price formation in the financial markets and how is it impacting on future price development? (ii) Why are prices of financial securities so volatile even when the underlying fundamentals show little or no change? What makes uncertainty different to risk? (iii) Can we predict the future movement of prices across different assets or asset classes using past historical data and some overall quantitative procedure? (iv) Might affirmative answers to any of the above violate either the efficient market hypothesis in traditional finance theory or the known laws of physics? “There is no known prescription for generating quantum solutions directly from classical ones.” - JOHN D. BARROW (New Theories of Everything) 1. Introduction – From Physics to Finance Classical physics can predict a future state arising from a particular past one. Given an initial set of starting conditions the future path for any object can be specified with great accuracy. But in Quantum physics, a future state is determined only as an appropriately weighted sum over all possible paths through space and time that the system could have taken. The issue to solve is how to reconcile these two very different approaches that have been confirmed countless times by experimental data. At the macro scale the orbits of planets obey the solutions to classical equations while at the sub-atomic level the behaviour of particles obey the „quantum-ized‟ versions of these equations. But a full theoretical model which accounts for the different scales and which can still yield solutions that confirm to every observation has not yet been found. The generalization of Einstein‟s equations to include quantum theory is one of the unsolved problems of modern physics. In this paper we look at this problem of unification theory from theoretical physics with an application to finance and time series data. The end result is a description of the market which is both deterministic as well as probabilistic, a model which we term Quantum Financial Gravity (QFG). The price for any financial security is continuously adjusting to itself through past, present and future effects. As a consequence equilibrium can never be fully reached at any point in time, somewhat contrary to the efficient market hypothesis, because essentially the data itself is incomplete. Yet despite this market clearing via the normal competitive forces of demand and supply continues to take place as usual. The only logical explanation is that the actual data is not observed initially and remains hidden. The system however is partially aware of this situation with respect to all of the known information that exists – in fact, it is for this precise reason that we observe the sudden and volatile movements which so frequently occur on the global financial markets. From the point of view of the majority of market participants it is no exaggeration to say that the market appears as though it is on a knife-edge from one day to the next. The 2 missing link is a model that can account for it, one ideally backed by strong empirical evidence. This paper seeks to fill that gap. Since the true data is not of the observed world it must instead be inferred through experiments. This inference is exactly what we seek to do at www.gmshadowtraders.com where we try to glimpse into the future ahead of time. **By looking at large samples of data we aim to reconstruct the quantum-ized versions of time series graphs which nevertheless obey a classical universe**1 The entire model of pricing behaviour in its quantum form is principally determined by the classical path, yet that same path is in of itself determined by its corresponding quantum description. The quantum path is strongly chosen by the hidden market forces while the classical path is strongly chosen by the observed market forces. The two need not precisely collapse into one exact observation which is what we would normally expect. Thus the future expected direction of price movement may be inferred by a comparison of the two data points. Only a Data Science approach combined with multiple layers of analytics can allow for such an exercise to be performed. The world of finance is ideally suited due to its large scale for an application of Big Data tools with advanced predictive analytics and statistical machine learning algorithms. ** For the full PDF version please register at www.gmshadowtraders.com ** 1 year membership is just £12 You then may access all PDF files, posts and gallery content Dr Nick Choudhury CEO of gmshadowtraders Email 1 [email protected] Email 2 [email protected] Mobile +44(0)7540329271 1 Alternatively stated the algorithm is designed to output „Big Data‟ directly from „Normal Data‟. In the financial markets we never see big data with the naked eye, nor can we go out and collect it. We can observe normal or standard data only. Yet data is data, and both these sources should be subject to the implications of physical or economic laws. This fundamental inconsistency is therefore shared by physics and finance alike. 3