How Scientists are Using Statistical Physics to Predict the Stock Market
By Ali Fazal
Those that invest in the stock market may know it to be a relatively volatile endeavor. Investors must keep up with the latest trends, exercise personal judgment, and create their own criteria for trading based on their predictions of price movement. But what if there was a way to (largely) take out the human emotional element? Could investors make more accurate decisions purely from a scientific and empirical standpoint?
That question is what researchers Hung Diep and Gabriel Desgranges aimed to answer. In their quest, they found simplified models of particle physics relating to spin states —which describe the internal state of an object—as well as temperature to price movement of a good and the behavior of investors. While spin states in particular physics can have various orientations like “up” and “down”, Diep and Desgranges’s models had five spin states for strong desire to sell a stock, moderate desire to sell, waiting, moderate desire to buy a stock, and strong desire to buy.
The two simulations drew upon the two distinct models: the Monte Carlo model and Mean-Field theory. The former generally involves using repeated random sampling to solve problems. The latter, assuming particles of different spin states belong to different communities (in this case, the communities being buyers and sellers), only the average value of the spin states in the community are taken into account, rather than the individual states of each member. Both relied on the key element of spin state fluctuations: when the temperature and applied magnetic field are both zero, the system’s spin orientations are randomized and the particles experience weak attraction to magnets.
In the Monte Carlo model, the spin states were buyers and sellers, and the temperature was analogous to how calm the markets were. An external measure, which was government intervention in this case, was also used as an analogous variable to the applied magnetic field. Both models demonstrated that at just below a critical temperature (analogous to the temperature at which market clearing occurs), if a market-boosting measure is applied, the price fluctuates greatly. When market conditions are at or above the market clearing temperature, the price is stabilized, given that buyers and sellers are equal. In the Mean-Field model, at a low temperature or below market clearing conditions, each group remained in their initial state of buying or selling. However, between two distinct critical temperatures (one for each community of buyers and sellers), the states of each group oscillate between buyers and sellers.
While both models are useful for predicting the role of economic temperature and influences on agent behavior, it is important to note the differences between the two. Unlike the Monte Carlo method, the Mean-Field theory is time-dependent; it is also better for predicting qualitative features rather than instantaneous fluctuations. Additionally, the Monte Carlo model does not incorporate the two distinct communities of buyers and sellers like its counterpart does.
The intersections between statistical physics and predictions of the stock market is an interesting representation of the emerging econophysics field as a whole. Many models have related financial markets to physical phenomena such as oscillations, Newtonian dynamics, and fluid mechanics. The statistical physics models outlined in Diep and Desgranges’s paper provides greater insight into an innovative approach that treats investors as particles whose behaviors can be predicted using particle theories.
Diep, H. T., & Desgranges, G. (2021). Dynamics of the Price Behavior in Stock Market: A Statistical Physics Approach. ArXiv:1912.11665 [Cond-Mat, Physics:Physics, q-Fin]. http://arxiv.org/abs/1912.11665
Brommer, P. E. (2005). Spin Fluctuations. In K. H. J. Buschow, R. W. Cahn, M. C. Flemings, B. Ilschner, E. J. Kramer, S. Mahajan, & P. Veyssière (Eds.), Encyclopedia of Materials: Science and Technology (pp. 1–6). Elsevier. https://doi.org/10.1016/B0-08-043152-6/02004-0
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