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Investors are inattentive to continuous information as opposed to discrete information, resulting in underreaction to continuous information. This paper aims to examine if the well-documented return predictability of the strategies based on the ratio of short-term to long-term moving averages can be enhanced by conditioning on information discreteness. Anchoring bias has been the popular explanation for the source of underreaction in the context of moving averages-based strategies. This paper proposes and studies another possible source based on investor inattention that can potentially result in superior performance of these strategies.

The paper uses portfolio sorting as well as Fama-MacBeth cross-sectional regressions. For examining the role of information discreteness in the return predictability of the moving average ratio, the sample stocks are double-sorted based on the moving average ratio and information discreteness measure. The returns to these portfolios are computed using standard approaches in the literature. The regression approach controls for various well-known return predictors.

This study finds that the equally-weighted monthly returns to the long-short moving average ratio quintile portfolios increase monotonically from 0.54% for the discrete information portfolio to 1.37% for the continuous information portfolio over the 3-month holding period. This study observes a similar pattern in risk-adjusted returns, value-weighted portfolios, non-January returns, large and small stocks, for alternative holding periods and the ratio of 50-day to 200-day moving average. The results are robust to control for well-known return predictors in cross-sectional regressions.

To the best of the authors’ knowledge, this is the first paper to document the significant role of investor inattention to continuous information in the return predictability of strategies based on the moving average ratios. There are many underreaction anomalies that have been reported in the literature, and the paper's results can be extended to those anomalies in subsequent research.

The findings of this paper have important practical implications. Strategies based on moving averages are an extremely popular component of a technical analyst's toolkit. Their profitability has been well-documented in the prior literature that attributes the performance to investors' anchoring bias. This paper offers a readily implementable approach to enhancing the performance of these strategies by conditioning on a straightforward measure of information discreteness. In doing so, this study extends the literature on the role of investor inattention to continuous information in anomaly profits.

While there is considerable literature on technical analysis, and especially on the performance of moving averages-based strategies, the novelty of this paper is the analysis of the role of information discreteness in strategy performance. Not only does the paper document robust evidence, but the findings suggest that the investor’s inattention to continuous information is a more dominant source of underreaction compared to anchoring. This is an important result, given that anchoring has so far been considered the source of return predictability in the literature.

As the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.

Firstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.

The authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.

Prior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.

This study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.

This paper evaluates the performance of actively managed conventional and Islamic equity funds in a developing economy with a focus to assess the performance-growth puzzle posited by Gruber (1993) (a.k.a Gruber’s puzzle). Under the context of an emerging market of Pakistan, this study explores if actively managed equity fund (AMEF) managers have been able to add value by outperforming the market in terms of stock-selection and market-timing abilities; and the comparative performance analysis of Islamic versus conventional AMEFs is also carried out.

We employ Sharpe and Treynor ratios, Capital asset pricing model, Fama–French three factors model (1993), Carhart four-factor model (1997) and Hendrickson (1981) market timing models on 45 equity funds comprising of 23 conventional and 22 Islamic equity funds operating in Pakistan for a period of 10 years. The overall sample period (2008–2018) is divided into two 5 years sub-periods (i.e. 2009–2013 and 2014–2018) and three 3 years sub-periods (2009–2011, 2012–2014 and 2015–2017) to be viewed in conjunction with the country's macro-economic condition.

We report that the actively managed equity funds (AMEFs) were unable to beat the market index with their stock selection or market timing capabilities. However, AMEFs depicted improved performance in the post-global financial crisis period where both conventional and Islamic AMEFs generated substantial rewards for the given amount of risk. Also, conventional AMEFs outperformed Islamic AMEFs potentially due to their holdings in highly leveraged value and large-cap stocks, while Islamic AMEFS invest more cautiously in small-cap and value firms. Analysis of market timing skills revealed that the funds have not been able to select the undervalued stocks and adopted a defensive strategy in the post-global financial crisis recovery period.

Our findings shed some interesting insights and raise some pertinent questions for research, policy, and practice – specifically for developing countries’ context. The no ‘return-growth’ configuration defies its fit with the ‘Gruber puzzle’ and somewhat presents a case of what we call the ‘Inverse Grubber puzzle’. This novel notion of the ‘Inverse Grubber puzzle’ should inform policy and practice to reflect on their practices, institutional arrangement, regulatory framework and policy design in developing economies characterized by lacklustre performance and growth of AMEFs. For example, the regulatory design may consider focusing on stimulating financial inclusion and deepening by motivating low-cost Index tracker funds (ITFs) – with lower fund management costs, while allocating the avoided cost to flow towards effective marketing campaigns driving greater awareness, financial deepening, and investor base diversification. For future research, financial development researchers may explore the implications and appropriateness of AMEFs versus ITFs in other developing economies.

The work reported in this paper is original and constitutes a valuable asset for ethno-religious-sensitive investors. The research has not been published in any capacity and is not under consideration for publication elsewhere.

This study aims to explore the moderating effects of strategy on the relationship between working capital management (WCM) and profitability.

A data sample of 72,444 firm-year observations of US-listed firms during 2000–2020 was used. The research hypotheses were tested using a panel regression analysis and an appropriate research instrument that signifies a firm’s strategic positioning.

The prospecting (defending) strategy has a decreasing (increasing) moderating effect on the relationship between WCM and profitability. The empirical findings are not affected by the level of earnings management, the presence of motives to meet earnings targets or the intensity of unreported intangible assets. Additionally, the reported empirical results remain robust within the context of propensity score matching regression analysis, in the presence of nonlinear effects of WCM on profitability, when alternative measures of WCM are used, and between firms with an increase or decrease in future profitability or different levels of efficiency on net WCM investments.

This study may stimulate future research exploring the moderating effects of various variables on the relationship between WCM and operating performance.

The findings highlight the importance of strategy for improving the performance evaluation of WCM policies and the prediction accuracy of the consequences of a strategy on short-term operating performance.

Prior empirical research has documented either a negative or positive relationship between WCM and profitability, which implies the presence of moderating effects of various factors. This study provides empirical evidence of the moderating effects of strategy on the relationship between WCM and profitability.

This paper aims to explore the development of investment decision tools by understanding the rationality behind the disposition effect. We suspect that not all disposition decisions are irrational. The decisions should be evaluated based on the bounded rationality of the individuals’ target and tolerance level, which is not covered in previous literature. Adding the context of individual preference (target and tolerance) in their decision could improve the classic measurement of disposition effect.

The laboratory web experiment is prepared to collect the responses in holding and selling the stocks within 14 days. Two groups of Gen Z investors are observed. The control group makes a decision based on their judgment without any system recommendation. In contrast, the second group gets help inputting their target and tolerance. Furthermore, the framing effect is also applied as a reminder of their target and tolerance to induce more holding decisions on gain but selling on loss.

The framing effect is adequate to mitigate the disposition effect but only at the early day of observation. Bounded rationality explains the rationality of liquidating the gain because the participants have reached their goal. The framing effect is not moderated by days to affect the disposition effect; over time, the disposition effect tends to be higher. A new measurement of the disposition effect in the context of bounded rationality is better than the original disposition effect coefficient.

Gen Z investors need a system aid to help their investment decisions set their target and tolerance to mitigate the disposition effect. Investment firms can make a premium feature based on real-time market data for investors to manage their assets rationally in the long run. Bounded rationality theory offers more flexibility in understanding the gap between profit maximization and irrational decisions in behavioral finance. The government can use this finding to develop a suitable policy and ecosystem to help beginner investors understand investment risk and manage their assets based on subjective risk tolerance.

The classic Proportion Gain Realized (PGR) and Proportion Loss Realized (PLR) measurements cannot accommodate several contexts of users’ targets and tolerance in their choices, which we argue need to be re-evaluated with bounded rationality. Therefore, this article proposed new measurements that account for the users’ target and tolerance level to evaluate the rationality of their decision.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

Large-scale industrialization, growth and development have come at the cost of severe environmental degradation, primarily measured in terms of carbon dioxide emissions. Apart from the several measures taken to reduce enviornmental degradation, provision of private capital is a necessity apart from the public capital. There is a debate on impact of carbon dioxide emissions with increase in affluence, technology, population and renewable energy. The purpose of the study is to look into the role of private equity investment on renewable energy and technological patents.

The study extends the use of stochastic impact by regression on population, affluence and technology model to include another factor for investments and capital, i.e. private equity along with renewable energy, population, technology and GDP growth on carbon emissions for the BRICS countries. The time period for the study is from 2002 to 2021, and the relationship between the variables has been tested using pooled mean group/autoregressive distributed lag, fully modified ordinary least squares and panel quantile regression.

First, the results depict a log-run relationship between the variables across the panel using cointegration. Private equity investments do not have a significant impact on carbon emissions. The study proposes important policy implications. There are two schools of thought on the impact of private equity on carbon emissions. For example, inherently private equity investments come with higher stakes and a shorter holding period because of which their primary focus remains on having higher returns instead of responsible investing. However, as private equity adds up to capital, which leads to an increase in productivity and eventually higher economic growth, this could affect carbon emissions. This study supports the first thought. Additionally, renewable energy also affects carbon emissions positively. The policymakers should look into the role and intent of the private equity investors in green investments and invest in technologies and patents that can lead to energy consumption.

The paper is the first of its kind, to the best of the authors’ knowledge, to look into the impact of private equity on renewable energy and technological patents.

With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.