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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 Using stock market returns of two stock exchanges in China, this paper employs MF-DCCA to investigate the non-linear cross-correlation between individual investor sentimentand Chinese stock market return. We find that there exists a power-law cross-correlation between individual investor sentiment and Chinese stock market return, and the cross-correlations are significantly multifractal. In addition, the cross-correlation between individual investor sentiment and Shenzhen Component Index (SZSE) return is more anti-persistent than that between individual investor sentiment and Shanghai Composite Index (SSEC) return, implying that individual investor sentiment has a stronger impact on small stocks. Besides, long-range correlations, fat-tailed distribution and extreme value all contribute to the multifractality for cross-correlation between individual investor sentiment and SSEC return, while long-range correlation is the main source of multifractality for the cross-correlation between individual investor sentiment and SZSE return. Highlights • Cross-correlation between investor sentiment and stock return is investigated. • Yu'ebao Sentiment Index and Shenzhen stock market return is more anti-persistent. • The sources of multifractal features are investigated. [ABSTRACT FROM AUTHOR]

The downside risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical Mean–Variance model concerning the asymmetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developed a new recursive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents some discontinuity and is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which continuous observations are available. In this paper, Athayde model is reformulated and clarified. Then, taking advantage on the robustness of the median, another nonparametric approach based on median kernel returns estimation is proposed in order to construct a portfolio frontier. A new version of Athayde’s algorithm will be exhibited. Finally, the properties of this improved portfolio frontier are studied and analysed on the French Stock Market. [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]

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]

Purpose -- The purpose of this paper is to compare and contrast the effect of individual and institutional sentiments on the US stock returns during a prolonged bull phase of ten years in the 1990s compared to shorter boom and bust cycles of the 2000s. The study is focussed on a set of stocks that are prone to sentiments and speculations. Design/methodology/approach -- To compare the dynamic interaction of individual and institutional sentiments and stock returns, the authors use the vector autoregression (VAR) approach. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series because it does not impose a priori restriction on the structure of the system. Using impulse response function, the authors determine how stock returns respond over time to a shock in institutional and individual sentiments. Findings -- The authors find that sentiments of individual investors can affect returns mostly when there is a prolonged upward trend in stock prices, while sentiments of institutional investors can impact the returns when stock market is more volatile. Originality/value -- This paper compares the effect of noise traders and rational investors' sentiment on stock returns during the persistent period of positive abnormal returns of the 1990s and the more volatile stock returns of the 2000s. [ABSTRACT FROM AUTHOR]

Despite years of study, the impact of firm-level governance on stock returns is not clear, especially in non-U.S. markets. We investigate the returns of governance-based trading strategies in Asia, using bias-free return data and CLSA governance ratings. We argue that poor governance should be associated with higher market risk. We find that a portfolio of poorly governed firms has a higher market beta, higher expected return and higher realized return, compared with a good governance portfolio. In contrast to some earlier studies, we find no abnormal returns after adjusting for risk and country effects. Only investors who can predict in advance which firms will improve their governance can earn abnormal returns. [ABSTRACT FROM AUTHOR]

Studies on investor sentiment are mostly focused on the stock market, but little attention has been paid to the effect of investor sentiment on the return of a specific industry. This paper constructs a proxy variable to examine the relationship between investor sentiment and the return of a specific industry, using the Principle Component Analysis, and finds that investor sentiment is positively correlated with the industry return of the current period and negatively correlated with that of one lag period; we classify investor sentiment as optimistic state and pessimistic state and find that optimistic investor sentiment has a positive effect on stock returns of most industries, while pessimistic investor sentiment has no effect on them; this paper further builds a two-state Markov regime switching model and finds that sentiment has different effect on different industries returns on different states of market. [ABSTRACT FROM AUTHOR]

Personalized recommended method is widely used to recommend commodities for target customers in e-commerce sector. The core idea of merchandise personalized recommendation can be applied to financial field, which can also achieve stock personalized recommendation. This paper proposes a new recommended method using collaborative filtering based on user fuzzy clustering and predicts the trend of those stocks based on money flow. We use M/G/1 queue system with multiple vacations and server close down time to measure practical money flow. Based on the indicated results of money flow, we can select the more valued stock to recommend to investors. The experimental results show that the proposed method provides investors with reliable practical investment guidance and receiving more returns. [ABSTRACT FROM AUTHOR]

In this study, the stock return rate of the company shares was estimated by means of the adaptive neurofuzzy inference system (ANFIS). For this aim, the data of financial ratios prepared for the input and output variables of the ANFIS was obtained by using the proportion parameters from the items of balance sheet and income statement of the ISE 100 companies in the industry between the years of 2003 and 2007 announced by 40 companies, and the obtained data was applied into the ANFIS. In the implemented experiment, the values estimated by the ANFIS were compared with the real values of the stock return rate, and significant 4 input variables among 14 input variables were determined to apply into the ANFIS. When the same experiment was implemented by using these determined significant input variables, the ANFIS model reached to approximately same estimation success. As a result, we showed that this model provides the same success by using less number of input variables and it can be used for decreasing the risk of companies and investors. [ABSTRACT FROM AUTHOR]

Abstract: Should investors diversify across emerging stock markets or across industries to achieve improvements in their risk–return tradeoffs especially during financial crisis periods? We examine the issue using individual firm data from a selection of emerging markets and including the period of the 1997 Asian financial crisis. We find that country effects were the dominant force behind the low co-movements among emerging stock market returns. There is evidence of increased industry effects beginning at the time of the Asian financial crisis, but this may have been a temporary phenomenon associated with contagion effects during the crisis. [Copyright &y& Elsevier]

Abstract: Using monthly foreign flows data on Istanbul Stock Exchange (ISE) and employing a structural VAR model, we analyze the interaction between foreigners'' trading and emerging stock returns. In contrast to most of the available theory and repeated previous findings on other markets, foreign investors negative-feedback-trade with respect to past local returns in ISE, however only in rising markets and especially under macroeconomic instability. Net foreign flows forecast future market returns, but not individual stock returns. Price impacts are permanent, suggesting that foreigners'' trading incorporates information. Overall, results reject previous conclusions that foreigners are uninformed positive feedback traders: rather, they are a heterogeneous group dominated by sophisticated investors able to rationally adjust their trading style in line with the market''s prevailing characteristics. [Copyright &y& Elsevier]

The article offers information on the strategy-proof test of portfolio returns, where the main problem of investors is to determine whether a portfolio is making positive returns as the risk-free rate or a stock market index. It states that Compound Excess Returns Test (CERT) is a conservative test, but it is unavoidable if the test is strategy proof. It mentions that the compound excess returns test guards against manipulations of different types.

Abstract: Weak-form efficiency in the stock markets of the Gulf Cooperation Council is examined using daily, weekly, and monthly index data for the 10-year period 2000–2009. Various variance ratio test specifications with specific homo- and heteroscedasticity assumptions found evidence of nonlinear dependence for the daily data, supporting the evidence in favor of a rejection of the random walk. A correction procedure for thin and nonsynchronous trading was applied but failed to produce significantly different results. Following an ARCH based model building procedure, conditional heteroscedasticity models are applied to the log return series. Significant differences in forecasting performance cannot be detected. The random walk hypothesis is generally rejected for daily but differences appear across markets using weekly and monthly data. The increased involvement of foreign institutional investors may play a role in the increased serial correlation in stock returns in the most recent period. [Copyright &y& Elsevier]

Abstract: This paper examines the link between over-subscription and pre-listing information in fixed-price IPOs. We find a strong negative relation between investors'' opportunity cost of fund and over-subscription. Since investors are required to make upfront payment at the time of IPO applications, lengthy offer period increases investors'' opportunity cost of fund and thus reduces their interests in the IPOs. To increase over-subscription, firms should time their IPOs to coincide with periods of large positive initial returns and low volume of IPO activity. We find that over-subscription is negatively correlated with offer price but is not significantly related to issue size. [Copyright &y& Elsevier]

Abstract: This paper has three main contributions to the literature on investor herds. First, it extends investor herding studies to an emerging yet relatively sophisticated Taiwanese stock market at the sector level by using firm level data. Second, it employs different methodologies designed to test the existence of investor herds to better understand the sources of herd behavior. Third, it discusses the implications of different herding measures for investors exposed to systematic and unsystematic risks. We find that the linear model based on the cross-sectional standard deviation (CSSD) testing methodology yields no significant evidence of herding. However, the non-linear model proposed by and the state space based models of lead to consistent results indicating strong evidence of herd formation in all sectors. We also find that the herding effect is more prominent during periods of market losses. Our results suggest limited diversification opportunities for investors in this market, especially during periods of market losses when diversification is most needed. Further research is necessary to see whether similar findings hold for other emerging markets. [ABSTRACT FROM AUTHOR]

Abstract: Portfolio selection is a resource allocation problem in a finance market. The investor’s asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation problem. However, the traditional ANN model cannot guarantee the summation of produced investment weight always preserves 100% in output layer. This article introduces a resource allocation neural network model to optimize investment weight of portfolio. This model will dynamically adjust the investment weight as a basis of 100% of summing all of asset weights in the portfolio. The experimental results demonstrate the feasibility of optimal investment weights and superiority of ROI of buy-and-hold trading strategy compared with benchmark Taiwan Stock Exchange (TSE). [Copyright &y& Elsevier]

The article explains the relationship between economic freedom and equity markets and proposes a strategy for global investors. It provides a review of the relationship between economic freedom and equity markets. Then it relates economic freedom to equity investment returns. It shows that the rate of increase in economic freedom is directly related to equity returns and that an investment strategy based on economic freedom earned attractive investment returns.