Dataset Information

Prior perceived losses and investment objectives after stock market crisis: a moderated-mediation model of risk tolerance and loss aversion.

ABSTRACT: This study explores whether prior perceived losses affect investment objectives via loss aversion as a mediator and whether the indirect effect is moderated by risk tolerance in a moderated-mediation model. Using retail investors who witnessed a market crash in Bangladesh and experienced losses, the model is tested by employing regression analyses and conditional process. The analyses reveal that prior perceived losses indirectly affect investment objectives (earning a higher expected return and building a financial reserve for future expenses) via mediation of loss aversion. Moderated-mediation model shows that for high-risk-tolerant investors, prior perceived losses indirectly affect investors to invest more for achieving a higher expected return objective and less to achieving building a financial reserve for future expenses, via a low level of loss aversion. These suggest that risk-tolerant investors continue to invest to earn a higher expected return even though they experienced prior losses and are loss-averse.

Supplementary information

The online version contains supplementary material available at 10.1007/s43546-022-00259-6.

SUBMITTER: Khan MTI

PROVIDER: S-EPMC9243710 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Prior perceived losses and investment objectives after stock market crisis: a moderated-mediation model of risk tolerance and loss aversion.

Khan Mohammad Tariqul Islam MTI

SN business & economics 20220628 7

This study explores whether prior perceived losses affect investment objectives via loss aversion as a mediator and whether the indirect effect is moderated by risk tolerance in a moderated-mediation model. Using retail investors who witnessed a market crash in Bangladesh and experienced losses, the model is tested by employing regression analyses and conditional process. The analyses reveal that prior perceived losses indirectly affect investment objectives (earning a higher expected return and ...[more]

PMID: 35789708

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Project description:Stock market prediction has long attracted the attention of academia and industry due to its potential for substantial financial returns. Despite the availability of various forecasting methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these models often faces limitations, particularly in single-objective optimization, where they can easily fall into local optima. To address this issue, this paper proposes an innovative multi-objective optimization algorithm-the Multi-Objective Escape Bird Algorithm (MOEBS)-and introduces the MOEBS-Transformer architecture to enhance the efficiency and effectiveness of hyper-parameter optimization for Transformer models. This study first validates the performance of MOEBS through a series of multi-objective benchmark tests on standard problem sets such as ZDT, DTLZ, and WFG, comparing it with other multi-objective optimization algorithms (e.g., MOMVO, MSSA, and MOEAD) using evaluation metrics such as GD, Spacing, IGD, and HV for comprehensive analysis. In the context of stock price prediction, we select the closing price datasets of Amazon, Google, and Uniqlo, using MOEBS to optimize the core hyper parameters of the Transformer while considering multiple objectives, including training set RMSE, testing set RMSE, and testing set error variance. In the experiments, this paper first compares CNN, LSTM, BiLSTM, GRU, and traditional Transformer models to establish the Transformer as the optimal model for stock market prediction. Subsequently, the study compares the MOEBS-Transformer with Transformer models optimized using various hyperparameter optimization methods, including MOMVO-Transformer, MSSA-Transformer, and MOEAD-Transformer. Additionally, it evaluates Transformer models optimized through conventional methods: Random Search (RS-Transformer), Grid Search (GS-Transformer), and Bayesian Optimization (BO-Transformer). By assessing the performance of these models using R2, RMSE, and RPD metrics on both training and testing sets, the results demonstrate that the Transformer model optimized by MOEBS significantly outperforms the other methods in terms of prediction accuracy and prediction stability. This research offers a new solution for complex optimization scenarios and lays a foundation for advancements in stock market prediction technologies.

Dataset Information

Prior perceived losses and investment objectives after stock market crisis: a moderated-mediation model of risk tolerance and loss aversion.

Supplementary information

Publications

Prior perceived losses and investment objectives after stock market crisis: a moderated-mediation model of risk tolerance and loss aversion.

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