Performance Manipulation and Fund Flow: Evidence from China

Qian, Meifen; Chongbo, Xu; Bin, Yu

doi:10.2753/ree1540-496x500312

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…This evidence contradicts the findings of prior studies that document a significant positive effect of manipulation on subsequent cash flows of fund managers (Qian & Yu, 2015). The current evidence is expected as the dynamics that drive the performance of funds are unlikely to be the same under different market conditions, while the prior analyses on the effect manipulation on fund flows are conducted in the context of stable market conditions and, thus, they are unable to provide explanations to influence of manipulation on fund flow under time-varying conditions of the market (Qian et al, 2014).…”

Section: Correlation Analysismentioning

confidence: 80%

“…Scholars prescribe various ways by which the prevalence of deceptive trading strategies among active managers could be mitigated, to ensure the protection of the financial interest of the average fund contributor while engendering a more robust financial system. Qian et al (2014) suggest that the actual performance of active managers relative to the performance of passive strategies is efficiently evaluated by the application of a more encompassing metric, the Manipulation Proof Performance Measure (MPPM) proposed by Goetzmann et al (2007). The MPPM is a standardized performance metric used as a benchmark measure to ascertain the degree of manipulation of fund performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…From the literature (Bollen & Pool, 2008), through this mechanism, funds can maintain marginal extraordinary returns while reducing the standard deviation concurrently as cited in Qian et al (2014, p. 5). However, with the application of the MPPM, the impact of this strategy on fund performance become limited, since the MPPM by formulation is dependent on the relative uniqueness of the marginal and variation of extraordinary returns (Qian et al, 2014). Thus, the MPPM represents effect metric of differentiating between manipulated funds and those who achieve extraordinary returns through superior trading ability.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

The effect of performance manipulation on fund flows under different market conditions in South Africa

Apau

Jeke

2022

Investment Management and Financial Innovations

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This study analyzes the effect of performance manipulation on mutual fund flows under different market conditions to provide explanations to the increased flow of investors’ funds to persistently underperforming active mutual fund managers in South Africa. The study employs a system GMM technique to analyze panel data of 52 South African actively managed equity mutual funds for the 2006–2019 period. From the analysis, it is found that past fund flows and fund size constitute a set of fund-level factors with predictive influences on fund flows, while market risk exerts systemic effect on the flow of investors’ assets to fund managers. The results show that market conditions do not impact the relationship between mutual fund flows and performance manipulation, which implies that manipulation strategies implemented by fund managers do not engender increased funds’ flow from asset owners. This study thus concludes that other non-performance factors drive convexity in the relationship between fund flows and performance in South Africa.

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Section: Correlation Analysismentioning

confidence: 80%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

The effect of performance manipulation on fund flows under different market conditions in South Africa

Apau

Jeke

2022

Investment Management and Financial Innovations

View full text Add to dashboard Cite

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“…As a result, the use of nonlinear models, such as the Markov switching framework, which endogenously determines the different market regimes of a given time series, help to obtain more accurate inferences about the behavior of the influencing variables. Furthermore, the conventional performance measures employed in the analysis are prone to dynamic manipulation by fund managers (Qian et al 2014). Hence, the reliance on these conventional metrics to test the performance of fund managers can lead to wrong conclusions about the performance of funds.…”

Section: Literature Reviewmentioning

confidence: 99%

Regime-Switching Determinants of Mutual Fund Performance in South Africa

2021

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This study assesses the effect of fund-level and systemic factors on the performance of mutual funds in the context of changing market conditions. A Markov regime-switching model is used to analyze the performance of 33 South African equity mutual funds from 2006 to 2019. From the results, fund flow and fund size exert more predictive influences on performance in the bearish state of the market than in the bullish state. Fund age, fund risk, and market risk were found to be the most significant factors driving the performance of active portfolios under time-varying conditions of the market. These variables exert more influence on fund performance under bearish conditions than under bullish conditions, emphasizing the flight-to-liquidity assets phenomenon and risk-aversion behavior of fund contributors during unstable conditions of the market. Consequently, fund managers need to maintain adequate asset bases while implementing policies that minimize dispersions in fund returns to engender persistence in performance. This study provides novel perspectives on how the determinants of fund performance change with market conditions as portrayed by the adaptive market hypothesis (AMH).

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