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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.

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.

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.

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.

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.

This paper aims to incorporate a market wide buying and selling pressure cost component into a spread decomposition model as spread cost component.

The paper extends a commonly used trade indicator spread decomposition model to include a component common to all stocks of a specialist firm and a market wide component common to all stocks.

Strong evidence is found that specialists consider this common factor cost component when they set bid and ask quotes. Some specialist firms also take the next logical step and specifically manage their firm wide stock inventories. The common factor is in percentage terms largest for securities with the highest trade frequencies.

The relative importance of the common factor spread component decreases as the pricing grid becomes finer, but remains highly significant under the decimal trading regime.

This is the first study to document not‐security‐specific spread cost components that are common to all stocks for which a specialist firm makes markets and to all stocks in the market. Using the model it is shown that market wide uncertainty translates into spreads of individual securities.

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 propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.

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 paper is to provide students with a sorting methodology to select securities and build portfolios.

This paper uses various accounting variables for all firms in the S&P 500, sorted by sector. The fundamental metrics are converted into standardized Z‐scores and then combined into a single score used to rank individual firms within each industry. Equity portfolios are then constructed using the aggregate Z‐scores.

In the authors' experience with student‐managed investment funds (SMIFs), students at the start of the course consistently ask how to begin selecting securities or seek to learn a new model for selecting securities. Discussions on stock selection are helpful to engage students in this area, but an attempt is made to further this by providing a comprehensive stock‐selection exercise to help students better understand how to appropriately pick stocks and create a portfolio.

In this exercise, students are reminded of the limitations surrounding the stock‐screening process and are provided with an alternative, more robust method for selecting securities that is commonly utilized by investment professionals. While the exercise described in this paper is done in reference to SMIFs, it is equally applicable to standard investment courses.

This paper provides an exercise which provides students a way to dive deeper into stock selection through stock sorting. Stock selection is typically a hot topic for most students in finance courses. Stock screens may permit a search on multiple variables simultaneously but typically do not allow for applying specific weights to each metric. A sorting method, avoids these issues by permitting the user to create custom variables, affords the opportunity to view all of the variables used in the screening process simultaneously, and includes the option to apply specific weights to each variable.