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zenodo40/100

Strategic selection of cryptocurrency investment portfolios based on discrete multi-criteria methods.

In recent years, cryptocurrencies have gone from an unknown plane to a protagonist one, with strategic investment in these assets becoming increasingly popular for organizations seeking to diversify their portfolios. However, cryptocurrencies carry a high risk due to their high volatility.  Based on the multi-criteria decision making theory, a decision support system was developed for the design of business investment portfolios in cryptocurrencies, starting from the definition of criteria based on historical data that characterize returns and risks in different ways, all this for short time windows of 7 and 15 days. Then, the importance of criteria was analyzed by various methods and their impact was evaluated assuming changes in stock prices. Finally, multi-criteria methods were applied for the design of cryptocurrency investment portfolios in the above-mentioned time windows. The developed system is applicable to companies of any size, since the studied methods provide bigger rationality and objectivity to the strategic decision-making process. The resulting method can be extended in the future to incorporate criteria that take advantage of information on the market capitalization of cryptocurrencies, as well as capturing investor sentiment or the expected shortfall of the investment.

opencc-by-4.0Jun 2022View details →

zenodo40/100

Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition

<ul> <li>Name: Microdata used to construct the Causal Diagrams to model investment decisions related to the energy transition</li> <li>Summary: This dataset contains answers from a panel of experts to build a) a taxonomy of determinants that explain the investment decision making on assets related to the energy transition, b) the individual contributions when sorting the taxonomy of determinantes on the different stages of the transtheoretical model for different archetypes of persons and c) the causal diagrams agreed between the different groups of experts.</li> <li>License: cc-BY-SA</li> <li>Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li>Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li>Collection Date: 22/07/2022</li> <li>Publication Date: 01/06/2024</li> <li>DOI: 10.5281/zenodo.11234441</li> <li>Other repositories:</li> <li>Author: University of Deusto</li> <li>Objective of collection: This data was originally collected to build a set of causal diagrams of the .</li> <li>Description: <ul> <li>Scenarios: This dataset contains the description of 20 different scenarios used in this research activity. </li> <li>File 1 - individual reasons to be coded This dataset compiles the reasons given by experts of different panels of the Intrinsic and Extrinsic Determinants, and the Barriers and potential Rebound effects of citizens towards a set of 20 different scenarios. The file contains the following sheets: <ul> <li>Methodology: Methodology followed by the coders.</li> <li>Help: Short summary of the Social Cognitive Theor and Self Determination Theory used for coding. </li> <li>Glossary: Glossary of terms build by the experts coding the answers. </li> <li>Appliances/Flexibility/Buildings/Mobility: The contributions of each expert, the code provided by the two researchers and the consensus achived. </li> <li>Summary: Assesment of the results.</li> </ul> </li> <li>File 2 - individual microdata to sort determinants into causal threads from expertsThis dataset includes the individual sortings made by the experts of the taxonomy of determinantes into each one of the stages of the transtheoretical model. The file includes one sheet per expert where he/she has sort each determinant for each arquetype into the stage he/she thinks is more relevant to advance to the next step of the TTM. </li> <li>File 3 - collective microdata to sort determinants into causal threads from EU and LATAM experts This dataset compiles the results, stage by stage, of the consensus reached by each panel regarding the determining factors that make up each of the archetypes in the contexts of Europe (EU) and Latin America (LATAM). And in which stage of the change of the Transtheoretical Model (TTM) the factors should appears. <ul> <li> Stage 1: The panels reached a consensus on the factors that describe each of the archetypes in their context. In the case of Latin America, for the panels of some countries, the existence of all eight archetypes was not evident. The number of archetypes analysed by each panel is indicated in parentheses in the following list: <ul> <li> European panels: Group – F (8), Group–A (8). Group–FF (8), Group–M (4) </li> <li> Latin America panels: Group-MX (5), Group-CO (8), Group-CL (7), Group-SV (7) </li> </ul> </li> </ul> <ul> <li> Stage 2: For each of the eight archetypes, the results of the consensus for each panel are consolidated in the tabs indicated in the list below. The column on the far right shows the weights (percentage) of each factor in each stage of the TTM: Archetype-EarlyAdopter, Archetype-Uninterested, Archetype-HomoEconomicus, Archetype-Fearful, Archetype-Stubborn, Archetype-Influencer, Archetype-Careful and Archetype-Activist. </li> <li> Stage3: In the "Archetypes - Consensus Results" tab, the weights of the factors for each archetype are consolidated. The far-right column calculates the average weight of each factor at each stage of the TTM (Transtheoretical Model of Change). </li> <li> Stage 4. In the “EU vs Latam - split context” sheet, it is presented a comparative assessment between the European and Latin American results. The comparison has four tables: <ul> <li> Table (s): Difference and Agreements between both context: European & Latin American Archetypes.  The table highlights the regions of determinants that mark the differences between both contexts for each archetype. If a determinant is identified by both contexts (EU, Latam), it is considered an agreement and allocated to the early TTM stage. The remaining determinants highlight the differences between the two contexts. European (-1) & Latin American (1) Archetypes FINAL Consensus (0) on TTM Stages. </li> <li> Table (t): This table shows the difference (E, L) and agreements (X) between both context: European (E) & Latin American (L) Archetypes. </li> <li> Table (t.1): This table shows just the agreements (X) between both context: European & Latin American Archetypes. </li> <li> Table (t.2): Show the difference between both context: European (E) & Latin American Archetypes (L). </li> <li> Table (t.3): This table shows the differences (E, L) and agreements (X) between both contexts: European (E) & Latin American (L) archetypes. In this table, the main regions of factors for each archetype are coloured to highlight the set of factors that make the main differences. </li> </ul> </li> </ul> </li> </ul> </li> <li>5 star: ⭐⭐⭐</li> <li>Preprocessing steps: Data transcription from written documents and oral discussions.</li> <li>Reuse: NA</li> <li>Update policy: No more updates are planned.</li> <li>Ethics and legal aspects: Names of the persons involved have been removed. </li> <li>Technical aspects: </li> <li>Other:</li> </ul>

opencc-by-4.0May 2024View details →

zenodo40/100

Dataset: Globalink Investment Inc. (GLLIW) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.