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This article studies the importance of stock market literacy and trust for stock ownership decisions. We find that these two distinct channels simultaneously explain not only the probability of participation, but, conditional on participation, also explain the share of investment in stocks. Once we account for stock market literacy, sociability is no longer significant for participation; what matters is literacy rather than sociability. Further, we observe that economic shocks and future expectations are key behavioral characteristics that explain a household’s decision to invest in stocks. However, upon participation, a larger set of behavioral characteristics explains the level of stock investment.

The article based on analyzing the theory of stock investment and the stock price prediction method, starting from the practical point of view, by describing the background and significance in Qinghai province listing Corporation stock price forecasting, which makes people aware of the importance of the stock prediction, introduces the stock prediction theory and the theory of BP neural network. This paper takes western mining and Qinghai gelatin which are two listing Corporation of Qinghai province as an example for inquiry. Contrasting the forecast accuracy and change direction of three periods and comparing the prediction accuracy of different trading systems, it draws the preliminary conclusion.

This study identifies segments of individual investors based on their money attitudes (attitude toward financial security, attitude toward stock investing, obsession with money, perceived immorality of the stock market, attitude toward gambling, interest in financial matters, attitude toward saving, frankness about finances). A cluster analysis of data from a representative mail survey conducted in Switzerland (N = 1,569) yielded four main segments of individual investors we term safe players, open books, money dummies, and risk-seekers. This typology has forecast value for behavior: Each type differed with regard to having investment portfolios, buying and selling securities, risk tolerance for maximization of capital, response to price fluctuations, and willingness to make environmentally and socially responsible investments.

Participants in 401(k) retirement plans violate the basic principle of diversification by investing significant fractions of their savings in their employers' equity. This paper characterizes investors' active changes to their company stock investment over time by analyzing new inflows and transfers. The average investor seems to base active changes on salient information, paying attention to past returns, volatility, and business performance. Past returns, over a three-year horizon, predict higher inflow allocations and transfers, whereas volatility and business performance only have a weak effect. The sensitivity to past returns is asymmetric, with investors reacting more strongly to positive and above-S&P500 returns. Copyright 2004, Oxford University Press.

Our study tries to identify if there are performance differences between men and women in stock investment decisions. We use experimental methodology and count on the participation of 1,050 volunteers who took decisions in virtual environment, similar to a real online home broker for stock trading. Through quantile regression models, we have found evidence that women get results lower than men when decisions involve gains when decisions involve losses, women lose as much as men. These results demonstrate behavioural biases associated with disposition and endowment effects, as well as aspects related to loss aversion. As a result, we present a contribution on the field of behavioural finance, especially on the literature of gender diversity, which presents a controversial debate in several studies.

This aim of the research is to analyze the different stock investment risk before and after make acquisition process. The research take 10 company for samples and have 60 days before and 60 days after acquisitions process. The research using the model of beta coefficient (β) and t-test paired sample test. The result of the research showed have the different stock investment risk before and after acquisition process with stock investment risk before acquisitions more than after acquisitions process.

The decision-making process in stock investment requires not only the rational trading rules practices, but also faith that market information is reliable. A trust metric is an indication of the degree to which one social actor trusts another. In our Agent-Based Model we built a stock-trading model that issues a daily stock trading signal. This paper introduces an agent-based model for finding the optimal degree of trust in stock transactions. The system has been evaluated in the context of Bank of America stock in the period of 1987–2014. The model outperformed both S&P 500 and buy-and-hold Bank of America stock strategy by two to three times.

This study examines how housing influences households’ risky asset holdings in multiple European countries, using the 2004 Survey of Health, Ageing and Retirement in Europe (SHARE) data set. This research provides three major findings. First, homeowners in bank-based economies have a significantly lower probability of participating in the stock market, whereas in market-based economies, homeownership has no significant impact on this probability. Second, homeowners tend to invest a lower share of their financial assets in stocks compared to renters. Third, households with a higher home value to wealth ratio invest a lower share of financial assets in stocks in countries with more developed mortgage markets. In contrast, in countries with underdeveloped mortgage markets, households with a higher home value to wealth ratio invest a larger share of financial assets in stocks. The results of this study suggest that recognizing differences in financial market structures is crucial to understanding the relationship between housing investment and stock investment.

People or companies canalize their money to consumption or retain it for the future. Their desire to use their savings to obtain extra income gave birth to the concept of investment. They do this in a frame of expectations about the future. Expectations are the foundation of all investment decisions. This chapter focuses on how an investment and portfolio management process should be and explains different portfolio management strategies. It also includes different types of stock investments. The chapter intends to teach how one can choose a stock and manage money effectively. For this aim, the chapter includes value investment style, growth investment sytle, technical investment style, momentum investment style, fundamental investment style, and beyond. It is very important to know which strategy best fits your aims and your characteristics, so you will be able to learn this through this chapter. In addition, it is important to know how these strategies can used together effectively. In this chapter, an investor will find answers to questions about stock investment.

Can fear trigger risk-taking? In this paper, we assess whether fear can be reinterpreted as a state of excitement as a result of contextual cues and promote, rather than discourage, risk-taking. In a laboratory experiment, the participants' emotional states were induced (fear vs. control), followed by a purportedly unrelated financial task. The task was framed as either a stock market investment or an exciting casino game. Our results showed that incidental fear (vs. control) induced risk-averse behaviour when the task was framed as a stock investment decision. However, fear encouraged risk-taking when the very same task was framed as an exciting casino game. The impact of fear on risk-taking was partially mediated by the excitement felt during the financial task.

Rate of return guarantees, included in many financial products, exist in two fundamentally different types. Maturity guarantees which are binding only at the expiration of the contract, and therefore, similar to financial options and multi-period guarantees which have the time to expiration divided into several subperiods with a binding guarantee for each subperiod. Relevant real-life examples are life insurance contracts and guaranteed investment contracts. We consider rate of return guarantees where the underlying rate of return is either (i) the rate of return on a stock investment or (ii) the short-term interest rates. Various types of these rate of return guarantees are priced in a general no-arbitrage Heath–Jarrow–Morton framework. We show that despite fundamental differences in the underlying rate of return processes ((i) or (ii)), the resulting pricing formulas for the guarantees are remarkably similar for maturity guarantees. For multi-period guarantees the presence of stochastic interest rates leads to intertemporal dependencies which complicates the valuation formulaes compared both to the case of maturity guarantees and the case of deterministic interest rates. Finally, we show how the term structure models of Vasicek (Vasicek, O., 1977. Journal of Financial Economics 5, 177–188) and Cox et al. (Cox, J.C., Ingersoll, Jr., J.E., Ross, S.A., 1985. Econometrica 53(2), 385–407) occur as special cases in our more general framework based on the model of Heath et al. (Heath, D., Jarrow, R.A., Morton, A.J., 1992. Econometrica 60 (1), 77–105).

The Artificial Neural Network (ANN) is a technique that is heavily researched and used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. Its generalization powers have not only received admiration from the engineering and scientific fields, but in recent years, the finance researchers and practitioners are taking an interest in the application of ANN. Bankruptcy prediction, debt-risk assessment and security market applications are the three areas that are heavily researched in the finance arena. The results, this far, have been encouraging as ANN displays better generalization power as compared to conventional statistical tools or benchmark. With such intensive research and proven ability of the ANN in the area of security market application and the growing importance of the role of equity securities in Singapore, it has motivated the conceptual development of this project in using the ANN in stock selection. With its proven generalization ability, the ANN is able to infer from historical patterns the characteristics of performing stocks. The performance of stocks is reflective of their profitability and the quality of management of the underlying company. Such information is reflected in financial and technical variables. As such, the ANN is used as a tool to uncover the intricate relationships between the performance of stocks and the related financial and technical variables. Historical data such as financial variables (inputs) and performance of the stock (output) are used in this ANN application. Experimental results obtained this far have been very encouraging.

In a previous issue of this journal, we proposed a model that concerns the encoding and retrieval of numerical data by accounting decision makers (Kida, T. & Smith, J. (1995). We argued in the model that decision makers will be better able to retrieve affective reactions to numerical data from memory structures as compared to other forms of encoded representations and that, as a consequence, affective reactions will significantly impact future data recall and decision making. This paper empirically examines these key propositions of the model. Three studies were conducted that investigate the memory retrievals and decisions of experienced managers in stock investment and financial difficulty contexts. The results are consistent with our expectations, pointing to the importance of considering affective reactions when attempting to better understand encoding, retrieval and decision making with accounting data.

This article examines the role of promotion motivation in decision making in the domain of gains. Using a stock investment paradigm in which individuals believed that they were making decisions that were real and consequential, we found that promotion motivation, and not prevention motivation, predicted the likelihood of switching between risky and conservative choices in the domain of gains. Promotion-focused participants chose a relatively risky option when their stock portfolio remained unchanged (stuck at 0, the status quo) but switched to a relatively conservative option when they had just experienced a large gain (Studies 1-4), both when regulatory focus was measured (Study 1) and manipulated (Studies 2-4). Studies in which progress was manipulated (Study 3) and measured (Study 4) provided evidence that it is perceptions of progress that underlie this tactical switch in risk preferences within the promotion system. We discuss the implications of these findings for decision making and the role of progress in self-regulation.

Over my thirty years as a professional investor I’ve heard every cocktail party stock tip and market timing success imagined; I’ve come to understand that with one hand wrapped around a highball, everyone’s a winner. Yet, if we listened to every hot tip or changed direction in the midst of big market moves based on casual advice advocated by friends or acquaintances, we would likely chase our tales right into bankruptcy. The only sure way to ensure we really do win over time is to stay the course we establish based on our personal and family objectives and to acknowledge that no investor or investment discipline can, or will, be right during every short-term period. If we adopt a strategy that lines up with our long-term goals and our investing temperament we are much more likely to succeed. So let’s begin by acquainting ourselves with the two primary stock investment style categories: growth and value.

This paper studies the importance of stock market literacy and trust for stock ownership decisions. We find that these two distinct channels simultaneously explain not only the probability of participation, but, conditional on participation, also explain the share of investment in stocks. Once we account for stock market literacy, sociability is no longer significant for participation; what matters is literacy rather than sociability. Further, we observe that economic shocks and future expectations are key behavioral characteristics that explain a household's decision to invest in stocks. However, upon participation, a larger set of behavioral characteristics explains the level of stock investment.

This paper presents an intelligent system to assist small investors to determine stock trend signals for investment in stock business. A pilot system is built providing three main categories of technical and analysis theories, namely momentum, moving average, and support/resistance line. It has extensive graphic interface design to facilitate the usage of the system. For novice investors, the system is associated with tutoring features and it supports analysis study of the rationale behind some system recommendations. Skilful investors can explore the various theories for the prediction by means of adjusting the weightings, combinations and even some independent variables allocated by the intelligent system. General users can therefore formulate their investment strategies upon system recommendations under different investment criteria accordingly.

Value at Risk (VAR) is a risk measurement method that use in risk investment calculation. VAR shows risk in nominal. This research calculate risk portfolio of stock using VAR method and measure whether VAR value overvalued or underestimated. Using historical simulation method is found VAR value tend to decrease when stock investment consist more stocks in the portfolio. Risk investment calculation consistent with standar devistion as risk measurement, which the more investment diversified the less the risk in the investment. Then, using backtesting reveal that VAR tend too high in portfolio consisting small number of stocks. VAR value can accepted in the portfolio that consist many stocks or the more investment diversified the more accurate VAR value as risk measurement.

In response to the ongoing debate in this journal about whether the risk of investments in the stock market is increased or decreased by lengthening the investment period, we show that Bodie's attempt to identify market risk with the price of “put” options leads to a circular argument within the workings of arbitrage-free option pricing models. Thus, Bodie failed to rebut the conventional view that longer investment horizons may be thought of as less risky. Bodie's put option prices are relevant, however, to determining “market” prices of stock investment insurance.

Investor in Indonesia there are not many women. It make Jakarta Stock Exchange (JSX) have special program to make promotion for potential women investor. In recent years Indonesian have a new phenomenon that there are many women have been working. Women not only become housewife but also can be work to make additional income in the family. This make women become potential investors for JSX that there are not happened in the past view years ago.

El presente artículo tiene por objetivo analizar el desempeño de la renta variable de los portafolios de las Administradoras de Fondos de Pensiones (AFP), aplicando el modelo de microatribución de las denominadas inversiones en el exterior de las AFP. La mayor parte de estudios en Perú sobre el desempeño de la cartera de las AFP se han realizado empleando modelos que aplican el esquema de la frontera eficiente. Otro aspecto de importancia se refiere a la decisión de las AFP de elegir inversionistas especializados para administrar la cartera dirigida hacia el exterior, política con la cual fracasó en alcanzar el objetivo de rendimientos por encima de los retornos obtenidos por los índices de mercado de las regiones donde se negociaron en un periodo posterior a la crisis financiera del 2007-2008., This article, applying the micro attribution model, has like objective to analyze the performance of stock investment of the pension fund managers (AFP) to the called investment abroad. Many studies in Perú respect to AFP ‘s performance portfolios had been conducted using the framework of the efficient frontier. Other important aspect refers to the AFP’s make decision of choosing investors specialized to portfolio management directed outward; policy which failed to achieve profitability over of returns of market indices of regions where they traded at a later period to 2007-2008 financial crises.

The objective of this study is to apply cluster analysis or also known as clustering on stocks data listed in LQ45 index at Indonesia Stock Exchange. The problem is that traders need a tool to speed up decision-making process in buying, selling and holding their stocks.The method used in this cluster analysis is k-means algorithm. The data used in this study were taken from Indonesia Stock Exchange. Cluster analysis in this study took data’s characteristics such as stocks volume and value. Results of cluster analysis were presented in the form of grouping of clusters’ members visually. Therefore, this cluster analysis in this study could be used to identify more quickly and efficiently about the members of each cluster of LQ45 index. The results of such identification can be used by beginner-level investors who have started interest in stock investment to help make decision on stocks trading.

This paper proposes a novel methodology for stock investing using the technique of episode mining and technical indicators. The time-series data of stock price is used for the construction of complex episode events and rules. Our experimental results show that the episode rule mining method not only improves a well-known technical indicator alone, but also assists it in outperforming the benchmark. Based upon the results obtained, we expect this episode mining methodology to advance the research in data mining for finance, and provide an alternative strategy to stock investment in practice.

This project explored the potential of Bayesian Networks in modeling financial ratios of Malaysian plantation stocks. Financial ratio is a crucial part of fundamental analysis which involves analyzing a company's financial statements to gain insights on its financial condition, future performance and aid decision-making in stock investment. Meanwhile, Bayesian Network is a popular graphical and probabilistic tool that is useful for reasoning under uncertainty in real life situations. The prospect of using Bayesian Network to model financial ratios in fundamental analysis is therefore explored in this project in the context of the Malaysian plantation industry. Using the local stock market data, relationships among financial ratios and stock price changes are modeled in a Bayesian Network, which will then be used to provide assistance in stock buying decision by predicting the stock price changes given observations of financial ratios. The network's reliability is tested on real data based on the counts of correct predictions and also comparison of portfolio mean return to that of a random portfolio. The resulting network is found to be reasonably reliable as it consistently predicted price changes correctly more than half of the times and also outperformed the random portfolios.

Stock turning point has been playing a significant role in the stock investment as buying or selling stocks around it can make a good return. The existing methods recognize the turning points according to different types of features but the effect is not so satisfactory. In this paper, we firstly build multiple types of features what reflect many aspects of characteristics of stock and utilize mutual information to partition them into several subsets. Then we use SVM to train the turning point recognition model on every feature subset. Finally, we combine the multiple models into an ensemble and apply Particle Swarm Optimization (PSO) to optimize the combination coefficients. Experimental results show that our method is more effective.

Many researches attempt to find out regularities of the stock market so as to gain higher profit within stock investment activities. In fact, the price changes over time, but the data from previous period reflects the future trend in some extent. Thus, a new method is proposed to investigate how to use the historical data to make correct investment decision in this paper. Since the technical indicators are efficient tools on stock prediction, we use technical indicators of every trading day in current year as the input variables to train a RBFNN based on L-GEM and make prediction of trading actions (buy, sell or hold) for days in the next year. Then, a Multiple Classifier System (MCS) is built to make the final prediction from all base RBFNNs which trained by data in different years. Experimental results show that the new information from past stock data is predictable. Data in different years causes different effects on the future stock market. Accumulating all information from base RBFNNs brings a considerable profit in our experiments.

The purpose of this research is to investigate the impact of cognitive style to recency effect in stock investment. This study expands Alvia and Sulistiawan (2010) work. Using cognitive style as moderating variable, the study explains the impact of type and order of information to recency bias when the information sequentially presented. This research uses unbalance ANOVA to examine the bias in stock investment decision making through laboratory experimental study. The participants are 172 bachelor degree students in accounting. They represent nonprofessional investor in stock market. The separation of treatment and control group is based on simple training about technical analysis knowledge. Control group did not received training about technical analysis knowledge. The results show that cognitive style enhances the impact of type and order of information to recency bias.

This book review discusses the bible of fundamental analysis and value investing drafted by the mentor of Mr. Warren Buffett, current world greatest investor.

Mestrado em Finanças Este trabalho tem como objectivo avaliar o impacto da taxa de câmbio na estrutura, rendibilidade e risco de carteiras óptimas de acções internacionais construídas com base num conjunto de índices de diversos países. Além disso, também se analisou o impacto da crise dos mercados financeiros de 2009 na constituição de carteiras óptimas. Para esse fim utilizou-se uma amostra constituída pelas cotações diárias de índices representativos de onze países da zona euro, Reino Unido, Suíça, Japão e EUA referente ao período compreendido entre Janeiro de 2007 e Junho de 2012. Como metodologia utilizou-se a comparação das rendibilidades e do Índice de Sharpe, da carteira óptima, avaliados nas diferentes moedas. Na análise efectuada concluiu-se que as variações nas taxas de câmbio influenciam a estrutura e a rendibilidade das carteiras óptimas. Não obstante, constatou-se que essa influência não é globalmente relevante ao analisar a significância estatística do impacto das taxas de câmbio através da ANOVA. Ao testar a significância estatística das diferenças entre pares de moedas relativamente à rendibilidade e ao Índice de Sharpe chegou-se praticamente às mesmas conclusões em ambos os casos, ou seja, a não existência de diferenças estatisticamente relevantes. The aim of this study is to evaluate the impact of the exchange rate in the structure, profitability and risk of optimal international equity portfolios constructed based on a set of stock indexes from different countries. In addition, we analyzed the impact of the financial market crisis of 2009 in the construction of optimal portfolios. For this purpose, we used a sample composed of daily prices of representative indexes from eleven countries of the Eurozone, UK, Switzerland, Japan and the U.S. for the period from January 2007 to June 2012. It was uses as methodology the comparison of the returns and the Sharpe ratio, the optimal portfolio, valued in different currencies. It was concluded, in the analysis carried out, that the variations in exchange rates have impact in the optimal portfolios structure and profitability. Nevertheless, it was found that this influence is not altogether relevant analyzing the statistical significance of the exchange rates impact by ANOVA. Testing the statistical significance of differences between currency pairs of the return and Sharpe ratio we reached practically the same conclusions in both cases, i.e. the lack of statistically significant.

This paper is the book review of one of the oldest books about stock investment and earliest exchange in Netherland: Confusion de Confusions.

The main challenge in Stock Exchange is choose stocks with uptrend in order to compose a profitable stock portfolio and also ensure security of investment, since the risk in stock investment is considered high. One tool that can help investors to identify the stocks behavior and help to select the right stocks becomes essential. The application of intelligent techniques, especially Artificial Neural Networks to forecast trends in stock prices generated good results. Thus, in this paper was created hybrid architecture, composed by the Markowitz Model and an Artificial Neural Network Multilayer Perceptron, in order to support the investor. For the experiments, the information of the ten most traded stocks on Stock Exchange of Sao Paulo was extracted. The hybrid architecture is given as follows: Processing Stock Information in Markowitz Model then result is presented as one of input variables of neural network. For analyze the results, was applied an investment simulator, where the investment return obtained point to the use of hybrid architecture in investments.

Abstract. The Shalibane Company of Camberley are tube manipulators and sheet metal workers, making parts mainly for the car industry. In 1991 they introduced Group Technology (GT). In eighteen months they reduced stocks by 60% (still falling); increased sales and output by 33%; increased the rate of return on investment by 24%; and increased the number of their employees by 12%. The cost of introducing the change was covered more than 3 times by the reduction in the stock investment. GT is shown to be a method which promotes growth and increases productivity, profitability and employment.

Candlestick pattern, as an efficient method in technical analysis, is widely used in decision support of stock investment. From historical data, there is a no 100% guarantee for a stock price increasing after the appearance of a bullish candlestick pattern. The main aim of this paper is to enhance the prediction ability of Candlestick Patterns using a Multiple Classifier System (MCS) consisting of Radial Basis Function Neural Network (RBFNN) trained by a Localized Generalization Error Model (L-GEM). The RBFNN classifies particular candlestick pattern to be a real bullish candlestick pattern or not based on training with past data. The MCS fusing RBFNN for different patterns makes the final prediction of the stock price trend. In this paper, stock price data of 40 stocks in Hong Kong Hang Seng Component Index is collected to carry out the investment simulation experiment. Experimental result shows that the proposed method yields statistically significant profit when compared with a random investment strategy and candlestick investment without RBFNN.

Analyzing the value of stock is the precondition to identify the valuable stock and invest reasonably. But how to select the indexes and distribute the weight of each index is the key to make the system of index effective. This paper selects the twelve financial indexes, and applied entropy weight method, Analytical Hierarchy Process (AHP) and TOPSIS method comprehensively to evaluate the value of the stock, so that investors can identify the valuable stock and invest reasonably according to the results.

Stock investment has become an important investment activity in Thailand. However, investors often lose money due to unclear investment objectives. Therefore, an investment decision support system to assist investors in making good decisions has become an important research issue. Thus, this paper introduces an intelligent decision-making model, based on the application of Neurofuzzy system (NFs) technology. Our proposed system can decide a trading strategy for each day and produce a high profit for of each stock. Our decision-making model is used to capture the knowledge in technical indicators for making decisions such as buy, hold and sell. Finally, the experimental results have shown higher profits than the Neural Network (NN) and “Buy & Hold” models for each stock index. The results are very encouraging and can be implemented in a Decision- Trading System during the trading day.

Despite the suggestion made by investment banks and brokerage firms for private investors to substitute one or all part of their stock investment by the associated option, the real benefit obtained from dealing with an option rather than with its underlying stock remains to be understood. To clarify the situation, instead of founding our argumentation on specific past data, we build a general analysis of the option return and a comparison of this last with the underlying asset relative change. Using market and synthetic data on option prices, it appears that for many situations investing in an option may be less interesting than investing directly in the related underlying stock. However there is no definitive answer about the superiority or not of investing in the option, since the situation depends on the level of asset change at the investment maturity. It is up to the investor to decide the best investment support to choose, depending on her views on the future asset relative change and on the option characteristics which are available on the markets. The formulas we derive here may be helping in performing this task.

Analysts and investors use Technical Analysis tools to create charts and price indicators that help them in decision making. Chart patterns and indicators are not deterministic and even analysts may have different interpretations, depending on their experience, background and emotional state. In this way, tools that allow users to formalize these concepts and study investment policies based on them can provide a more solid basis for decision making. In this paper, we present a tool we have built to formally model stock investment contexts as Partially Observable Markov Decision Processes (POMDP), so that investment policies in the stock market can be generated and simulated, taking into consideration the accuracy of Technical Analysis techniques. In our models, we assume that the trend for the future prices is part of the state at a certain time and can be "partially observed" by means of Technical Analysis techniques. Historical series are used to provide probabilities related to the accuracy of Technical Analysis techniques, which are used by an automated planning algorithm to create policies that try to maximize the profit. The tool also provides flexibility for trying and comparing different models.

This chapter discusses the continuous effort to explore stock price and trend prediction from finance perspective as well as from the integration of three major research areas namely data mining, artificial intelligence and decision support. These areas have been explored to design a hybrid stock price prediction model with relevant techniques into the stock price analysis and prediction activities.

This study aims to compare between maximum losses that could be arising to sharia based stock and non-sharia based stock investment due to the differences characteristic of these stocks. As the consequence of Conditional Variance Phenomenon, this study will use EWMA model and GARCH model. The finding show that EWMA model provides better performance and gives more conservative of VAR value than GARCH model. It is also shown that Sharia based stock is riskier during February to March and May to July. Finally, this study tries to give information on risk characteristic in Indonesia’s Capital Market.

Analyzing the relationships of time series is an important problem for many applications, including climate monitoring, stock investment, traffic control, etc. Existing research mainly focuses on studying the relationship between a pair of time series. In this paper, we study the problem of discovering leaders among a set of time series by analyzing lead-lag relations. A time series is considered to be one of the leaders if its rise or fall impacts the behavior of many other time series. At each time point, we compute the lagged correlation between each pair of time series and model them in a graph. Then, the leadership rank is computed from the graph, which brings order to time series. Based on the leadership ranking, the leaders of time series are extracted. However, the problem poses great challenges as time goes by, since the dynamic nature of time series results in highly evolving relationships between time series. We propose an efficient algorithm which is able to track the lagged correlation and compute the leaders incrementally, while still achieving good accuracy. Our experiments on real climate science data and stock data show that our algorithm is able to compute time series leaders efficiently in a real-time manner and the detected leaders demonstrate high predictive power on the event of general time series entities, which can enlighten both climate monitoring and financial risk control.

Despite the suggestion made by investment banks and brokerage firms for private investors to substitute one or all part of their stock investment by the associated option, the real benefit obtained from dealing with an option rather than with its underlying stock remains to be understood. Using our general results (in Theorems 1 and 4), market and synthetic data on option prices, it appears that for many situations investing in an option may be less interesting than investing directly in the related underlying stock. However there is no definitive answer about the superiority or not of investing in the option, since the situation depends on the level of asset change at the investment maturity. Our contribution here to the above question is to provide a systematic analysis of the return structures associated with the two alternative investments. Then, it is up to the investor to decide the best investment support to choose depending on her views on the future asset relative change and on the option characteristics which are available on the markets. The fomulas we derive here may be helping in performing this task.

Earlier behavioral finance research has found that individual investors’ familiarity with companies’ products may spill over to affect their decisions to invest in the companies’ stocks. However, what has not been confirmed is whether also subjective product evaluations spill over to affect investment decisions. To examine this question, the authors utilize a randomized experiment, conducted with Finnish individual investors. The experiment reveals that an individual investor’s subjective evaluation of a company’s products has positive effect on his interest to invest in the company’s stock; that this effect is independent of familiarity; and that the effect may be especially reinforced for non-domestic companies.

This paper investigates the scale-free characteristics of Chinese stock network which includes Shenzhen stock network and ShangHai stock network. We analyze characteristics of the growth and preferential attachment of stock networks. In particular, we study other three important properties of stock networks, such as the rapid communication, robustness and clustering. Through empirical theory and numerical study, we find that the two stock networks have not scale-free characteristics, which is different to many real complex networks.

This paper constructs 3-level evaluation model based on index system for the listed corporations and confirm the weights by the factor analysis, then compute the investment value of listed companies in chlor-alkali industry making use of fuzzy comprehensive evaluation method and illustrate the method. The objective and reasonable basis for making investment decision is provided.