Impact of Stock Market Structure on Intertrade Time and Price Dynamics

We analyse times between consecutive transactions for a diverse group of stocks registered on the NYSE and NASDAQ markets, and we relate the dynamical properties of the intertrade times with those of the corresponding price fluctuations. We report that market structure strongly impacts the scale-invariant temporal organisation in the transaction timing of stocks, which we have observed to have long-range power-law correlations. Specifically, we find that, compared to NYSE stocks, stocks registered on the NASDAQ exhibit significantly stronger correlations in their transaction timing on scales within a trading day. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing on scales within a trading day, indicating influences of market structure. We also report a persistent decrease in correlation strength of intertrade times with increasing average intertrade time and with corresponding decrease in companies'' market capitalization–a trend which is less pronounced for NASDAQ stocks. Surprisingly, we observe that stronger power-law correlations in intertrade times are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of intertrade times and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ markets, we demonstrate that the stronger correlations we find in intertrade times for NASDAQ stocks are associated with stronger correlations in absolute price returns and with higher volatility, suggesting that market structure may affect price behavior through information contained in transaction timing. These findings do not support the hypothesis of universal scaling behavior in stock dynamics that is independent of company characteristics and stock market structure. Further, our results have implications for utilising transaction timing patterns in price prediction and risk management optimization on different stock markets.     

Does corn market uncertainty impact the US ethanol prices?

The growing interest in biofuel as a green energy source has intensified the linkages between corn and ethanol markets, especially in the United States that represents the largest producing and exporting country for ethanol in the world. In this study, we examine the effect of corn market uncertainty on the price changes of US ethanol applying a set of GARCH‐jump models. We find that the US ethanol price changes react positively to the corn market volatility shocks after controlling for the effect of oil price uncertainty. In addition, we document that the impact of corn price volatility on the US ethanol prices appears to be asymmetric. Specifically, only the positive corn market volatility shocks are found to influence the ethanol market returns. Our findings also suggest that time‐varying jumps do exist in the ethanol market.     

A Financial Market Model Incorporating Herd Behaviour

Herd behaviour in financial markets is a recurring phenomenon that exacerbates asset price volatility, and is considered a possible contributor to market fragility. While numerous studies investigate herd behaviour in financial markets, it is often considered without reference to the pricing of financial instruments or other market dynamics. Here, a trader interaction model based upon informational cascades in the presence of information thresholds is used to construct a new model of asset price returns that allows for both quiescent and herd-like regimes. Agent interaction is modelled using a stochastic pulse-coupled network, parametrised by information thresholds and a network coupling probability. Agents may possess either one or two information thresholds that, in each case, determine the number of distinct states an agent may occupy before trading takes place. In the case where agents possess two thresholds (labelled as the finite state-space model, corresponding to agents’ accumulating information over a bounded state-space), and where coupling strength is maximal, an asymptotic expression for the cascade-size probability is derived and shown to follow a power law when a critical value of network coupling probability is attained. For a range of model parameters, a mixture of negative binomial distributions is used to approximate the cascade-size distribution. This approximation is subsequently used to express the volatility of model price returns in terms of the model parameter which controls the network coupling probability. In the case where agents possess a single pulse-coupling threshold (labelled as the semi-infinite state-space model corresponding to agents’ accumulating information over an unbounded state-space), numerical evidence is presented that demonstrates volatility clustering and long-memory patterns in the volatility of asset returns. Finally, output from the model is compared to both the distribution of historical stock returns and the market price of an equity index option.     

Realized Volatility and Absolute Return Volatility: A Comparison Indicating Market Risk

Measuring volatility in financial markets is a primary challenge in the theory and practice of risk management and is essential when developing investment strategies. Although the vast literature on the topic describes many different models, two nonparametric measurements have emerged and received wide use over the past decade: realized volatility and absolute return volatility. The former is strongly favored in the financial sector and the latter by econophysicists. We examine the memory and clustering features of these two methods and find that both enable strong predictions. We compare the two in detail and find that although realized volatility has a better short-term effect that allows predictions of near-future market behavior, absolute return volatility is easier to calculate and, as a risk indicator, has approximately the same sensitivity as realized volatility. Our detailed empirical analysis yields valuable guidelines for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods.     

Financial Symmetry and Moods in the Market

This paper studies how certain speculative transitions in financial markets can be ascribed to a symmetry break that happens in the collective decision making. Investors are assumed to be bounded rational, using a limited set of information including past price history and expectation on future dividends. Investment strategies are dynamically changed based on realized returns within a game theoretical scheme with Nash equilibria. In such a setting, markets behave as complex systems whose payoff reflect an intrinsic financial symmetry that guarantees equilibrium in price dynamics (fundamentalist state) until the symmetry is broken leading to bubble or anti-bubble scenarios (speculative state). We model such two-phase transition in a micro-to-macro scheme through a Ginzburg-Landau-based power expansion leading to a market temperature parameter which modulates the state transitions in the market. Via simulations we prove that transitions in the market price dynamics can be phenomenologically explained by the number of traders, the number of strategies and amount of information used by agents, all included in our market temperature parameter.     

Universal Behavior of Extreme Price Movements in Stock Markets

Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model—adding a slow, but significant, fluctuation to the standard deviation of the process—accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here.     

Transient and selective overexpression of dopamine D2 receptors in the striatum causes persistent abnormalities in prefrontal cortex functioning

Increased activity of D2 receptors (D2Rs) in the striatum has been linked to the pathophysiology of schizophrenia. To determine directly the behavioral and physiological consequences of increased D2R function in the striatum, we generated mice with reversibly increased levels of D2Rs restricted to the striatum. D2 transgenic mice exhibit selective cognitive impairments in working memory tasks and behavioral flexibility without more general cognitive deficits. The deficit in the working memory task persists even after the transgene has been switched off, indicating that it results not from continued overexpression of D2Rs but from excess expression during development. To determine the effects that may mediate the observed cognitive deficits, we analyzed the prefrontal cortex, the brain structure mainly associated with working memory. We found that D2R overexpression in the striatum impacts dopamine levels, rates of dopamine turnover, and activation of D1 receptors in the prefrontal cortex, measures that are critical for working memory.     

Study on Market Stability and Price Limit of Chinese Stock Index Futures Market: An Agent-Based Modeling Perspective

This paper explores a method of managing the risk of the stock index futures market and the cross-market through analyzing the effectiveness of price limits on the Chinese Stock Index 300 futures market. We adopt a cross-market artificial financial market (include the stock market and the stock index futures market) as a platform on which to simulate the operation of the CSI 300 futures market by changing the settings of price limits. After comparing the market stability under different price limits by appropriate liquidity and volatility indicators, we find that enhancing price limits or removing price limits both play a negative impact on market stability. In contrast, a positive impact exists on market stability if the existing price limit is maintained (increase of limit by10%, down by 10%) or it is broadened to a proper extent. Our study provides reasonable advice for a price limit setting and risk management for CSI 300 futures.     

Emergent Group Level Navigation: An Agent-Based Evaluation of Movement Patterns in a Folivorous Primate

The foraging activity of many organisms reveal strategic movement patterns, showing efficient use of spatially distributed resources. The underlying mechanisms behind these movement patterns, such as the use of spatial memory, are topics of considerable debate. To augment existing evidence of spatial memory use in primates, we generated movement patterns from simulated primate agents with simple sensory and behavioral capabilities. We developed agents representing various hypotheses of memory use, and compared the movement patterns of simulated groups to those of an observed group of red colobus monkeys (Procolobus rufomitratus), testing for: the effects of memory type (Euclidian or landmark based), amount of memory retention, and the effects of social rules in making foraging choices at the scale of the group (independent or leader led). Our results indicate that red colobus movement patterns fit best with simulated groups that have landmark based memory and a follow the leader foraging strategy. Comparisons between simulated agents revealed that social rules had the greatest impact on a group’s step length, whereas the type of memory had the highest impact on a group’s path tortuosity and cohesion. Using simulation studies as experimental trials to test theories of spatial memory use allows the development of insight into the behavioral mechanisms behind animal movement, developing case-specific results, as well as general results informing how changes to perception and behavior influence movement patterns.     

Simulation and inference for stochastic volatility models driven by Levy processes

We study Ornstein-Uhlenbeck stochastic processes driven by Lévyprocesses, and extend them to more general non-Ornstein-Uhlenbeckmodels. In particular, we investigate the means of making thecorrelation structure in the volatility process more flexible.For one model, we implement a method for introducing quasi long-memoryinto the volatility model. We demonstrate that the models canbe fitted to real share price returns data.     

Cross-Correlation Asymmetries and Causal Relationships between Stock and Market Risk

We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock prices from the Standard & Poor''s 500 index for 1994–2013. We focus on the behavior of the cross-correlations at the times of financial crises with significant jumps of market volatility. The observed historical dynamics showed that the dependence between the risks was almost linear during the US stock market downturn of 2002 and after the US housing bubble in 2007, remaining at that level until 2013. Moreover, the averaged cross-correlation function often had an asymmetric shape with respect to zero lag in the periods of high correlation. We develop the analysis by the application of the linear response formalism to study underlying causal relations. The calculated response functions suggest the presence of characteristic regimes near financial crashes, when the volatility of an individual stock follows the market volatility and vice versa.     

Quantifying Stock Return Distributions in Financial Markets

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales.     

Statistical Properties and Pre-Hit Dynamics of Price Limit Hits in the Chinese Stock Markets

Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders’ short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners.     

The Effect of Wind Exposure on the Web Characteristics of a Tetragnathid Orb Spider

Studies on spiders in their natural habitats are necessary for determining the full range of plasticity in their web-building behaviour. Plasticity in web design is hypothesised to be important for spiders building in habitats where environmental conditions cause considerable web damage. Here we compared web characteristics of the orb spider Metellina mengei (Araneae, Tetragnathidae) in two different forest habitats differing in their wind exposure. We found a notable lack of differences in web geometry, orientation and inclination between webs built along an exposed forest edge and those built inside the forest, despite marked differences in wind speed. This suggests that M. mengei did not exhibit web-building plasticity in response to wind in the field, contrasting with the findings of laboratory studies on other species of orb spiders. Instead, differences in prey capture and wind damage trade-offs between habitats may provide an explanation for our results, indicating that different species employ different strategies to cope with environmental constraints.     

Variability in thermal and phototactic preferences in Drosophila may reflect an adaptive bet‐hedging strategy

Organisms use various strategies to cope with fluctuating environmental conditions. In diversified bet‐hedging, a single genotype exhibits phenotypic heterogeneity with the expectation that some individuals will survive transient selective pressures. To date, empirical evidence for bet‐hedging is scarce. Here, we observe that individual Drosophila melanogaster flies exhibit striking variation in light‐ and temperature‐preference behaviors. With a modeling approach that combines real world weather and climate data to simulate temperature preference‐dependent survival and reproduction, we find that a bet‐hedging strategy may underlie the observed interindividual behavioral diversity. Specifically, bet‐hedging outcompetes strategies in which individual thermal preferences are heritable. Animals employing bet‐hedging refrain from adapting to the coolness of spring with increased warm‐seeking that inevitably becomes counterproductive in the hot summer. This strategy is particularly valuable when mean seasonal temperatures are typical, or when there is considerable fluctuation in temperature within the season. The model predicts, and we experimentally verify, that the behaviors of individual flies are not heritable. Finally, we model the effects of historical weather data, climate change, and geographic seasonal variation on the optimal strategies underlying behavioral variation between individuals, characterizing the regimes in which bet‐hedging is advantageous.     

Carving the cognitive niche: optimal learning strategies in homogeneous and heterogeneous environments

A model learning system is constructed, in which an organism samples behaviors from a behavioral repertoire in response to a stimulus and selects the behavior with the highest payoff. The stimulus and most rewarding behavior may be kept in the organism's long-term memory and reused if the stimulus is encountered again. The value of the memory depends on the reliability of the stimulus, that is, how the corresponding payoffs of behaviors change over time. We describe how the inclusion of memory can increase the optimal sampling size in environments with some stimulus reliability. In addition to using memory to guide behavior, our organism may use information in its memory to choose the stimulus to which it reacts. This choice is influenced by both the organism's memory state and how many stimuli the organism can observe (its sensory capability). The number of sampled behaviors, memory length, and sensory capability are the variables that define the learning strategy. When all stimuli have the same reliability, there appears to be only a single optimal learning strategy. However, when there is heterogeneity in stimulus reliability, multiple locally optimal strategies may exist.     

Fish is food--the FAO's fish price index

World food prices hit an all-time high in February 2011 and are still almost two and a half times those of 2000. Although three billion people worldwide use seafood as a key source of animal protein, the Food and Agriculture Organization (FAO) of the United Nations-which compiles prices for other major food categories-has not tracked seafood prices. We fill this gap by developing an index of global seafood prices that can help to understand food crises and may assist in averting them. The fish price index (FPI) relies on trade statistics because seafood is heavily traded internationally, exposing non-traded seafood to price competition from imports and exports. Easily updated trade data can thus proxy for domestic seafood prices that are difficult to observe in many regions and costly to update with global coverage. Calculations of the extent of price competition in different countries support the plausibility of reliance on trade data. Overall, the FPI shows less volatility and fewer price spikes than other food price indices including oils, cereals, and dairy. The FPI generally reflects seafood scarcity, but it can also be separated into indices by production technology, fish species, or region. Splitting FPI into capture fisheries and aquaculture suggests increased scarcity of capture fishery resources in recent years, but also growth in aquaculture that is keeping pace with demand. Regionally, seafood price volatility varies, and some prices are negatively correlated. These patterns hint that regional supply shocks are consequential for seafood prices in spite of the high degree of seafood tradability.     

Wall Street's assessment of plastic surgery--related technology: a clinical and financial analysis

Many plastic surgeons develop technologies that are manufactured by Wall Street-financed companies. Others participate in the stock market as investors. This study examines the bioengineered skin industry to determine whether it integrates clinical and financial information as Wall Street tenets would predict, and to see whether the financial performance of these companies provides any lessons for practicing plastic surgeons. In efficient markets, the assumptions on which independent financial analysts base their company sales and earnings projections are clinically reasonable, the volatility of a company's stock price does not irrationally differ from that of its industry sector, and the buy/sell recommendations of analysts are roughly congruent. For the companies in this study, these key financial parameters were compared with a benchmark index of 69 biotech companies of similar age and annual revenues (Student's t test). Five bioengineered skin companies were included in the study. Analysts estimated that each company would sell its product to between 24 and 45 percent of its target clinical population. The average stock price volatility was significantly higher for study companies than for those in the benchmark index (p < 0.05). Similarly, buy/sell recommendations of analysts for the study companies were significantly less congruent than those for the benchmark companies (p < 0.05). These results indicate clinically unrealistic projections for market penetration, significantly high price volatility, and significantly high discordance among professional analysts. In all cases, the market is inefficient-an unusual finding on Wall Street. A likely explanation for this market failure is a cycle of poor clinical correlation when assigning sales projections, which in turn leads to price volatility and discordance of buy/sell recommendations. This study's findings have implications for plastic surgeons who develop new technology or who participate in the equities markets as investors. Plastic surgeons who develop new medical devices or technology cannot universally depend on the market to drive clinically reasonable financial performance. Although inflated sales estimates have benefits in the short term, failure to meet projections exacts severe financial penalties. Plastic surgeons who invest in the stock market, because of their unique clinical experience, may sometimes be in the position to evaluate new technologies and companies better than Wall Street experts. Well-timed trades that use this expertise can result in opportunities for profit.     

Understanding Financial Market States Using an Artificial Double Auction Market

The ultimate value of theories describing the fundamental mechanisms behind asset prices in financial systems is reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidence from the fields of finance, mathematics, and even physics, previous theories that attempt to address the complexities of financial markets in full have been inadequate. We propose an artificial double auction market as an agent-based model to study the origin of complex states in financial markets by characterizing important parameters with an investment strategy that can cover the dynamics of the financial market. The investment strategies of chartist traders in response to new market information should reduce market stability based on the price fluctuations of risky assets. However, fundamentalist traders strategically submit orders based on fundamental value and, thereby stabilize the market. We construct a continuous double auction market and find that the market is controlled by the proportion of chartists, P_c. We show that mimicking the real state of financial markets, which emerges in real financial systems, is given within the range P_c = 0.40 to P_c = 0.85; however, we show that mimicking the efficient market hypothesis state can be generated with values less than P_c = 0.40. In particular, we observe that mimicking a market collapse state is created with values greater than P_c = 0.85, at which point a liquidity shortage occurs, and the phase transition behavior is described at P_c = 0.85.     

Structural Correlates of Antibodies Associated with Acute Reversal of Amyloid ��-related Behavioral Deficits in a Mouse Model of Alzheimer Disease

Immunotherapy targeting of amyloid β (Aβ) peptide in transgenic mouse models of Alzheimer disease (AD) has been widely demonstrated to resolve amyloid deposition as well as associated neuronal, glial, and inflammatory pathologies. These successes have provided the basis for ongoing clinical trials of immunotherapy for treatment of AD in humans. Acute as well as chronic Aβ-targeted immunotherapy has also been demonstrated to reverse Aβ-related behavioral deficits assessing memory in AD transgenic mouse models. We observe that three antibodies targeting the same linear epitope of Aβ, Aβ_3–7, differ in their ability to reverse contextual fear deficits in Tg2576 mice in an acute testing paradigm. Reversal of contextual fear deficit by the antibodies does not correlate with in vitro recognition of Aβ in a consistent or correlative manner. To better define differences in antigen recognition at the atomic level, we determined crystal structures of Fab fragments in complex with Aβ. The conformation of the Aβ peptide recognized by all three antibodies was highly related and is also remarkably similar to that observed in independently reported Aβ:antibody crystal structures. Sequence and structural differences between the antibodies, particularly in CDR3 of the heavy chain variable region, are proposed to account for differing in vivo properties of the antibodies under study. These findings provide a structural basis for immunotherapeutic strategies targeting Aβ species postulated to underlie cognitive deficits in AD.