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The purpose of this paper is to analyse the performance of UK property funds using the dual sources of active management, Active Share and tracking error, to distinguish between the types of active management styles used by funds.

The authors use data on 38 UK real estate funds and classify them into five active management categories using the dual sources of active management, Active Share and tracking error. Then, the authors compare their return performance against Active Share, tracking error, fund size and leverage. Therefore the paper is able to answer two of the fundamental questions of investment: does active management add value and what form of active management, stock selection or factor risk, is better at adding value to the fund?

There are three main conclusions. First, the approach of Cremers and Petajisto (2009) and Petajisto (2010) is able to classify real estate funds in the UK on their management activity into categories that makes intuitive sense and seem stable over time. Second, balanced funds show relatively low Active Shares and particularly low tracking errors, due to the benefits of property-type diversification. In contrast, specialists funds display higher Active Shares and both low and high tracking errors depending on their stock-picking approach; diversified or concentrated. Third, an analysis over different time periods confirmed that funds in the sample essentially remained in the same categories within the sample period, even during markedly different market return periods. This implies that investors need to constantly monitor changes in the market and switch between fund management styles, if at all possible.

The analysis was only based on 38 funds with complete data over the sample period and the relationship between fees and active management was not examined, even though ultimately investors are concerned with returns after management fee. It would be instructive therefore if the number of funds and time period was expanded to see if the results are robust and to see whether management fees outweigh the benefits of active manager.

The findings should enable investors to make a more informed investment decisions in the future.

To the best of the author’s knowledge this is the first paper to apply the dual sources of active management, Active Share and tracking error, in the UK real estate market.

The purpose of this study is twofold. On the one hand, it studies the impact of IFRS 9 adoption on the firm value; and on the other hand, it investigates the impact of the classification of financial assets on the firm value.

The study covers a sample of 55 listed banks in the Middle Eastern and North African (MENA) region. Data is collected for three years (2017–2019).

The findings show that banks’ value is not impacted by IFRS 9 adoption but by financial assets’ classification. Firm value is positively affected by fair value through other comprehensive income assets, while it is negatively affected by amortized cost and fair value through profit or loss assets. The results of the additional analysis show consistent outcomes.

This research reveals important managerial implications. Priority should be given to the financial assets’ classification strategy following the adoption of IFRS 9 to boost the market valuation of banks. It may be useful for investors, managers and regulators in their decision-making.

This study enriches previous research as IFRS 9 is a new standard, and its adoption consequences need to be investigated. A few recent studies have focused on IFRS 9 as a whole or on other parts of IFRS 9, namely, the impairment regime and hedge accounting and concern developed contexts. However, this research adds to the knowledge of capital market studies by investigating the application of IFRS 9 in terms of classification in the MENA region.

This book is a policy proposal aimed at the democratic left. It is concerned with gradual but radical reform of the socio‐economic system. An integrated policy of industrial and economic democracy, which centres around the establishment of a new sector of employee‐controlled enterprises, is presented. The proposal would retain the mix‐ed economy, but transform it into a much better “mixture”, with increased employee‐power in all sectors. While there is much of enduring value in our liberal western way of life, gross inequalities of wealth and power persist in our society.

To provide an overview of the US Securities and Exchange Commission’s (SEC) recently adopted rule 22e-4 (Rule 22e-4) under the Investment Company Act of 1940, as amended (1940 Act) regarding investment company liquidity risk management programs.

Reviews and summarizes the specific requirements of Rule 22e-4 to better enable investment companies and their boards to comply by the general compliance date of December 1, 2018 (smaller complexes have until June 1, 2019).

The SEC clarifies that each fund should tailor its particular Program to ensure that it is adequately assessing and managing its specific liquidity risk based on its investment strategies and risks; however, it is not expected that a fund would eliminate all adverse impacts of liquidity risk. In addition, under the final rule, while the board does have certain duties and responsibilities with respect to certain aspects of a fund’s Program, the SEC pared back much of what had been in the Proposing Release to ensure that the board’s role remains one of oversight and not management.

Although the compliance date does not occur for almost two years, funds and their boards should begin reviewing the Rule 22e-4 requirements now and developing their Program.

Practical guidance from experienced investment management attorneys that provides insight into expectations for compliance with Rule 22e-4.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

The purpose of this paper is to examine the performance of Chinese mutual funds during the period of January 2000 to July 2013. Emerging market funds provide investors with alternative risk exposure for their portfolios. The Chinese market has developed rapidly and differs from developed markets regarding wide range of market and economic characteristics, including size, liquidity, and regulation. The performance of these funds is investigated by using various risk adjusted measures. The study also compares performances of mutual fund subgroups and explains the factors influencing their performances.

This is an empirical paper using various risk performance measures. These measures include the Sharpe ratio, Information ratio, Treynor ratio, M-squared and Jensen’s α. The data comprises 1,037 funds. These funds are further divided into ten subgroup of funds based on their classification: equity (484); aggressive allocation (95 funds); conservative allocation (18 funds); moderate allocation (85 funds); aggressive bond (92 funds); normal bond (52 funds); guaranteed (29 funds); money market (53 funds); and QDII funds (119 funds). A cross-sectional analysis of fund performance is performed using Sharpe and Jensen’s measures as dependent variables and fund-specific variables (Age, Turnover, Tenure, Frontload, Redemption fees, and Management fees), market-specific variables (P/E ratio, P/B ratio, Market capitalization), and fund types as independent variables.

The findings show that Chinese funds generate positive αs for their investors. The highest return is provided with aggressive allocation funds followed by moderately aggressive allocation funds. The average Jensen’s α is the highest in aggressive allocation funds. QDII funds do not provide significant positive αs; in several instances αs are negative. Further analysis of sub-periods show that Chinese funds do not consistently provide excess returns and show great variations. The study also finds that older funds, funds with higher fees, high price to book ratio, and smaller funds continue to perform better than other funds.

This study adds value by focussing on Chinese funds and risk/return characteristics of these funds. The research will further explore factors explaining these returns.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

Allyn Young′s lectures, as recorded by the young Nicholas Kaldor, survey the historical roots of the subject from Aristotle through to the modern neo‐classical writers. The focus throughout is on the conditions making for economic progress, with stress on the institutional developments that extend and are extended by the size of the market. Organisational changes that promote the division of labour and specialisation within and between firms and industries, and which promote competition and mobility, are seen as the vital factors in growth. In the absence of new markets, inventions as such play only a minor role. The economic system is an inter‐related whole, or a living “organon”. It is from this perspective that micro‐economic relations are analysed, and this helps expose certain fallacies of composition associated with the marginal productivity theory of production and distribution. Factors are paid not because they are productive but because they are scarce. Likewise he shows why Marshallian supply and demand schedules, based on the “one thing at a time” approach, cannot adequately describe the dynamic growth properties of the system. Supply and demand cannot be simply integrated to arrive at a picture of the whole economy. These notes are complemented by eleven articles in the Encyclopaedia Britannica which were published shortly after Young′s sudden death in 1929.

For those who plan research funds and assess the research performance from the funds, it is necessary to overcome the limitations of the conventional classification of evaluated papers published by the research funds. Besides, they need to promote the objective, fair clustering of papers, and analysis of research performance. Therefore, the purpose of this paper is to find the optimum clustering algorithm using the MATLAB tools by comparing the performances of and the hybrid particle swarm optimization algorithms using the particle swarm optimization (PSO) algorithm and the conventional K-means clustering method.

The clustering analysis experiment for each of the three fields of study – health and medicine, physics, and chemistry – used the following three algorithms: “K-means+Simulated annealing (SA)+Adjustment of parameters+PSO” (KASA-PSO clustering), “K-means+SA+PSO” clustering, “K-means+PSO” clustering.

The clustering analyses of all the three fields showed that KASA-PSO is the best method for the minimization of fitness value. Furthermore, this study administered the surveys intended for the “performance measurement of decision-making process” with 13 members of the research fund organization to compare the group clustering by the clustering analysis method of KASA-PSO algorithm and the group clustering by research funds. The results statistically demonstrated that the group clustering by the clustering analysis method of KASA-PSO algorithm was better than the group clustering by research funds.

This study examined the impact of bibliometric indicators on research impact of papers. The results showed that research period, the number of authors, and the number of participating researchers had positive effects on the impact factor (IF) of the papers; the IF that indicates the qualitative level of papers had a positive effect on the primary times cited; and the primary times cited had a positive effect on the secondary times cited. Furthermore, this study clearly showed the decision quality perceived by those who are working for the research fund organization.

There are still too few studies that assess the research project evaluation mechanisms and its effectiveness perceived by the research fund managers. To fill the research void like this, this study aims to propose PSO and successfully proves validity of the proposed approach.