arXiv subjects

Peter Cauwels

Publications and source records attributed to Peter Cauwels.

11 recordsLinked to original sources

arXiv2019

The fair reward problem: the illusion of success and how to solve it

Humanity has been fascinated by the pursuit of fortune since time immemorial, and many successful outcomes benefit from strokes of luck. But success is subject to complexity, uncertainty, and change - and at times becoming increasingly unequally distributed. This leads to tension and confusion over to what extent people actually get what they deserve (i.e., fairness/meritocracy). Moreover, in many fields, humans are over-confident and pervasively confuse luck for skill (I win, it's skill; I lose, it's bad luck). In some fields, there is too much risk taking; in others, not enough. Where success derives in large part from luck - and especially where bailouts skew the incentives (heads, I win; tails, you lose) - it follows that luck is rewarded too much. This incentivizes a culture of gambling, while downplaying the importance of productive effort. And, short term success is often rewarded, irrespective, and potentially at the detriment, of the long-term system fitness. However, much success is truly meritocratic, and the problem is to discern and reward based on merit. We call this the fair reward problem. To address this, we propose three different measures to assess merit: (i) raw outcome; (ii) risk adjusted outcome, and (iii) prospective. We emphasize the need, in many cases, for the deductive prospective approach, which considers the potential of a system to adapt and mutate in novel futures. This is formalized within an evolutionary system, comprised of five processes, inter alia handling the exploration-exploitation trade-off. Several human endeavors - including finance, politics, and science -are analyzed through these lenses, and concrete solutions are proposed to support a prosperous and meritocratic society.

econ.GN↗

arXiv2016

Discrete hierarchy of sizes and performances in the exchange-traded fund universe

Using detailed statistical analyses of the size distribution of a universe of equity exchange-traded funds (ETFs), we discover a discrete hierarchy of sizes, which imprints a log-periodic structure on the probability distribution of ETF sizes that dominates the details of the asymptotic tail. This allows us to propose a classification of the studied universe of ETFs into seven size layers approximately organized according to a multiplicative ratio of 3.5 in their total market capitalization. Introducing a similarity metric generalising the Herfindhal index, we find that the largest ETFs exhibit a significantly stronger intra-layer and inter-layer similarity compared with the smaller ETFs. Comparing the performance across the seven discerned ETF size layers, we find an inverse size effect, namely large ETFs perform significantly better than the small ones both in 2014 and 2015.

q-fin.GN↗

arXiv2014

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.

physics.data-an↗

arXiv2014

Financial bubbles: mechanisms and diagnostics

We define a financial bubble as a period of unsustainable growth, when the price of an asset increases ever more quickly, in a series of accelerating phases of corrections and rebounds. More technically, during a bubble phase, the price follows a faster-than-exponential power law growth process, often accompanied by log-periodic oscillations. This dynamic ends abruptly in a change of regime that may be a crash or a substantial correction. Because they leave such specific traces, bubbles may be recognised in advance, that is, before they burst. In this paper, we will explain the mechanism behind financial bubbles in an intuitive way. We will show how the log-periodic power law emerges spontaneously from the complex system that financial markets are, as a consequence of feedback mechanisms, hierarchical structure and specific trading dynamics and investment styles. We argue that the risk of a major correction, or even a crash, becomes substantial when a bubble develops towards maturity, and that it is therefore very important to find evidence of bubbles and to follow their development from as early a stage as possible. The tools that are explained in this paper actually serve that purpose. They are at the core of the Financial Crisis Observatory at the ETH Zurich, where tens of thousands of assets are monitored on a daily basis. This allow us to have a continuous overview of emerging bubbles in the global financial markets. The companion report available as part of the Notenstein white paper series (2014) with the title ``Financial bubbles: mechanism, diagnostic and state of the World (Feb. 2014)'' presents a practical application of the methodology outlines in this article and describes our view of the status concerning positive and negative bubbles in the financial markets, as of the end of January 2014.

q-fin.RM↗

arXiv2014

A Creepy World

Using the mechanics of creep in material sciences as a metaphor, we present a general framework to understand the evolution of financial, economic and social systems and to construct scenarios for the future. In a nutshell, highly non-linear out-of-equilibrium systems subjected to exogenous perturbations tend to exhibit a long phase of slow apparent stable evolution, which are nothing but slow maturations towards instabilities, failures and changes of regimes. With examples from history where a small event had a cataclysmic consequence, we propose a novel view of the current state of the world via the logical scenarios that derive, avoiding the traps of an illusionary stability and simple linear extrapolation. The endogenous scenarios are "muddling along", "managing through" and "blood red abyss". The exogenous scenarios are "painful adjustment" and "golden east".

physics.soc-ph↗

arXiv2013

Is There A Real Estate Bubble in Switzerland?

We have analyzed the risks of possible development of bubbles in the Swiss residential real estate market. The data employed in this work has been collected by comparis.ch, and carefully cleaned from duplicate records through a procedure based on supervised machine learning methods. The study uses the log periodic power law (LPPL) bubble model to analyze the development of asking prices of residential properties in all Swiss districts between 2005 and 2013. The results suggest that there are 11 critical districts that exhibit signatures of bubbles, and seven districts where bubbles have already burst. Despite these strong signatures, it is argued that, based on the current economic environment, a soft landing rather than a severe crash is expected.

q-fin.GN↗

arXiv2013

Dynamics and Spatial Distribution of Global Nighttime Lights

Using open source data, we observe the fascinating dynamics of nighttime light. Following a global economic regime shift, the planetary center of light can be seen moving eastwards at a pace of about 60 km per year. Introducing spatial light Gini coefficients, we find a universal pattern of human settlements across different countries and see a global centralization of light. Observing 160 different countries we document the expansion of developing countries, the growth of new agglomerations, the regression in countries suffering from demographic decline and the success of light pollution abatement programs in western countries.

physics.soc-ph↗

arXiv2012

When games meet reality: is Zynga overvalued?

On December 16th, 2011, Zynga, the well-known social game developing company went public. This event followed other recent IPOs in the world of social networking companies, such as Groupon or Linkedin among others. With a valuation close to 7 billion USD at the time when it went public, Zynga became one of the biggest web IPOs since Google. This recent enthusiasm for social networking companies raises the question whether they are overvalued. Indeed, during the few months since its IPO, Zynga showed significant variability, its market capitalization going from 5.6 to 10.2 billion USD, hinting at a possible irrational behavior from the market. To bring substance to the debate, we propose a two-tiered approach to compute the intrinsic value of Zynga. First, we introduce a new model to forecast its user base, based on the individual dynamics of its major games. Next, we model the revenues per user using a logistic function, a standard model for growth in competition. This allows us to bracket the valuation of Zynga using three different scenarios: 3.4, 4.0 and 4.8 billion USD in the base case, high growth and extreme growth scenario respectively. This suggests that Zynga has been overpriced ever since its IPO. Finally, we propose an investment strategy (dated April 19th, 2012 on the arXive), which is based on our diagnostic of a bubble for Zynga and how this herding / bubbly sentiment can be expected to play together with two important coming events (the quarterly financial result announcement around April 26th, 2012 followed by the end of a first lock-up period around April 30th, 2012). On the long term, our analysis indicates that Zynga's price should decrease significantly. The paper ends with a post-mortem analysis added on May 24th, 2012, just before going to press, showing that we have successfully predicted the downward trend of Zynga. Since April 27th, 2012, Zynga dropped 25%.

q-fin.GN↗

arXiv2011

Valuation of Zynga

On December 16, Zynga, the well-known social game developing company went public. This event is following other recent IPOs in the world of social networking companies, such as Groupon, Linkedin or Pandora to cite a few. With a valuation close to 7 billion USD at the time when it went public, Zynga has become the biggest web IPO since Google. This recent enthusiasm for social networking companies, and in particular Zynga, brings up the question whether or not they are overvalued. The common denominator of all these IPOs is that a lot of estimates about their valuation have been circulating, without any specifics given about the methodology or assumptions used to obtain those numbers. To bring more substance to the debate, we propose a two-tiered approach. First, we introduce a new model to forecast the global user base of Zynga, based on the analysis of the individual dynamics of its major games. Next, we model the revenues per user using a logistic growth function, a standard model for growth in competition. This leads to bracket the valuation of Zynga using three different scenarios (base one, optimistic and very optimistic): 4.17 billion USD in the base case, 5.16 billion in the high growth and 7.02 billion in the extreme growth scenario respectively. Thus, only the unlikely extreme growth scenario could potentially justify today's 6.6 billion USD valuation of Zynga. This suggests that Zynga at its IPO has been overpriced.

q-fin.GN↗

arXiv2011

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 based on two ingredients: (i) revenues and profits 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. We illustrate the methodology with a detailed analysis of facebook, one of the biggest of the social-media giants. There is 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 with 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. To prove the wider applicability of our methodology, the analysis is repeated on Groupon, the well-known deal-of-the-day website which is expected to go public in November 2011. The results are in line with the facebook analysis. Customer growth will plateau. By not taking this fundamental property of the growth process into consideration, estimates of its IPO are wildly overpriced.

q-fin.GN↗

arXiv2009

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.

q-fin.ST↗