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In 2014, FERC adopted a new theory of capital markets for the purpose of setting authorized returns-on-equity: that all observed dividends and interest rates were anomalous and therefore unreliable indicators of the returns necessary for investors to hold the stocks and bonds of transmission owners. The new theory, used to justify higher authorized ROEs, rejected evidence of fundamental conditions in capital markets and explicitly adopted arguments by investor-owned utilities regarding unreliable markets. After a judicial remand, FERC abandoned the new theory, but consumers in New England and the Midwest were arbitrarily charged hundreds of millions of dollars over several years due to unjust and unreasonable rates for transmission service. [ABSTRACT FROM AUTHOR]

In the traditional portfolio models, the return rates of risky assets are usually defined as random variables or fuzzy variables. However, when some events such as financial crises or wars occur that can cause instability in financial markets, the results of these assumptions are not always satisfactory. Therefore, some scholars try to use uncertain variables to express the return rates. In this paper, a two-factor uncertain portfolio problem under optimistic value criteria is studied, where we use the optimistic value instead of expected value to describe the investment return and study the impact of economic circumstance factor and corporation's specificity factor on the return rates. Firstly, a two-factor optimistic value-variance-entropy model with background risk is constructed, in which the total investment return consists of the risky asset return and the background asset return. Then, considering the investors with two kinds of risk preferences, a three-step method is applied to convert the bi-objective optimization model. Moreover, a moment estimation method is used to process the expert-estimated data on the return rates. Finally, a numerical simulation is presented for showing the applicability of our models and solution method. • We use optimistic value to represent the return rather than expected value. • The multiple factors and background risk are considered simultaneously. • Three-step method is used to convert the model into two single-objective models. [ABSTRACT FROM AUTHOR]

With the development of financial market, people invest their savings into different financial products to increase return. However, financial market is an extremely complex system, where investment opportunities and risk often coexist. For investors, it is necessary to take effective measures to avoid risk while obtaining considerable return. In recent decades, actively managing investment in the form of a portfolio is widely accepted by investors. In this paper, we propose a new three-objective Sortino ratio-expected shortfall-turnover rate (SR-ES-TR) portfolio optimization (PO) model. The Sortino ratio is used to measure the risk-adjusted rate of return. The expected shortfall is used to measure the tail risk of a portfolio. Portfolio liquidity risk is characterized by turnover rate. We constructed a portfolio of ten stocks and solved the SR-ES-TR model by the novel MOPSO-SODE algorithm. Compared with other four multi-objective algorithms, the better performance and validity in solving SR-ES-TR model by MOPSO-SODE algorithm have been verified. • Established a three-objective SR-ES-TR portfolio optimization model with liquidity ratio. • Designed a MOPSO-SODE algorithm based on the MOPSO framework. • Verified the effectiveness of the MOPSO-SODE algorithm in solving SR-ES-TR model. [ABSTRACT FROM AUTHOR]

Abstract The aim of this study is to analyze whether managers, practitioners and individual investors could obtain higher risk-adjusted returns by allocating their investments to funds that integrate specific levels of socially responsible (SR) criteria in their portfolios. This is achieved by comparing the performance of mutual funds according to their SR characteristics: environmental, governance, social, and sustainability attributes. For a large sample of 3920 equity SR mutual funds around the world, performance is measured using a multifactor model that incorporates relevant benchmarks according to the fund investment objectives, and using Carhart's (1997) methodology to measure mutual fund performance persistence. In general, fund performance is not significant, the average being negative and close to zero. Funds achieving relatively high levels of SR attributes in their portfolios seem to experience overall worse performances. This evidence is, however, mainly driven by the behavior of worst-performing funds. Moreover, investing in the previous best SR funds could lead investors to greater overall returns in most areas and levels of SR attributes considered. This evidence highlights the role of managers in enhancing the returns of a portfolio with a well-defined SR investment policy. Therefore, there is no incompatibility between pursuing higher ethical (and sustainable) values as well as greater financial performances from investments—provided managers have the skills necessary to choose the right SR funds. Highlights • We compare the performance of socially responsible (SR) funds. • Funds with high SR attributes experience overall worse performances. • Investing in the previous best SR funds leads to greater overall returns. • The evidence highlights managers' role for designing SR investment policies. • Ethical and sustainable values are compatible with greater performances. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we test for predictability of excess stock returns for 18 emerging markets. Using a range of macroeconomic and institutional factors, through a principal component analysis, we find some evidence of in-sample predictability for 15 countries. In-sample predictability is corroborated by out-of-sample tests. Using a mean-variance investor framework, we show that investors in most of these emerging markets can make significant profits from dynamic trading strategies. Finally, we show that investors in most countries where short-selling is prohibited could make significant gains if limited borrowing and short-selling were allowed. [Copyright &y& Elsevier]

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]

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: 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 tests how effective global models are at pricing the cross section of emerging market (EM) stock returns over a recent post-liberalization period. We apply the tests of Kan et al. (2009). Our results show that conditional models and currency factors do perform better than unconditional models and single factor models and there are some differences in the models in the two subperiods of our data. The important implication of this paper for international investors is none of our results are significant when we allow for model misspecification and none of the alternative models specifically outperform the World CAPM. [Copyright &y& Elsevier]

Abstract: In strategy of investment, an important thing for investors is to correctly predict firm’s revenue growth rate (RGR), which is an effective evaluation indicator for them to see how big the potential power of future development is and measure how about the growth of future development for a target firm that may be selected to investment portfolios. However, conventional methods of forecasting RGR have some shortcomings such as statistical methods based on strict assumptions of linearity and/or normality limit applications in real world. Additionally, due to rapid changing of information technology (IT) today, some techniques (i.e. rough sets and data mining tools) have become important research trends to both practitioners and academicians. With these reasons above, a new procedure, using the feature selection method and rough sets classifier, is proposed to extract decision rules and improve accuracy rate for classifying RGR. In empirical study, an actual RGR dataset collected from publicly traded company of stock markets is employed to illustrate the proposed procedure. The experimental results of RGR dataset analyses indicate that the proposed procedure surpasses the listing methods in terms of both higher accuracy and fewer attributes, and the output of proposed procedure is to generate a set of easily understandable decision rules that are readily applied in knowledge-based investment systems by investors. [Copyright &y& Elsevier]

Abstract: Lowering psychological pressure of investors and increasing the futures trading profit are the main purposes of this paper. First of all, this study aims to transfer profit curve (PC) generated by a non-AI-based trading strategy into technical indices, and enable clustering of high-low points of PC to display high–low point signals through some AI-based methods such as Grey Clustering, SOM and K-mean. Next, it attempts to close the transaction with high-point signal, and then open a position at low-point one, thus constructing three groups of profit refiners: GCR (Grey Clustering Refiner), SOMR (SOM Refiner) and KMR (K-Mean Refiner). Finally, the features of these refiners are analyzed to evaluate the test results using some performance indices. SOMR could improve the profit to the greatest possible extent, followed by GCR and KMR; on the other hand, KMR could lower psychological pressure, followed by SOMR and GCR. As a whole, three groups of refiners can really improve the profit and alleviate psychological burden of investors. [Copyright &y& Elsevier]

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]

This paper looks at the investment allocation process employed by portfolio investors in emerging markets. In particular, it examines the first of a two-stage decision process: first, investors create a sub-set of investable countries to be analyzed later in further detail; second, they weigh expected returns versus risk and subsequently allocate their funds. This study hypothesizes that the determination of whether a country is investable or not is influenced by a number of factors, especially related to size, quality of “housekeeping” (macro-policies, political economy, local financial markets, corporate governance, etc.) and efficiency of “plumbing” (legal and regulatory framework, custody, clearing and settlement, taxes, etc.). By interviewing many types of these investors in both the United States and the United Kingdom, we delve into their decision-making processes as well as attempt to uncover the factors they indicate matter most in defining the investable universe. We determine the relative importance of such housekeeping and plumbing factors while highlighting the role of external issues, such as index benchmarking and U.S. foreign policy. We recognize from the outset that the most profound effects on investment flows, or the required minimum expected returns, arise from improvements or deteriorations in macro-policies. However, at the margin, improvements can be made in country policies that will, for a given macro-situation, improve the ability of a country to attract international investment flows. [Copyright &y& Elsevier]

• A cloud theory-based multi-objective portfolio selection model is proposed. • The crisp numerical characteristics of the cloud model theory are defined. • The acceptance and rejection functions with variable risk appetite are constructed. • A compromise programming approach is utilized to aggregate the objectives. • Several schemes as per investors' preferences regarding all objectives are devised. This study proposes a cloud theory-based multi-objective portfolio selection model with variable risk appetite, which incorporates four objectives of mean, variance, skewness, and liquidity constrained by several realistic constraints. Cloud model theory is employed to characterize the return rates and liquidity of assets due to the superiority of simultaneously capturing the ambiguity and randomness of information. The crisp numerical characteristics of the cloud model are defined to obtain the crisp form of the proposed model. To highlight and portray the investors' risk (averse-neutral-seeking) appetites, the generalized acceptance and rejection functions are modeled by using the extreme values of each objective and introducing a variable risk appetite parameter. Thus the corresponding model is transformed with the objective functions of maximizing acceptance and minimizing rejection, which is solved through the compromise programming approach. The extended model provides investors with an opportunity to adjust risk parameters according to current market status. Moreover, the preference ratio vector is introduced when optimizing, which provides investors with overall control over the preferences regarding all objectives, so that investors can derive optimal portfolios well compatible with their expectations through customized weighting schemes. A real-world empirical application is presented to demonstrate the effectiveness of the proposed model [ABSTRACT FROM AUTHOR]