Uncertainty avoidance and mutual funds

Keswani, Aneel; Medhat, Mamdouh; Miguel, António F.; Ramos, Sofía

doi:10.1016/j.jcorpfin.2020.101748

Cited by 18 publications

(6 citation statements)

References 79 publications

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“…Stochastic predictors also provide potential help in the early days when not much data is available for machine learning approaches [12]. Such stochastic models also seem to work even for huge societies, such as India [13]. Therefore, when artificial intelligence is applied in the first days of forecast periods, the results are mostly related to a single region or country.…”

Section: )) X T D F X T X T X T Nmentioning

confidence: 99%

Neural Networks and Forecasting COVID-19

Dadyan

Avetisyan

2021

Opt. Mem. Neural Networks

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For analysis tasks, time counts are of interest – values recorded at some, usually equidistant, points in time. The calculation can be performed at various intervals: after a minute, an hour, a day, a week, a month, or a year, depending on how much detail the process should be analyzed. In time series analysis problems, we deal with discrete-time, when each observation of a parameter forms a time frame. The same can be said about the behavior of Covid-19 over time. In this paper, we solve the problem of predicting Covid-19 diseases in the world using neural networks. This approach is useful when it is necessary to overcome difficulties related to non-stationarity, incompleteness, unknown distribution of data, or when statistical methods are not completely satisfactory. The problem of forecasting is solved with the help of the analytical platform Deductor Studio, developed by specialists of the company Intersoft Lab of the Russian Federation. When solving this problem, appropriate methods were used to clean the data from noise and anomalies, which ensured the quality of building a predictive model and obtaining forecast values for tens of days ahead. The principle of time series forecasting was also demonstrated: import, seasonal detection, cleaning, smoothing, building a predictive model, and predicting Covid-19 diseases in the world using neural technologies for 30 days ahead.

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Section: )) X T D F X T X T X T Nmentioning

confidence: 99%

Neural Networks and Forecasting COVID-19

Dadyan

Avetisyan

2021

Opt. Mem. Neural Networks

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“…It is shown in [3] that artificial neural networks have certain characteristics that make them useful in the development of controllers at different control levels, which micro nets must include to be economical, efficient, and able to meet the quality and quantity of energy requirements. The goal of this work is to draw attention to the promising applicability of artificial neural networks for controlling the distributed generation of microgrid sources, as well as for planning, power distribution, control, and optimization.…”

Section: Neural Network In Evaluating the Effectiveness Of A Mutual F...mentioning

confidence: 99%

Estimation of the Efficiency of the Investment Fund

Dadyan¹

2021

Preprint

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In this article, a search for a calculation method and an analysis of performance indicators of mutual investment funds is carried out. Many factors can influence the return on investments in portfolio investments, which makes the choice of the fund incredibly difficult. However, in addition to the fact that it is difficult to determine which indicators should be given more attention and which should be omitted, it is not so easy to get these data. Some of them are publicly available on the Internet, while others can only be found in trading systems that are not accessible to people outside of this area. The article proves that a well-trained neural network can easily find existing patterns between risk and expected return on investment. It is a well-trained neural network that provides the ability to use the "what-if" function to justify your choice on real factors, as well as the ability to download available data and calculate the estimated income and its changes. This makes it much easier to choose a Fund, especially for inexperienced investors. The article also presents the results of a study of the dependence of estimated income on correlation, standard deviation, and volatility using a trained neural network. According to the theory, higher values of these three factors correspond to a higher amount of income. The obtained graphs of the calculated income dependence on correlation, standard deviation, and volatility confirmed the correctness of the neural network training and compliance with the relations described in the theory. The paper presents graphs of the dependence of the estimated income on the beta and alpha coefficients. The higher the beta and alpha indicators, the higher the expected return on investment. This corresponds to the dependency accepted in the model. When the values of the beta and alpha coefficients increase, the income also increases, which is completely consistent with the theory.

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“…Recently, by analysing the Chinese mutual fund market, Li, Massa, and Zhang (2017) show that trust can influence disposition effect in investment. Using a comprehensive sample of mutual fund from 25 countries, Keswani, Medhat, Miguel, and Ramos (2019) find that cultural differences across countries can explain cross‐sectional differences in flow‐performance sensitivity.…”

Section: Review Of Literature and Related Hypothesesmentioning

confidence: 99%

Do determinants of fees differ between Islamic and conventional funds?

et al. 2020

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This paper examines the effects of country conditions on fund manager fees. By using an international sample of 607 funds from 23 countries, the authors show that legal, culture, and political risk have robust effects on fund manager fees. The findings indicate that Islamic and conventional funds in developing countries with weaker legal settings and higher corruption have higher fixed and performance fees. However, performance fees are lower and fixed fees are higher in countries with higher political risk. Overall, Hofstede's cultural values significantly affect the fund fees with robust differential effects between Islamic and conventional funds.

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Uncertainty avoidance and mutual funds

Cited by 18 publications

References 79 publications

Neural Networks and Forecasting COVID-19

Neural Networks and Forecasting COVID-19

Estimation of the Efficiency of the Investment Fund

Do determinants of fees differ between Islamic and conventional funds?

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