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Stock portfolio optimization is always an interesting and attractive research problem due to the variety of stock markets. To find a useful stock portfolio, metaheuristic-based approaches have been presented to obtain diverse group stock portfolios (DGSPs) by considering the diversity of stock portfolios in the past. However, in the existing DGSP algorithms, two problems remain to be solved. The first is how to set a suitable group size, and the second is that the evolution process is time-consuming. To solve these problems, in this paper, an approach using grouping genetic algorithms (GGAs) was proposed for optimizing a DGSP. For setting a suitable group size, the proposed approach utilized two attributes of group stocks, including the return on equity and the price/earnings ratio. Then, to derive better stock groups, a cluster validation factor was designed, which was used as part of a fitness function. To solve the time-consumption problem, using the designed temporary chromosome, the number of stock portfolios that need to be evaluated could be reduced in the proposed approach to speed up the evolution process. Finally, experiments on two real stock datasets containing 31 and 50 stocks were conducted to show that the proposed approach was effective and efficient. The results indicated that the proposed approach could not only achieve similar returns but also accelerate the evolution process when compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

In this article, gold is analyzed from an investment perspective as an asset that allows you to increase your wealth. The analysis is twofold. First, it is about examining to what extent changes in gold prices in the world markets translate into changes in the prices of shares of companies that extract gold. second, it was checked whether there is a financial leverage effect, which in this case means that changes in the price of shares of gold mining companies are greater than changes in the price of gold itself. Methodically, the sharpe model was used and two basic parameters of the model were estimated, i.e. the intercept (alpha), and the beta coefficient as a measure of systematic risk, for the gold market and the equity market of gold mining companies and etFs based on these companies. the research carried out in accordance with the logic of the sharpe model shows that the obtained value of the alpha parameter for the stock market was positive, while for the gold market it was negative. at the same time, higher levels of this parameter are beneficial to the investor, which means that an advantage of the stock market over the gold market exists. In turn, the estimated beta for the stock market is much lower than for the gold market. the systematic risk level for stocks is 0.45, and for the gold market it is 1.98, which is a significant difference. the stocks of gold mining companies can be classified as defensive against the stock market (the rate of return of the gold mine stock is insensitive to market movements) and aggressive against the gold market (the rate of return of the gold mine shares reacts more strongly than the movement in the price of gold). [ABSTRACT FROM AUTHOR]

This study aims to determine the effects of R&D and marketing expenditures of companies that force marketing and finance to act together on stock return, return on assets, and return on equity. To this end, the quarterly frequency data of nine companies that were continuously traded in the BIST Technology Index between March 2009 and December 2020 were examined with panel-data analysis. In line with the purpose of the research, analyzes were carried out in three different models. First of all, we determined which tests should be performed on the models based on the cross-sectional dependence, homogeneity/heterogeneity, and panel unit root test results obtained for the established models. The results of panel least squares test carried out to determine the effect of R&D and marketing expenditures on stock return showed that the effect of R&D expenditures on stock return was not statistically significant while marketing expenditures had a positive and significant effect on stock return. Analyzes should be continued with cointegration tests according to the characteristics of the two models established to determine the effect of R&D and marketing expenditures on return on assets and return on equity. The results implied a positive and significant relationship between R&D expenditures and return on both assets and equity. While no statistically significant relationship was found between marketing expenditures and return on assets, there was a positive and significant relationship between marketing expenditures and return on equity. [ABSTRACT FROM AUTHOR]

The purpose of this research is to identify the equity effects associated with the implementation of IFRS 15 and the variations produced in the performance indicators of companies listed in the Colombian and Santiago de Chile Stock Exchanges. The methodology used is Gray's conservatism index, which is applied to the variations presented in the financial statements of the adopting entities. The results show a contrast of equity effects, since Colombian companies presented positive impacts in 70%, while only 21% of Chilean companies showed positive variation. Similarly, of the total number of entities analyzed, it was found that 9% of Colombian entities and 4% of Chilean entities did not include any information associated with the application of IFRS 15. [ABSTRACT FROM AUTHOR]

There has been an increased focus among recent studies on bank profitability. However, there is a gap in the literature in terms of the relationship and causalities between profitability and bank concentrations. To close this gap, this study investigates whether the concentrations of 5 and 7 banks have an impact on bank profitability by using the wavelet coherence technique covering the period 1995Q1-2017Q3. As expected, it is observed that bank profitability and banking concentrations of 5 and 7 banks significantly fluctuated in the period 2000 and 2003. In Turkey, bank concentrations significantly cause the banks' profitability at different time periods. [ABSTRACT FROM AUTHOR]

Following the recent efforts made to achieve a predictable capital market, this study attempted to explore the interlocking relationships between the stock returns of companies listed on Tehran stock exchange (TSE). For that purpose, data concerning 36 industry classes between 2000 and 2013 were examined through clustering and association rule. Preparation and initial refining of data suggested that only 25 out of 36 industries met the requirement for 13-year membership at TSE. Finally, a total of 249,061 records were evaluated, and the results were presented in the form of several rules and recommendations for investors. The results suggested that there were no two-item rules (rules with one antecedent) within industries. The best rules entailed three and four items with a lift of more than two, confidence more than 81% and support more than 1%. [ABSTRACT FROM AUTHOR]

Abstract A preponderance of research evidence shows that normal distributions cannot capture the behaviour of stock returns. In some empirical experiments, alternatives to normal distributions have been applied to stock data on a case-by-case basis, but no simple and practical general solutions exist to capture stock behaviour. As a simple methodology, the normal mixture model is a linear combination of normal distributions that can be directly used to approximate the characteristics of stock returns. In this paper, we recommend usage of the normal mixture model as a general method to understand stock behaviour. We also compare the performance of different normal mixture models with kernel density estimations for ten major stock market indexes and two individual stocks from the years 2000 to 2016. Empirical results show that the normal mixture model with three components better represents the behaviour of stock returns, both statistically and economically, than models based on normal distributions and kernel density estimations. Highlights • The normal distribution is not suitable for modelling distributions of stock returns. • Normal mixture models with k = 3 better represent the behaviour of stock returns both statistically and economically. • A normal mixture method is more suitable for capturing the characteristics of joint distribution of intermarket. [ABSTRACT FROM AUTHOR]

Unsupervised machine learning can provide an objective and comprehensive broad-level sector decomposition of stocks [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a new measurement of time irreversibility based on information measures method to analyze the financial stock markets. In order to examine the effectiveness of this method, we employ it into ARFIMA models. Applying the new method to quantifying time irreversibility of 33 financial indices evolving over the period 2002–2016, we conclude that the stock daily prices of the companies are indeed time irreversible and the degree of irreversibility varies with time for each company. According to the values of irreversibility, we could rank the companies. Also we obtain that the values of annualized irreversibility may have little effect on the coefficient of variation. Moreover, in order to find patterns arising among different periods, we use the principal component analysis (PCA) and hierarchical clustering, the results obtained by these two standard techniques in data mining are in agreement. Highlights • A new measurement of time irreversibility based on information measures is proposed to analyze the time series. • The information measures method incorporates elements of both information-based and word statistics-based categories. • We find that the volatility and irreversibility are not correlated. • The new measure of time irreversibility can reveal complementary and valuable information on the evolution for the financial time series. [ABSTRACT FROM AUTHOR]

We propose a nonparametric procedure to test for changes in correlation matrices at an unknown point in time. The new test requires constant expectations and variances, but only mild assumptions on the serial dependence structure, and has considerable power in finite samples. We derive the asymptotic distribution under the null hypothesis of no change as well as local power results and apply the test to stock returns. [ABSTRACT FROM AUTHOR]

Purpose - People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe. Design/methodology/approach - In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe. Findings - The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the followingmonth. Research limitations/implications - The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and ultimately, enhance the profitability of recognition heuristic strategies. Practical implications - Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic. Originality/value - The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature. [ABSTRACT FROM AUTHOR]

We study the various sectors of the Bombay Stock Exchange (BSE) for a period of eight years from January 2006-March 2014. Using the data of the daily returns of a period of eight years we investigate the financial cross-correlation co-efficients among the sectors of BSE and Price by Earning (PE) ratio of BSE Sensex. We show that the behavior of these quantities during normal periods and during crisis is very different. We show that the PE ratio shows a particular distinctive trend in the approach to a crash of the financial market and can therefore be used as an indicator of an impending catastrophe. We propose that a model of analysis of crashes in a financial market can be built using two parameters: (i) the PE ratio and (ii) the largest eigenvalue of the cross-correlation matrix. [ABSTRACT FROM AUTHOR]

While the concept of corporate governance is a concept that continues its existence for a long time with different applications, even if not with its present meaning, especially after Enron, which took place in 2001 in the United States, and Parmalat scandals that emerged in Italy in 2003, it was a phenomenon that attracted the attention of all countries not only those countries. Corporate governance is a philosophy of management in itself, a philosophy that holds regulations on all aspects of businesses and aims to manage the relationship of all these aspects to each other based on a solid ground. To do this, the principles laid down by the OECD are regulated under such headings as i) shareholder rights and fundamental property functions, ii) equitable treatment of shareholders, iii) roles of stakeholders, iv) public disclosure and transparency, and v) responsibilities of board of directors. On the light of these information, The BIST Corporate Governance Index is calculated on the basis of the price and performance of companies whose shares are traded on the Stock Exchange Istanbul markets (except for the Close Monitoring Market and C and D lists) and which have at least 7 out of 10 compliance scores and at least 6.5 out of 10 aimed to measure return performance Methods In this study, there are 50 companies listed on the Borsa Istanbul Corporate Governance Index, which have issued corporate governance rating notation announcements in 2015. The study aims to determine how investors evaluate the announcement of the corporate governance index by examining the movement in stocks. In this direction, the event study method will be utilized. In the literature the event study aims to reveal the effect of the event by evaluating the values before and after the date or dates determined with a number of statistical methods. After calculating the real rate of return for 50 companies and the market rates of return for 50 companies, the abnormal returns will be calculated for each day from 15 days before the announcements to 15 after the announcements. Cumulative abnormal return rates for each event window will then be calculated and compared. At the end of these calculations, cumulative abnormal return rates before the announcement date will be compared with cumulative abnormal return rates after the announcement date. On the other hand, the reactions of company stocks to the announcements for 2015 were evaluated and interpreted in three different event windows. Findings It is seen that the average of 13 days from the average abnormal returns obtained have negative returns. Six of them were realized after the announcements and seven before the announcements. This can be implied that stocks are not always come up with positive reactions after the province and that there may also be value decreases due to profit taking or other reasons. Another point to note is that the highest ratio within the mean abnormal returns is the day after the announcement day (t + 1 = 0.88). On the day of the announcement, it has the fourth highest average. It is understandable that when the notice is taken at the time of the announcement, the day of the announcement gives less abnormal returns compared to the next day. On the third day following the announcement, the abnormal return was negative. The average abnormal return on the day of the announcement and the following two days is calculated as 1.70%. When the 31-day event window is examined, the cumulative abnormal return rate from the announcement date to the day before the announcement is 2.40%, while at the end of the 15th day the rate is up to 4.56%. A similar situation can be seen from the 21-day event window. Cumulative abnormal return up to the day before the announcement day is 1.03%, but it rises to 3.09% at the end of the 10th day. Lastly, when taking into account the possibility that the announcement effect is much shorter, the cumulative abnormal return rate from the announcement date to the day before the announcement was calculated as 0.29% for the 11-day window, at the end of the 5th day this rate was 2.11%. In all three event windows, there are positive increments between the day before the announcement and the day at the end of the window. From this point, similar results are obtained in the new event window of 15 days before the event and 2 days after the event. Cumulative abnormal return before the announcement was 2.70%, but at the end of the second day, this ratio increased to 4.09%. Another noteworthy situation is that the third trading day after the announcement is below the market return. This situation is likely to be regarded as a result of profit taking (profit realization) of stock market users for speculative purposes. Conclusion In this study, cumulative abnormal return ratios after the announcement obtained on a daily basis in 3 different event windows has higher return than before announcement. After the announcement of the rating note, stock returns increased, so the market value of the companies also increased. The semi-strong form, the second degree of the Efficient Market Hypothesis, assumes that investors can not make abnormal returns by exploiting the companies public statements in addition to historical data. (Karan, 2004). The results obtained in the study contradict the Effective Markets Hypothesis. Hence, the result is that the BIST is not even effective in the semi-strong form. This study was based on the announcement of the rating marks of the old and new companies that were included in the same year. Similarly, a study from a previous year can be evaluated as a research topic and provided with the possibility of comparison. It will be possible to diversify and improve this work by making use of the differences in abnormal return calculation methods. [ABSTRACT FROM AUTHOR]

This study examined potential effects of financial literacy on household portfolio choice and investment return, an indicator of financial wellbeing. Using data from the 2014 Chinese Survey of Consumer Finance, financial literacy was measured and further categorized into basic financial literacy and advanced financial literacy. This study tested the hypothesis that financial literacy affects household choice between stock and mutual fund. The results indicated that households with higher financial literacy, especially those with higher level of advanced financial literacy tended to delegate at least part of their portfolio to experts and invest in mutual fund. However, households who were overconfident about their financial literacy tended to invest by themselves and were more likely to hold only stocks in their portfolios. The findings also indicated that households with higher financial literacy had a better chance of receiving a positive investment return, suggesting that higher financial literacy may result in a better financial outcome. [ABSTRACT FROM AUTHOR]

Research background: The liquidity of assets in the financial market is understood generally as costs, and the easiest way in which different types of assets can be converted into cash, or to put it simply, sold at the currently available price on the market. For a considerable period of time this category had not been duly considered in the framework of modern finance theory. As a result, a number of basic models constructed within the framework of this theory in its classical form did not include problems with liquidity. This applies to a number of aspects related to liquidity, with one of the most important being the relationship between the liquidity of trading in shares and the results obtained from these rates of return. Purpose of the article: The aim of the article is to determine whether the rate of return on shares increases with the increase in share liquidity and the incremental rate of return on this account decreases with increasing liquidity. The applied research methodology is similar to that described by Pastor and Stambaugh (2003). The model used in the empirical study is the expanded model of Fama and Francha (1993) for the liquidity factor. Methods: In this paper I present various factors which will affect the liquidity. The paper will also provide the results of research concerning the relations between spread and stock return on the Warsaw Stock Exchange (WSE). The evidence drawn from WSE stock returns over the period 2004-2012 indicates that Amihuda measure and other variables have a significant effect on stock return using the multifactorial Pastor-Stambaugh model. Findings & Value added: In the case of the Polish market, it can be stated that in the analysis based on the Pastor-Stambaugh model not all the variables included in this model are statistically significant. However, directional parameters associated with liquidity risk were statistically significant in all analyzed periods, which allows us to confirm the hypothesis that liquidity has a significant influence on the rate of return on shares listed on the Stock Exchange in Warsaw. [ABSTRACT FROM AUTHOR]

Summary Self-Help Groups (SHGs) create a platform that allows women to meet on a weekly basis to save and to take loans if needed. Strict records of all saving and lending are important, both to avoid conflicts in the group and to obtain access to bank loans. Accounting is either done by a group member (internal accountant), or by another villager (external accountant). Using first-hand data on SHGs in Northern India, this paper studies whether economic and non-financial returns differ depending on whether SHGs meet as independent entities or with external involvement. Economic theory suggests that repeated interaction between individuals can help build social capital. The presence of an external accountant hampers this process. Indeed, repeated interaction is more likely to create non-financial benefits in the form of mutual assistance and collective action when the accountant is a group member. However, these benefits come at a cost, as SHGs with internal accountants distribute financial benefits more unequally and the accountants themselves receive larger shares than the other members of the groups. The analysis shows that the larger shares cannot be explained as a compensation for a better financial performance, but that some form of elite capture occurs. However, other group members implicitly agree on the unequal distribution. Indeed, members are not more likely to leave groups, possibly because the loss in financial benefits is outweighed by the gain in non-financial benefits. [ABSTRACT FROM AUTHOR]

The paper deals with the application possibilities of the intermarket analysis for the investment portfolio formation. The aim of this paper is to analyze how to adapt various financial market relations in order to assess the attraction of the different asset classes and taking into account it to form the investment portfolio. In the paper three different investment portfolio construction strategies have been analyzed. The first investment portfolio is formed from the decreasing financial markets, the second - from the rising financial markets, and the third portfolio includes all the financial markets. The study results showed that investing in equal proportions into stocks, bonds and commodities is more effective solution than investing in the market or in the reverse market. Including the currency into the basic portfolio has reduced the risk, but due to the declined returns the efficiency also decreased. The study results showed that the 'permanent portfolio' formation of the three asset classes is sufficiently effective in order to reduce investment portfolio risk and to achieve a reasonable return. [ABSTRACT FROM AUTHOR]

Football is a sport that moves thousands of people and millions of euros. Since 1983, several clubs entered the stock markets with shares, and now twenty two clubs are listed in the Stoxx Football Index. In this study, we analyse the behaviour of the return rates of such shares, with Detrended Fluctuation Analysis and Detrended Cross-Correlation Analysis (and its correlation coefficient). With Detrended Fluctuation Analysis, we are able to observe that the shares of several clubs are far from the behaviour of a random walk, which is expected by the theory. Using Detrended Cross-Correlation Analysis, we calculate the cross correlations of clubs’ returns with national indexes and then with the Stoxx Football Index. Although almost all of them are positive, they do not seem to be strong. [ABSTRACT FROM AUTHOR]

The multifractality in stock returns have been investigated extensively. However, whether the autocorrelations in portfolio returns are multifractal have not been considered in the literature. In this paper, we detect multifractal behavior of returns of portfolios constructed based on two popular trading rules, size and book-to-market (BM) ratio. Using the multifractal detrended fluctuation analysis, we find that the portfolio returns are significantly multifractal and the multifractality is mainly attributed to long-range dependence. We also investigate the multifractal cross-correlation between portfolio return and market average return using the detrended cross-correlation analysis. Our results show that the cross-correlations of small fluctuations are persistent, while those of large fluctuations are anti-persistent. [ABSTRACT FROM AUTHOR]

In this paper, we advocate the provincial TV audience rating as the novel proxy for the provincial investor sentiment (PIS) and investigate its relation with stock returns. The empirical results firstly show that the PIS is positively related to stock returns. Secondly, we provide direct evidence on the existence of home bias in China by observing that the provincial correlation coefficient is significantly larger than the cross-provincial correlation coefficient. Finally, the PIS can explain a large proportion of provincial comovement. To sum up, all these findings support the role of the non-traditional information sources in understanding the “anomalies” in stock market. [ABSTRACT FROM AUTHOR]

The Indonesia Stock Exchange (IDX) Composite is an index of all stocks that are traded on the IDX. It is known as a tool used by investors and financial managers to depict market and to compare the return on specific investments. Recently, IDX Composite rate is very fluctuated resulting in difficulty to predict the trend of series data. A lot of time series-based model had been developed to make future predictions of IDX Composite. Neural Networks, one of the most popular approaches in financial worlds due to its parsimonious data requirements and can produce a more accurate model especially nonlinear, was used in this study. The study aims to investigate the suitable model and forecast future of IDX Composite using Feed Forward Neural Networks (FFNN). The IDX Composite series from January 2011 to July 2016 was used to construct the model of FFNN. The study found that FFNN architecture with three inputs and four hidden neurons showed the best forecast accuracy measured by RMSE and MAE. The selected model was then used to predict 12 months ahead of IDX Composite along with their confidence interval. The capital gain of IDX Composite forecasting was calculated to compute the profit with an assumption the investment was 1 million (IDR). The results indicated that if the investors sold the shares in next 12 months, there would be significant profit or capital gain obtained on August and September 2016. While selling the share on remaining periods would yield a noticeable loss or capital loss. [ABSTRACT FROM AUTHOR]

We propose a new stock selection strategy that exploits rebalancing returns and improves portfolio performance. To effectively harvest rebalancing gains, we apply ideas from elliptical-copula graphical modelling and stability inference to select stocks that are as independent as possible. The proposed elliptical-copula graphical model has a latent Gaussian representation; its structure can be effectively inferred using the regularized rank-based estimators. The resulting algorithm is computationally efficient and scales to large data-sets. To show the efficacy of the proposed method, we apply it to conduct equity selection based on a 16-year health care stock data-set and a large 34-year stock data-set. Empirical tests show that the proposed method is superior to alternative strategies including a principal component analysis-based approach and the classical Markowitz strategy based on the traditional buy-and-hold assumption. [ABSTRACT FROM PUBLISHER]

We use methods of non-extensive statistical physics to describe quantitatively the memory effect involved in returns of companies from WIG 30 index on the Warsaw Stock Exchange. The entropic approach based on the generalization of the Boltzmann-Gibbs entropy to non-additive Tsallis q-entropy is applied to fit fat tailed distribution of returns to q-normal (Tsallis) distribution. The existence of long term memory effects in price returns generated by two-point autocorrelations are checked via calculation of the Hurst exponent within detrended fluctuation analysis approach. The results are collected for diversified frequency of data sampling. We confirm the perfect inverse cubic power law for low time-lags (≈1 min) of returns for the main WIG 30 index as well as for the most of separate stocks, however this relationship does not hold for longer time-lags. The particular emphasis is given to a study of an independent fit of probability distribution of positive and negative returns to q- normal distribution. We discuss in this context the asymmetry between tails in terms of the Tsallis parameters q±. A qualitative and quantitative relationship between the frequency of data sampling, the parameters q and q±, and the corresponding main Hurst exponent H is provided to analyze the effect of memory in data caused by linear and nonlinear autocorrelations. A new quantifier based on asymmetry of the Tsallis index instead of skewness of distribution is proposed which we believe is able to describe the stage of market development and its robustness to speculation. [ABSTRACT FROM AUTHOR]

This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes. [ABSTRACT FROM AUTHOR]

We study the various sectors of the Bombay Stock Exchange (BSE) for a period of 8 years from April 2006 to March 2014. Using the data of daily returns of a period of eight years we make a direct model free analysis of the pattern of the sectorial indices movement and the correlations among them. Our analysis shows significant auto correlation among the individual sectors and also strong cross-correlation among sectors. We also find that auto correlations in some of the sectors persist in time. This is a very significant result and has not been reported so far in Indian context. These findings will be very useful in model building for prediction of price movement of equities, derivatives and portfolio management. We show that the Random Walk Hypothesis is not applicable in modeling the Indian market and mean–variance–skewness–kurtosis based portfolio optimization might be required. We also find that almost all sectors are highly correlated during large fluctuation periods and have only moderate correlation during normal periods. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to portfolio management using stocks. The investment models tested incorporate a fundamental and technical approach using financial ratios and technical indicators. A Multi-Objective Evolutionary Algorithms (MOEA) with two objectives, the return and the risk, are used to optimize the models. Three different chromosomes are used for representing different investment models with real constraints equivalent to the ones faced by portfolio managers. To validate the present solution three case studies are presented for the S&P 500 for the period June 2010 until 2014. Simulations demonstrate that the stock selection based on financial ratios can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. The increase of fundamental indicators enhances the quality of the chromosomes found by the MOEA, and the results of real simulations become more precise. Some of the best chromosomes found by the algorithms invest in stocks with high return on equity (ROE), in conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential, it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage. [ABSTRACT FROM AUTHOR]

Abstract: As an aggregate measure of the variations in individuals, the analysis of mobility provides a substantial and comprehensive perspective into the complexity of socio-economic systems. In this paper, we introduced the ranking mobility index to measure the ranking variations of the stocks in Chinese stock market over time. Using the daily data of 837 constituent stocks of the Shanghai A-Stock Composite Index from January 1, 2002 to December 31, 2012, we examined respectively the dependence of ranking mobility with respect to the absolute return, trading volume and turnover ratio on the sampling time interval. The scaling property is observed in all three relations. The fact of long relaxation times gives evidence of long memory property in the stock ranking orders. [Copyright &y& Elsevier]

Efficient, accurate, and fast Markov Chain Monte Carlo estimation methods based on the Implicit approach are proposed. In this article, we introduced the notion of Implicit method for the estimation of parameters in Stochastic Volatility models. Implicit estimation offers a substantial computational advantage for learning from observations without prior knowledge and thus provides a good alternative to classical inference in Bayesian method when priors are missing. Both Implicit and Bayesian approach are illustrated using simulated data and are applied to analyze daily stock returns data on CAC40 index. [ABSTRACT FROM PUBLISHER]

Black-Scholes option pricing model's normality assumption that the stock returns are normally distributed has long been disputed by the empirical data. In this paper we investigate in detail the return distributions of NIFTY, JKSE, STI, and S&P500 index of the Indian stock market in different time intervals for the period 1st January 1991-31st December 2010. Firstly, for daily stock returns the null hypothesis of normality is clearly rejected for α = 0.05, in all the four markets. This result is in accordance with the Central Limit Theorem which says that the longer the time interval for which returns are computed, the more the resulting distribution should conform to the normal distribution. Secondly, we fit the Student's t-distribution to the daily return distribution and find that the shape parameter ? has similar value as obtained for the developed markets. [ABSTRACT FROM AUTHOR]

The pa package provides tools for conducting performance attribution for long-only, single currency equity portfolios. The package uses two methods: the Brinson-Hood-Beebower model (hereafter referred to as the Brinson model) and a regression-based analysis. The Brinson model takes an ANOVA-type approach and decomposes the active return of any portfolio into asset allocation, stock selection, and interaction effect. The regression-based analysis utilizes estimated coefficients, based on a regression model, to attribute active return to different factors. [ABSTRACT FROM AUTHOR]

The article focuses on the relationship between uncertainty related to inflation and the equity market. It states that high uncertainty leads to higher risk premia in the equity market. A study conducted by the authors reveals that uncertainty is vital to know the differences in excess returns in the market. It highlights that consumption growth is associated with high premiums during high uncertainty.

Financial advisors commonly recommend that the investment horizon should be rather long in order to benefit from the ‘time diversification’. In this case, in order to choose the optimal portfolio, it is necessary to estimate the risk and reward of several alternative portfolios over a long-run given a sample of observations over a short-run. Two interrelated obstacles in these estimations are lack of sufficient data and the uncertainty in the nature of the return generating process. To overcome these obstacles researchers rely heavily on block bootstrap methods. In this paper we demonstrate that the estimates provided by a block bootstrap method are generally biased and we propose two methods of bias reduction. We show that an improper use of a block bootstrap method usually causes underestimation of the risk of a portfolio whose returns are independent over time and overestimation of the risk of a portfolio whose returns are mean-reverting. [ABSTRACT FROM AUTHOR]

We propose a simple time-series model based on information asymmetry that allows us to test the predictive power of equity and debt issues with respect to future market returns. Using this method, we find that managers’ new equity and debt issue decisions have predictive power for future market returns, when we take into account potential feedback from past market returns and structural breaks. We also take into account a cointegration relation among stock prices, equity issues and debt issues. This finding is robust with respect to various measures of market returns and consistent with the managerial timing hypothesis. [ABSTRACT FROM AUTHOR]

Este artigo examina o procedimento de estimação de betas para empresas cujas ações não são negociadas todos os dias. Os betas são estimados para 3 métodos: repetição da última cotação (RUC), trade-to-trade (TT) e ajuste de Scholes e Williams (SW). Há 3 periodicidades de retorno: diária, semanal e mensal. O objetivo é verificar a consistência dos betas estimados nos diferentes métodos de cálculo e nas diferentes periodicidades de retorno, com base em dados simulados. Os resultados indicam que, para ações que não negociam todos os dias, os betas podem ser estimados com melhor precisão com o método TT para periodicidade diária. [ABSTRACT FROM AUTHOR]

Este artigo faz uso do Capital Asset Pricing Model (CAPM), em sua versão canônica e com extensões não lineares, visando a apreçar um painel de 75 fundos de investimento em ações no Brasil, ao longo dos últimos 11 anos. O resultado sugere que a versão linear desse arcabouço não seja capaz de apreçar ou de prever retornos reais de fundos que possuam elevados patrimônio líquido (PL) e outperformance, em relação ao índice da Bolsa de Valores de São Paulo (Ibovespa), corroborando evidências anteriores. A versão não linear com thresholds baseados no PL parece lidar melhor com a questão de alfas de Jensen significativos, apesar de ser estatisticamente indicada apenas para poucos fundos com elevado PL, mas baixa outperformance. Essa é uma evidência de que, apesar de o tamanho influenciar na gestão e, possivelmente, na performance de um fundo, a modelagem de apreçamento desse efeito deve ser feita linearmente. [ABSTRACT FROM AUTHOR]

The article focuses on the bond stock earnings yield differential (BSEYD) model, which relates the yield on stocks to the yield on nominal Treasury bonds. It says that an optimal asset allocation between bonds and stocks is related to their relative yields. It mentions that the BSEYD model causes an equity market correction if the market adjustment is large. It adds that the standard confidence intervals based on a Gaussian assumption proved inaccuracy.

Aggregate development impact fees must be apportioned among developers in a manner commensurate with benefits provided by new infrastructure. We propose a methodology to directly, rather than iteratively, apportion aggregate development impact fees so that all users receive an internal rate of return equal to the municipality's cost of capital. The methodology explicitly recognizes that users may enjoy the benefits of infrastructure for different periods of time. The model provides for proportionate share assessment of impact fees whether infrastructure investment occurs before, during, or after development and is flexible in addressing changes in demand and in the municipality's cost of capital. [ABSTRACT FROM AUTHOR]

This work analyzes the efficiency of the companies in the sector of civil construction in generating return to the shareholders in the years of 2009 and 2010, by means of Data Envelopment Analysis, considering Equity as input, and net profits, shares and variation in the share price, as outputs. The data were obtained by collecting in Economática ® software and database available on BM&FBovespa website, and analyzed in SIAD software. As main result, it was verified that Helbor Empreendimentos S.A. was the most efficient in generating return to the shareholder within the analyzed group, while Viver Incorporadora e Construtora S.A. was the least efficient. [ABSTRACT FROM AUTHOR]

Abstract: We examine the performance of passively managed exchange-traded funds (ETFs) that provide exposure to global emerging markets equities. We find that the tracking errors of these funds are substantially higher than previously reported levels for developed markets ETFs. ETFs that use statistical index replication techniques turn out to be especially prone to high tracking errors, and particularly so during periods of high cross-sectional dispersion in stock returns. At the same time, we find no convincing evidence that these funds earn higher returns than ETFs that rely on full-replication techniques. [Copyright &y& Elsevier]