Academic literature on the topic 'Mutual funds Classification Statistical methods'
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Journal articles on the topic "Mutual funds Classification Statistical methods"
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Homocianu, Daniel. "A Methodology of Discovering Comparable Models. The Case of Investing in Retirement Accounts when Considering Age, Main Residence and Education before 1989 vs. Globalization." Scientific Annals of Economics and Business 67, SI (2020): 19–31. http://dx.doi.org/10.47743/saeb-2020-0026.
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This paper provides a way to discover strong individual influences on investments in retirement accounts. Data are from SHARE-ERIC (Wave7). Principal residences in ex-communist countries or not and full-time education before 1989 served as filters. Two particular models with good classification accuracy resulted based on data mining, variable selection methods, and logistic regressions. A statistical script generated tables with comparable coefficients (average marginal effects). Common influences from the same financial category as the outcome emerged (having life insurance or ever investing in mutual funds or stocks). The younger respondents, those with computer skills or exposed to high stress, are more likely to invest in retirement accounts regardless of the presence of the communist heritage. Specific influences (personality traits and life experiences) also resulted despite the increasing globalization, which, in the case of people over a certain age, was not able to erase some behavioral differences reflected until today.
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Amin, Moch. "Performance Comparison of Islamic Mutual Funds with Conventional Mutual Funds." Jihbiz : jurnal ekonomi, keuangan dan perbankan syariah 3, no. 1 (January 22, 2019): 38–54. http://dx.doi.org/10.33379/jihbiz.v3i1.787.
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The purpose of this study is to determine whether or not there is a difference in mutual fund performance between sharia mutual funds and conventional mutual funds from 2016 to 2018. The data used is secondary data in the form of NVA report data of 34 mutual funds consisting of 16 sharia mutual funds and 18 conventional mutual funds. The data analysis method used is the Jensen Index, Sharpe index, Treynor Index, MM Index, and TT Index methods and uses the t-test to see whether there are differences in mutual fund performance. The results of this study conclude that quantitatively there is no difference in mutual fund performance between sharia mutual funds and conventional mutual funds. Likewise, the statistical test with the t-test shows that there is no difference in performance in terms of the Jensen Index, Sharpe index, Treynor Index, MM Index, and TT Index.
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Čomić, Dragan Ratko. "Forest Estates/Organisational Units Ranking Model - The MRG Model." South-east European forestry 10, no. 1 (March 22, 2019): 39–51. http://dx.doi.org/10.15177/seefor.19-03.
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Background and Purpose: The fact that new organizational concepts require comparison and ranking of some business entities, implies the analogy that, in forestry, ranking should create the basis for differentiation of Forest Estates (FE) (seen as profit centers) according to their capability to allocate funds from rent for the utilization of forests and forest land. In this sense, it was necessary to determine the basic criteria and variables, and then to create the model for FE ranking on the basis of ecological and production potentials, and business results (economic indicators). The main idea was to create a model that can be used primarily by forest owners (which are, in certain countries such as Bosnia and Herzegovina, Croatia, Serbia, and Montenegro, mainly governments) and by public forest enterprises. The proposed models may serve to all other scientific, professional, research and other institutions, as the starting point for further research and as suggestions for possible improvements of the proposed solutions. Materials and Methods: The research was carried out within the project "Differential rent in the Republic of Srpska forestry". Total sample for the survey was 44 interviewed parties, with 118 questionnaires filled in. The methods of classification, content analysis, desk research, analysis, synthesis and comparison were used. In the concrete application of the Forest Estates/Organisational Units Ranking Model (hereinafter MRG Model; Model rangiranja šumskih gazdinstava, in Bosnian), the following methods were used: brainstorming, focus groups, survey, desk research method, Pareto analysis, modelling and induction. The statistical methods used were descriptive statistics and rank correlation. By using these methods and by combining them, a new model for forest estates ranking was created. Different input data and variables that refer to economic and natural indicators were used for ranking, all in accordance with the values for areas for which the ranking was carried out. Results: The main results are used for defining and proposal of the new model for forests estates ranking, i.e. the MRG Model. This model includes the following steps: (1) Survey, (2) Selection and scoring of specific variables, (3) Determining the intervals for specific variables, (4) Ranking of forest estates, and (5) Validation and rank correlation. This paper presented the algorithm of implementation of specific steps within the MRG Model, together with all activities that need to be implemented in order to perform forest estates ranking. It is necessary to emphasize that forest estates ranking was performed in accordance with the following three ranks: (1) for all analyzed variables, (2) for economic variables, and (3) for natural variables. Additionally, three modules for the calculation of scores for individual forest estates are the result of this research. Conclusions: The MRG Model is based on FE ranking according to deviation from the average value of the selected variables. The quality of the model lies in the fact that it is relatively simple (there are no complex statistical or other methods, necessary data can be collected easily), and that it can be applied again for similar surveys. Implementation of the MRG Model involves 5 basic steps with 7 phases to be performed in the order specified in this paper. The selection of variables which will be part of the MRG Model is crucial. The survey sample must include representatives that are directly or indirectly involved in the forestry sector. Although it might seem that all selected variables are significant, it is always necessary to give each variable the importance in accordance with the survey results. It is necessary to validate the defined model, data and final ranks on a pilot sample. Since there are three ranks, it is necessary to consider their mutual correlation, by performing statistical analysis rank correlation.
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Andriani, Fitria. "INVESTASI REKSADANA SYARIAH DI INDONESIA." AT-TIJARAH: Jurnal Penelitian Keuangan dan Perbankan Syariah 2, no. 1 (October 16, 2020): 44–65. http://dx.doi.org/10.52490/at-tijarah.v2i1.816.
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The development of Islamic investment in Indonesia is progressing very rapidly. The principle of every investment made by each individual must be based on sharia principles. This study aims to explain the principles of sharia mutual funds investing which are currently growing rapidly as well as the development and procedures for investing in Islamic mutual funds. The results of this study indicate that the statistical development of Islamic mutual funds tends to increase significantly, the NAV of Islamic mutual funds in August 2020 reached 67.71 trillion. Players in sharia mutual fund investments include investors and investment managers who are trusted by investors in managing their mutual funds. The procedure for investing in Islamic mutual funds includes two ways, namely the method of buying or referred to as subcription and redemption or the method of selling Islamic mutual funds, a good understanding of these two methods will determine the risks and benefits that investors and investment managers get.
Keyword: Mutual Fund Investment, Investment Procedures.
Abstrak
Perkembangan investasi syariah di Indonesia melaju dengan sangat pesat, Prinsip dari setiap investasi yang dilakukan oleh setiap individu harus berlandaskan dengan prinsip syariah. Penelitian ini bertujuan untuk menjelaskan prinsip dari investasi reksadana syariah yang saat ini sedang berkembang pesat serta perkembangannya dan tata cara melakukan investasi pada reksadana syariah. hasil penelitian ini menunjukkan perkembangan statistik reksadana syariah cenderung naik signifikan, NAB reksadana syariah agustus 2020 mencapai 67,71 Triliun. Pelaku dalam investasi reksadana syariah meliputi investor dan manajer inverstasi yang dipercayai oleh investor dalam mengelola reksadana miliknya. Tata cara investasi pada reksadana syariah meliputi dengan dua cara yaitu cara pembelian atau disebut dengan subcription dan redemtion atau cara penjualan pada reksadana syariah, pemahaman yang baik akan kedua cara ini akan menentuan resiko dan keuntungan yang di dapatkan oleh investor dan manajer investasi.
Kata kunci: Investasi Reksadana, Tata Cara Invetasi Reksadana.
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Danilov, Igor Sergeevich, and Galina Viktorovna Tretyakova. "Theoretical aspects of collective investment in the international financial market." Mezhdunarodnaja jekonomika (The World Economics), no. 12 (December 1, 2020): 31–41. http://dx.doi.org/10.33920/vne-04-2012-04.
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The global fi nancial crisis of 2008–2009 and the crisis of the end of 2014–2015 had a negative impact on the state of the collective investment market, which became a constraining factor for its development. These problems of the formation and development of the market for collective investments in Russia, including mutual funds, dictate the need for theoretical research on these issues and their practical implementation. This determined the relevance and signifi cance of the chosen research topic. The concept of «collective investment» has not been suffi ciently researched in the works of Russian and foreign economists, it is not contained in the regulatory documents of the Russian Federation. That is why this topic is relevant: there are a large number of unexplored issues that need to be considered. The purpose of this research is to study and generalize the theoretical and practical aspects of the formation and development of mutual funds and ETFs as one of the most promising forms of collective investment. The main research methods used a systematic approach, statistical and economic analysis, and other general scientifi c methods. The article examines the aspects of collective investment in the international fi nancial market, as well as the mechanism of the collective investment market functioning. Moreover, a comparative description of mutual funds and ETFs are presented. In the course of research activities, directions for the development of mutual funds and ETFs were proposed as important elements of the collective investment system. The implementation of these proposals will increase the role of mutual funds and ETFs in the collective investment market in Russia and abroad, stabilize their reliability and attractiveness for investors.
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Kurniawan, Elan. "PENGARUH INFLASI, JAKARTA ISLAMIC INDEX, BAGI HASIL BANK SYARIAH TERHADAP INVESTASI REKSA DANA SYARIAH." Kinerja 2, no. 01 (April 18, 2020): 113–21. http://dx.doi.org/10.34005/kinerja.v2i02.799.
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The purpose of this study is to obtain empirical evidence about the problems discussed, namely "Influence of Inflation, Jakarta Islamic Index, and Profit-Sharing of Islamic Banks on Sharia Mutual Investment". Investment is proxied by the Net Asset Value of Sharia Funds.
Presentation and analysis of research data in 2013-2017, using descriptive statistical analysis, panel data regression models, data estimation methods namely Chow Test, classical assumption analysis using normality test, multicollinearity test, and hypothesis testing.
Based on the results of the analysis of this research data, positive inflation has a significant positive effect on Sharia Mutual Fund Investment, the Jakarta Islamic Index has a significant positive effect on Sharia Mutual Fund Investment, while Sharia Bank Profit Sharing is also significantly negative on Sharia Mutual Fund Investment.
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Darmayanti, Ni Putu Ayu, Ni Putu Santi Suryantini, Henny Rahyuda, and Sayu Ketut Sutrisna Dewi. "PERBANDINGAN KINERJA REKSA DANA SAHAM DENGAN METODE SHARPE, TREYNOR, DAN JENSEN." Jurnal Riset Ekonomi dan Bisnis 11, no. 2 (August 28, 2018): 93. http://dx.doi.org/10.26623/jreb.v11i2.1079.
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<p>Reksa dana saham merupakan reksa dana yang menawarkan keuntungan yang tinggi namun juga memiliki risiko yang tinggi karena dipengaruhi oleh fluktuasi yakni penurunan harga saham yang dipengaruhi mekanisme pasar di bursa efek. Oleh karena itu para calon investor harus memiliki pengetahuan dalam memilih reksa dana mana yang akan dipilih. Dalam penelitian ini ingin membandingkan antara metode pengukuran kinerja Treynor, Sharpe, dan Jensen. Tujuan dari penelitian ini adalah untuk mengetahui ranking kinerja reksa dana saham yang dihasilkan menggunakan ketiga metode tersebut, membandingkan kinerja reksa dana saham dengan suatu standar pengukuran (<em>benchmark</em>) yaitu kinerja IHSG, dan kemudian untuk mengetahui ada atau tidaknya perbedaan ranking yang dihasilkan oleh ketiga metode tersebut. Berdasarkan hasil penilaian kinerja dengan metode Sharpe, jika dibandingkan dengan IHSG sebagai <em>benchmark</em>, sebanyak 17 (18,5 persen) reksa dana memiliki kinerja yang <em>outperform</em> atau kinerjanya di atas kinerja portofolio pasar. Sisanya sebanyak 75 reksa dana ditemukan <em>underperform</em> atau kinerjanya di bawah portofolio pasar. Hasil penilaian kinerja dengan metode Treynor dan Jensen, sebanyak 33 (35,87 persen) reksa dana memiliki kinerja yang <em>outperform</em> atau kinerjanya di atas kinerja portofolio pasar. Sisanya sebanyak 59 reksa dana ditemukan <em>underperform. </em>Reksa dana yang <em>outperform</em> dapat dipertimbangkan oleh investor sebagai alternatif investasi. Dari hasil pengujian statistik mengenai perbedaan ranking kinerja reksa dana dengan menggunakan metode Sharpe, Treynor, dan Jensen, dapat disimpulkan bahwa ketiga metode penilaian kinerja tidak menghasilkan ranking kinerja yang berbeda-beda secara signifikan</p><p> </p><p><em>Equity funds are mutual funds that offer high profits but also have a high risk because they are influenced by fluctuations in the decline in stock prices which are influenced by market mechanisms on the stock exchange. Therefore, potential investors must have knowledge in choosing which mutual fund to choose. In this study wanted to compare the performance measurement methods of Treynor, Sharpe, and Jensen. The purpose of this study was to determine the ranking performance of equity funds generated using these three methods, compare the performance of equity funds with a benchmark standard, namely the JCI performance, and then to find out whether or not there are ranking differences generated by these three methods. . Based on the results of the performance evaluation with the Sharpe method, when compared with the JCI as a benchmark, as many as 17 (18.5 percent) mutual funds have outperformed performance or performance above the market portfolio performance. The results of the performance appraisal with the Treynor and Jensen methods, as many as 33 (35.87 percent) mutual funds have outperformed performance or performance above the market portfolio performance. Mutual funds that are outperformed can be considered by investors as an alternative investment. From the results of statistical tests regarding differences in the ranking of mutual fund performance using the Sharpe, Treynor, and Jensen methods, it can be concluded that the three methods of performance appraisal do not produce performance ratings that differ significantly.</em><em></em></p>
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Jain, Nidhi, and Bikrant Kesari. "Impact of Behavioral Biases in Financial Risk Tolerance Ability of Mutual Fund Investors." Tobacco Regulatory Science 7, no. 5 (September 30, 2021): 2748–65. http://dx.doi.org/10.18001/trs.7.5.1.45.
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Objective: The Behavioral bias is the term that deals with the investors’ psychology about their investment decision with their investment expertise. Every individual is biased, according to standard economic theory by his behavior and experiences which are rational. Methods: This research seeks to segregate mutual fund holders into various groups (persons and professionals) based on Behavioral biases and then investigates whether these Behavioral biases are influencing the level of knowledge of investors and the financial risk tolerance of certain mutual funds. Statistical tools compare investors characteristics and analyse how Behavioral biases are associated. Results: The factors analysed are financial circumstance, Type of Investors, Asset class preference, Time Horizon and Purpose of Investment. The primary information was gathered from 250 Central India mutual fund investors dependent on Judgment sampling. CFA, Correlation, MANOVA and Regression. Conclusions: Findings shows the effect of the behavior bias has positive impact on mutual fund investor awareness and financial risk tolerance.
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Meng, Ming, Luyang Dai, Qingshan She, Yuliang Ma, and Wanzeng Kong. "Crossing time windows optimization based on mutual information for hybrid BCI." Mathematical Biosciences and Engineering 18, no. 6 (2021): 7919–35. http://dx.doi.org/10.3934/mbe.2021392.
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<abstract> <p>Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.</p> </abstract>
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Ziyadinov, Vadim V., and Maxim V. Tereshonok. "MATHEMATICAL MODELS AND RECOGNITION METHODS FOR MOBILE SUBSCRIBERS MUTUAL PLACEMENT." T-Comm 15, no. 4 (2021): 49–56. http://dx.doi.org/10.36724/2072-8735-2021-15-4-49-56.
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The challenge of mobile subscribers’ groups and crowd’s behavior prediction during the mass events is now increasingly important. Operative methods application of this task solution is difficult; accordingly, development and application of technical methods is necessary. The method of this problem solution consists of subscribers’ telephone conversations recording in a zone of mass action, and the following speech recognition, the semantic analysis and statistical processing application. However, there is a tendency demand decrease for mobile systems voice services with simultaneous demand growth for data traffic nowadays. The purpose of this paper is to create a mathematical model of mobile networks subscribers’ mutual placement types, applicable for automatization of the subscribers’ activities nature prediction systems. The research method consists of mathematical simulation model development for pseudo-random examples generation of subscribers’ mutual placement types set, creation of training dataset, convolution neural network training and usage of training results to recognize the new examples. The results obtained. A mathematical model is proposed allowing to create a representative training and validation dataset of mobile networks subscribers’ mutual placement types for neural network training and testing. The convolution neural network trained using these samples has shown high classification accuracy results with a wide class of subscribers’ mutual placement types.
Dissertations / Theses on the topic "Mutual funds Classification Statistical methods"
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Huang, Chi-Nien, and 黃綺年. "Using Statistical Methods and Artificial Neural Networks to Classify the Investment Performance and Forecast the Rate of Return - A Study of Open-end Equity Mutual Funds in Taiwan." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/45009877707971780073.
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碩士國立成功大學
統計學系碩博士班
92
In this low-interest era, interest incomes of deposit cannot catch up the inflation rate. Therefore, the diverse investment products start to be popular. Among all outlets for investment, mutual fund is one of investor’s favorites. Due to its characteristic of accumulation and less risk, investors having less financial support also can get a chance to make the profit from investment portfolio. Moreover, authorizing a professional manager to handle their funds could save cost of time, so mutual fund gradually becomes a popular product in the commercial market. The purpose of this research focuses on Equity Mutual Funds and includes two main directions. First of all, different funds are classified based on their performance. Data is collected from Jan. 2001 to Dec. 2003, and the evaluation index of Mutual Funds includes net asset value, turnover rate, Sharpe Index, Beta coefficient, and Treynor Index; Secondly, based on historical data of rate of return from Jan. 1999 to Dec. 2003, this research explores the relationship between ROR and the macroeconomic indicators including the wholesale price index, M1b and M2 of money supply, Prosperity Score, refunding rate, interest rate, net value of foreign exchange, and import and export balance of trade. This research proceeds by using Statistical Methods and Artificial Neural Networks and compares to get the best result. For classification, judging model good or not by the rate of accurate classification, and matching up SOM and PNN as result get better effect. As for Forecasting, judging from Residual, the result of BPN is better than other models. In conclusion, we infer that Artificial Neural Networks could be more appropriate than statistical methods based on the data type of this research.
Book chapters on the topic "Mutual funds Classification Statistical methods"
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Lisi, Francesco, and Edoardo Otranto. "Clustering mutual funds by return and risk levels." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 183–91. Milano: Springer Milan, 2010. http://dx.doi.org/10.1007/978-88-470-1481-7_19.
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Basso, Antonella, and Stefania Funari. "Socially Responsible Mutual Funds: An Efficiency Comparison Among the European Countries." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 69–79. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02499-8_6.
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Basso, Antonella, and Stefania Funari. "The Role of Fund Size and Returns to Scale in the Performance of Mutual Funds." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 21–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_5.
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Cinar, Dilaysu. "A Market Analysis Approach to Portfolio Theories." In Global Strategies in Banking and Finance, 241–52. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4635-3.ch016.
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Stocks are affected by general economic conditions in different ways and differing severities. Various parameters affect different securities. Through diversification by making a mixture of the securities, which are affected by different states, transactions increase the benefit of the investor and this situation, which is called portfolio management. Portfolio management is deciding when securities are removed and when securities will be added. Traditional portfolio theory ignores the relationship between mutual funds and quantitative data. This is done by Modern Portfolio Theory, which uses the mathematical and statistical methods in the second half of the 20th century. Undoubtedly, market analysis within the scope of this theory will provide great convenience to investors. Thus, the aim of this study is to express some basic concepts to discuss the both traditional and modern portfolio theories and their importance in the technical and fundamental analyses.
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Li, Tengyue, and Simon Fong. "Similarity Measure of Breast Cancer Datasets Using Fuzzy Rule-Based Classification by Attribute." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, 247–65. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch014.
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To compare with two datasets based on attributes by using classification algorithms, for the attributes, the authors need to select them by rules and the system is known as rule-based reasoning system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, the authors propose a classifier ensemble-based method for comparison of two breast cancer datasets. The ensemble data mining learning methods are applied to rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two breast cancer datasets. This article introduces a novel fuzzy rule-based classification method called FURIA, to obtain a relationship between two breast cancer datasets. Hence, it can find the similarity between these two datasets. The new method is compared vis-à-vis with other classical statistical approaches such as correlation and mutual information gain.
Conference papers on the topic "Mutual funds Classification Statistical methods"
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Dmitriev, E. V., T. V. Kondranin, P. G. Melnik, and S. A. Donskoy. "Statistical texture analysis of forest areas from very high spatial resolution satellite images." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.64.23.009.
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Aerospace images with a spatial resolution of less than 1 m are actively used by regional services to obtain and update information about various environmental objects. Considerable efforts are being devoted to the development of remote sensing methods for forest areas. The structure of the forest canopy depends on various parameters, most of which are determined by ground-based methods during forest management works. Remote sensing methods for assessing the structural parameters of forest stands are based on texture analysis of panchromatic and multispectral images. A statistical approach is often used to extract texture features. The basis of this approach is the description of the distributions characterizing the mutual arrangement of image pixels in grayscale. This paper compares the effectiveness of matrix based statistical methods for extracting textural features for solving the problem of classifying various natural and manmade objects, as well as structures of the forest canopy. We consider statistics of various orders based on estimates of the distributions of gray levels, as well as the mutual occurrence, frequency, difference and structuring of gray levels. The results of assessing the informativeness of statistical textural characteristics in determining various structures of the forest canopy are presented. Dependences of the classification results on the choice of distribution parameters are determined. For the quantitative validation of the results obtained, data from ground surveys and expert visual classification of very high resolution WorldView-2 images of the territories of Savvatyevkoe and Bronnitskoe forestries are used.