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.     

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.     

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.     

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)²PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)²PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)²PCA to forecast the next day''s stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.     

Confidence and the Stock Market: An Agent-Based Approach

Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior.     

The speculator's bread: what is behind rising food prices?

Speculators increasingly invest into food markets for financial gain, with potentially devastating consequences for millions of poor people who cannot afford food at inflated prices....the market price of agricultural commodities is more important than those of nearly all other productsMost citizens in developed countries buy and consume their food without any consideration of how it is produced or how it gets from the field or slaughterhouse to the supermarket. They take for granted that they can afford it and do not care about its production and the economic, financial and other factors that eventually determine its price on the supermarket shelf. However, the market price of agricultural commodities is more important than those of nearly all other products. Increasing prices can cause hunger for millions of people and enormous political repercussions. In 2007–2008, a price explosion for grain and other commodities caused malnutrition among an estimated 115 million people and triggered hunger revolts in several nations. The prices subsequently dropped, only to soar again three years later (Fig 1), surpassing previous highs by the end of 2010. The revolt in Tunisia in January 2011 that eventually led to the government''s downfall was originally triggered by rising food prices.Open in a separate windowFigure 1FAO Food Price Index values from 1990 to 2010“The FAO Food Price Index is a measure of the monthly change in international prices of a basket of food commodities. It consists of the average of five commodity group price indices (representing 55 quotations), weighted with the average export shares of each of the groups for 2002–2004” (http://www.fao.org). FAO, Food and Agricultural Organization.Which factors or mechanisms determine the market price of food? If a drought or a flood were to destroy harvests in wheat-exporting countries such as Australia or Russia, it would certainly drive up the price of wheat. Yet, there is also ongoing debate about whether and how the 2007–2008 price spike might have been driven by financial speculation in commodity markets. This is not only a media debate, but also of scientific interest as it gets to the heart of economic theory; indeed, various research articles have tried to analyse and explain the causes of the 2007–2008 price spike.The revolt in Tunisia in January 2011 that eventually led to the government''s downfall was originally triggered by rising food pricesThe global market-prices for agricultural commodities are determined in different ways, depending on the commodity. Some products, such as rice, are mainly traded nationally, with only a small share being traded internationally; other commodities are traded in large quantities on international commodity exchanges, particularly in the USA. As the USA is one of the main producers and exporters of wheat, corn and soybean—and has a liberal market tradition—these exchanges are important for both the US and the global agricultural industry. In Europe, commodity exchanges for agricultural products play a lesser role, partly owing to the former Common Agricultural Policy of the European Union (EU), which tightly regulated the production of foodstuffs. However, this policy is now changing and exchanges are set to have a more important role in Europe too. The Paris commodity exchange is already a relevant marketplace for wheat, and the London commodity exchange has an important role in the global trade of coffee, cocoa and sugar.The price of any commodity should reflect the levels of supply and demand. Of course, fluctuations occur and are sometimes justified by fundamental factors, for example a bad harvest or increased demand. However, other external factors—such as a lack of information, asymmetries, externalities, conflicts of interest and agency problems—can also influence prices on commodity markets. In addition, outright speculation (for instance by hoarding), price bubbles and even market manipulation can repeatedly influence prices. The largest grain companies in the world, such as ADM, Cargill, Dreyfus and Bunge, have an interest in maximizing their profits and do so by buying and selling commodities at the most suitable time. Even farmers speculate on commodity markets, for example by withholding their harvest when they expect a price rise. To keep these factors and interests under control it is necessary and indeed legitimate to regulate and control markets, not just for food commodities.Commodities are not only traded physically on ‘spot'' or cash markets, but also subject to forward buying through ‘futures''. A future is a contract between a producer—that is, a farmer—and a buyer that specifies the amount, the price and the delivery date of a purchase. Similarly, buyers—such as millers—can use futures to buy a certain amount of grain at a guaranteed price ahead of time. Many farmers and end-users take advantage of futures to pre-sell or pre-purchase agricultural goods to insure themselves against market fluctuations. This ‘hedging'' reduces their risks and enables them to invest more safely.Intermediary traders ensure that the two sides meet. Traditionally, these traders are established firms that buy and sell futures from producers and to consumers, thereby providing the necessary liquidity. They shoulder the risks and gain their profits from the difference between the price stipulated in a future and the final market-price. These firms, naturally, have a profound knowledge and understanding of the commodity markets in which they are trading.In addition, such trading can take place both on exchanges (then called ‘futures trading'') and bilaterally ‘over-the-counter'' (OTC). Modern trading in commodity futures began in the USA during the mid-nineteenth century. Chicago, where the first modern wheat futures were traded, is still the largest and most important marketplace for agricultural commodities in the world, even though Asian countries have contested this in recent years.As futures no longer require the seller to possess the actual goods and because physical delivery is replaced by cash exchanges, their volume can be separated from the actual quantity of the commodity; their volume can also increase indefinitely as long as enough intermediaries want to deal with them. In the past, though, relatively few investors and intermediaries speculated on future markets. Moreover, regulatory agencies can and have imposed rules to limit the extent of speculation, for instance by regulating delivery dates, delivery locations, the timeframe for buying, certified stocks, storage fees, position limits, price limits and other factors.However, an increasing number of investors from outside the traditional markets—including banks, and pension and investment funds—have begun to speculate on agricultural futures exchanges. These large investors not only push the exploitation of price trends, but also—in contrast to the traditional intermediaries—are often not familiar with the cash market and the fundamentals. These outside speculators also often invest for reasons that have nothing to do with the cash market, for instance to protect themselves against price fluctuations on financial markets....an increasing number of investors from outside the traditional markets—including banks, and pension and investment funds—have begun to speculate on agricultural futures exchangesThis is the main reason that the US government imposed strict limits for financial speculation on commodity future exchanges. Only commercial participants with an interest in hedging were exempted. However, these rules and limits have been slowly eroded or removed. In 1991, one financial investor managed to get an official exemption from the limits in order to hedge his financial risk. In the following years, more traders were granted such exemptions or limit expansions. In 2000, the Commodity Futures Modernization Act exempted OTC trading from regulatory oversight and control. As a result of laxer oversight, other speculators joined the market, especially after the beginning of the financial crisis in 2006. These newcomers include banks such as Goldman Sachs, JP Morgan and Deutsche Bank; pension funds, such as the California State Teachers'' Retirement System; and hedge funds. A good deal of their trading is carried out through ‘swaps'', a type of OTC instrument.As these new and powerful speculators have entered the market, the total volume of new speculative investments in commodity indexes has increased more than tenfold in five years...As these new and powerful speculators have entered the market, the total volume of new speculative investments in commodity indexes has increased more than tenfold in five years: from an estimated $15 billion in 2003 to around $200 billion in 2008. ‘Index funds'', which aim to imitate the cash markets with futures, rose particularly high: between 2006 and 2008, index traders increased the demand for wheat futures from 33% to 100%. The number of daily outstanding contracts held by index traders on the Chicago Mercantile Exchange grew from approximately 30,000 in early 2004 to 220,000 in mid-2008 (US Senate PSI, 2009).The unexpected price hike in 2007–2008 has triggered a lively debate among economists about whether this increased speculation in futures has driven up cash prices. This discussion is both a theoretical debate about how futures markets work and an empirical debate about the reasons behind the price rise. The main questions are: Can speculation alone move the prices of futures and can there be excessive, that is, harmful, speculation in futures? Can futures prices influence the cash markets, and if so, how?Some claim that that the amount of trading in futures is irrelevant to the real price, because it is always a “zero-sum game” between traders (Irwin & Sanders, 2010). For every position that bets on a rising price (long position), there is a counterparty which bets on a falling price (short position). By this view, the amount of trading is detached from the price level. Indeed, it is not possible to demonstrate an unequivocal relationship between the amount of trading and the price.Yet, a large in-flow or out-flow of money can create a price shift. Statistical research has demonstrated the growing interdependence of commodity markets, both between the markets themselves and with financial markets. Tang & Xiong (2010) found that “concurrent with the rapidly growing index investment in commodities markets since the early 2000s, futures prices of different commodities in the US became increasingly correlated with each other. [...] In contrast, such commodity price co-movements were absent in China, which refutes growing commodity demands from emerging economies as the driver.”Silvennoinen & Thorp (2010) observe, “higher and more variable correlations between commodity futures and stock returns.” This trend—often called financialization—has also been observed by the United Nations Conference on Trade and Development (UNCTAD, 2009; Mayer, 2009). Similarly, an investigation by the US Senate took the view that the price of US futures had been influenced by excessive speculation (US Senate PSI, 2009).The second question, which is more relevant to consumers, remains: how can futures prices influence the cash price? Theoretically, the cash price should always converge with the futures price once the future is delivered. Some economists therefore assume that if futures are over-priced, the cash market will simply solve this problem by speculative arbitrage trading: buying something at a lower price and immediately reselling it for a higher price. Futures markets, in this view, are always driven by the cash markets, which themselves are determined by the fundamental mechanisms of supply and demand (Irwin & Sanders, 2010). However, it is logical to assume that futures markets have an influence on cash markets because, as all economists agree, they should predict the future price on the cash markets.Thus: how does speculation in futures influence prices on cash markets and how long does the effect last? Some scientists at the UN Food and Agricultural Organization were able to identify only short-term effects (Dreschler et al, 2010), but what does short-term mean? Different economists use different definitions: some define short-term as one day, others one week and some others one month. However, if the same effect leads to a one-month deviation, why should it not cause a deviation of many months? And what is the effect of a month-long deviation for people who need to buy food every day? As the famous economist John Maynard Keynes noted, in the long run we are all dead. Indeed, financial speculators cannot suspend the laws of supply and demand in the long-term, but they are able to cause short- to medium-term price increases, which, for the world at large, is bad enough.Traders are usually open about the effects of their trading. In April 2006, a hedge fund manager commented: “There is so much money going into commodity markets that it almost doesn''t matter how fundamentals behave” (WDM, 2010). At the same time, the investment bank Merill Lynch estimated that commodity prices had increased by 50% through speculation (Thornton, 2006). One of the most well-known speculators, George Soros, commented that, “Every speculation is also rooted in reality [however] speculators create the bubble that lies above everything. Their expectations, their gambling on futures help drive up prices, and their business distorts prices, which is especially true for commodities. It is like hoarding food in the midst of a famine, only to make profits on rising prices. That should not be possible” (WDM, 2010).Furthermore, if the futures price is higher than the cash price, traders on the cash market are inclined to store food in order to gain higher incomes. This is a common occurrence in hard commodity markets, such as oil or metal. However, hoarding of agricultural commodities driven by expectations of higher prices can also take place. Finally, divergent cash and futures prices, along with market volatility, cause other problems; higher costs are required for risk management and hedging, which harms the food business and ultimately affects food supply and prices (US Senate PSI, 2009).Many observers initially argued that the price spike of 2007–2008 was related to bad harvests, rising demand from importing countries—notably China—and the growing production of biofuels. A leading study by the World Bank was perhaps most influential at the time (World Bank, 2008). However, even when it became clear in early 2008 that harvests had recovered, the prices still rose. Moreover, prices on the cash and futures markets plummeted from mid-2008 onwards although demand from emerging countries remained high, even during the financial crisis. Some researchers are still not convinced that the 2007–2008 price spike was caused by speculation and continue to point to the increasing demand for biofuels, depreciation of the US dollar and the rising price of oil to explain this phenomenon (Headey & Fan, 2010).Nonetheless, criticism of financial speculations on commodity markets has been growing. In 2009, US hedge fund manager Michael W. Masters testified to the US Senate that passive investment, such as index funds, “provides no benefits to the markets while it exacts a heavy toll” (Masters, 2009). Accordingly, the US Senate and various scholars found signs of excessive and harmful speculation in US wheat markets (US Senate PSI, 2009; Lines, 2010; Gilbert, 2010). Headey & Fan (2010) reject the argument that rising demand from emerging countries could have caused the spike, writing that “low interest rates, and investment portfolio adjustments in favour of commodities” have an important role in price formation. The World Bank, in a recent working paper (Baffes & Haniotis, 2010), has also recognized the influence of financial speculators on prices: “We conjecture that index fund activity [...] played a key role during the 2008 price spike. Biofuels played some role too, but much less than initially thought. And we find no evidence that alleged stronger demand by emerging economies had any effect on world prices.” In a more recent paper by the UN Special Rapporteur on the Right to Food, Olivier de Schutter (2010) found that “a significant portion of the price increases and volatility of essential food commodities can only be explained by the emergence of a speculative bubble.”Another reason to assume that speculation is a harmful influence is that the oil-price peak of 2008 also seems to have been caused by speculation (Masters, 2009; Chevalier et al, 2010). This is not an independent explanatory variable for the price rise in agricultural commodities, but it highlights the impact of speculation.In addition to index funds, hedge funds have become increasingly important players in commodity markets. These funds, which can invest more freely than any other type of fund, often take highly speculative long and short positions to profit from rising or falling prices. Hedge funds can also move huge amounts of money. In July 2010, a single hedge fund bought almost all cocoa futures on the London commodity exchange, in an attempt to force cocoa buyers to buy from it at a monopolistic price. Afterwards, a group of cocoa processing companies called on the London International Financial Futures and Options Exchange to prevent such speculations and threatened to go to the New York commodity exchange, where tighter regulations are in force.Today, there is again a debate about whether speculation has a role in rising prices. On the one hand, harvest losses for wheat crops in July 2010 would justify a slight price rise. On the other hand, National Farmers Union representative Doug Sombke said at a US Commodity Futures Trading Commission hearing in the USA, “I think speculators have created a huge mess here for us. Farmers are feeling this today” (Reuters, 2010). Klaus Josef Lutz, CEO of BayWa, one of Europe''s biggest grain traders, commented that, “70 percent of the price rise can be blamed on speculators” (Handelsblatt, 2010). Finally, wheat is not nearly as scarce as the price rise would suggest: the global 2010 harvest is estimated to be the third largest of all time (FAO, 2010a).Higher food prices not only cause immediate problems; by reducing the available money for health care and education, they also produce negative long-term effectsTwo-thirds of developing countries are net importers of basic food commodities, even if the percentage of farmers in these countries is much higher than in industrialized countries. Furthermore, the relative household expenditure on food is much higher in developing countries: 60–80% compared with approximately 15% in the EU. This makes developing countries particularly vulnerable to price rises. They were hit hard in 2007–2008 and are again facing serious problems; the recent revolt in Tunisia being the most visible uprising sparked by food prices. Higher food prices not only cause immediate problems; by reducing the money available for health care and education, they also produce negative long-term effects.Some developing countries are commodity producers. As such, they profit, more or less, from price increases. However, their small-scale farmers are the weakest link in the production chain and profit the least from price rises. Apart from speculators, it is larger intermediaries, retailers or bigger farms that reap most of the profits (Höffler & Owour Ochieng, 2009).Growing ‘financialization'' makes it vital to reform commodity futures markets and set clear limits for speculation. Trading by financial speculators must take place on regulated and transparent commodity exchanges. The number and influence of speculators must be controlled through market and position limits. As Ann Berg, former commodity trader, stressed at a recent FAO special committee, “Over 150 years of futures trading history demonstrates that position limits are necessary in commodities of finite supply to curb excessive speculation and hoarding” (FAO, 2010b). Furthermore, some types of investment, such as index funds, could be strongly restricted. Generally, a legal demarcation between the commodities futures markets and the financial markets and a special agency to oversee it is required, such as the US Commodity Futures Trading Commission.The USA has learned its lesson from the past few years and is once again restricting financial speculation through reforms introduced in July 2010The USA has learned its lesson from the past few years and is once again restricting financial speculation through reforms introduced in July 2010. The US government aims to return OTC trading—mostly carried out as swaps—to multilateral trading and clearing platforms. Higher transparency requirements will apply and financial speculators will once again be limited by stricter position limits, without exemptions.As mentioned above, fewer agricultural commodities are traded on a large scale in the EU, but the London and Paris commodity exchanges still exert an influence. Moreover, stricter regulations in the USA could induce speculators to move their activities to European exchanges, even though there are strong position limits, at least at the Paris commodity exchange. Reforms of the financial markets in the EU are therefore necessary, and these are currently being debated. Michel Barnier, the European Commissioner for Internal Market and Services, has rightfully called speculation with food commodities a scandal. Whether his words will be followed with actions remains to be seen.In September 2010, the European Commission released draft regulations for OTC derivatives that include plans to create new trading platforms called ‘central counterparties''. The draft regulations require that OTC trades are limited and fulfil transparency requirements (EC, 2010). Along with these, two other directives will be revised: one on markets in financial instruments, such as futures, and one on market abuse. However, the EU has not yet acknowledged that commodities markets are not the same as financial markets. It is therefore not certain whether they will propose and pass appropriate regulation, which ought to include a special regulatory body, full transparency and position limits....farmers and buyers have a strong interest in managing their risks, and futures markets have proven to be an appropriate, if imperfect, mechanism...Given the problems that commodity futures markets have caused, it might be tempting to renounce them. Conversely, farmers and buyers have a strong interest in managing their risks, and futures markets have proven to be an appropriate, if imperfect, mechanism by which to do so. Other measures such as harvest assurances bring their own disadvantages. Moreover, local markets can also cause problems, as can political measures, especially when these include export bans.Nonetheless, it is prudent to explore alternatives. These could include regional or bilateral treaties between states, which have been successfully practised in several cases in Asia. The build-up of higher, more reliable reserves at the national, regional or global level is another option for dealing with volatility and uncertainty. Such reserves could also be virtual, as has been suggested by one leading agricultural researcher, Professor Joachim von Braun from Bonn University in Germany (von Braun, 2010).In the meantime, banks and hedge funds have also begun to invest in cash markets. In 2009, Goldman Sachs, Barclays and JP Morgan reportedly controlled physical commodities worth £16 billion—more than three times the amount they controlled in 2008. The head of one cocoa retail company commented on this development: “A lot of branch-alien money has poured into the market. The banks that are part of the game now are not giving us loans anymore or require much more collateral, as the markets have become more volatile. This is really grotesque” (Handelsblatt, 2010). This seems to be the next step in the ‘financialization'' of commodity markets, but the central question is whether banks should be able to buy our food or if they should get back to their initial purpose: serving the economy with credit.Food markets should serve the interests of people and not those of financial investorsFood markets should serve the interests of people and not those of financial investors. In this regard, politics has failed to protect food markets from excessive speculation. As former US President Bill Clinton said in a speech at the United Nations'' World Food Day on 16 October, 2008, “We need the World Bank, the IMF, all the big foundations, and all the governments to admit that, for 30 years, we all blew it, including me when I was President. We were wrong to believe that food was like some other product in international trade, and we all have to go back to a more responsible and sustainable form of agriculture” (Clinton, 2008).Given that hunger still exists in the world, even small price increases that are driven by financial investment are scandalous. We must not allow food to become a purely financial asset.? Open in a separate windowMarkus Henn

Science & Society Series on Food and Science

This article is part of the EMBO reports Science & Society series on ''food and science'' to highlight the role of natural and social sciences in understanding our relationship with food. We hope that the series serves a delightful menu of interesting articles for our readers.     

Exploring Market State and Stock Interactions on the Minute Timescale

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.     

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.     

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.     

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.     

Dynamic Evolution of Cross-Correlations in the Chinese Stock Market

The analysis of cross-correlations is extensively applied for the understanding of interconnections in stock markets and the portfolio risk estimation. Current studies of correlations in Chinese market mainly focus on the static correlations between return series, and this calls for an urgent need to investigate their dynamic correlations. Our study aims to reveal the dynamic evolution of cross-correlations in the Chinese stock market, and offer an exact interpretation for the evolution behavior. The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving window with a size of 400 days. The evolutions of the statistical properties of the correlation coefficients, eigenvalues, and eigenvectors of the correlation matrices are carefully analyzed. We find that the stock correlations are significantly increased in the periods of two market crashes in 2001 and 2008, during which only five eigenvalues significantly deviate from the random correlation matrix, and the systemic risk is higher in these volatile periods than calm periods. By investigating the significant contributors of the deviating eigenvectors in different time periods, we observe a dynamic evolution behavior in business sectors such as IT, electronics, and real estate, which lead the rise (drop) before (after) the crashes. Our results provide new perspectives for the understanding of the dynamic evolution of cross-correlations in the Chines stock markets, and the result of risk estimation is valuable for the application of risk management.     

Health Risk Assessment of Consumption of Heavy Metals in Market Food Crops from Sialkot and Gujranwala Districts,Pakistan

This study was performed to investigate the potential health risk of heavy metals (HMs) through consumption of market food crops (MFCs) in the Sialkot and Gujranwala districts, Pakistan. Both study areas are located in industrialized regions of the country, where atmospheric pollution is a problem and irrigation of food crops is mostly practiced on the use of wastewater/contaminated water. For the purpose of this study, MFCs samples were collected and assessed for HMs (Cr, Ni, Cd, Pb, Mn, Cu, Zn, and Fe) by using flame atomic absorption spectrophotometry. Concentration of HMs such as Pb and Cd exceeded the Food and Agriculture/World Health Organization's recommended limits in all MFCs, while Cr in most of the vegetables of the Sialkot and Gujranwala districts also exceeded that limit. The health risk index was >1 in Triticum aestivum for Pb and Cd intake in the Sialkot district and only Pb in the Gujranwala district. Therefore, this study suggests pretreatment of wastewater and its utilization for lawns and green belts irrigation, rather than for food crops. This study also suggests a regular monitoring of HMs in the irrigation water, subsequent soil, air, and MFCs in order to prevent or reduce health hazards.     

Is There Any Overtrading in Stock Markets? The Moderating Role of Big Five Personality Traits and Gender in a Unilateral Trend Stock Market

Overtrading is a common anomaly among stock investors. This study examines the relationship between overtrading and investment returns and the impact of the Big Five traits and gender on overtrading in a unilateral trend stock market using a simulated stock investment system. The data were derived from a sample of undergraduates from six universities who performed in a simulated stock investment situation and had their personality traits measured by the Big Five Personality Questionnaire. The results indicate that: (1) Overtrading was significant in rising stock markets, but not significant in falling markets. (2) The degree of female investors who overtraded was significant in rising markets. (3) The degree of overtrading investors who were high in extroversion or agreeableness was significant in rising markets. The implications of these results for more effective investment strategies are discussed.     

Effects of market price on the dynamics of a spatial fishery model: Over-exploited fishery/traditional fishery

We present a dynamical model of a spatial fishery describing the time evolution of the fish stock, the fishing effort and the market price of the resource. The market price is fixed by the gap between the supply and the demand. Assuming two time scales, we use “aggregation of variables methods” in order to derive a reduced model governing fish density and fishing effort at a slow time scale. The bifurcation analysis of the reduced model is performed. According to parameters values, three main cases can occur: (i) a stable fishery free equilibrium, (ii) a stable persistent fishery equilibrium and (iii) coexistence of three strictly positive equilibria, two of them being stable separated by a saddle. In this last case, a stable equilibrium corresponds to a traditional fishery with large fish stock, small fishing effort and small market price. The second stable one corresponds to over-exploitation of the resource with small fish stock, large fishing effort and large market price.     

Establishing an index arbitrage model by applying neural networks method--a case study of Nikkei 225 index.

This paper applies the neural network method to establish an index arbitrage model and compares the arbitrage performances to that from traditional cost of carry arbitrage model. From the empirical results of the Nikkei 225 stock index market, following conclusions can be stated: (1) The basis will get enlarged for a time period, more profitability may be obtained from the trend. (2) If the neural network is applied within the index arbitrage model, twofold of return would be obtained than traditional arbitrage model can do. (3) If the T_basis has volatile trend, the neural network arbitrage model will ignore the peak. Although arbitrageur would lose the chance to get profit, they may reduce the market impact risk.     

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.     

Measurement error affects risk estimates for recruitment to the Hudson River stock of striped bass

We examined the consequences of ignoring the distinction between measurement error and natural variability in an assessment of risk to the Hudson River stock of striped bass posed by entrainment at the Bowline Point, Indian Point, and Roseton power plants. Risk was defined as the probability that recruitment of age-1+ striped bass would decline by 80% or more, relative to the equilibrium value, at least once during the time periods examined (1, 5, 10, and 15 years). Measurement error, estimated using two abundance indices from independent beach seine surveys conducted on the Hudson River, accounted for 50% of the variability in one index and 56% of the variability in the other. If a measurement error of 50% was ignored and all of the variability in abundance was attributed to natural causes, the risk that recruitment of age-1+ striped bass would decline by 80% or more after 15 years was 0.308 at the current level of entrainment mortality (11%). However, the risk decreased almost tenfold (0.032) if a measurement error of 50% was considered. The change in risk attributable to decreasing the entrainment mortality rate from 11 to 0% was very small (0.009) and similar in magnitude to the change in risk associated with an action proposed in Amendment #5 to the Interstate Fishery Management Plan for Atlantic striped bass (0.006)--an increase in the instantaneous fishing mortality rate from 0.33 to 0.4. The proposed increase in fishing mortality was not considered an adverse environmental impact, which suggests that potentially costly efforts to reduce entrainment mortality on the Hudson River stock of striped bass are not warranted.     

Unraveling Hidden Order in the Dynamics of Developed and Emerging Markets

The characterization of asset price returns is an important subject in modern finance. Traditionally, the dynamics of stock returns are assumed to lack any temporal order. Here we present an analysis of the autocovariance of stock market indices and unravel temporal order in several major stock markets. We also demonstrate a fundamental difference between developed and emerging markets in the past decade - emerging markets are marked by positive order in contrast to developed markets whose dynamics are marked by weakly negative order. In addition, the reaction to financial crises was found to be reversed among developed and emerging markets, presenting large positive/negative autocovariance spikes following the onset of these crises. Notably, the Chinese market shows neutral or no order while being regarded as an emerging market. These findings show that despite the coupling between international markets and global trading, major differences exist between different markets, and demonstrate that the autocovariance of markets is correlated with their stability, as well as with their state of development.     

Network Environment and Financial Risk Using Machine Learning and Sentiment Analysis

Under the network environment, the trading volume and asset price of a financial commodity or instrument are affected by various complicated factors. Machine learning and sentiment analysis provide powerful tools to collect a great deal of data from the website and retrieve useful information for effectively forecasting financial risk of associated companies. This article studies trading volume and asset price risk when sentimental financial information data are available using both sentiment analysis and popular machine learning approaches: artificial neural network (ANN) and support vector machine (SVM). Nonlinear GARCH-based mining models are developed by integrating GARCH (generalized autoregressive conditional heteroskedasticity) theory and ANN and SVM. Empirical studies in the U.S. stock market show that the proposed approach achieves favorable forecast performances. GARCH-based SVM outperforms GARCH-based ANN for volatility forecast, whereas GARCH-based ANN achieves a better forecast result for the volatility trend. Results also indicate a strong correlation between information sentiment and both trading volume and asset price volatility.     

High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.