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The purpose of this paper is to analyse the current rhetoric of predictability in investment theory. After making the case for unpredictability, a new rhetoric for investment theory is proposed.

McCloskey's project of the rhetoric of economics provides the background and approach for the author's investigation. In particular the author will use the notions of metaphor, prediction, discourse analysis, and virtue ethics.

The current rhetoric equals the original rhetoric in the seminal work of Markowitz. The current rhetoric is based on predictability and rational behaviour. The proposed new rhetoric for investment theory denies predictability. The new rhetoric aims to cope with statistics by stressing that statistics is supportive but not decisive: handling investment theory is about judgements, combining virtues with historical and theoretical insights.

The investigation of the rhetoric of investment theory has practical relevance because the theory constitutes investment practice, and can put financial wealth at risk. The new rhetoric for investment theory invites practitioners and researchers to reflect on the epistemology of investment theory, and its consequences for the field.

The rhetoric of investment theory is to the author's knowledge not yet analysed in the literature. The rhetorical analysis of the current rhetoric and the proposal of a new rhetoric aim to contribute to the literature on the rhetoric of investment theory.

Human existence is characterized by discontinuities, chaos, instabilities, constant changes, and paradoxes (Firat and Venkatesh, 1996). A challenge to social scientists is to construct theories which explain human behavior, given the plurality and complexity of human behavior. At the same time, business decision makers face the challenge of managing in an environment of constant change and flux. The economist, Joseph Schumpeter (1943), described this process well:

Hall (2001a) argues that the value of intangible assets can be inferred from firms’ stock market value and the value of tangible assets, which suggests rational valuation in the market. This paper investigates the relationship between firms’ future stock returns and their inferred intangibles and indirectly tests Hall’s hypothesis by using various trading strategies. It is found that the inferred intangibles have predictive power for stock returns, which might be because of mean‐reverting misvaluation by the stock market; and the way the inferred intangibles predict stock returns is consistent with the three‐factor model of Fama and French (1992). However, I find that the predictive power of inferred intangibles is consistent with market inefficiency, rather than a rational premium for distress risk related to the book‐to‐market equity ratio. Thus the intangible assets hypothesis of Hall does not hold and the discrepancy between market equity and book equity suggests market inefficiency.

How can managers optimally distribute rewards among individuals in a job group? While the management literature on compensation has established the need for equitable reimbursements for individuals holding similar positions in a function or group, an objective grounding of rewards allocation has certainly escaped scrutiny. This paper aims to address this issue.

Using an optimization model based on a financial rubric, the portfolio approach allows organizations to envision human capital assets as a set (i.e. a team, group, function), rather than independent contractors. The portfolio can be organized and managed for meeting various organizational objectives (e.g. optimizing returns and instrumental benefits, assessing resource allocations).

This research introduces an innovative portfolio management scheme for employee rewards distribution. Akin to investing in capital assets, organizations invest considerable resources in their human capital. In doing so, organizations, over time, create a portfolio of human capital assets. The findings reduce large variances in rewards distribution yet serving employee and management considerations.

The research has tremendous implications for managers who can mitigate serious equitable rewards distribution issues by creating a process that exemplifies rewards distribution using four different rewards allocation scenarios based on varying managerial prerogatives.

This research is a unique model that addresses a pressing human resource issue by solution based on a usable and feasible optimization mechanism from financial portfolio theory.

This research mainly intends to ascertain the stimulus of investor investment tendencies on the amount of capital investment in the share market.

Utilizing a sample of 477 individual investors who actively trade on the Bangladesh capital market, this empirical study was conducted. The objective of this examination is to ascertain the investment trading behavior of retail investors in the Bangladesh capital market using multiple regression, hypothesis testing and correlation analysis.

The coefficients of market categories, preferred share price ranges and investment source reveal negative predictor correlations; all predictors are statistically significant, with the exception of investment source. Positive predictive correlations exist between investor category, financial literacy degree, investment duration, emotional tolerance level, risk consideration, investment monitoring activities, internal sentiment and correct investment selection. Except for risk consideration and investment monitoring activities, all components have statistically significant predictions. The quantity of capital invested in the stock market is heavily influenced by the investment duration, preferred share price ranges, investor type, emotional toleration level and decision-making accuracy level.

This investigation was conducted exclusively with Bangladeshi individual stockholders. Therefore, the existing study can be extended to institutional investors and conceivably to other divisions. It is possible to conduct this similar study internationally. And the query can enlarge with more sample size and use a more sophisticated econometric model. Despite that the outcomes of this study help the regulatory authorities to arrange more informative seminars and consciousness programs.

The conclusions have practical implications since they empower investors to modify their portfolios based on elements including share price ranges, investment horizons and emotional stability. To improve chances of success and reach financial objectives, they stress the significance of bettering financial understanding, active monitoring and risk analysis. Results can also be enhanced by distributing ownership over a number of market sectors and price points. The results highlight the value of patience and giving potential returns enough time.

This study on the trading behavior of investors in Bangladesh is unique and based on field study, and the findings of this study will deliver information to the stakeholders of the capital market regarding the investors’ trading behavior belonging to different categories, financial literacy level, investment duration, emotional tolerance level and internal feeling.

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.

The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

The paper aims to help explain how certain smaller university endowments are able to provide investment results that are more typical of much larger endowments. Investment teams' characteristics and risk-reward perceptions are examined in relation to portfolio composition and performance.

This exploratory study uses a grounded-theory approach consisting of 20 in-depth interviews of financial officers at US colleges and universities with assets between $100 million and $200 million. Ten were conducted from the top performance quartile and ten from the bottom quartile. Interviews were transcribed and coded; afterward, emerging themes and constructs were identified. Objective investment performance over a ten-year period was employed from a well-known industry survey.

Top-performing endowments were described as having endowment teams with greater investment expertise, efficacy, decision-making independence and learning commitment than teams from the low-performing endowments. Teams from top-performing endowments assessed alternative investments more favorably and made greater portfolio allocations to them as compared to teams from low-performing endowments.

Because of the chosen research approach, the research results may not be generalizable.

The paper includes implications for colleges and universities in the management of their endowments, and particularly in the selection of committee and other team members.

The paper is original in exploring certain team characteristics and practices of institutional investment decision-makers and their relationship to portfolio composition and performance.