Statistics of the p-values of the peak predictions
<p>Each box plot represents the p-values of the predictions for 1000 parameter selections o... more <p>Each box plot represents the p-values of the predictions for 1000 parameter selections of <i>S</i>, <i>a</i> and <i>b</i> defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128908#pone.0128908.e012" target="_blank">Eq (12)</a>. The lower and upper horizontal edges (blue lines) of box represent the first and third quartiles. The red line in the middle is the median. The lower and upper black lines are the 1.5 interquartile range away from quartiles. Points out of black lines are outliers. The lower the p-value is, the stronger the prediction power.</p
City logistics networks based on online freight orders in China
Physica A: Statistical Mechanics and its Applications
Abstract The development of logistics accelerates the transportation process, which in turn incre... more Abstract The development of logistics accelerates the transportation process, which in turn increases the exchanging efficiency between cities. To understand such exchanging interactions among cities, we build a city logistics network based on a unique data set, which contains millions of online freight orders spanning a period of one year. It is found that this network exhibits a disassortative configuration and its link weight (respectively, node strength) follows a Weibull (an exponential) distribution. We also find that the hierarchical structure of the city logistics network is highly consistent with the strategic layout of constructing modern comprehensive transportation system plan in China’s “Thirteenth Five-Year Plan”. Furthermore, it is observed that the cities with high Gross Domestic Product (GDP) have high centrality in the city logistics network and the logistics flows between two cities are proportional to the product of their GDPs. Our results not only uncover the topological and hierarchical structure of the city logistics network in China, but also provide an understanding of the structural shaping force on the growth of city logistics network.
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause... more Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repos market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations.
RESEARCH ARTICLE Forecasting Financial Extremes: A Network Degree Measure of Super-Exponential Growth
Investors in stock market are usually greedy during bull markets and scared during bear markets. ... more Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend rever-sal that corrects the previous overreaction. In this paper, we construct an indicator to mea-sure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major interna-tional stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.
By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on ... more 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 (LPPL) 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. The power of the LPPL model is illustrated by two recent real-life predictions performed recently by our group: the peak of the Oil price bubble in early July 2008 and the burst of a bubble on the Shanghai stock market in early August 2009. We then present the concept of "negative bubbles", which are the mirror images of positive bubbles. We argue that similar positive feedbacks are at work to fuel these accelerated downward price spirals. We adapt the LPPL model to these negative bubbles and implement a pattern recognition method to predict the end times of the negative bubbles, which are characterized by rebounds (the mirror images of crashes associated with the standard positive bubbles). The out-of-sample tests quantified by error diagrams demonstrate the high significance of the prediction performance.
Highway freight transportation (HFT) plays an important role in the economic activities. Predicti... more Highway freight transportation (HFT) plays an important role in the economic activities. Predicting HFT networks is not only scientifically significant in the understanding of the mechanism governing the formation and dynamics of these networks, but also of practical significance in highway planning and design for policymakers and truck allocation and route planning for logistic companies. In this work we apply parameter-free radiation models to predict the HFT network in mainland China and assess their predictive performance using metrics based on links and fluxes, which can be done in reference to the real directed and weighted HFT network between 338 Chinese cities constructed from about 15.06 million truck transportation records in five months. It is found that the radiation models exhibit relatively high accuracy in predicting links but low accuracy in predicting fluxes on links. Similar to gravity models, radiation models also suffer difficulty in predicting long-distance links and the fluxes on them. Nevertheless, the radiation models perform well in reproducing several scaling laws of the HFT network. The adoption of population or gross domestic product in the model has a minor impact on the results, and replacing the geographic distance by the path length taken by most truck drivers does not improve the results.
Using a unique data set containing about 15.06 million truck transportation records in five month... more Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability pij from one city to another. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product (GDP), and find quantitatively very similar results. We find that the transportation probabilities have nice power-law tails with the tail exponents close to 0.5 for all the models. The two transportation probabilities in each model fall around the diagonal pij=pji but are often not the same. In addition, the corresponding transportation probabilities calculated from the raw radiation model and the cost-based radiation model also fluctuate around the diagonal pijgeo=pijc...
Transfer entropy measures the strength and direction of information flow between different time s... more Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin & Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extract important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the composite sector is an information source of the whole stock market, while the nonbank financial sector is the information sink. We also find that the non-bank finance, bank, computer, media, real estate, medical biology and non-ferrous metals sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the non-bank finance and bank sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector acts as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks.
The gravity law has been documented in many socioeconomic networks, which states that the flow be... more The gravity law has been documented in many socioeconomic networks, which states that the flow between two nodes positively correlates with the strengths of the nodes and negatively correlates with the distance between the two nodes. However, such research on highway freight transportation networks (HFTNs) is rare. We construct the directed and undirected highway freight transportation networks between 338 Chinese cities using about 15.06 million truck transportation records in five months and test the traditional and modified gravity laws using GDP, population, and per capita GDP as the node strength. It is found that the gravity law holds over about two orders of magnitude for the whole sample, as well as the daily samples, except for the days around the Spring Festival during which the daily sample sizes are significantly small. Accordingly, the daily exponents of the gravity law are stable except during the Spring Festival period. The results also show that the gravity law has h...
Investors in stock market are usually greedy during bull markets and scared during bear markets. ... more Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous over reaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.
Physica A: Statistical Mechanics and its Applications, 2012
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause... more Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreement (repo) market size is a very important element in calculating the overall leverage in a financial market. Therefore, studying the behavior of repo market size can help to understand a process that can contribute to the birth of a financial crisis. We hypothesize that herding behavior among large investors led to massive over-leveraging through the use of repos, resulting in a bubble (built up over the previous years) and subsequent crash in this market in early 2008. We use the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles and behavioral finance to study the dynamics of the repo market that led to the crash. The JLS model qualifies a bubble by the presence of characteristic patterns in the price dynamics, called log-periodic power law (LPPL) behavior. We show that there was significant LPPL behavior in the market before that crash and that the predicted range of times predicted by the model for the end of the bubble is consistent with the observations.
Sornette for his excellent guidance. He opened the wonderful undiscovered science world to me and... more Sornette for his excellent guidance. He opened the wonderful undiscovered science world to me and led me on the road to dig out the fantastic treasure in this world. I am deeply grateful to Dr. Ryan Woodard, who has been taking care of me in every tiny aspect in the past three years. With his guidance and help, I am making progress rapidly to be a mature researcher and a complete person. I am also grateful to Prof. Dr. Wei-Xing Zhou and Reda Rebib for their hard work and useful advices. Part of the research written in this thesis is the joint work with them. My special thanks go to my family and Rujun Jia for their love and support.
Financial markets are well known for their dramatic dynamics and consequences that affect much of... more Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framework to understand and diagnose financial bubbles from rational expectations and was recently extended to negative bubbles and rebounds. Using the JLS model, we develop an alarm index based on an advanced pattern recognition method with the aim of detecting bubbles and performing forecasts of market crashes and rebounds. Testing our methodology on 10 major global equity markets, we show quantitatively that our developed alarm performs much better than chance in forecasting market crashes and rebounds. We use the derived signal to develop elementary trading strategies that produce statistically better performances than a simple buy and hold strategy.
Identifying unambiguously the presence of a bubble in an asset price remains an unsolved problem ... more Identifying unambiguously the presence of a bubble in an asset price remains an unsolved problem in standard econometric and financial economic approaches. A large part of the problem is that the fundamental value of an asset is, in general, not directly observable and it is poorly constrained to calculate. Further, it is not possible to distinguish between an exponentially growing fundamental price and an exponentially growing bubble price. In this paper, we present a series of new models based on the Johansen-Ledoit-Sornette (JLS) model, which is a flexible tool to detect bubbles and predict changes of regime in financial markets. Our new models identify the fundamental value of an asset price and a crash nonlinearity from a bubble calibration. In addition to forecasting the time of the end of a bubble, the new models can also estimate the fundamental value and the crash nonlinearity, meaning that identifying the presence of a bubble is enabled by these models. Besides, the crash nonlinearity obtained in the new models presents a new approach to possibly identify the dynamics of a crash after a bubble. We test the models using data from three historical bubbles ending in crashes from different markets. They are: the Hong Kong Hang Seng index 1997 crash, the S&P 500 index 1987 crash (black Monday) and the Shanghai Composite index 2009 crash. All results suggest that the new models perform very well in describing bubbles, forecasting their ending times and estimating fundamental value and the crash nonlinearity. The performance of the new models is tested under both the Gaussian residual assumption and non-Gaussian residual assumption. Under the Gaussian residual assumption, nested hypotheses with the Wilks statistics are used and the p-values suggest that models with more parameters are necessary. Under non-Gaussian residual assumption, we use a bootstrap method to get type I and II errors of the hypotheses. All tests confirm that the generalized JLS models provide useful improvements over the standard JLS model.
Physica A: Statistical Mechanics and its Applications, 2012
We introduce the concept of ''negative bubbles'' as the mirror (but not necessarily exactly symme... more We introduce the concept of ''negative bubbles'' as the mirror (but not necessarily exactly symmetric) image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate describing the collective buying pressure of noise traders. The price fall occurring during a transient negative bubble can be interpreted as an effective random down payment that rational agents accept to pay in the hope of profiting from the expected occurrence of a possible rally. We validate the model by showing that it has significant predictive power in identifying the times of major market rebounds. This result is obtained by using a general pattern recognition method that combines the information obtained at multiple times from a dynamical calibration of the JLS model. Error diagrams, Bayesian inference and trading strategies suggest that one can extract genuine information and obtain real skill from the calibration of negative bubbles with the JLS model. We conclude that negative bubbles are in general predictably associated with large rebounds or rallies, which are the mirror images of the crashes terminating standard bubbles.
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Papers by Wanfeng Yan