Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors'' network structure by measuring the investors'' communication patterns. Our results showed that the investors'' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors'' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum''s reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.     

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.     

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.     

Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis

This paper looks at the relationship between negative news and stock markets in times of global crisis, such as the 2008/2009 period. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.     

Evidence of multifractality from emerging European stock markets

We test for the presence of multifractality in the daily returns of the three most important stock market indices from Central and Eastern Europe, Czech PX, Hungarian BUX and Polish WIG using the Empirical Mode Decomposition based Multifractal Detrended Fluctuation Analysis. We found that the global Hurst coefficient varies with the q coefficient and that there is multifractality evidenced through the multifractal spectrum. The exercise is replicated for the sample around the high volatility period corresponding to the last global financial crisis. Although no direct link has been found between the crisis and the multifractal spectrum, the crisis was found to influence the overall shape as quantified through the norm of the multifractal spectrum.     

Stock Market Returns and Clinical Trial Results of Investigational Compounds: An Event Study Analysis of Large Biopharmaceutical Companies

Background

For biopharmaceutical companies, investments in research and development are risky, and the results from clinical trials are key inflection points in the process. Few studies have explored how and to what extent the public equity market values clinical trial results.

Methods

Our study dataset matched announcements of clinical trial results for investigational compounds from January 2011 to May 2013 with daily stock market returns of large United States-listed pharmaceutical and biotechnology companies. Event study methodology was used to examine the relationship between clinical research events and changes in stock returns.

Results

We identified public announcements for clinical trials of 24 investigational compounds, including 16 (67%) positive and 8 (33%) negative events. The majority of announcements were for Phase 3 clinical trials (N = 13, 54%), and for oncologic (N = 7, 29%) and neurologic (N = 6, 24%) indications. The median cumulative abnormal returns on the day of the announcement were 0.8% (95% confidence interval [CI]: –2.3, 13.4%; P = 0.02) for positive events and –2.0% (95% CI: –9.1, 0.7%; P = 0.04) for negative events, with statistically significant differences from zero. In the day immediately following the announcement, firms with positive events were associated with stock price corrections, with median cumulative abnormal returns falling to 0.4% (95% CI: –3.8, 12.3%; P = 0.33). For firms with negative announcements, the median cumulative abnormal returns were –1.7% (95% CI: –9.5, 1.0%; P = 0.03), and remained significantly negative over the two day event window. The magnitude of abnormal returns did not differ statistically by indication, by trial phase, or between biotechnology and pharmaceutical firms.

Conclusions

The release of clinical trial results is an economically significant event and has meaningful effects on market value for large biopharmaceutical companies. Stock return underperformance due to negative events is greater in magnitude and persists longer than abnormal returns due to positive events, suggesting asymmetric market reactions.     

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.     

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.     

Catastrophe Risk Analysis: A Financial Perspective

This article examines how catastrophe events affect risk analysis from a financial perspective. Data from different industries such as Advanced Sustainable Performance Indices, gold, energy, real estate, and insurance were collected and analyzed. The performance of these funds was compared by using various financial ratios. Then we tested the stock selecting and market timing abilities. We also assessed whether a particular catastrophe event has affected stock prices by analysis of two event studies, the 9/11 terrorist attacks in the United States and the collapse through bankruptcy of Lehman Brothers. We examined how an asset portfolio performs under catastrophic events and under the situation of adding Advanced Sustainable Performance Indices into an investor's portfolio basket. We found that the 9/11 terrorist attacks affected the Dow Jones Real Estate Index's prices a lot. Lehman Brothers' bankruptcy filing had a positive impact on the CBOE Gold Index, and had a large impact on the Fidelity US Bond Index. The ASPI Index in our optimization problem gave us better diversification. From our analysis, we conclude that portfolio diversification is a good way to hedge against catastrophic risk.     

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.     

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.     

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.     

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users’ behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012–2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a “wisdom-of-the-crowd” effect that allows to exploit users’ activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.     

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios.     

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 Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.     

Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor's 100

Today´s connected world allows people to gather information in shorter intervals than ever before, widely monitored by massive online data sources. As a dramatic economic event, recent financial crisis increased public interest for large companies considerably. In this paper, we exploit this change in information gathering behavior by utilizing Google query volumes as a "bad news" indicator for each corporation listed in the Standard and Poor´s 100 index. Our results provide not only an investment strategy that gains particularly in times of financial turmoil and extensive losses by other market participants, but reveal new sectoral patterns between mass online behavior and (bearish) stock market movements. Based on collective attention shifts in search queries for individual companies, hence, these findings can help to identify early warning signs of financial systemic risk. However, our disaggregated data also illustrate the need for further efforts to understand the influence of collective attention shifts on financial behavior in times of regular market activities with less tremendous changes in search volumes.     

The Application of Cash-Flow-at-Risk to Risk Management in a Deregulated Electricity Market

The current level of deregulation in electricity markets is continuing to expand. Although each of these markets has individual operational and financial structures, one common characteristic is volatility. This volatility is significant and time-varying, and the persistence of this volatility makes the management of financial risk a priority among market participants.

This article considers two applications of an innovative spot price model to risk management in such a market. The first application is the empirical estimation of risk premia in the market considered here. The results support other approaches, which find the risk premia to be both significant and time-variant. In addition, this work considers the application of a Cash-Flow-at-Risk (CFaR) approach to measuring and comparing financial risk among various portfolio alternatives. These portfolios are considered from the perspective of the electricity producer, and the electricity purchaser. This approach is flexible and practical, allowing the comparison among portfolios and across seasons. The results of this analysis show that derivative instruments are significantly over-priced in this market, and that producers have the opportunity to earn significant profits, above those justified by the inherent risk in the market.     

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.     

Spawning ecology of flannelmouth sucker, Catostomus lattipinnis (Catostomidae), in two small tributaries of the lower Colorado River

We report the first published accounts of spawning behavior and spawning site selection of the flannelmouth sucker in two small tributaries of the lower Colorado River in the Grand Canyon, Arizona. Spawning was observed on 20 March 1992 and from 28 March to 10 April 1993 in the Paria River, and from 16 to 19 March 1993 in Bright Angel Creek. Flannelmouth suckers exhibited promiscuous spawning behavior–individual females were typically paired with two or more males for a given event and sometimes changed partners between events. Multiple egg deposits by different females sometimes occurred at one spawning site. Flannelmouth sucker selected substrates from 16 to 32 mm diameter in both streams. Spawning occurred at depths of 10 to 25 cm in the Paria River and 19 to 41 cm in Bright Angel Creek. Mean column water velocities at spawning locations ranged from 0.15 to 1.0 m sec^-1 in the Paria River and from 0.23 to 0.89 m sec^-1 in Bright Angel Creek. Water temperatures recorded during spawning ranged from 9 to 18° C in the Paria River and 13 to 15° C in Bright Angel Creek. Spawning flannelmouth sucker ascended 9.8 km upstream in the Paria River and 1.25 km in Bright Angel Creek. Spawning females (410–580 mm) were significantly larger than spawning males (385–530 mm) in the Paria River. The mean size of spawning fish in the Paria River was significantly smaller than the entire stock, averaged throughout the study period (380–620 mm). However, fish spawning in 1992–1993 averaged 53 mm larger than fish spawning in the same reach of the Paria River in 1981, indicating a shift in the size structure of this stock.