The cumulative prospect theory and fund flows in emerging markets

ABSTRACT

This study is the first to examine the efficacy of the Cumulative Prospect Theory value of the past return (CPTV) to explain fund flow in emerging markets funds by considering the impact of fund managers’ characteristics and behavioural biases on fund investors’ investment decisions. Besides, this study also investigates the impact of fund investors’ ability to identify fund managers’ skills. The findings suggest that CPTV has a significant and positive association with subsequent fund flow and emerging market funds’ performance. Notably, the study documents a negative relationship between fund flow and fund size and also highlights that newer funds generate greater fund flows. Furthermore, the findings indicate that investors investing in emerging market funds tend to behave differently, allocate money for alpha, and avoid giving distorted income for factor-related returns to fund managers. The results demonstrate that managers’ skill and portfolio concentration decision affects fund flow and CPTV relationships. The findings illustrate that fund managers who concentrate their portfolio and invest in high CPTV stocks tend to generate higher fund flow than their diversified counterparts.

KEYWORDS:

• cumulative prospect theory (CPT)
• managerial skills
• portfolio concentration
• fund flow
• portfolio performance

JEL CODES:

• G1
• G11
• G14
• G17

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Henceforth, CPTV represents the cumulative prospect theory value of the past return.

2 This study considers mangers’ skills as proxy of fund characteristics, while for behavioural biases it uses managers portfolio concentration as proxy.

3 For further details, please see following link https://economictimes.indiatimes.com/mf/analysis/all-you-need-to-know-about-emerging-market-funds/articleshow/88846869.cms?from=mdr

4 The Bayesian agent paradigm is a probability theory framework for rational agents, utilising Bayes's theorem to update hypothesis probabilities based on available information, facilitating decision-making under uncertainty. Here, investors’ allocation of funds for alpha shows investors are making rational decisions that align with the Bayesian agent paradigm's (BAP) assumptions. It suggests BAP can be used for further modelling to understand fund investors’ behaviour.

5 Different from Brown et al. (2020), we add a model error term to calculate fund alpha in Equation (7)(7) $\mathrm{ALPH}{A}_{i,t}=\alpha +e_{i,t}$(7) , whereas we calculated FRR from 12-month return only, as per Equation (8)(8) $\mathrm{FR}{R}_{i,t}={\displaystyle \frac{1}{12}\sum _{\tau =t-12}^t[{\beta }_{3}M{R}_{i,\tau }+{\beta }_4\mathrm{SM}{B}_{i,\tau }+{\beta }_5\mathrm{HM}{L}_{i,\tau }+{\beta }_6\mathrm{MO}{M}_{i,\tau }]}$(8) to follow Barras et al. (2022).

6 Please see Kenneth R. French's website at following link https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

7 We checked the robustness of the results by sub-sample analysis of small and large funds, pre- and post-2008 Global Financial Crisis (GFC) and the COVID-19 period. Analysis results are presented in Appendix Tables 3(A) and 4(A).

8 To reduce the length of the article, we have not included quantile regression results in the main body of the paper as there are not any significant differences in results from the fixed effect model. Please see Figure 3 in the Appendix.

9 We thank the anonymous reviewers for their suggestions, to investigate this research question, which help to improve understanding of this topic.

10 For robustness, we ran quantile fixed-effect regression and found similar result for each quantile (see Figures (4) and (5) in the Appendix.

11 For robustness, we ran quantile fixed-effect regression and found similar result for each quantile (see Figures (6) and (7) in the Appendix.