Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios

González, Eduar Fernando Aguirre; Velásquez, Pablo César Manyoma; ManotasDuque, Diego Fernando Article Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: González, Eduar Fernando Aguirre; Velásquez, Pablo César Manyoma; ManotasDuque, Diego Fernando (2024) : Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-30, https://doi.org/10.1080/23311975.2024.2371072 This Version is available at: https://hdl.handle.net/10419/326382 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios Eduar Fernando Aguirre González, Pablo César Manyoma Velásquez & Diego Fernando Manotas-Duque To cite this article: Eduar Fernando Aguirre González, Pablo César Manyoma Velásquez & Diego Fernando Manotas-Duque (2024) Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios, Cogent Business & Management, 11:1, 2371072, DOI: 10.1080/23311975.2024.2371072 To link to this article: https://doi.org/10.1080/23311975.2024.2371072 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 10 Jul 2024. Submit your article to this journal Article views: 563 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Banking & Finance | ReseaRch aRticle Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2371072 Prioritization of CIFs taking into account multiple criteria for the construction of efficient portfolios eduar Fernando aguirre gonzález , Pablo césar Manyoma Velásquez and Diego Fernando Manotas-Duque school of industrial engineering, universidad del Valle, Cali, Colombia ABSTRACT this study presents a methodology for making decisions on the number of collective investments Funds (ciF) for portfolio construction using Multi-criteria Decision analysis (McDa) techniques, particularly a combination called PratoVi. the proposed methodology includes the integration of economic and financial theories of investment in equity portfolios with the multi-criteria techniques described in PratoVi, which allows a finite number of alternatives to be evaluated hierarchically under qualitative and quantitative criteria. this methodology was evaluated with funds managed by fiduciaries and with information from the end of 2021, which is contained in the technical sheets of each fund, and, the compares with information about 2022 and 2023. the computational results show the importance and efficiency of successfully integrating traditional investment criteria in stock portfolios and multi-criteria methodologies to generate a ranking of those ciFs with which portfolios can be created and an adequate balance between multiple criteria, including profitability and risk, can be found. this approach also considers the investment decision-making process regarding assets managed by colombian sFs. the proposed methodology can be applied to other emerging markets such as colombia and can manage this alternative type of investment. 1. Introduction there are many mechanisms through which to invest money; most investors commonly look for a portfolio composed of stocks that have high-level returns and low-level volatility. in recent decades, the increasing degree of competition among companies, financial institutions, and organizations, globalization of financial markets, rapid economic and social evolution, and technological changes have led to an increase in uncertainty and instability in finance and business environments. according to Doumpos (2018), the main areas of interest in the literature are corporate finance, financial economics, behavioral finance, valuation, risk management, and financial engineering. in this new context, the importance of making efficient financial decisions and the complexity of the financial decision-making process have increased (constantin Zopounidis & Doumpos, 2002). this complexity is evident in the existing variety and volume of new financial resources, products and services. Financial professionals and researchers in these fields recognize the need to address financial decision-making problems through integrated and realistic approaches based on sophisticated quantitative analysis techniques. therefore, the connection between financial theory and mathematical modelling has become evident, which has led to financial decision-making becoming analytical with a high level of modelling and methodological sophistication (Doumpos, 2018). in parallel, investments in non-traditional assets have been gaining importance worldwide in recent decades because this type of investment can offer interesting returns and opportunities for portfolio diversification, thus contributing to a reduction in the degree of inherent risk of the activity. according © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT eduar Fernando aguirre gonzález [email protected] school of industrial engineering, universidad del Valle, Cali 760001, Colombia. https://doi.org/10.1080/23311975.2024.2371072 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 28 February 2024 Revised 22 May 2024 accepted 13 June 2024 KEYWORDS Prioritization; collective investments funds; alternatives investments; portfolio construction; McDa REVIEWING EDITOR David McMillan, University of stirling, United kingdom of great Britain and northern ireland SUBJECTS quantitative finance; decision analysis; financial management 2 e. F. agUiRRe gOnZÁleZ etal. to lopez and hurtado (2008), this type of investment has emerged as a response to the lack of profitability of traditional investments, such as a fall in the yield of government bonds. as a result, investors have progressively built an important part of institutional investment portfolios—mutual and pension funds. Because sophisticated investors witnessed the poor performance of traditional investments, many turned to alternative investments to meet their return objectives and a means of controlling risk. For example, as noted by Baker and Filbeck (2013), alternative investments provide the opportunity to obtain a reasonable return with a manageable level of risk. some alternative investments offer opportunities to participate in different markets and apply investment strategies that are not available to the general investing public. according to some researchers (P. chen et al., 2011), (amin & kat, 2003), (h. c. chen etal., 2005) and (anson, 2002), these types of investments achieve superior performance in terms of the inclusion of alternative investments independently or as part of a portfolio composed of traditional assets. the remainder of this paper is structured as follows: section 2 presents the literature review. section 3 explains the study’s research methodology, a section will consist of the results and discussion. Finally, section 5 concludes the study. 2. Literature review 2.1. Optimal portfolio theory the use of theory for the optimal selection of efficient portfolios originated from (Markowitz, 1959) and has been enriched with essential contributions, such as those of sharpe (1964) and (stephen, 1976), among other authors who have made significant progress in terms of the problems that stand out in portfolio selection decisions, especially concerning the choice of indices or criteria to be considered. as described by loraschi and tettamanzi (2002), the central problem of portfolio theory concerns the selection of asset weights in a portfolio that minimize a certain measure of risk for any given level of expected return. to construct an optimal portfolio, (Markowitz, 1959) suggested minimizing the risk of the portfolio at a certain level of its expected return. this model is called the variance-mean model and is widely used because of its ease of use and simplicity. in (loraschi & tettamanzi, 2002)) the autors explained that Markowitz proposed the use of “seivmariance”, defined as the weighted average of the squared distances of the returns below the mean of the distribution, as an index of risk, due realizing that the investor perceives risk only on the downside part of the return distribution. likewise, as described in Prigent (2007), since Markowitz introduced a seminal analysis of mean-variance in 1959, portfolio management theory has been expanded to consider different characteristics: Dynamic optimization of the portfolio according to Merton • choice of new decision criteria based on risk aversion (utility functions) or risk measures (value at risk (VaR), conditional VaR (cvaR), etc.). • Market imperfections, such as transaction costs and • specific portfolio strategies, such as portfolio insurance or alternative methods (hedge funds). however, as mentioned in kim et al. (2014), the model is highly sensitive to its inputs due to the random nature of asset returns. according to Black and litterman (1992), assets often have unintuitive or extreme weights, such as investment positions with large negative weights, which cannot be considered in active trading. Other approaches have been reported in the literature, such as the mean absolute deviation (MaD) found by konno and Yamazaki (1991) and the cVaR approach reviewed by Rockafellar and Uryasev (2000). in the above three studies, the measure for returns (yields) is the average and what varies is the measure for risk (variance, MaD, and cVaR). another approach explained by Doumpos and grigoroudis (2013) is the upside deviation-downside deviation (UD-DD) approach, which uses terms used by financial investors known as “good volatility” and “bad volatility.” as a novelty, in order to select the optimal portfolio, in garcía etal. (2020) the authors extends the stochastic mean-semivariance model to a fuzzy multiobjective model, Particularly, it optimizes the expected return, the semivariance and the cardinality constraint and upper and lower bound constraints; defining the credibilistic sortino ratio as cOgent BUsiness & ManageMent 3 the ratio between the credibilistic risk premium and the credibilistic semivariance. the constrained portfolio optimization problem resulting is solved using the algorithm nsga-ii. in (Brandtner et al., 2020) they incorporate the concept of convex shortfall risk measures in portfolio optimisation. additionally, in garcía etal. (2022) they construct pareto efficient portfolios using a fuzzy multicriteria portfolio selection model with real-world constraints. Recently, in huang and Ma (2023) using uncertainty theory, the authors discusses a portfolio selection problem considering inflation, a most popular multiplicative background risk, in such situations, and analyses the effect of uncertain inflation on investment in risky and risk-free assets. 2.2. MCDA and portfolio selection considering the wide variety of objectives that are part of the optimization process that the investor must conduct, the use of multi-criteria decision analysis (McDa) is both relevant and versatile. For example, in search of delimiting the problem and some methods used in the present study (Bahmani et al., 1987) applied the analytic hierarchy process (ahP) to the asset selection process for a given investment. Zopounidis in c. Zopounidis (1999), the contribution of McDa to the resolution of financial decision problems. according to Xidonas etal. (2009), who focused on the selection of assets through a specific method, the relevance of the use of McDM is reinforced. this study argues that the McDM paradigm provides a broad methodological approach for effectively addressing portfolio selection problems. some of the first papers published on the selection of equity portfolios and using multi-criteria methods were those of hababou and Martel (1998), (costa etal., 2004) and (Bouri et al., 2002). in (hababou & Martel, 1998), a methodology comprising the following four stages: (1) defining a list of potential solutions to the problem considered, (2) defining a list of critical criteria, (3) evaluating the performance of each solution according to each criterion, and (4) aggregating these returns using the PROMethee ii multi-criteria method. (costa etal., 2004) presented a model for selecting a portfolio based on the results of fund managers’ fieldwork and using direct scoring, optimization, and MacBeth techniques. in (Bouri etal., 2002), the authors included the investor’s attitude toward solvency and liquidity, solving a portfolio problem with a multi-criteria issue, which must be addressed using appropriate techniques. in (Vásquez et al., 2021), a hybrid method between two multi-criteria techniques applied directly to the optimal selection of financial portfolios in search of a more robust and complete optimization process was proposed. this process is one of the most decisive criteria or objectives for investors. 2.3. Alternative investments alternative investments are vital in large investment portfolios worldwide, especially in emerging countries. likewise, owing to the lack of structured information on this type of investment, the large portfolios of institutional entities, such as pension funds and trust funds, have adopted the position of investors with ends and not means. that is, large portfolios do not guarantee profitability, but depend on the performance of the investments within. according to P. chen et al. (2011), this type of investment seeks greater profitability by including alternative investments in isolation or as part of a portfolio comprising traditional assets. in (Baker & Filbeck, 2013), these types of investment into two large groups: • traditional alternative investments: this group includes real estate, private capital, raw materials and • Modern alternative investments: this group includes managed futures, hedge funds, and distressed securities of companies or government entities that are already in default, under bankruptcy protection, in danger, or heading toward such a condition. the most common types of distressed securities are bonds and bank debts. in addition, emerging alternative investments present highly important research topics related to information asymmetry and agency costs, governance and monitoring, and legal and economic 4 e. F. agUiRRe gOnZÁleZ etal. conditions (cumming & Zhang, 2016). according to Begué hoyos (2012), to define alternative investments, traditional ones must first be delimited. When discussing these investments, reference is made to stocks (variable income), bonds, treasury bonds (tess), or certificates of deposit (cDs), as they are considered traditional because they are the most common options people use to invest their money. as alternative types of investment, reference mades to private equity funds (FcPs), real estate funds (real estate investment trusts (Reits)), commodities, and hedge funds) and a special type of “speculative” collective portfolio called a collective investment fund (ciF). 2.4. CIF’s in Colombia according to (Decreto 2555, 2555, 2010), “collective speculation portfolios are understood to be those whose primary objective is to carry out operations of a speculative nature, including the possibility of carrying out operations for amounts greater than those contributed by investors (leverage)”. later, in Decreto 1242, 1242 (2013) defined ciFs as “any mechanism or vehicle for the collection or administration of sums of money or other assets.” ciFs defined as the mechanisms or vehicles through which to capture or manage sums of money or other assets with the contributions of many people, which can be determined once the fund is operational. in addition, ciFs collectively manage resources to obtain collective economic results (asociación de Fiduciarias de colombia, 2013). in colombia, financing assets in a capital market can be segmented as a) individual administration, headed by the client, and b) delegated administration, which can occur through individual or collective investment vehicles. according to Maiguashca (2016), in the first case, investors act on their own and can count on an intermediary to carry out market transactions on their behalf and on some type of agent to act as a depositary for their investments. however, investment and ownership decisions are concentrated on investors themselves. in the second case, a professional manager makes investment decisions within a general mandate defined by the client who acts as the principal. this mandate can be from a client, in which case, it is considered an individual delegated administration. the individual management actors offered by the colombian market are fundamentally third-party portfolio managers of stock brokerage companies (sBcs) and individual investment trusts of trust companies (sFs). according to asofiduciarias (2021), at the end of 2019, managed assets were distributed to more than 24,900 businesses, a figure that increased by 1% compared to that at the end of the previous year. the trust sector manages approximately 690 billion pesos in assets, with an annual growth of 19%. the income from the administration of these businesses amounted to $ 1.88 billion, representing an increase of 12%. conversely, the profits of the trust sector grew by 33%, exceeding 710,000 million pesos. iin 2021, the fiduciary sector took advantage of economic recovery to continue articulating businesses in the different sectors of the economy. as a result, the assets managed by the sector exceeded $700 billion through 25,257 businesses, with the last figure being registered as a historical record in terms of the number of businesses managed by fiduciaries. Figure 1 shows the evolution of the assets managed by sFs, which show constant growth. as of December 2021, assets under management represent 65% of gross domestic product (gDP), as shown in Figure 2. Within the obligations of ciF management companies, they keep investors informed, at least through the following mechanisms. • Regulation • Prospectus • technical data • investor account statement • accountability report the system of categorization of ciFs (siciF) consists of establishing homologous groups of ciFs so that they are objectively comparable and allow the investor to know, evaluate, and make decisions about which funds best suit the needs of the investor (asofiduciarias etal., 2021). the ciF, for improved administration and monitoring, can be categorized into six macro categories. Figure 3 shows the cOgent BUsiness & ManageMent 5 percentage share of each macro category in the total assets under management at the end of January 2022. considering the above factors, the objective of this research was to generate a ranking that would allow the prioritization of these funds based on the technical information available for each ciF and through a combination of methodologies for multi-criteria decision-making (McDM) and help in the construction of optimal portfolios for this type of investment in colombia. 3. Methodology the proposed methodology for the development of this study is divided into five stages, which are summarized in Figure 4. 3.1. Stage 1: Data collection 3.1.1. Download information on ciFs was collected from the asofiduciarias website, which groups together the sFs who have been authorized to manage this type of investment in colombia and have reported information since 2017. information was downloaded individually for each entity. Figure 1. total assets of the financial system and total assets managed by supervisory entities (in billions of pesos). Source: own elaboration from the annual information of asofiduciarias. Figure 2. trust assets as a percentage of gDP. Source: annual report of asofiduciarias (2021). 6 e. F. agUiRRe gOnZÁleZ etal. 3.1.2. Debugging For data purification, the “extract, transform, load” (etl) data-processing methodology was used. according to Duque Méndez et al. (2016), the activities of this methodology are framed as key activities in the context of databases because, through their combination, they allow for the transfer of data from one source to another. 3.2. Stage 2: Heuristic selection at the time of this research, approximately 400 funds had been managed by 18 trust companies. each trust company can manage any number of funds, as long as they are categorized in one of the 6 currently established macro-categories. For the heuristic selection, funds were selected from each trust company that accounted for at least 80% of the total funds under management, regardless of the number of assets under management. in the end, 72 funds were selected from the 18 trust companies. 3.3. Stage 3: Analysis of technical data in (Decreto 2555, 2555, 2010) established the technical file as one of the mechanisms through which management companies can keep investors informed about all aspects inherent to ciFs. likewise, the Ministerio de hacienda Pública defines a technical file as a standardized information document for ciFs that contains the basic information of each fund that must be updated and published monthly within five (5) business days following the last calendar day of the month. the information cut-off date was the last calendar day of the month being reported. similarly, siciF determined the categorization summarized in table 1. in total, 6 macro categories and 19 micro categories were established. 3.4. Stage 4: Multi-criteria analysis the purpose of this stage is to form the initial matrix, which identifies and summarizes the essential elements for the application of multi-criteria methodologies. Figure 3. total assets managed by CiFs. Source: Quarterly report of CiFs (asofiduciarias et al., 2021). Figure 4. Proposed methodology. Source: own elaboration. cOgent BUsiness & ManageMent 7 3.5. Initial matrix formation 3.5.1. Selection of criteria in this study, the selected criteria have been validated and are available in the quarterly reports of all funds under evaluation. accordingly, three dimensions have been established to categorize the criteria: Financial dimension: this encompasses three criteria: Profitability (c1), Risk (c2), and Fund Value (c3), which specifically evaluate financial performance and the size of the fund. Operational dimension: this includes two criteria that assess the fund’s operations, namely the number of investors (c4) and the average investment duration (c5). Administrative dimension: this consists of two criteria that appraise the credit rating (c6) and the manager’s experience (c7). 3.5.2. Selection of alternatives alternatives are the units or objects that serve as options to achieve the objective or solve the problem. in our research, these alternatives will be represented by the various funds. 3.5.3. Determination of performance indicators the evaluation for each alternative must consider all attributes connected from the point of view considered pertinent. considering what is described in Manyoma-Velásquez (2022), to make this comparison, it was essential to measure the performance of each action based on these criteria. Performance can be measured in diverse ways, through grades and scores, characterized by a number, or by being a verbal statement, among others. 3.6. Selection of the methodology the most traditional approach in the multi-criteria world is to use a method to obtain an order or hierarchy of the alternatives analyzed, to make decisions in accordance with the possibilities of the decision-making center. the decision process includes evaluating the possible alternatives and choosing the best one. But from this traditional application, some questions arise that haunt decision-makers after performing these procedures: is there a method that makes more sense than another method for a particular problem, and therefore leads to a better solution than another method? according to Romero (2000) the answer is far from easy, as the decision-making construction of each of them is different and this can lead to problems of comparison. Table 1. Categories and subcategories of CiFs in Colombia. MaCRo Cat Cat MaCRo Cat Cat Fixed-income Funds High-Performance Weights 1 1 Fixed-income Pesos Liquidity 1 2 national Fixed-income Public entities 1 3 short-term Fixed income Pesos 1 4 Medium-term Fixed-income Pesos 1 5 Long-term Fixed-income Pesos 1 6 international Fixed-income Funds 1 7 stock Funds national stock Funds 2 1 international stock Funds 2 2 Balanced Funds Balanced Funds with Lower Risk 3 1 Moderately Balanced Funds 3 2 Balanced Funds with Higher Risk 3 3 exchange traded funds Beta stock exchange Funds 4 1 smart Beta stock exchange Funds 4 2 Real estate Funds national Development Real estate Funds 5 1 national income Real estate Funds 5 2 other Funds Credit asset Funds 6 1 other equity Funds 6 2 other Funds 6 3 Source: own elaboration. 14 e. F. agUiRRe gOnZÁleZ etal. with the ranking. each multi-criteria methodology, which is part of the same initial matrix, is shown in table 7 (see the annexes). in the case of PROMethee ii, seven steps are executed to obtain the final hierarchical table. similarly, the tOPsis methodology concludes with a final hierarchical matrix after six steps. For the VikOR methodology, the proposed six steps are completed to derive the final hierarchy matrix. Upon acquiring the hierarchies or rankings from these three methodologies, the Borda count is utilized to conduct the final ranking. table 8 (see annexes) summarizes these rankings. 4.8. Stage 5: Prioritization and ranking With the help of the MatlaB® tool, a heatmap was established to better visualize the ranking obtained in stage 4, the result of the combination of multi-criteria methodologies, and the final ranking of the Borda count. Figure 6 shows the heatmap for the zero (0) scenario, that is, that of equal weights for the criteria. in this map, we can identify that the funds in blue colors are ranked in the first place, that is, the best qualified and with a high probability of being selected for the portfolio; then, the funds in red colors are those in last place, which indicates their inferior performance and null selection for the portfolio. the funds that belong to the FiD 17 and FiD 18 sFs have a score that places them in the first five places of the classification and, therefore, many opportunities to be selected. in contrast, the funds that belong to the FiD 2 trust are located last in the classification. the results by micro category are as follows: Macro category: Fixed-income fund according to asofiduciarias et al. (2021), these funds have portfolios exclusively invested in fixed-income instruments registered in the national Registry of securities and issuers (RnVe) or a world-renowned stock exchange (international Organization of securities commissions (iOscO) list). Category: national high-yield fund this is the ciF with at least 50% of the value of the fund (including the available fund) invested in the fixed-income instruments described in the macro category, denominated by local currency, either from local issuers issued in the local or international market or from foreign issuers issued in the local market, and a maximum of 50% of the fund’s value in fixed-income instruments from local or foreign issuers issued in international markets denominated by foreign currency, with a maximum of 30% in foreign issuers issued in international markets denominated by foreign currency. the performance of these funds is shown in Figure 7. this category includes a single fund, corresponding to FiD 18, and has a score that places it in any of the scenarios, first in the classification and, therefore, makes this fund a viable option to be selected. Category: national fixed-income fund liquidity this category includes fixed-income ciFs with a conservative investment policy, whose main objective is to preserve capital, with a total duration of the fund’s value of less than or equal to 240 days, Figure 5. Hierarchy of the problem. Source: own elaboration. cOgent BUsiness & ManageMent 15 Table 7. initial decision matrix for 2021. Dimension Financial operation Management Criteria (C1) (C2) (C3) (C4) (C5) (C6) (C7) indicator expected Value Returns standard Deviation Returns Balance at Closing Date amount at Closing Date term (Days) Credit Risk experience Manager Vector (+) (-) (+) (+) (+) (+) (+) units % % number number number normalized scale number a1 1.63% 0.38% 278.60 219,367 268 1 11 a2 0.80% 0.23% 58.90 67 234 1 11 a5 2.69% 2.66% 897.82 9,737 279.06 0.875 11 a6 0.37% 0.34% 306.91 1,998 129 1 26 a7 0.41% 0.47% 257.40 33,134 232 1 26 a8 1.65% 0.16% 9.28 58 75 1 26 a9 1.27% 0.34% 68.59 225 129 1 26 a10 1.75% 0.16% 234.42 234 75 1 26 a11 0.95% 0.16% 126.98 8,691 75 1 26 a12 −0.36% 0.33% 186.36 11,566 212.73 1 17 a13 0.00% 0.00% 0.02 1 212.73 1 17 a14 −0.21% 0.33% 5.17 321 212.73 1 17 a15 0.05% 0.33% 0.16 10 212.73 1 17 a16 −0.21% 0.33% 116.59 7,236 212.73 1 17 a17 0.45% 0.33% 0.31 19 212.73 1 17 a18 0.81% 0.33% 0.02 1 212.73 1 17 a19 0.70% 0.37% 0.00 23 212 1 8 a20 1.11% 0.28% 5.45 36 190 1 8 a22 1.10% 0.37% 135.63 20 212 1 8 a23 −0.30% 0.35% 66.08 3,778 177.16 1 10 a24 0.19% 0.35% 0.16 9 177.16 1 10 a25 −0.94% 0.27% 5.78 36 84526 1 10 a26 −0.06% 0.27% 12.84 80 84526 1 10 a27 0.49% 0.31% 3,139.72 651,982 194841 1 12 a28 0.33% 0.36% 2.78 7 211516 1 10 a29 0.80% 0.36% 40.93 103 211516 1 10 a30 0.44% 0.36% 683.91 1,721 211516 1 10 a32 0.62% 0.49% 2.53 186 295222 1 10 a33 −0.86% 0.49% 679.09 50,011 295222 1 10 a34 −0.57% 0.49% 1.05 77 295222 1 10 a35 0.87% 0.29% 130.77 47,152 202 1 15 a36 0.87% 0.29% 40.73 14,687 202 1 15 a37 0.37% 0.23% 136.68 291 144.28 1 18 a38 0.49% 0.28% 269.08 121 269.57 1 10 a39 1.09% 0.32% 128.52 81 321.24 1 10 a40 0.39% 0.32% 4.76 3 321.24 1 10 a41 0.39% 0.32% 334.78 211 321.24 1 10 a42 0.21% 0.29% 783.32 76,558 205.95 1 15 a43 0.22% 0.29% 105.47 10,308 205.96 1 15 a44 0.34% 0.17% 259.99 75 128.02 1 15 a45 −0.08% 0.32% 72.35 107 160.32 1 19 a46 4.40% 0.84% 102.22 23 198.76 1 19 a47 0.61% 0.32% 72.35 129 160.32 1 19 a48 4.40% 0.84% 102.22 4 198.76 1 19 a49 0.45% 0.72% 10.12 223 508.22 1 19 a50 3.47% 0.84% 102.22 1,311 198.76 1 19 a51 3.07% 0.84% 102.22 12 198.76 1 19 a52 −0.15% 0.32% 72.35 80 160.32 1 19 a53 3.68% 0.84% 102.22 3 198.76 1 19 a54 0.71% 0.33% 37.71 9 155.2 1 23 a55 0.49% 0.33% 9.87 100 155.2 1 23 a56 −0.90% 1.23% 7.27 109 529.88 1 23 a57 0.67% 0.33% 8.24 96 155.2 1 23 a58 6.47% 0.10% 45.84 775 936.01 0.833 25 a59 0.20% 0.28% 4.19 453 192.48 1 25 a60 −0.30% 0.28% 2.85 308 192.48 1 25 a61 −0.25% 0.46% 3.94 21 463.89 1 25 a62 0.67% 0.29% 534.71 1,433 181.11 1 17 a63 0.71% 0.24% 208.05 171 123.24 1 10 a64 0.97% 0.29% 534.71 676 181.11 1 17 a65 1.43% 0.29% 534.71 80 181.11 1 17 a66 −0.11% 0.29% 493.71 94 199.87 1 18 a67 0.67% 0.15% 624.51 315 88.07 1 18 a68 0.67% 0.25% 51.57 2,446 169.7 1 15 a69 0.41% 0.25% 6.87 326 169.7 1 15 (Continued) 16 e. F. agUiRRe gOnZÁleZ etal. and at least 80% of its portfolio invested in instruments whose national rating is greater than or equal to the second-highest rating in the long term, according to the scale used by the rating agencies in instruments with a term of more than one (1) year, whose national rating is equal to the maximum in force in the short term according to the scale used for this term in instruments with a term of less than or equal to one (1) year, or whose investments are exposed to national risk. the performance of these funds is shown in Figure 8. the two (2) funds that belong to the FiD 18 trust have scores that place them in the first four places of the classification and therefore make them viable options to be selected. in contrast, three (3) funds that belong to the FiD 2 and FiD 6 trusts are in the last place of classification. the other funds in this category occupy places from the middle down. Category: national fixed-income fund for public entities ciFs with a total duration of the fund value (including available) less than or equal to 365 days, with investment policies that comply with the provisions of Decree 1525 of 2008 and its amendments and that at the request of the participating entity that manages them, are included in this category. the performance of these funds is shown in Figure 9. two (2) of the funds that belong to the FiD 4 trust, as well as one of the FiD 5 trust funds, have a score that places them in the first 10 places of the classification and, therefore, makes them viable options for selection. One of the FiD 4 funds drops considerably if scenario eleven is presented, which gives 40% importance to the profitability criterion. in contrast, three (3) funds that belong to the FiD 16 trust are located in the last place of the classification, regardless of the scenario under consideration. in the case of two (2) of the FiD 5 funds, it is observed that they lose their position considerably in scenario 6 (where 50% of the weight is carried by the operation dimension), in scenario 10 (where 40% of the weight is carried by the operation dimension and 20% is carried by the value of the fund), and in scenario 15 (where 40% of the weight is carried by the criterion of the average investment term in the operation dimension). Category: short-term national fixed-income fund this category includes the ciF for its investment objectives, maintains the total duration of the fund’s value in the range of 240 to 540 days, and at least 80% of its portfolio invested in instruments whose national rating is higher or equal to the second-highest in force in the long term according to the scale used by the rating agencies with instruments with a term of more than one (1) year; investments whose national rating is equal to the maximum in force in the short term according to the scale used for this term with instruments with a term of less than or equal to one (1) year, or investments exposed to national risk. the performance of these funds is shown in Figure 10. this category includes one (1) of the funds that belong to the FiD 5 trust and have scores in any scenario that places them in the first five places of the classification and, therefore, makes them viable options to be selected. One of the FiD 12 funds is generally located in the first quarter of the general classification but is considerably better in position if scenario 1 was presented (where 60% of the weight is carried by the financial dimension), in scenario 2 (where two financial criteria would Dimension Financial operation Management Criteria (C1) (C2) (C3) (C4) (C5) (C6) (C7) indicator expected Value Returns standard Deviation Returns Balance at Closing Date amount at Closing Date term (Days) Credit Risk experience Manager Vector (+) (-) (+) (+) (+) (+) (+) units % % number number number normalized scale number a70 0.87% 0.25% 0.08 4 169.7 1 15 a71 0.67% 0.25% 101.59 4,818 169.7 1 15 a72 0.19% 0.42% 52.60 1,231 273.97 1 15 a73 0.19% 0.42% 52.60 4,978 273.97 1 15 a75 0.22% 0.53% 112.27 4,606 120.25 1 6 a76 0.22% 1.23% 1.97 81 120.25 1 6 a77 1.64% −1.01% 45.35 39 878.39 1 6 Source: own elaboration. Table 7. Continued. cOgent BUsiness & ManageMent 17 Table 8. Final ordering. Promthee ii topsis Vikor sumatoria Ránking A1 25 68 25 118 31 A2 57 32 55 144 24 A5 68 71 71 210 2 A6 12 15 11 38 62 A7 11 24 10 45 60 A8 8 7 3 18 69 A9 10 9 7 26 65 A10 6 5 2 13 71 A11 9 10 6 25 66 A12 45 59 50 154 18 A13 40 29 31 100 38 A14 47 55 49 151 19 A15 43 47 46 136 25 A16 44 54 48 146 22 A17 42 34 39 115 34 A18 36 20 33 89 43 A19 67 53 66 186 7 A20 64 36 61 161 14 A22 66 40 64 170 11 A23 65 63 67 195 5 A24 63 61 65 189 6 A25 56 65 57 178 10 A26 52 60 52 164 13 A27 0 17 0 17 70 A28 15 49 17 81 45 A29 14 33 14 61 54 A30 16 43 16 75 49 A32 3 38 5 46 59 A33 5 64 8 77 47 A34 7 62 9 78 46 A35 38 22 35 95 40 A36 46 23 40 109 36 A37 33 28 30 91 42 A38 60 50 60 170 12 A39 58 31 58 147 21 A40 61 56 63 180 9 A41 62 57 62 181 8 A42 41 41 38 120 30 A43 53 42 51 146 23 A44 37 30 32 99 39 A45 30 44 36 110 35 A46 20 1 12 33 64 A47 26 21 28 75 50 A48 19 2 13 34 63 A49 31 58 45 134 26 A50 23 4 20 47 57 A51 24 8 24 56 56 A52 32 48 37 117 33 A53 22 3 18 43 61 A54 1 6 1 8 72 A55 21 16 21 58 55 A56 27 67 56 150 20 A57 4 12 4 20 68 A58 2 0 22 24 67 A59 13 19 15 47 58 A60 17 37 19 73 51 A61 18 45 23 86 44 A62 35 25 34 94 41 A63 59 39 59 157 17 A64 34 14 29 77 48 A65 29 11 27 67 52 A66 39 46 41 126 28 A67 28 13 26 67 53 A68 50 27 44 121 29 A69 51 35 47 133 27 A70 48 18 42 108 37 A71 49 26 43 118 32 A72 55 52 54 161 15 A73 54 51 53 158 16 A75 69 66 68 203 4 A76 70 69 69 208 3 A77 71 70 70 211 1 Source: own elaboration. 18 e. F. agUiRRe gOnZÁleZ etal. Figure 6. Ranking of funds in the zero (0) scenario. Source: own elaboration. Figure 7. Ranking of funds in the national high-performance fund category. Source: own elaboration. Figure 8. Ranking of funds in the national fixed-income fund liquidity category. Source: own elaboration. cOgent BUsiness & ManageMent 19 carry 50% of the weight), and in scenario 11 (where a single criterion, risk (Ri), carries 40% of the weight of the decision). Other funds constantly occupy places in the middle of the table upward, regardless of the setting, and no sudden changes or jumps are observed in the classification of this category. Category: Medium-term national fixed-income fund this category includes the ciF for its investment objectives, which maintains the total duration of the fund’s value in the range of 240 to 540 days and at least 80% of its portfolio invested in instruments whose national rating is higher or equal to the second highest in force in the long term according to the scale used by the rating agencies in instruments with a term of more than one (1) year; investments whose national rating is equal to the maximum in force in the short term according to the scale used for this term in instruments with a term of less than or equal to one (1) year; or investments exposed to national risk. the performance of these funds is shown in Figure 11. Figure 9. Ranking of funds in the national fixed-income fund public entities category. Source: own elaboration. Figure 10. Ranking of funds in the short-term national fixed-income fund category. Source: own elaboration. 20 e. F. agUiRRe gOnZÁleZ etal. this category includes a single fund, corresponding to FiD 13, which has a score that places it in most scenarios in the first 20 places of the classification and, therefore, makes it a medium-low option to be selected. the fund shows a considerable decrease in scenario 7 (where 40% of the decision is carried by the management dimension and 30% of the decision is carried by the operation dimension) and in scenario 17 (where 50% of the decision is carried by the operation dimension). Macro category: exchange traded Funds according to (asofiduciarias et al., 2021), these are ciFs whose purpose is to replicate or track a national or international index, investing at least 90% of the fund in any or all of the assets in the basket comprising the index. Category: Beta stock exchange Funds stock exchange-traded ciFs that exclusively replicate equity indices, at least 90% in equity investments that comprise or replicate the index. the performance of these funds is shown in Figure 12. Figure 11. Ranking of funds in the medium-term national fixed-income fund category. Source: own elaboration. Figure 12. Ranking of funds in the Beta stock exchange Funds category. Source: own elaboration. cOgent BUsiness & ManageMent 21 this category includes a single fund, corresponding to FiD 7, which has a score that places it in the bottom 25 of the ranking in most scenarios and thus makes it a medium-low option to select. the fund shows a slight rise in scenario 17 (where 50% of the decision rests on the operation dimension). Macro category: Other Funds Category: credit asset Fund Funds whose investment policy is the acquisition of securities with credit content not registered in the RnVe through the discount modality, whose underlying is represented by invoices, promissory notes, promissory notes, among others, investing in this type of securities a minimum of 60% of the market value of its portfolio. the performance of these funds is shown in Figure 13. this category includes a single fund, corresponding to FiD 14, which has a score that places it in the bottom of the ranking in most scenarios and thus makes it a low option to select. Category: Other Funds those ciFs whose investments do not fit any of the above definitions. this category shall not be subject to awards or comparisons. this category shall not be subject to awards to unitholders. the performance of these funds is shown in Figure 14. Figure 13. Ranking of funds in the Credit asset Fund category. Source: own elaboration. Figure 14. Ranking of funds in the other funds category. Source: own elaboration. 22 e. F. agUiRRe gOnZÁleZ etal. this category includes one (1) of the funds that belong to the FiD 1 trust and have scored in any scenario that places them in the top 3 places of the classification and, therefore, make them very viable options to be selected. two of the FiD 10 funds are generally located in the top ten of the general classification in anything scenario. By the other way, the fund that belongs to FiD 2 is the worst classified in this category. Other funds constantly occupy places in the middle of the table upward, regardless of the setting, and no sudden changes or jumps are observed in the classification of this category. 4.9. Stage 6: Scenario analysis and robustness each of the seventeen scenarios generates an ordering, that is, an interpretation of the behavior of each fund. Figure 15 summarizes the positions of each fund in each scenario and allows us to observe the performance or position of each fund. there is no significant variation in the first 10 positions between the zero (0) scenario of equal weights for all criteria and the rest of the scenarios. For scenarios one (1) to five (5), where the financial dimension carries the highest percentage, the stability of the ranking is evident. in scenarios six (6), eight (8) and ten (10), some funds of FiD 4 and FiD 5 that belong to the national fixed-income fund for public entities fall from the top ten positions of the other scenarios; given that, in these scenarios, the values of the criteria of the operation dimension are given greater weights, but these alternatives (funds) have low numbers of investors and short average investment terms, they are almost sight funds. in scenario eleven (11), where each criterion is given a weight greater than 40%, the ranking is not affected by major alterations in the top twenty (20) places, even in the last twenty (20) and thirty (30) places. however, it is subtly appreciated that in scenario 12, in which the risk criterion has the greatest weight, there are considerable variations in FiD 3 and FiD 16 funds, which decrease significantly and belong to the national fixed-income fund for public entities category. to strengthen the applicability and robustness of our conclusions, we have compared the data from 2022 and 2023. tables 9 and 10 in the annexes display the initial values to be evaluated using the McDa methodologies for 2022 and 2023, respectively. Figures 16 and 17 depict the ratings for 2022 and 2023, respectively. in this map, the scale is preserved in which we can identify that the funds in blue colors are ranked first, i.e. the best rated and with a high probability of being selected to form the portfolio. at the end of 2022, the funds belonging to sF FiD 17 and FiD 18 have, once again, a score that places them in the Figure 15. absolute ranking of funds for all categories and all scenarios. Source: own elaboration. cOgent BUsiness & ManageMent 23 Table 9. initial decision matrix for 2022. Dimension Financial operation Management Criteria (C1) (C2) (C3) (C4) (C5) (C6) (C7) indicador expected Value Returns standard Deviation Returns Balance at Closing Date amount at Closing Date term (Days) Credit Risk experience Manager Vector (+) (–) (+) (+) (+) (+) (+) units % % number number number normalized scale number a1 4.28% 0.47% 5,129,036.09 280,653 188.14 1 12 a2 4.48% 0.47% 18,311.92 1,002 188.14 1 12 a5 7.64% 0.29% 2,644,956.36 1,127 171 0.875 22 a6 11.98% 0.56% 1,757,045.45 2,894 291 1 9 a7 10.46% 0.74% 1,134,476.98 220 245 1 26 a8 11.70% 0.33% 2,962,778.57 90 90 1 5 a9 11.98% 0.56% 138,426.53 228 291 1 9 a10 11.81% 0.33% 95,698.65 370 90 1 5 a11 10.30% 0.33% 2,867,079.91 11,085 90 1 5 a12 5.26% 0.44% 32,256.13 11,142 240 1 17 a13 0.00% 0.00% 5,790.01 2 240 1 17 a14 5.74% 0.44% 1,189,846.43 411 240 1 17 a15 6.05% 0.44% 40,530.05 14 240 1 17 a16 5.75% 0.44% 29,222.17 10,094 240 1 17 a17 6.47% 0.44% 34,740.04 12 240 1 17 a18 6.78% 0.44% 2,895.00 1 240 1 17 a19 7.04% 0.28% 1,064,860.40 2 124 1 9 a20 5.47% 0.57% 5,334.97 29 170 1 9 a22 7.35% 0.28% 1,064,860.40 23 124 1 9 a23 5.17% 0.35% 278,087.19 4,368 136.66 1 12 a24 4.28% 0.34% 38,245.31 44 96.946 1 12 a25 5.20% 0.34% 90,398.01 104 96.946 1 12 a26 5.69% 0.35% 278,087.19 8 136.66 1 12 a27 6.69% 0.34% 11,676,558.70 662,566 194 1 13 a28 6.33% 0.34% 3,153,691.12 5 173.44 1 25 a29 7.12% 0.35% 3,507,963.37 178 204 1 25 a30 −0.05% 1.46% 1,333,527.20 1,720 211 1 11 a32 6.86% 0.34% 810,559.92 119 173.44 1 25 a33 6.55% 0.34% 2,343,131.20 344 173.44 1 25 a34 5.85% 0.35% 3,507,963.37 4 204 1 25 a35 5.69% 0.31% 323,500.96 76,307 147.91 1 15 a36 5.71% 0.33% 373,073.05 88 147.91 1 15 a37 12.73% 0.53% 636,247.75 286 212.11 1 27 a38 7.08% 0.29% 1,097,829.35 127 202.592 1 12 a39 6.93% 0.34% 436,998.51 83 151.876 1 12 a40 6.17% 0.34% 21,060.17 4 151.876 1 12 a41 6.17% 0.34% 1,237,284.93 235 151.876 1 12 a42 7.05% 0.35% 3,047,210.00 76,944 111.79 1 12 a43 7.06% 0.35% 433,612.79 10,949 111.79 1 12 a44 7.49% 0.27% 1,131,560.79 87,286 93.7 1 12 a45 6.02% 0.30% 419,656.81 10,817 160 1 19 a46 6.48% 0.41% 46,847.01 2 140.49 1 19 a47 6.75% 0.30% 419,656.81 147 160 1 19 a48 6.48% 0.41% 46,847.01 2 140.49 1 19 a49 5.48% 0.86% 21,422.03 160 183.18 1 19 a50 6.48% 0.41% 46,847.01 2 140.49 1 19 a51 6.48% 0.41% 46,847.01 2 140.49 1 19 a52 6.02% 0.30% 419,656.81 125 160 1 19 a53 6.48% 0.41% 46,847.01 2 140.49 1 19 a54 5.95% 0.31% 9,627.09 6 139.4 1 29 a55 5.73% 0.31% 141,197.38 88 139.4 1 29 a56 1.83% 1.37% 17,790.72 75 516.89 1 29 a57 6.29% 0.32% 142,801.90 89 139.4 1 29 a58 8.91% 0.21% 89,369.26 486 1352.11 0.833 25 a59 5.72% 0.31% 49,558.29 448 78.8 1 25 a60 5.20% 0.31% 37,389.96 338 78.8 1 25 a61 8.57% 0.12% 93,685.11 553 1413.74 1 25 a62 14.66% 0.61% 1,540,170.73 1,323 277 1 21 a63 13.24% 0.63% 1,655,409.00 148 218 1 26 a64 14.66% 0.61% 605,358.11 520 277 1 21 a65 14.66% 0.61% 94,296.17 81 277 1 21 a66 4.88% 0.51% 971,660.53 94 152.03 1 18 a67 6.94% 0.31% 2,905,228.01 387 77.18 1 18 a68 6.88% 0.28% 14,389.50 240 95.61 1 15 a69 6.60% 0.28% 719,475.09 120 95.61 1 15 (Continued) 30 e. 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