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Mutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.

The authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.

The study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.

The authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.

The results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?

Mutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.

Research has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

This study investigates the impact of the enhanced continuous disclosure regime introduced in December 2002 on several measures of information risk in NZX‐listed stocks. We employ two microstructure models and an intraday data set to measure information risk in a sample of 71 stocks. Our empirical results show that the reforms enacted in December 2002 had no significant effect on either the level of information‐based trading or the adverse selection component of market spreads in our sample of NZX‐listed stocks.

The purpose of this paper is to contrast three investment choices within the reach of individual investors: naive portfolios of Brazilian stocks; actively managed stock funds; and the Ibovespa index, which represents passive management as well as to offer insights on the performance of professional asset managers in this large emerging market.

Equally weighted portfolios contained between 5 and 30 stocks to keep transaction costs low. Stock selection used the Ibovespa constituents and considered value (dividend yield (DY) and price-to-book ratio), momentum (past returns), and liquidity, as well as the Sharpe ratio (SR) over the 2003-2012 period, rebalancing three times a year.

Cumulative returns of naive portfolios are large. They frequently outperform the index for all values of n. They also outperform stock funds, particularly when the invested amount exceeds US$25,000, due to transaction costs. Yet, expected out-of-sample SRs corrected for errors in estimates are very low, suggesting that one should not count on this historical performance in the future. Naive portfolios may simply be more exposed to additional value, size, and momentum risks. Results are sensitive to time period selection.

Naive portfolios may be attractive to individual investors in Brazil relative to stock funds, which seem to strive to keep volatility low and may be better when the investment amount is low. There may be merit for value or momentum stock selection strategies when forming small equally weighted portfolios.

The paper contrasts realistic stock investing alternatives for individuals, it provides a view of stock fund performance in Brazil, and offers practical implications that may be pertinent in other emerging stock markets.

Contrastar três opções de investimento ao alcance de investidores individuais: carteiras ingênuas de ações brasileiras; fundos de ações de gestão ativa; e o índice Ibovespa, que representa a gestão passiva. Oferecer informações sobre o desempenho de gestores de ativos profissionais neste grande mercado emergente.

As carteiras igualmente ponderadas continham entre 5 e 30 ações para manter os custos de transação baixos. A seleção de ações utilizou os componentes do Ibovespa e considerou o valor (rendimento de dividendos e relação preço/valor patrimonial), momentum (retornos passados) e liquidez, bem como o Índice de Sharpe no período 2003-2012, rebalanceando três vezes ao ano.

Os retornos acumulados de carteiras ingênuas são grandes. Eles frequentemente superam o índice para todos os valores de N. Eles também superam os fundos de ações, particularmente quando o montante investido excede US$ 25,000, devido aos custos de transação. Contudo, os Índices de Sharpe esperados fora de amostra corrigidos por erros nas estimativas são muito baixos, sugerindo que não se deve contar com este desempenho histórico no futuro. As carteiras ingênuas podem simplesmente estar mais expostas a fatores riscos adicionais, tal como os de valor, tamanho e momentum. Os resultados são sensíveis à seleção do período de tempo.

As carteiras ingênuas podem ser atrativas para os investidores individuais no Brasil em relação aos fundos de ações, que parecem se esforçar para manter a volatilidade baixa e podem ser melhores quando o valor do investimento é baixo. Pode haver mérito para estratégias de seleção de ações de valor ou momentum ao formar carteiras igualmente ponderadas pequenas.

O artigo contrasta alternativas realistas de investimento em ações para indivíduos, oferece uma visão do desempenho dos fundos de ações no Brasil e oferece implicações práticas que podem ser pertinentes em outros mercados emergentes.

Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively.

The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously.

It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection.

Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments.

The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice.

The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.

Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.

Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.

The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.

One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.

Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.

This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

Describes the establishment and work of the Black and minority Ethnic Stock Group (BSG) in Hackney libraries. The BSG was an attempt to involve black staff, at all levels in the library service, in the selection of stock for the black community. As a first step, 25 per cent of the stock fund was allocated to the purchase of black stock. This helped to make up for historical underfunding and was part of a longer term strategy to match library resources to the community profile. The BSG was a successful experiment in staff empowerment and led to the selection of stock which was relevant to the black community.

The purpose of this study is to adopt data envelopment analysis (DEA) to construct portfolios, and compare their return rates with the market index to examine whether DEA portfolios created superior returns. In addition, this study investigated whether using the “size effect” as a stock selection strategy is appropriate in Taiwan.

This study applied two DEA models to evaluate the efficiency of the firms and construct portfolios by selecting stocks with high efficiency. Furthermore, the return rates of the portfolios constructed by small‐size firms, DEA models and market indices were compared via empirical data analysis.

The results showed that size effect seems inappropriate as a stock selection strategy in the Taiwan stock market. However, the portfolios constructed by DEA models achieved noticeable superior returns.

Future studies can apply DEA models to other stock markets in different countries to confirm the effectiveness of DEA methods in stock selection.

This study is the first attempt to select stocks using DEA models and compares the performances of the portfolios composed by DEA analysis, small‐size firms and the stock market indices. The proposed approach provides useful managerial implications in stock selection and insight to improve financial efficiencies of corporations.

The purpose of this paper is to evaluate two different asset selection methodologies and further examine these by forming optimal portfolios.

This paper deals with the problem of portfolio formation, broadly in two steps: asset selection and asset allocation by using the two different approaches for the first step and then well‐known mean variance portfolio optimization. In addition, the resulting portfolios are compared using Sharpe ratio.

The empirical observations prove the applicability of the methodology adopted in the research design, ordered weighted averaging (OWA)‐heuristic algorithm gives us a better portfolio from the sample observations. Also the asset selection procedures adopted in the research proves to be of help when an investor has to narrow down the number of assets to invest in.

The analysis provides two different methodologies for portfolio formation – though the asset allocation is based on the mean variance portfolio optimization, the asset selection methods adopted provide a systematic approach to select the efficient securities.

This paper shows that OWA can be used to decide the order of inputs for the heuristic algorithm. Also an attempt is made to use data envelopment analysis to find a solution to the problem of portfolio formation.

The purpose of this paper is to study the individual investors’ preferences towards stock selection in social media environments. The study is conducted to understand the implications and conceptual directions for the corporates and financial advisors to understand the choices of individual investors applied in financial markets. Further, this study aims to examine the selection of the most preferred social media platform and behavioral intentions of investors towards selection of investment portfolios in Indian stock markets.

A questionnaire was designed based on the technique of conjoint analysis and was responded by 428 respondents belonging to the Northern region of India. The estimation of preference functions in Conjoint Analysis was designed by using orthogonal arrays and was calculated using the ordinary least square regression technique.

This study reveals that while making selection of desired investment portfolios, the investors give highest preference to social media platforms in terms of highest utility value and range followed by their preference for behavioral intentions to invest. Among different social media platforms, the investors preferred Twitter the most, followed by Facebook and the primary interest of investors was observed towards Intra-day trading purposes and balanced portfolio investments in financial markets. The major reason behind opting the social media platforms was selection of speculative stocks.

The actual individual investment behavior cannot be observed through the survey, which limits the external validity of the study.

The paper presents a very important practical tool that can help financial advisors, opinion leaders and corporates in defining their target audience more sharply for investment-related advice. The findings revealed by the study will put them in a better position to understand how investors differ behaviorally and they will get acquainted with their choices and preferences while making investment decisions in the backdrop of social media environments. The preferences of the investors based on social media usage discovered by the study will not only enable the individual investors understand their own preferences, but those of the other investors as well in terms of planned investment decisions and choices.

The paper is a first of its kind to empirically identify the individual investors and their preferences and choices by applying conjoint analysis in the new social media environment. The study thus integrates the gap between marketing theories and emerging theories of behavioral finance to understand the investor behavior in a better way.

This paper seeks to review and extend previous research on the performance of Islamic mutual funds (IMFs) by evaluating the relative performance of IMFs and conventional funds during the global economic crisis in the context of the Saudi Arabian capital market.

This paper compares the market timing and stock selection abilities of 159 mutual funds listed on the Saudi Arabian stock market from 2007 to 2011 by using the CAPM regression and Treynor and Mazuy models. The paper addresses the benchmark problem from which most prior IMFs studies suffered by using appropriate regional benchmarks. As a robustness check, coefficients of IMFs and conventional funds are compared by using the differences in mean and standard deviation analysis obtained from the standard CAPM model on individual funds.

The empirical results show evidence of better performance of IMFs relative to conventional funds during periods of economic crisis. In addition, although there is no evidence of relative superiority in market timing ability, managers of IMFs appear to have better stock selection ability during times of economic crisis.

The combination of superior stock selection ability of IMFs and the negative market timing ability of conventional funds suggest that IMFs offer better hedging opportunities for investors during periods of economic downturn.

The findings of this paper suggest that IMFs can provide hedging benefits during adverse economic conditions – an issue of great importance due to the current and forecast insecurity surrounding the global capital markets. By holding a portion of their investment portfolio in IMFs, investors can experience a higher degree of confidence in terms of investment security, growth and returns. Similarly, managers of conventional funds can improve risk adjusted performance by following similar screening criteria as IMFs during economic slowdowns.

This paper represents the first comprehensive study on the comparative performance of Islamic and conventional mutual funds during the current financial crisis by including all fund managers listed on the Saudi Arabian stock market. The paper extends the knowledge of the emerging literature of Islamic finance and mutual fund performance.