The impact of international financial reporting standards on fund performance

Rubanov, Dmitrij; Nnadi, Matthias

doi:10.1108/arj-01-2017-0020

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…But, in a later section of our paper, we also estimate abnormal returns using the Fama & French 3-factor model (Eq. ( 7)) which has been used in some papers in the area (e.g., Rubanov and Nnadi 2018;Su et al 2019;Li et al 2020).…”

Section: Methodsmentioning

confidence: 99%

Loss sensitive investors and positively biased analysts in Hong Kong stock market

Choudhry

Dissanaike

Jayasekera

et al. 2021

Rev Quant Finan Acc

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The Hong Kong stock market is known to be highly volatile. Professional investors have a strong demand for timely information because of the infrequent nature of Hong Kong analysts’ interim reports (Cheng et al., 2003). Our paper provides a comprehensive study of investor reactions to analysts’ recommendations in the Hong Kong stock market from 2009 to 2014 under different sentiment scenarios. We find that analysts’ recommendation upgrades and downgrades deliver significant information to the Hong Kong stock market. However, analysts’ initiation coverages convey little information and bring about limited impact to the stock market. In addition, analysts’ upgrades and downgrades result in significant differential price impacts in bullish and the bearish phases.

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Section: Methodsmentioning

confidence: 99%

Loss sensitive investors and positively biased analysts in Hong Kong stock market

Choudhry

Dissanaike

Jayasekera

et al. 2021

Rev Quant Finan Acc

Self Cite

View full text Add to dashboard Cite

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“…Different studies have suggested that the business cycle has nonlinear properties and economic variables display asymmetries throughout economic booms and busts [11][12][13]. The nonlinear models are interchangeable LSTM and RNN, whereas the LR and ARIMA are linear and sublinear models.…”

Section: Literature Workmentioning

confidence: 99%

Influence of Artificial Intelligence on Forecasting Net Asset Value and Return Volatility in Indian Mutual Fund

Mary Antony,

Natarajan

2024

AAIML

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Predicting the critical net asset value (NAV) in the financial market is difficult for investors and fund agencies. The present study introduces machine learning (ML) and deep learning (DL) models such as linear regression, deep long short-term memory recurrent neural network (DLSTM-RNN), and autoregressive integrated moving averages (ARIMA) for predicting the NAV. The five different equity sectoral technology mutual fund direct growth plans from January 2013 to December 2022 have been collected. The novelty of the current study is deeply examining, which ML or DL model devotedly predicts the NAV closing price. The major key findings of the experimental results proved that the DLSTM-RNN model makes statistically viable predictions, whereby the mean absolute percent error (MAPE) average prediction accuracy value is 0.02. Based on the accuracy of a superior model, we compute the annualized return volatility to compare the risk of investments with annual return periods over different time horizons. The Jarque-Bera statistics of the return volatility over time Gaussian distribution is rejected at the 0.01 level. Statistical paired t-test and Pearson correlation coefficient are used to compare the effects of the proposed three models. In addition, the benchmark portfolio strategy yields a Sharpe ratio of 7.0193 and the maximum drawdown is 0.3743. The AI performed deep LSTM neural network model simulation, especially when using a daily and monthly MAPE strategy giving 81% and 84% highest NAV prediction consistency than the linear regression and ARIMA models.

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