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.     

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.     

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.     

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.     

Investment strategies used as spectroscopy of financial markets reveal new stylized facts

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This enormous dataset allows us to compare (i) a closed national market (A-shares) with an international market (B-shares), (ii) individuals and institutions, and (iii) real traders to random strategies with respect to timing that share otherwise all other characteristics. We find in general that more trading results in smaller net return due to trading frictions, with the exception that the net return is independent of the trading frequency for A-share individual traders. We unveiled quantitative power laws with non-trivial exponents, that quantify the deterioration of performance with frequency and with holding period of the strategies used by traders. Random strategies are found to perform much better than real ones, both for winners and losers. Surprising large arbitrage opportunities exist, especially when using zero-intelligence strategies. This is a diagnostic of possible inefficiencies of these financial 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.     

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.     

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.     

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.     

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.     

Ecuadorian Banana Farms Should Consider Organic Banana with Low Price Risks in Their Land-Use Portfolios

Organic farming is a more environmentally friendly form of land use than conventional agriculture. However, recent studies point out production tradeoffs that often prevent the adoption of such practices by farmers. Our study shows with the example of organic banana production in Ecuador that economic tradeoffs depend much on the approach of the analysis. We test, if organic banana should be included in economic land-use portfolios, which indicate how much of the land is provided for which type of land-use. We use time series data for productivity and prices over 30 years to compute the economic return (as annualized net present value) and its volatility (with standard deviation as risk measure) for eight crops to derive land-use portfolios for different levels of risk, which maximize economic return. We find that organic banana is included in land-use portfolios for almost every level of accepted risk with proportions from 1% to maximally 32%, even if the same high uncertainty as for conventional banana is simulated for organic banana. A more realistic, lower simulated price risk increased the proportion of organic banana substantially to up to 57% and increased annual economic returns by up to US$ 187 per ha. Under an assumed integration of both markets, for organic and conventional banana, simulated by an increased coefficient of correlation of economic return from organic and conventional banana (ρ up to +0.7), organic banana holds significant portions in the land-use portfolios tested only, if a low price risk of organic banana is considered. We conclude that uncertainty is a key issue for the adoption of organic banana. As historic data support a low price risk for organic banana compared to conventional banana, Ecuadorian farmers should consider organic banana as an advantageous land-use option in their land-use portfolios.     

Tracking traders' understanding of the market using e-communication data

Tracking the volume of keywords in Internet searches, message boards, or Tweets has provided an alternative for following or predicting associations between popular interest or disease incidences. Here, we extend that research by examining the role of e-communications among day traders and their collective understanding of the market. Our study introduces a general method that focuses on bundles of words that behave differently from daily communication routines, and uses original data covering the content of instant messages among all day traders at a trading firm over a 40-month period. Analyses show that two word bundles convey traders' understanding of same day market events and potential next day market events. We find that when market volatility is high, traders' communications are dominated by same day events, and when volatility is low, communications are dominated by next day events. We show that the stronger the traders' attention to either same day or next day events, the higher their collective trading performance. We conclude that e-communication among traders is a product of mass collaboration over diverse viewpoints that embodies unique information about their weak or strong understanding of the market.     

Consentaneous Agent-Based and Stochastic Model of the Financial Markets

We are looking for the agent-based treatment of the financial markets considering necessity to build bridges between microscopic, agent based, and macroscopic, phenomenological modeling. The acknowledgment that agent-based modeling framework, which may provide qualitative and quantitative understanding of the financial markets, is very ambiguous emphasizes the exceptional value of well defined analytically tractable agent systems. Herding as one of the behavior peculiarities considered in the behavioral finance is the main property of the agent interactions we deal with in this contribution. Looking for the consentaneous agent-based and macroscopic approach we combine two origins of the noise: exogenous one, related to the information flow, and endogenous one, arising form the complex stochastic dynamics of agents. As a result we propose a three state agent-based herding model of the financial markets. From this agent-based model we derive a set of stochastic differential equations, which describes underlying macroscopic dynamics of agent population and log price in the financial markets. The obtained solution is then subjected to the exogenous noise, which shapes instantaneous return fluctuations. We test both Gaussian and q-Gaussian noise as a source of the short term fluctuations. The resulting model of the return in the financial markets with the same set of parameters reproduces empirical probability and spectral densities of absolute return observed in New York, Warsaw and NASDAQ OMX Vilnius Stock Exchanges. Our result confirms the prevalent idea in behavioral finance that herding interactions may be dominant over agent rationality and contribute towards bubble formation.     

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.     

Predicting Power for Longitudinal Studies with Attrition

Power analyses play an integral role in designing biomedical studies and are customary in biomedical research proposals, for example, submitted to federal regulatory and medical research agencies. An underpowered study potentially leads to inconclusive inferences and consequently misspends valuable time and financial resources allocated to the study. The occurrence of attrition or dropout further increases the risk of conducting an underpowered study. In some applications, it is desirable to provide additional assurance against insufficient statistical power by producing conservative power predictions. We propose two new methods for predicting power when expecting attrition, and both methods employ a simple probability model for the number of dropouts. One approach allows conservative power predictions that reduce the chance of underpowering studies. The second procedure gives the minimum mean squared error predictor of conditional power, given dependence on a random number of unobserved values or dropouts. We illustrate our methods for predicting power using a longitudinal study of depression. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

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.     

A Note on Trader Sharpe Ratios

Traders in the financial world are assessed by the amount of money they make and, increasingly, by the amount of money they make per unit of risk taken, a measure known as the Sharpe Ratio. Little is known about the average Sharpe Ratio among traders, but the Efficient Market Hypothesis suggests that traders, like asset managers, should not outperform the broad market. Here we report the findings of a study conducted in the City of London which shows that a population of experienced traders attain Sharpe Ratios significantly higher than the broad market. To explain this anomaly we examine a surrogate marker of prenatal androgen exposure, the second-to-fourth finger length ratio (2D∶4D), which has previously been identified as predicting a trader''s long term profitability. We find that it predicts the amount of risk taken by traders but not their Sharpe Ratios. We do, however, find that the traders'' Sharpe Ratios increase markedly with the number of years they have traded, a result suggesting that learning plays a role in increasing the returns of traders. Our findings present anomalous data for the Efficient Markets Hypothesis.     

Profitability of Contrarian Strategies in the Chinese Stock Market

This paper reexamines the profitability of loser, winner and contrarian portfolios in the Chinese stock market using monthly data of all stocks traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange covering the period from January 1997 to December 2012. We find evidence of short-term and long-term contrarian profitability in the whole sample period when the estimation and holding horizons are 1 month or longer than 12 months and the annualized return of contrarian portfolios increases with the estimation and holding horizons. We perform subperiod analysis and find that the long-term contrarian effect is significant in both bullish and bearish states, while the short-term contrarian effect disappears in bullish states. We compare the performance of contrarian portfolios based on different grouping manners in the estimation period and unveil that decile grouping outperforms quintile grouping and tertile grouping, which is more evident and robust in the long run. Generally, loser portfolios and winner portfolios have positive returns and loser portfolios perform much better than winner portfolios. Both loser and winner portfolios in bullish states perform better than those in the whole sample period. In contrast, loser and winner portfolios have smaller returns in bearish states, in which loser portfolio returns are significant only in the long term and winner portfolio returns become insignificant. These results are robust to the one-month skipping between the estimation and holding periods and for the two stock exchanges. Our findings show that the Chinese stock market is not efficient in the weak form. These findings also have obvious practical implications for financial practitioners.     

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.