Peter Cauwels

Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles

By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite and Shenzhen Stock Exchange Component indexes exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in Oct. 2007 and 2) from Nov. 2008, bursting in the beginning of Aug. 2009. We successfully predicted time windows for both crashes in advance with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble and the peak in July 2008 of the global oil bubble. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them.

Quis Pendit Ipsa Pretia: Facebook Valuation and Diagnostic of a Bubble Based on Nonlinear Demographic Dynamics

We present a novel methodology to determine the fundamental value of firms in the social-networking sector, motivated by recent realized IPOs and by reports that suggest sky-high valuations of firms such as facebook, Groupon, LinkedIn Corp., Pandora Media Inc, Twitter, Zynga. Our valuation of these firms is based on two ingredients: (i) revenues and profits of a social-networking firm are inherently linked to its user basis through a direct channel that has no equivalent in other sectors; (ii) the growth of the number of users can be calibrated with standard logistic growth models and allows for reliable extrapolations of the size of the business at long time horizons. Illustrating the methodology with facebook, one of the biggest of the social-media giants, we find a clear signature of a change of regime that occurred in 2010 on the growth of the number of users, from a pure exponential behavior (a paradigm for unlimited growth) to a logistic function describing the evolution towards an asymptotic plateau (a paradigm for growth in competition). We consider three different scenarios, a base case, a high growth and an extreme growth scenario. Using a discount factor of 5%, a profit margin of 29% and 3.5 USD of revenues per user per year yields a value of facebook of 15.3 billion USD in the base case scenario, 20.2 billion USD in the high growth scenario and 32.9 billion USD in the extreme growth scenario. According to our methodology, this would imply that facebook would need to increase its profit per user before the IPO by a factor of 3 to 6 in the base case scenario, 2.5 to 5 in the high growth scenario and 1.5 to 3 in the extreme growth scenario in order to meet the current, widespread, high expectations.

Identification and critical time forecasting of real estate bubbles in the USA

We present a hybrid model for diagnosis and critical time forecasting of real estate bubbles. The model combines two elements: (1) the Log Periodic Power Law Singular model to describe endogenous price dynamics originated from positive feedback loops among economic agents; and (2) a diffusion index that creates a parsimonious representation of multiple macroeconomic variables. We explicitly compare the in-sample and out-sample behaviour of our model on the housing price indices of 380 US metropolitan areas. Empirical results suggest that the model is able to forecast the end of the bubbles and to identify the variables that are highly relevant during the bubble regime.

Volatility Is Risk

The observation of a stronger noise or a higher volatility in financial markets is usually interpreted as a situation with a higher implied risk profile and vice versa. It is argued here that this is an idea that should be forgotten. When investors enter a market because of the prospect of higher returns, they start buying because the price goes up. This sets off a feedback mechanism causing the price to spiral even higher. This is what we call a financial bubble. The reverse situation, when investors sell because the price goes down, is a crash. Many studies have shown that an asset’s volatility is negatively correlated with its return. This is called “the leverage effect.” As a consequence, volatility may be the lowest at the crest of a bubble, when the price but also the risk has spiralled the highest. Alternatively, volatility may be the highest at the trough of the crash, when the opportunity is the highest.

Forecasting Future Oil Production in Norway and the UK: A General Improved Methodology

We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the standard methodology that tends to underestimate remaining oil reserves, our method gives a better description of future oil production, as validated by our back-tests starting in 2008. Specifically, we predict remaining reserves extractable until 2030 to be 188 ± 10 million barrels for Norway and 98 ± 10 million barrels for the UK, which are respectively 45% and 66% above the predictions using the standard methodology.

Identification and Critical Time Forecasting of Real Estate Bubbles in the U.S.A and Switzerland

We present a hybrid model for diagnosis and critical time forecasting of real estate bubbles. The model combines two elements: 1) the Log Periodic Power Law (LPPL) model to describe endogenous price dynamics originated from positive feedback loops between economic agents; and 2) a diffusion index method that creates a parsimonious representation of multiple macroeconomic variables. We examine the behavior of our model on the housing price indices of 380 US metropolitan areas, using 15, 35, and 90 national-level macroeconomic time series and a dynamic forecasting methodology. Empirical results suggests that the model is able to forecast the end of the bubbles and to identify variables highly relevant during the bubble regime. In addition, the same methodology is applied to the national housing price index of Switzerland, diagnosing a bubble in which global imbalances and Switzerlands status as a safe haven seem to be playing a dominant role.