# Decision Support Using Machine Learning Indication for Financial Investment
## 6 Conclusions  
This paper was dedicated to implementing Machine Learning algorithms with the ability to act as financial investment advisors for new investors. The proposed objectives were achieved, namely: identify market and investor profile data that are needed to generate reliable investment suggestions; perform preprocessing and database assembly; define algorithms that will be used. implement Machine Learning algorithms; perform validation and performance tests. The Fundamentalist Analysis was used as a basis for evaluating and generating investment indications, using the Fundamentalist Indicators of each stock as variables in the Database. The Fundamentalist Indicators used in the Database were obtained from the Oceans14 website [62]. However, the Fundamental Indicators alone were not enough for the implementation. It was necessary to label each registered stock as good (Class \"1\") or bad (Class \"0\") to invest in. Thus, it was necessary to compare the variation of each share's price with the variation of the Selic Rate in the same period. If the stock variation was greater than the Selic Rate variation, the stock in question was labeled as Class \"1\"; otherwise, it was labeled as Class \"0\". To perform the calculations, it was to use the Yahoo Finance quotation history and the Brazilian Central Bank's Basic Interest Rate history. In addition, preparation was carried out in the database, removing unnecessary variables for the implementation of the work and stocks from companies in the financial sector. The Machine Learning techniques used were: Multilayer Perceptron, Decision Tree, and Logistic Regression. The implementation was completed in Python language, using Google Colaboratory and the Machine Learning library Sikit-learn. The settings for each algorithm were defined using the GridSearchCV function to find the best settings for the implementation of this work. With the parameter settings of each Artificial Intelligence technique defined, the algorithms were implemented. At first, only the database with records from 2014 to 2019 would be used, but the results obtained with records from only this period were not satisfactory, because the classification metrics for Classes \"0\" were very low. For this reason, new tests were subsequently performed with the Database containing records from 1998 to 2019, which in turn obtained better accuracy, precision, recall, and F1-score. The results obtained with each algorithm were compared using the metrics: accuracy, precision, recall, and F1-score. The algorithm that obtained the best classification metrics was Decision Tree. The accuracy obtained with Decision Tree was 0.77. The metrics for Class \"0\" were accuracy of 0.79, recall of 0.73, and F1-score of 0.76. Metrics for Class \"1\" were accuracy of 0.75, recall of 0.80, and F1-score of 0.78. Regarding the Decision Tree accuracy, the results lead us to consider that the value achieved was not better due to the market behavior itself, which has numerous variables that exceed what is \"predicted\" by the indicators of Fundamental Analysis, for example: economic crises, wars, natural disasters, etc. As the work was based on Fundamental Analysis, the forecast made by the models is limited to the information provided by the indicators (used as inputs to the models). Future WorksFor the future development of the research in the area of the present work, some points can be developed, such as using a database with more records, such as the one provided by Economica, which is paid, but very complete. This would help in the training of algorithms and consequently in the results achieved by them. It could also be beneficial to use more or different Fundamental Indicators. In this work we have used some of the main indicators, but there are many others that if well chosen, in conjunction with other indicators can assist in the analysis of stock valuation by the algorithms.