US Stock Market Interaction Network as Learned by the Boltzmann Machine

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Price formation on a financial market is a complex problem: It reflects opinion of investors about true value of the asset in question, policies of the producers, external regulation and many other factors. Given the big number of factors influencing price, many of which unknown to us, describing price formation essentially requires probabilistic approaches. In the last decades, synergy of methods from various scientific areas has opened new horizons in understanding the mechanisms that underlie related problems. One of the popular approaches is to consider a financial market as a complex system, where not only a great number of constituents plays crucial role but also non-trivial interaction properties between them. For example, related interdisciplinary studies of complex financial systems have revealed their enhanced sensitivity to fluctuations and external factors near critical events with overall change of internal structure. This can be complemented by the research devoted to equilibrium and non-equilibrium phase transitions.

In general, statistical modeling of the state space of a complex system requires writing down the probability distribution over this space using real data. In a simple version of modeling, the probability of an observable configuration (state of a system) described by a vector of variables s can be given in the exponential form

p(s) = Z−1 exp {−βH(s)} —– (1)

where H is the Hamiltonian of a system, β is inverse temperature (further β ≡ 1 is assumed) and Z is a statistical sum. Physical meaning of the model’s components depends on the context and, for instance, in the case of financial systems, s can represent a vector of stock returns and H can be interpreted as the inverse utility function. Generally, H has parameters defined by its series expansion in s. Basing on the maximum entropy principle, expansion up to the quadratic terms is usually used, leading to the pairwise interaction models. In the equilibrium case, the Hamiltonian has form

H(s) = −hTs − sTJs —– (2)

where h is a vector of size N of external fields and J is a symmetric N × N matrix of couplings (T denotes transpose). The energy-based models represented by (1) play essential role not only in statistical physics but also in neuroscience (models of neural networks) and machine learning (generative models, also known as Boltzmann machines). Given topological similarities between neural and financial networks, these systems can be considered as examples of complex adaptive systems, which are characterized by the adaptation ability to changing environment, trying to stay in equilibrium with it. From this point of view, market structural properties, e.g. clustering and networks, play important role for modeling of the distribution of stock prices. Adaptation (or learning) in these systems implies change of the parameters of H as financial and economic systems evolve. Using statistical inference for the model’s parameters, the main goal is to have a model capable of reproducing the same statistical observables given time series for a particular historical period. In the pairwise case, the objective is to have

⟨sidata = ⟨simodel —– (3a)

⟨sisjdata = ⟨sisjmodel —– (3b)

where angular brackets denote statistical averaging over time. Having specified general mathematical model, one can also discuss similarities between financial and infinite- range magnetic systems in terms of phenomena related, e.g. extensivity, order parameters and phase transitions, etc. These features can be captured even in the simplified case, when si is a binary variable taking only two discrete values. Effect of the mapping to a binarized system, when the values si = +1 and si = −1 correspond to profit and loss respectively. In this case, diagonal elements of the coupling matrix, Jii, are zero because s2i = 1 terms do not contribute to the Hamiltonian….

US stock market interaction network as learned by the Boltzmann Machine

Sustainability of Debt

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For economies with fractional reserve-generated fiat money, balancing the budget is characterized by an exponential growth D(t) ≈ D0(1 + r)t of any initial debt D0 subjected to interest r as a function of time t due to the compound interest; a fact known since antiquity. At the same time, besides default, this increasing debt can only be reduced by the following five mostly linear, measures:

(i) more income or revenue I (in the case of sovereign debt: higher taxation or higher tax base);

(ii) less spending S;

(iii) increase of borrowing L;

(iv) acquisition of external resources, and

(v) inflation; that is, devaluation of money.

Whereas (i), (ii) and (iv) without inflation are essentially measures contributing linearly (or polynomially) to the acquisition or compensation of debt, inflation also grows exponentially with time t at some (supposedly constant) rate f ≥ 1; that is, the value of an initial debt D0, without interest (r = 0), in terms of the initial values, gets reduced to F(t) = D0/ft. Conversely, the capacity of an economy to compensate debt will increase with compound inflation: for instance, the initial income or revenue I will, through adaptions, usually increase exponentially with time in an inflationary regime by Ift.

Because these are the only possibilities, we can consider such economies as closed systems (with respect to money flows), characterized by the (continuity) equation

Ift + S + L ≈ D0(1+r)t, or

L ≈ D0(1 + r)t − Ift − S.

Let us concentrate on sovereign debt and briefly discuss the fiscal, social and political options. With regards to the five ways to compensate debt the following assumptions will be made: First, in non-despotic forms of governments (e.g., representative democracies and constitutional monarchies), increases of taxation, related to (i), as well as spending cuts, related to (ii), are very unpopular, and can thus be enforced only in very limited, that is polynomial, forms.

Second, the acquisition of external resources, related to (iv), are often blocked for various obvious reasons; including military strategy limitations, and lack of opportunities. We shall therefore disregard the acquisition of external resources entirely and set A = 0.

As a consequence, without inflation (i.e., for f = 1), the increase of debt

L ≈ D0(1 + r)t − I − S

grows exponentially. This is only “felt” after trespassing a quasi-linear region for which, due to a Taylor expansion around t = 0, D(t) = D0(1 + r)t ≈ D0 + D0rt.

So, under the political and social assumptions made, compound debt without inflation is unsustainable. Furthermore, inflation, with all its inconvenient consequences and re-appropriation, seems inevitable for the continuous existence of economies based on fractional reserve generated fiat money; at least in the long run.