EdgarAndTrump.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Is Trump any good ? - The Evolution of Company Profits and R&D\nDonald Trump is the best, smartest, most beautiful, richest, youngest ... President that was ever elected in the USA. \n\nHe is also the best that could happen to the profites of the companies which are located in the US. \n\nBut is this actually true ?\n\nWe use the values from the Edgar database to analyse the profits of the filing US companies over time.\n\n### Setup\nWe load the neccessary libraries with the help of Maven"},{"metadata":{"trusted":true},"cell_type":"code","source":"%classpath config resolver maven-public http://software.pschatzmann.ch/repository/maven-public/\n%%classpath add mvn \nch.pschatzmann:smart-edgar:1.0.3-SNAPSHOT\nch.pschatzmann:jupyter-jdk-extensions:1.0.0","execution_count":28,"outputs":[{"output_type":"display_data","data":{"method":"display_data","application/vnd.jupyter.widget-view+json":{"version_minor":0,"model_id":"","version_major":2}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"And we import the packages that we want to use"},{"metadata":{"trusted":true},"cell_type":"code","source":"import ch.pschatzmann.common.table._\nimport ch.pschatzmann.edgar.reporting._\nimport ch.pschatzmann.edgar.utils._\nimport ch.pschatzmann.display._\nimport scala.collection.JavaConverters._\nimport java.util.Arrays\n","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"import ch.pschatzmann.common.table._\nimport ch.pschatzmann.edgar.reporting._\nimport ch.pschatzmann.edgar.utils._\nimport ch.pschatzmann.display._\nimport scala.collection.JavaConverters._\nimport java.util.Arrays\n<console>:6: error: Class ch.pschatzmann.stocks.errors.UniverseException not found - continuing with a stub.\n  lazy val $print: String =  {\n           ^\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"To make sure that the Edgar Table is displayed as table in BaekerX we use a custom Displayer..."},{"metadata":{"trusted":true},"cell_type":"code","source":"Displayers.setup()","execution_count":30,"outputs":[{"output_type":"execute_result","execution_count":30,"data":{"text/plain":"true"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Getting the Data from Smart-Edgar with a Query \n\nIn my first posts, I was using webservices to query the Smart EDGAR database. In this example - since we use Scala - we can directly use the underlying Java query classes: \n- The EdgarModel implements the database model and \n- the Table class is used to define the table columns and rows.\n\nWe also only use 10K filings and for the parameter name we select \"NetIncomeLoss\". We restrict the selection on USD values and we take the values for the period from 2005 up to 2018. \n\nWe exclude 2019 because we do not have all the filings yet."},{"metadata":{"trusted":true},"cell_type":"code","source":"var model = new EdgarModel()\nmodel.create()\n\n// set filter values\nmodel.getNavigationField(\"values\", \"parametername\").setFilterValues(Arrays.asList(\"NetIncomeLoss\"))\nmodel.getNavigationField(\"values\", \"numberofmonths\").setFilterValues(Arrays.asList(\"12\"))\nmodel.getNavigationField(\"values\", \"form\").setFilterValues(Arrays.asList(\"10-K\",\"10-K/A\"))\nmodel.getNavigationField(\"values\", \"unitref\").setFilterValues(Arrays.asList(\"USD\"))\nmodel.getNavigationField(\"values\", \"segment\").setFilterValues(Arrays.asList(\"\"))\nmodel.getNavigationField(\"values\", \"segmentdimension\").setFilterValues(Arrays.asList(\"\"))\n","execution_count":31,"outputs":[{"output_type":"execute_result","execution_count":31,"data":{"text/plain":"values.segmentDimension"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"// define the result table\nvar tableIncome = new ch.pschatzmann.edgar.reporting.Table()\ntableIncome.setValueField(model.getTable(\"values\").getValueField())\ntableIncome.addColumn(model.getNavigationField(\"states\",\"country\",\"location\").setFilterValues(Arrays.asList(\"USA\")))\ntableIncome.addRow(model.getNavigationField(\"values\",\"year\",null).setFilterRange(2005,2018))\ntableIncome.execute(model)\n\n// we double check the generated SQL query\nnew TableFormatterSQL(model).format(tableIncome)","execution_count":32,"outputs":[{"output_type":"execute_result","execution_count":32,"data":{"text/plain":"SELECT states.country, to_char(date,'yyyy') AS year, SUM(value) AS value FROM values INNER JOIN company ON (values.identifier = company.identifier )  INNER JOIN states ON (company.location = states.id )  WHERE values.parameterName IN ('NetIncomeLoss') AND values.numberOfMonths IN ('12') AND values.form IN ('10-K','10-K/A') AND values.segment IN ('') AND values.segmentDimension IN ('') AND values.unitRef IN ('USD') AND to_char(date,'yyyy') IN ('2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018') AND states.country IN ('USA') GROUP BY states.country, year"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"tableIncome","execution_count":33,"outputs":[{"output_type":"display_data","data":{"method":"display_data","application/vnd.jupyter.widget-view+json":{"version_minor":0,"model_id":"2f48f7f9-0302-45fa-b917-16fd58a60957","version_major":2}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"## Chart the Evolution of Profits \n\nNow we are raeady to plot the result. I have marked the election year of "},{"metadata":{"trusted":true},"cell_type":"code","source":"val plot = new Plot\nplot.add(new Line { x = tableIncome.getValues(\"year\").asScala.toSeq; y = tableIncome.getValues(\"USA\").asScala.toSeq })\nplot.add(new ConstantLine {\n    x = 2009  // Obama\n    y = 0\n    style = StrokeType.DOT\n    color = Color.blue\n})\nplot.add(new ConstantLine {\n    x = 2017 // Trump\n    y = 0\n    style = StrokeType.DOT\n    color = Color.red\n})","execution_count":34,"outputs":[{"output_type":"display_data","data":{"method":"display_data","application/vnd.jupyter.widget-view+json":{"version_minor":0,"model_id":"b850dbf2-751e-41aa-a392-7447a75f8fd8","version_major":2}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Well, the result is not very conclusive. One might argue that Trump is just riding the upturn that was created by Obama. But there is no denying that the profits were rising to record levels.\n\nSo let's have a quick look into the Revenues and Reasearch & Development. A high R&D should lead to high future profits:\n\n## Revenues"},{"metadata":{"trusted":true},"cell_type":"code","source":"// define the result table\nmodel.getNavigationField(\"values\", \"parametername\").setFilterValues(Arrays.asList(\"Revenues\"))\n\nvar tableRevenue = new ch.pschatzmann.edgar.reporting.Table()\ntableRevenue.setValueField(model.getTable(\"values\").getValueField())\ntableRevenue.addColumn(model.getNavigationField(\"states\",\"country\",\"location\").setFilterValues(Arrays.asList(\"USA\")))\ntableRevenue.addRow(model.getNavigationField(\"values\",\"year\",null).setFilterRange(2005,2018))\ntableRevenue.execute(model)\n\nval plot = new Plot\nplot.add(new Line { x = tableRevenue.getValues(\"year\").asScala.toSeq; y = tableRevenue.getValues(\"USA\").asScala.toSeq })\nplot.add(new ConstantLine {\n    x = 2009\n    y = 0\n    style = StrokeType.DOT\n    color = Color.blue\n})\nplot.add(new ConstantLine {\n    x = 2017\n    y = 0\n    style = StrokeType.DOT\n    color = Color.red\n})","execution_count":35,"outputs":[{"output_type":"display_data","data":{"method":"display_data","application/vnd.jupyter.widget-view+json":{"version_minor":0,"model_id":"bcac3dd1-94c2-447e-80ee-3365cf0189d4","version_major":2}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"We can see that the Revenues of the Companies are actually declining with Trump. So the higher profits are not created because the companies were sellimg more! \n\n## Reaserch & Development"},{"metadata":{"trusted":true},"cell_type":"code","source":"model.getNavigationField(\"values\", \"parametername\").setFilterValues(Arrays.asList(\"ResearchAndDevelopmentExpense\"))\n\n// define the result table\nvar tableRD = new ch.pschatzmann.edgar.reporting.Table()\ntableRD.setValueField(model.getTable(\"values\").getValueField())\ntableRD.addColumn(model.getNavigationField(\"states\",\"country\",\"location\").setFilterValues(Arrays.asList(\"USA\")))\ntableRD.addRow(model.getNavigationField(\"values\",\"year\",null).setFilterRange(2005,2018))\ntableRD.execute(model)\n\nval plot = new Plot\nplot.add(new Line { x = tableRD.getValues(\"year\").asScala.toSeq; y = tableRD.getValues(\"USA\").asScala.toSeq })\nplot.add(new ConstantLine {\n    x = 2009\n    y = 0\n    style = StrokeType.DOT\n    color = Color.blue\n})\nplot.add(new ConstantLine {\n    x = 2017\n    y = 0\n    style = StrokeType.DOT\n    color = Color.red\n})","execution_count":36,"outputs":[{"output_type":"display_data","data":{"method":"display_data","application/vnd.jupyter.widget-view+json":{"version_minor":0,"model_id":"0b3e3af7-615c-4243-9821-c1eedde41541","version_major":2}},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"This finally looks very promising: Companies are spending more on R&D then ever!"},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"scala","display_name":"Scala","language":"scala"},"language_info":{"nbconverter_exporter":"","codemirror_mode":"text/x-scala","name":"Scala","mimetype":"","file_extension":".scala","version":"2.11.12"},"toc":{"nav_menu":{},"number_sections":false,"sideBar":false,"skip_h1_title":false,"base_numbering":1,"title_cell":"Table of 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