“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion and a bubble regime represented by the stochastic super-exponential Sornette and Andersen (Int J Mod Phys C 13(2):171–188, 2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005–2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is some predictability obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.     

The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

Cycles,determinism and persistence in agent-based games and financial time-series: part II

The present article constitutes part II of a series of two reports in which we study the decomposition of synthetic and real financial time-series into a superposition of weighted Hamiltonian cycles on graphs. Part II further analyses the cycle-decomposition method introduced in part I for the Minority Game (MG), the Majority Game (MAJG) and the Dollar Game ($G), in order to gain insight into the ‘illusion of control’ that certain of these games demonstrate, i.e. the fact that the strategies outperform the agents that deploy them. We also illustrate both numerical and analytical methods for extracting cycles from a given time-series and apply the method to a number of different real-world data sets, in conjunction with an analysis of persistence.     

Inefficiency and predictability in the Brexit Pound market: a natural experiment

Exploiting the near-experimental conditions provided by the GBPUSD exchange rate during the Brexit vote of 2016, we quantify a significant delay of the market price in reflecting the increasing probability of a Brexit outcome over the vote counting period. We claim that the Brexit outcome could realistically have been predicted hours before the market adjusted to the outcome. This inefficiency is identified by comparing the market-implied probability of a Brexit outcome with a separate probability, estimated by a standard Monte-Carlo algorithm based on a simple linear regression model, representative of what should have been easily possible in real time. The core of the method is the real-time re-calibration of ex-ante ‘pollster’ predictions for the voting district outcomes by regressing the observed voting results onto them. For comparative purposes, a study of the MXNUSD exchange rate in the 2016 US Presidential Election was done, finding that the market-implied and model-estimated probabilities moved more consistently toward the Trump outcome. Put together, this identifies a somewhat anomalous breakdown in market efficiency in the case of the Brexit vote, which we attribute to its novelty as well as a kind of political bubble and subsequent crash, generated by confirmation bias and social herding.     

Should Banks Be Banned From Creating Money? An Analysis From the Perspective of Hierarchical Money

We extend the concept of “hierarchy of money” to our current monetary and financial system based on fiat money, with monetary policy that is conducted through the sale and purchase of securities and credit intermediation by non-bank financial intermediaries. This exposes a feedback loop between the upper and lower level of the hierarchy, which allows for more than full use of otherwise dormant capital, but that also increases inherent instabilities manifested in asset booms and busts. From the perspective of hierarchical money, we find that the call to ban banks from creating money neglects the significant role of securities-based financing in the global financial markets at the lower level, as well as the money creation capacity of central banks at the highest level of the hierarchy. Moreover, the inherently expansive nature of the hierarchy of money contradicts the long-term feasibility of full-reserve banking.     

Decision trees unearth return sign predictability in the S&P 500

Technical trading rules and linear regression models are often used by practitioners to find trends in asset returns. However, these models typically neglect interaction terms between the lagged daily directional movements. We propose a decision tree forecasting model that has the flexibility to capture arbitrary interaction patterns. To study the importance of interaction terms, we construct a binary Markov process with a deterministic component that cannot be predicted without interaction terms between the lagged directional movements. We show that some tree based strategies achieve trading performance significant at the 99% confidence level on the S&P 500 over the past 20 years, after adjusting for multiple testing. The best strategy breaks even with the buy-and-hold strategy at 21 bps in transaction costs per round trip. A four-factor regression analysis shows significant intercept, and correlation with the market. The directional predictability is strongest during the bursts of the dotcom bubble, financial crisis, and European debt crisis. The return sign predictability during these periods confirms the necessity of interaction terms to model daily returns.     

Quantitative determination of the level of cooperation in the presence of punishment in three public good experiments

Strong reciprocity is a fundamental human characteristic associated with our extraordinary sociality and cooperation. Laboratory experiments on social dilemma games and many field studies have quantified well-defined levels of cooperation and propensity to punish/reward. The level of cooperation is observed to be strongly dependent on the availability of punishments and/or rewards. Here, we propose an operational approach based on the evolutionary selection of prosocial behaviors to explain the quantitative level of the propensity to punish in three experimental set-ups. A simple cost/benefit analysis at the level of a single agent, who anticipates the action of her fellows, determines an optimal level of altruistic punishment, which explains quantitatively experimental results on a third-party punishment game, the ultimatum game and an altruistic punishment game. We also report numerical simulations of an evolutionary agent-based model of repeated agent interactions with feedback by punishments, which confirms that the propensity to punish is a robust emergent property selected by the evolutionary rules of the model. The cost-benefit reasoning is not to be taken literally but rather to embody the result of the selection pressure of co-evolving agents that have make them converge to their preferences (which can be seen as either hard-wired and/or culturally selected). In this view, the prosocial preference of humans is a collective emergent process, robustly selected by adaptation and selection. Our main contribution is to use evolutionary feedback selection to quantify the value of the prosocial propensity to punish, and test this prediction on three different experimental set-ups.