osiris_financial_statements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Osiris Therapeutics - A trip to the \"underworld\" and back"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On November 2, 2017, the U.S. Securities and Exchange Commission filed [charges](https://www.sec.gov/news/press-release/2017-207) against Osiris Therapeutics for fraudulent financial statements. Osiris Therapeutics settled the case by [paying a penalty of USD 1.5 million](https://www.sec.gov/news/press-release/2017-207). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The SEC alleges that Osiris Therapeutics routinely **overstated company performance** and issued fraudulent financial statements for a period of nearly two years.  According to the SEC’s complaint, the company **improperly recognized revenue using artificially inflated prices**, backdated documents to **recognize revenue in earlier periods**, and **prematurely recognized revenue upon delivery of products to be held on consignment**.  Osiris Therapeutics and its executives also allegedly used pricing data that they knew was false and attempted to book revenue on a fictitious transaction, among other accounting improprieties. (Source: [SEC Press Release](https://www.sec.gov/news/press-release/2017-207), highlights added)\n",
    "\n",
    "The full SEC complaint can be downloaded here: [Case 1:17-cv-03230-CCB](https://www.sec.gov/litigation/complaints/2017/comp-pr2017-207.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of today, the top executives mentioned in the SEC complaint, [Lode B. Debrabandere](https://www.law360.com/articles/1020367/ex-biotech-ceo-blasts-sec-s-guilt-by-association-suit) (then CEO, still a CEO for other biotech companies according to his [LinkedIn profile](https://www.linkedin.com/in/lode-debrabandere-5911aa16b)), Gregory I. Law (then CFO), and Bobby Dwayne Montgomery (Chief Business Officer) are still facing litigation. Only Philip R. Jacoby (then CFO) was [sentenced](https://www.sec.gov/litigation/admin/2018/34-84010.pdf) to two years of supervised release and a penalty of USD 10,000.\n",
    "\n",
    "[Osiris Therapeutics, Inc.](http://www.osiris.com) is still in business and on August 1, 2018, re-listed on the NASDAQ. The corporate website states:\n",
    "\n",
    ">Osiris Therapeutics, Inc., researches, develops, manufactures and commercializes regenerative medicine products intended to improve the health and lives of patients and lower overall healthcare costs. We have achieved commercial success with products in orthopedics, sports medicine and wound care, including the Grafix product line, Stravix, BIO4®, and Cartiform. We continue to advance our research and development (“R&D”) by focusing on innovation in regenerative medicine, including the development of bioengineered stem cell and tissue-based products. (Source: [Osiris](http://www.osiris.com/about/))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fraud at Osiris began to come to light during the third quarter of 2015 when\n",
    "Osiris’ Auditor requested additional information regarding Osiris’ recognition of revenue. On November 16, 2015, Osiris filed its [Form 10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000155837015002707/osir-20150930x10q.htm#Item4Controls_821072) for the third quarter of 2015 and, within that\n",
    "filing, disclosed a material weakness in internal controls over financial reporting and restated\n",
    "certain transactions that are detailed below:\n",
    "\n",
    "> Under the supervision and with the participation of our management, including our Chief Executive Officer and Chief Financial Officer, we evaluated the effectiveness of the design and operation of our disclosure controls and procedures as of the end of the period covered by this Quarterly Report on Form 10-Q. Based on this evaluation, our Chief Executive Officer and Chief Financial Officer concluded that our disclosure controls and procedures were not effective as of the end of the period covered by this Quarterly Report on Form 10-Q solely due to the material weakness in internal control over financial reporting described below.\n",
    "\n",
    "\n",
    "\n",
    "In a [Form 8-K filed on November 20, 2015](https://www.sec.gov/Archives/edgar/data/1360886/000110465915080467/a15-23865_18k.htm), Osiris\n",
    "disclosed that it would be restating its financial statements for the first and second quarters of\n",
    "2015 due to material errors, \n",
    "\n",
    "> Management identified the following control deficiency that constituted a material weakness in our internal control over financing reporting that existed as of December 31, 2014, March 31, 2015, and June 30, 2015: The Company did not design and maintain controls to ensure that (1) adequate written documentation existed for contracts with distributors in order to ensure that persuasive evidence of the contractual arrangement existed and the terms were fixed and determinable with respect to revenue recognition and (2) adequate analysis and documentation existed for new distributor contracts to ensure timely and accurate recording of revenue in accordance with GAAP.\n",
    "\n",
    "and in a [Form 8-K filed on December 17, 2015](https://www.sec.gov/Archives/edgar/data/1360886/000110465915085545/a15-25185_18k.htm), Osiris disclosed that\n",
    "its Auditor intended to resign. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a Form 8-K filed on March 15, 2016, Osiris stated that its financial statements\n",
    "issued for 2014 and the first three quarters of 2015 should not be relied upon and that the company intended to restate its financial statements for all of 2014 and the first three quarters of 2015. Osiris’ restated Form 10-K/A for 2014 was filed on March 27, 2017. In that filing, Osiris\n",
    "disclosed material weaknesses in internal controls and corrected approximately $10.3 million in\n",
    "revenue overstatements for 2014. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case study, we analyze:\n",
    "* The [court filing by the SEC](https://www.sec.gov/litigation/complaints/2017/comp-pr2017-207.pdf) and\n",
    "* The financial statements of Osiris, which are part of this [dataset](https://www.sec.gov/dera/data/financial-statement-and-notes-data-set.html).\n",
    "\n",
    "Our goal is to replicate the fraudulent activities using the data from the financial statements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the court filings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [SEC court filings](https://www.sec.gov/litigation/complaints/2017/comp-pr2017-207.pdf) are our starting point because they explain what happened when and how the fraud was structured and carried out. The court filing summarizes the fraud as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> During **all four quarters of 2014** and the **first\n",
    "three quarters of 2015**, Osiris and its former senior officers engaged in a wide-ranging fraud to artificially inflate the company’s reported revenue. In addition to directing the misstatement of Osiris’ financial results, the company’s former senior officers engaged in numerous other fraudulent and deceptive acts, including **entering into undisclosed side agreements with\n",
    "distributors**, **recognizing revenue in direct contradiction to their disclosed accounting policies**,\n",
    "lying to Osiris’ independent registered public accounting firm (“Auditor”), **using false pricing data**, and **backdating and falsifying documents**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The court filings list the elements of the fraud:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">* **prematurely recognized revenue** in periods before sales had been made and critical agreement terms were finalized;\n",
    ">* **recognized revenue using higher, inaccurate prices**, while disregarding data explicitly providing lower, actual revenue numbers; and \n",
    ">* **recognized revenue on consignment inventory**, directly contradicting Osiris’ disclosed accounting policies. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Generally Accepted Accounting Principles (GAAP) state that revenue can be recognized if ([Case 1:17-cv-03230-CCB](https://www.sec.gov/litigation/complaints/2017/comp-pr2017-207.pdf), p. 9):\n",
    "* title and risk of loss passes to the customer\n",
    "* persuasive evidence of an arrangement exists\n",
    "* sales amounts are fixed or determinable\n",
    "* collectability is reasonably assured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More specifically, the SEC mentions examples of fraudulent activities about transactions with four distributors A, B, C, and D."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Fraud|Transaction|Explanation|Source|\n",
    "|-----|-----------|-----------|------|\n",
    "|Violation of reporting standards | Sales revenue from Distributor D | In 2014 and 2015, Distributor D had a 30-day payment goal. However, Osiris tolerated payments that came in substantially later. D only paid when Osiris products were sold to end-customers. Still, revenue from D was recognized. Hence, D had accounts receivable of more than USD 7 million (2 million were more than 120 days due.). During this time, USD 14 million in sale revenue were prematurely recognized. Osiris also misled the auditors.|Summary Point 10|\n",
    "|Prematurely recognized revenue|Sales revenue of USD 1.1 million from Distributor A| Negotiation about converting consignment stock into sales for Q4 2014 between December 23, 2014 and January 13, 2015; Distributor A agreed to buy inventory for USD 1.7 million; Osiris recognized 1.1 million to meet Q4 sales revenue targets. The remaining consignment stock was recognized in Q1 2015 despite a year-long payment plan with sell-through requirements. During Audit, Osiris misled its external accountants. |Summary Point 7|\n",
    "|Recognized fictitious revenue|Sales activities with Distributor B| B made only partial payments in Q1 2015, had no regulatory approval to import Osiris products into home country, did not request further products but Osiris still reported almost USD 2 million in revenue for Q1 to Q3 2015 (including revenue on products not requested by distributor B). However, the auditors made clear that these transactions had to be reversed in Q4 2015.|Summary Point 8|\n",
    "|Violation of reporting standards | Sales revenue from Distributor C | Osiris' financial statements state that revenue on consignment stock will be recognized when it was used in surgery. However, the CEO emailed that Osiris \"cannot wait until we receive PO's to book these as sale\", despite concerns of the assistant controller. Osiris also actively misled the auditors. |Summary Point 9|\n",
    "|Inaccurate valuation of revenue & delaying reconciliation| Sales revenue from Distributor C | From Q1 2015 to Q3 2015, Osiris valued revenue at \"list price\" although the monthly reporting by Distributor C showed that the actual revenue is lower than the list price. Although Osiris states in its financial statement that it would reconcile differences, they never did.| Summary Point 9|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SEC states that pressure to increase revenue was the primary driver for the fraud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The fraudulent scheme, misstatements, and omissions were driven by Osiris’ culture [...] During at least 2014 and the first three quarters of 2015, Osiris was focused on recognizing gross revenue, which Osiris executives and employees often referred to as “top line” revenue. In particular, Osiris was focused on **demonstrating consistent revenue growth each quarter**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SEC argues that for 2014, Osiris' actual revenue was about 17 percent lower than reported and for the three quarters of 2015, it was about 9 percent lower than reported. The fraud, however, helped Osiris to paint a successful picture for investment analysts (Facts, Point 31). Not surprisingly, during this fraudulent period, the company announced that it would [issue 750,000 additional shares as part of the employee compensation program](https://www.sec.gov/Archives/edgar/data/1360886/000110465914069872/0001104659-14-069872-index.htm). For instance, one executive sold company stock in May 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the financial statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-20T18:28:35.627732Z",
     "start_time": "2018-04-20T18:28:35.612855Z"
    }
   },
   "source": [
    "Some initialization to make life easier. **Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:13:11.367538Z",
     "start_time": "2018-05-25T20:13:11.353589Z"
    }
   },
   "outputs": [],
   "source": [
    "#Allow multiple outputs for each cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "#Show simple plots in the notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following libraries:\n",
    "* [Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics.\n",
    "* [Altair](https://altair-viz.github.io) is a visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:13:12.627191Z",
     "start_time": "2018-05-25T20:13:12.617304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('notebook')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from altair import *\n",
    "import numpy as np\n",
    "#Show up to 500 rows\n",
    "pd.set_option('display.max_rows', 500)\n",
    "#Format number without four decimals\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "#Show altair plots in the notebook\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T16:32:55.639860Z",
     "start_time": "2018-05-25T16:32:55.631986Z"
    }
   },
   "source": [
    "### Osiris' quarterly and yearly financial statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SEC provides all filings as a [dataset](https://www.sec.gov/dera/data/financial-statement-and-notes-data-set.html). Because this dataset is quite large (from 8GB up to ~90GB), you will find an extract of Osiris' documents here.\n",
    "\n",
    "The data consists of several related files:\n",
    "\n",
    "* The `SUB` file contains the individual filings that have been **sub**mitted to the SEC.\n",
    "* The `NUM` file contains the **num**eric information and \"includes one row for each distinct amount from each submission\n",
    "included in the SUB data set.\" (Source: [SEC](https://www.sec.gov/files/aqfsn_1.pdf))\n",
    "* The `PRE` file \"provides information about how the tags and numbers were\n",
    "**pre**sented in the primary financial statements.\" (Source: [SEC](https://www.sec.gov/files/aqfsn_1.pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin our analysis with the `SUB` file. The following columns are important for our analysis of the `SUB` file:\n",
    "\n",
    "| Column | Description (Source: [SEC](https://www.sec.gov/files/aqfsn_1.pdf))|\n",
    "| -------- |:-------------:|\n",
    "|adsh|  A unique identifier assigned automatically to an accepted submission by the EDGAR Filer System. The identifier has the format `CIK - Year - Count`. |\n",
    "|cik |  A unique numerical identifier assigned by the EDGAR system to filers. |\n",
    "|name| Name of filer. This corresponds to the name of the legal entity as recorded in EDGAR as of the filing date.|\n",
    "|sic| Name of registrant. This corresponds to the name of the legal entity as recorded in EDGAR as of the filing date.|\n",
    "|fye| Fiscal Year End Date with the format (MMDD).|\n",
    "|form| The submission type of the registrant's filing.|\n",
    "|period| Balance Sheet Date with the format (YYMMDD).|\n",
    "|fy| Fiscal Year Focus with the format (YYYY). |\n",
    "|pf| Fiscal Period Focus (10-Q form is for the quarters Q1,Q2, and Q3, 10-K form is for FY)|\n",
    "|fs_year| Year from folder name |\n",
    "|fs_quarter| Quarter from the folder name|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the Osiris `SUB` file. This contains all the filings made by Osiris Therapeutics, Inc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_os = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/osiris_sub.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter this filter to include only the relevant columns (see Table above.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_os = sub_os.loc[:,['adsh', 'cik', 'name', 'sic', 'fye', 'form', 'period', 'fy', 'fp', 'fs_year', 'fs_quarter']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the types of filings made by Osiris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:13:30.529164Z",
     "start_time": "2018-05-25T20:13:30.513040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10-Q', '10-K', '10-K/A'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_os['form'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A `10-Q` filing represents [quarterly reports](https://en.wikipedia.org/wiki/Form_10-Q).\n",
    "* A `10-K` filing represents the [annual report](https://en.wikipedia.org/wiki/Form_10-K)\n",
    "* A `10-K/A` filing represents an [amended annual report](https://en.wikipedia.org/wiki/SEC_filing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we filter the submissions to only include these three types. (This step is actually redundant but if the underlying dataset changes, other form types may be included in the data). Better safe than sorry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:14:14.726223Z",
     "start_time": "2018-05-25T20:14:14.717222Z"
    }
   },
   "outputs": [],
   "source": [
    "sub_os = sub_os[sub_os['form'].isin(['10-Q','10-K', '10-K/A'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus on the timeframe that is mentioned in the SEC documents. We compare the fraudulent period with the data available prior and beyond the fraudulent period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:14:41.731885Z",
     "start_time": "2018-05-25T20:14:41.721883Z"
    }
   },
   "outputs": [],
   "source": [
    "sub_os['reporting_period'] = pd.to_datetime(sub_os['period'],format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:15:25.038766Z",
     "start_time": "2018-05-25T20:15:25.020562Z"
    }
   },
   "outputs": [],
   "source": [
    "sub_os = sub_os[(sub_os['reporting_period']>='2012-03-31') &\\\n",
    "                (sub_os['reporting_period']<='2015-09-30') &\\\n",
    "                (sub_os['fs_year']!=2017)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have four statements for each of the years between 2012 and 2014. Additionally, we have three statements for the first three quarters of 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T20:15:26.385701Z",
     "start_time": "2018-05-25T20:15:26.378013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sub_os)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following table provides the relevant financial statements:\n",
    "\n",
    "| Q/Y  | **2012** | **2013** | **2014** | **2015** |\n",
    "|--------|------|------|------|-----|\n",
    "|**Q1**|[10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465912035988/a12-7712_110q.htm#StatementsOfComprehensivelossInco_111614) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465913039896/a13-7919_110q.htm#CondensedStatementsOfComprehensiv_170757) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465914037626/a14-9603_110q.htm#StatementsOfComprehensiveLoss_151930) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465915036502/a15-7834_110q.htm#CondensedStatementsOfComprehensiv_152747)|\n",
    "|**Q2**|[10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465912056210/a12-13252_110q.htm#CondensedStatementsOfOperationsAn_125530) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465913062017/a13-13627_110q.htm#CondensedStatementsOfComprehensiv_203935) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465914059084/a14-14047_110q.htm#OsirisTherapeuticsinc_StatementsO_080703) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465915057928/a15-12062_110q.htm#CONDENSEDSTATEMENTSOFCOMPREHENSI_110514) | \n",
    "|**Q3**|[10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465912076280/a12-19567_110q.htm#CondensedStatementsOfOperationsAn_144800) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465913083236/a13-19541_110q.htm#CondensedStatementsOfComprehensiv_162604) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000110465914078750/a14-19766_110q.htm#StatementsOfComprehensiveIncomelo_161017) | [10-Q](https://www.sec.gov/Archives/edgar/data/1360886/000155837015002707/osir-20150930x10q.htm#COMPREHENSIVEINCOMELOSS_506191) |\n",
    "|**Q4**|[10-K](https://www.sec.gov/Archives/edgar/data/1360886/000104746913002788/a2213414z10-k.htm#Comp) | [10-K](https://www.sec.gov/Archives/edgar/data/1360886/000104746914003199/a2219393z10-k.htm#otsci) |[10-K](https://www.sec.gov/Archives/edgar/data/1360886/000104746915002532/a2223659z10-k.htm#aa4) |\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Content of the financial statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `NUM` file \"includes one row for each distinct amount from each submission\n",
    "included in the SUB data set.\" (Source: [SEC](https://www.sec.gov/files/aqfsn_1.pdf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read the Osiris `NUM` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.148902Z",
     "start_time": "2018-05-25T19:34:51.577193Z"
    }
   },
   "outputs": [],
   "source": [
    "num_os = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/osiris_num.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following columns are important for the following analysis:\n",
    "\n",
    "| Column | Description (Source: [SEC](https://www.sec.gov/files/aqfsn_1.pdf)) |\n",
    "| -------- |:-------------:|\n",
    "|adsh| \"A unique identifier assigned automatically to an accepted submission by the EDGAR Filer System.\" The identifier has the format `CIK - Year - Count`. |\n",
    "|tag | \"The unique identifier (name) for a tag in a specific taxonomy release.\" |\n",
    "|version| \"For a standard tag, an identifier for the taxonomy; otherwise the accession number where the tag was defined.\" |\n",
    "|ddate | \"The end date for the data value, rounded to the nearest month end.\"|\n",
    "|qtrs | \"The count of the number of quarters represented by the data value, rounded to the nearest whole number. \"0\" indicates it is a point-in-time value.|\n",
    "|uom| \"The unit of measure for the value.\"|\n",
    "|value| \"The value. This is rounded to four digits to the right of the decimal point.\"|\n",
    "|footnote| \"The plain text of any superscripted footnotes on the value, if any, as shown on the statement page, truncated to 512 characters.\"|\n",
    "|fs_year| Year from folder name |\n",
    "|fs_quarter| Quarter from the folder name|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter the `NUM` file to include only numerical information on the relevant submissions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.165838Z",
     "start_time": "2018-05-25T19:34:52.156377Z"
    }
   },
   "outputs": [],
   "source": [
    "num_os = num_os[num_os['adsh'].isin(sub_os['adsh'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Osiris's financial years ends on December, 31 ([Osiris](http://investor.osiris.com/financial-information/sec-filings)). This means that:\n",
    "* Q1 - January, 1 to March, 31.\n",
    "* Q2 - April, 1 to June, 30.\n",
    "* Q3 - July, 1 to September, 30.\n",
    "* Q4 - October, 1 to December, 31."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.198114Z",
     "start_time": "2018-05-25T19:34:52.171764Z"
    }
   },
   "outputs": [],
   "source": [
    "num_os = pd.merge(num_os, sub_os[['adsh', 'period']], how='left', on='adsh')\n",
    "num_os = num_os[num_os['ddate']==num_os['period']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add the financial quarter information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.862855Z",
     "start_time": "2018-05-25T19:34:52.202217Z"
    }
   },
   "outputs": [],
   "source": [
    "num_os['fin_quarter'] = pd.PeriodIndex(num_os['ddate'], freq='Q-DEC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later, we want to use the financial quarter on the x-axis of our visualizations. However, currently, `fin_quarter` is a TimePeriod object, which cannot be used for this purpose. Thus, we have to transform it into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.908941Z",
     "start_time": "2018-05-25T19:34:52.865289Z"
    }
   },
   "outputs": [],
   "source": [
    "num_os['fin_quarter_viz'] = num_os['fin_quarter'].dt.strftime('%F-Q%q')\n",
    "num_os.drop('fin_quarter', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have all the financial statement data in one handy dataframe. We are ready to start the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As stated above, all of the fraudulent activities were targeted toward inflating the sales revenue. Thus, we begin our analysis with the quarterly sales revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quarterly Sales Revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to understand what types of revenue the company is reporting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could look at the actual financial statements or the chart of accounts used by Osiris. But, we can also simply search for the word `Revenue`. We search in the column `tag` because here we find the \"unique identifier[s]\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.928587Z",
     "start_time": "2018-05-25T19:34:52.912676Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SalesRevenueGoodsNet', 'SalesRevenueServicesNet', 'Revenues',\n",
       "       'RoyaltyRevenue', 'CostOfRevenue',\n",
       "       'IncreaseDecreaseInDeferredRevenue', 'SalesRevenueNet',\n",
       "       'ReimbursementRevenue'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_os[(num_os['tag'].str.contains('Revenue'))]['tag'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the 2013 10-K, we learn that Osiris had two operations:\n",
    "* Therapeutics\n",
    "* Biosurgery\n",
    "\n",
    "In 2013, Osiris discontinued the therapeutics operations. Previous financial statements reported on the therapeutics operations using `SalesRevenueNet` and `SalesRevenueServicesNet`. In the 2013 10-K, final reports were made on the therapeutics business using `RoyaltyRevenue` and `ReimbursementRevenue`. \n",
    "\n",
    "Thus, we focus on the tags used to describe the biosurgery business:\n",
    "* `SalesRevenueGoodsNet`\n",
    "* `Revenues`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.952128Z",
     "start_time": "2018-05-25T19:34:52.934106Z"
    }
   },
   "outputs": [],
   "source": [
    "revenue_os = num_os[(num_os['tag'].str.contains('Revenues')) | \\\n",
    "                    (num_os['tag'].str.contains('SalesRevenueGoodsNet'))\n",
    "                   ].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to understand that elements in the financial statement represent a value at a given point in time. This is represented by the field `qtrs`. A `0` means that it is a value for a given point in time. Any value larger than `0` refers to the number of quarters. For instance, the sales revenue refers to a quarter. Thus, we have to filter for the quarterly value (`qtrs` = `1`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:52.971701Z",
     "start_time": "2018-05-25T19:34:52.960454Z"
    }
   },
   "outputs": [],
   "source": [
    "quarterly = revenue_os[revenue_os['qtrs']==1].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T18:33:14.157117Z",
     "start_time": "2018-05-25T18:33:14.143217Z"
    }
   },
   "source": [
    "In 2012, the `Revenue` contains additional USD 179,000.00 from the former Therapeutics business. We manually remove this figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.210327Z",
     "start_time": "2018-05-25T19:34:52.980417Z"
    }
   },
   "outputs": [],
   "source": [
    "quarterly.loc[quarterly['adsh']=='0001047469-13-002788', value] = \\\n",
    "quarterly.loc[quarterly['adsh']=='0001047469-13-002788', value] - 179000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the chart of the quarterly sales revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.277161Z",
     "start_time": "2018-05-25T19:34:53.213760Z"
    }
   },
   "outputs": [
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"
     },
     "metadata": {
      "jupyter-vega": "#fcd3cc63-0383-47d1-b376-3dbc20d2ec7f"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rev_chart = alt.Chart(quarterly).mark_bar().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('value', axis=alt.Axis(title='Revenue')),\n",
    ")\n",
    "rev_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see rising quarterly sales revenue. Let's have a look at the growth rate of the quarterly sales revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sum up all the relevant revenues for each quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.292696Z",
     "start_time": "2018-05-25T19:34:53.280983Z"
    }
   },
   "outputs": [],
   "source": [
    "growth = quarterly.groupby('fin_quarter_viz')['value'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the change in revenue from quarter to quarter (i.e. Q1 2013 versus Q2 2013)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "growth['q_rate'] = growth['value'].pct_change(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the growth rate of the revenue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.399429Z",
     "start_time": "2018-05-25T19:34:53.341489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-384ef1f467aa30226802dcdb45bb3d44\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Revenue\"}, \"field\": \"q_rate\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-384ef1f467aa30226802dcdb45bb3d44\": [{\"fin_quarter_viz\": \"2012-Q1\", \"value\": 1137000.0, \"q_rate\": null}, {\"fin_quarter_viz\": \"2012-Q2\", \"value\": 1626000.0, \"q_rate\": 0.4300791556728232}, {\"fin_quarter_viz\": \"2012-Q3\", \"value\": 2151000.0, \"q_rate\": 0.3228782287822878}, {\"fin_quarter_viz\": \"2012-Q4\", \"value\": 2935000.0, \"q_rate\": 0.36448163644816356}, {\"fin_quarter_viz\": \"2013-Q1\", \"value\": 4055000.0, \"q_rate\": 0.3816013628620103}, {\"fin_quarter_viz\": \"2013-Q2\", \"value\": 5291000.0, \"q_rate\": 0.30480887792848343}, {\"fin_quarter_viz\": \"2013-Q3\", \"value\": 6882000.0, \"q_rate\": 0.3006993006993006}, {\"fin_quarter_viz\": \"2013-Q4\", \"value\": 8080000.0, \"q_rate\": 0.17407730310956127}, {\"fin_quarter_viz\": \"2014-Q1\", \"value\": 10054000.0, \"q_rate\": 0.2443069306930694}, {\"fin_quarter_viz\": \"2014-Q2\", \"value\": 13290000.0, \"q_rate\": 0.32186194549433056}, {\"fin_quarter_viz\": \"2014-Q3\", \"value\": 17204000.0, \"q_rate\": 0.29450714823175317}, {\"fin_quarter_viz\": \"2014-Q4\", \"value\": 19319000.0, \"q_rate\": 0.12293652638921171}, {\"fin_quarter_viz\": \"2015-Q1\", \"value\": 21003000.0, \"q_rate\": 0.0871680728816191}, {\"fin_quarter_viz\": \"2015-Q2\", \"value\": 23688000.0, \"q_rate\": 0.12783888015997724}, {\"fin_quarter_viz\": \"2015-Q3\", \"value\": 25331000.0, \"q_rate\": 0.0693600135089496}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"bf01b38d-b38a-4124-90a2-e6d96427f273\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x113ecfe48>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#bf01b38d-b38a-4124-90a2-e6d96427f273"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#bf01b38d-b38a-4124-90a2-e6d96427f273"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_growth_chart = alt.Chart(growth).mark_bar().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('q_rate', axis=alt.Axis(title='Revenue')),\n",
    ")\n",
    "q_growth_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally, we see a diminishing growth rate for the revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should become more apparent when we compare the yearly quarters (i.e., Q1 2012 v Q1 2013)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.412582Z",
     "start_time": "2018-05-25T19:34:53.403469Z"
    }
   },
   "outputs": [],
   "source": [
    "growth['y_rate'] = growth['value'].pct_change(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.460314Z",
     "start_time": "2018-05-25T19:34:53.420727Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-7e2e27a55e36fc2e7c6ce379942a395d\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Revenue\"}, \"field\": \"y_rate\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-7e2e27a55e36fc2e7c6ce379942a395d\": [{\"fin_quarter_viz\": \"2012-Q1\", \"value\": 1137000.0, \"q_rate\": null, \"y_rate\": null}, {\"fin_quarter_viz\": \"2012-Q2\", \"value\": 1626000.0, \"q_rate\": 0.4300791556728232, \"y_rate\": null}, {\"fin_quarter_viz\": \"2012-Q3\", \"value\": 2151000.0, \"q_rate\": 0.3228782287822878, \"y_rate\": null}, {\"fin_quarter_viz\": \"2012-Q4\", \"value\": 2935000.0, \"q_rate\": 0.36448163644816356, \"y_rate\": null}, {\"fin_quarter_viz\": \"2013-Q1\", \"value\": 4055000.0, \"q_rate\": 0.3816013628620103, \"y_rate\": 2.5664028144239226}, {\"fin_quarter_viz\": \"2013-Q2\", \"value\": 5291000.0, \"q_rate\": 0.30480887792848343, \"y_rate\": 2.2539975399753995}, {\"fin_quarter_viz\": \"2013-Q3\", \"value\": 6882000.0, \"q_rate\": 0.3006993006993006, \"y_rate\": 2.199442119944212}, {\"fin_quarter_viz\": \"2013-Q4\", \"value\": 8080000.0, \"q_rate\": 0.17407730310956127, \"y_rate\": 1.7529812606473594}, {\"fin_quarter_viz\": \"2014-Q1\", \"value\": 10054000.0, \"q_rate\": 0.2443069306930694, \"y_rate\": 1.47940813810111}, {\"fin_quarter_viz\": \"2014-Q2\", \"value\": 13290000.0, \"q_rate\": 0.32186194549433056, \"y_rate\": 1.5118125118125119}, {\"fin_quarter_viz\": \"2014-Q3\", \"value\": 17204000.0, \"q_rate\": 0.29450714823175317, \"y_rate\": 1.4998546934030803}, {\"fin_quarter_viz\": \"2014-Q4\", \"value\": 19319000.0, \"q_rate\": 0.12293652638921171, \"y_rate\": 1.3909653465346534}, {\"fin_quarter_viz\": \"2015-Q1\", \"value\": 21003000.0, \"q_rate\": 0.0871680728816191, \"y_rate\": 1.0890192958026654}, {\"fin_quarter_viz\": \"2015-Q2\", \"value\": 23688000.0, \"q_rate\": 0.12783888015997724, \"y_rate\": 0.782392776523702}, {\"fin_quarter_viz\": \"2015-Q3\", \"value\": 25331000.0, \"q_rate\": 0.0693600135089496, \"y_rate\": 0.47239014182748207}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"e68de55d-9831-4927-a0dc-77fa63e53f4f\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11836abe0>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#e68de55d-9831-4927-a0dc-77fa63e53f4f"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#e68de55d-9831-4927-a0dc-77fa63e53f4f"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_growth_chart = alt.Chart(growth).mark_bar().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('y_rate', axis=alt.Axis(title='Revenue')),\n",
    ")\n",
    "y_growth_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the growth rate has a negative trend (which is not unusual) but in 2014 the growth rate remains fairly stable. This is in line with the fraudulent activities, which were targeted at improving the revenue numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T18:41:22.371951Z",
     "start_time": "2018-05-25T18:41:22.362310Z"
    }
   },
   "source": [
    "### Quarterly Accounts Receivables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important part of the fraud in relationship with Distributor D was to declare consignment inventory as accounts receivable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter the data to include information on Accounts Receivable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.478617Z",
     "start_time": "2018-05-25T19:34:53.464489Z"
    }
   },
   "outputs": [],
   "source": [
    "ar_os = num_os[num_os['tag'].str.contains('AccountsReceivableNetCurrent')].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the accounts receivable along the quarters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.518583Z",
     "start_time": "2018-05-25T19:34:53.485866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-a72d5e66b2afe0e9f56e481213b9a792\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Accounts Receivable in USD\"}, \"field\": \"value\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-a72d5e66b2afe0e9f56e481213b9a792\": [{\"value\": 16890000.0, \"fin_quarter_viz\": \"2014-Q2\"}, {\"value\": 21446000.0, \"fin_quarter_viz\": \"2014-Q3\"}, {\"value\": 11763000.0, \"fin_quarter_viz\": \"2014-Q1\"}, {\"value\": 5346000.0, \"fin_quarter_viz\": \"2013-Q1\"}, {\"value\": 6406000.0, \"fin_quarter_viz\": \"2013-Q2\"}, {\"value\": 6773000.0, \"fin_quarter_viz\": \"2013-Q3\"}, {\"value\": 24307000.0, \"fin_quarter_viz\": \"2014-Q4\"}, {\"value\": 997000.0, \"fin_quarter_viz\": \"2012-Q1\"}, {\"value\": 2231000.0, \"fin_quarter_viz\": \"2012-Q3\"}, {\"value\": 1393000.0, \"fin_quarter_viz\": \"2012-Q2\"}, {\"value\": 7459000.0, \"fin_quarter_viz\": \"2013-Q4\"}, {\"value\": 37857000.0, \"fin_quarter_viz\": \"2015-Q3\"}, {\"value\": 38598000.0, \"fin_quarter_viz\": \"2015-Q2\"}, {\"value\": 3063000.0, \"fin_quarter_viz\": \"2012-Q4\"}, {\"value\": 32022000.0, \"fin_quarter_viz\": \"2015-Q1\"}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"1393088a-f704-461b-8e74-8b91eb531e04\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x1191f4f98>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#1393088a-f704-461b-8e74-8b91eb531e04"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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"
     },
     "metadata": {
      "jupyter-vega": "#1393088a-f704-461b-8e74-8b91eb531e04"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ar_chart = alt.Chart(ar_os.loc[:,['value', 'fin_quarter_viz']]).mark_line().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('value', axis=alt.Axis(title='Accounts Receivable in USD')),\n",
    ")\n",
    "ar_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In line with the SEC complaint, we see a significant rise in accounts receivable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accounts Receivable Turnover Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we analyze the accounts receivable turnover rate, which is a measurement for  how quickly Osiris collects account receivables. Intentionally not collecting accounts receivables to hide consignment should reflect in a decreasing accounts receivable turnover rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We merge the Accounts Receivable data with the Quarterly Revenue Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.543495Z",
     "start_time": "2018-05-25T19:34:53.522944Z"
    }
   },
   "outputs": [],
   "source": [
    "rtr = pd.merge(ar_os.loc[:, ['adsh', 'tag', 'value', 'fin_quarter_viz']], \\\n",
    "               quarterly.loc[:, ['adsh', 'tag', 'value', 'fin_quarter_viz']], \\\n",
    "               how='left', on=['adsh', 'fin_quarter_viz'], \\\n",
    "               suffixes=('_ar', '_sales')).sort_values('fin_quarter_viz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Accounts Receivable Turnover Rate is\n",
    "\\begin{equation*}\n",
    "Quarterly \\ Receivables \\ Turnover \\ Ratio = \\frac{Quarterly \\ Sales \\ Revenue}{\\Bigl(\\frac{Accounts \\ Receivables_{Previous \\ Quarter} + Accounts \\ Receivables_{Current \\ Quarter}}{2})}\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the average accounts receivable for each quarter by averaging the accounts receivables for each quarter and its predecessor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.555365Z",
     "start_time": "2018-05-25T19:34:53.548647Z"
    }
   },
   "outputs": [],
   "source": [
    "rtr['avg_ar'] = (rtr['value_ar'] + rtr['value_ar'].shift(1))/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We divide the quarterly sales revenue by the accounts receivable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.569912Z",
     "start_time": "2018-05-25T19:34:53.562841Z"
    }
   },
   "outputs": [],
   "source": [
    "rtr['ar_turnover'] = rtr['value_sales']/rtr['avg_ar']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the accounts receivable turnover rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-4ee7e3c032de496e03f2b4b70cb77942\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Accounts Receivable Turnover\"}, \"field\": \"ar_turnover\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-4ee7e3c032de496e03f2b4b70cb77942\": [{\"ar_turnover\": null, \"fin_quarter_viz\": \"2012-Q1\"}, {\"ar_turnover\": 1.3606694560669457, \"fin_quarter_viz\": \"2012-Q2\"}, {\"ar_turnover\": 1.1870860927152318, \"fin_quarter_viz\": \"2012-Q3\"}, {\"ar_turnover\": 1.1088024178315075, \"fin_quarter_viz\": \"2012-Q4\"}, {\"ar_turnover\": 0.9644428588417172, \"fin_quarter_viz\": \"2013-Q1\"}, {\"ar_turnover\": 0.9004424778761062, \"fin_quarter_viz\": \"2013-Q2\"}, {\"ar_turnover\": 1.0443888003642159, \"fin_quarter_viz\": \"2013-Q3\"}, {\"ar_turnover\": 1.135469364811692, \"fin_quarter_viz\": \"2013-Q4\"}, {\"ar_turnover\": 1.046093018416398, \"fin_quarter_viz\": \"2014-Q1\"}, {\"ar_turnover\": 0.9276515548110146, \"fin_quarter_viz\": \"2014-Q2\"}, {\"ar_turnover\": 0.8975375626043406, \"fin_quarter_viz\": \"2014-Q3\"}, {\"ar_turnover\": 0.8444910716237186, \"fin_quarter_viz\": \"2014-Q4\"}, {\"ar_turnover\": 0.7457260025919153, \"fin_quarter_viz\": \"2015-Q1\"}, {\"ar_turnover\": 0.670858113848768, \"fin_quarter_viz\": \"2015-Q2\"}, {\"ar_turnover\": 0.6626381531619907, \"fin_quarter_viz\": \"2015-Q3\"}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"0ddf69e1-96b9-4137-8380-7c2167e79b3a\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x119184048>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#0ddf69e1-96b9-4137-8380-7c2167e79b3a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#0ddf69e1-96b9-4137-8380-7c2167e79b3a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rtr_chart = alt.Chart(rtr.loc[:,['ar_turnover', 'fin_quarter_viz']]).mark_line().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('ar_turnover', axis=alt.Axis(title='Accounts Receivable Turnover')),\n",
    ")\n",
    "rtr_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the period of fraudulent activities, we see a constant decrease in the accounts receivable turnover rate. This is in line with the SEC complaint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consigned Inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "inventory = num_os[num_os['tag'].str.contains('InventoryNet')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.671718Z",
     "start_time": "2018-05-25T19:34:53.628324Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-0b0b2cb95413a72ea2b2d08b643fdbf9\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Inventory in USD\"}, \"field\": \"value\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-0b0b2cb95413a72ea2b2d08b643fdbf9\": [{\"value\": 5540000.0, \"fin_quarter_viz\": \"2014-Q2\"}, {\"value\": 8840000.0, \"fin_quarter_viz\": \"2014-Q3\"}, {\"value\": 2337000.0, \"fin_quarter_viz\": \"2014-Q1\"}, {\"value\": 1611000.0, \"fin_quarter_viz\": \"2013-Q1\"}, {\"value\": 1696000.0, \"fin_quarter_viz\": \"2013-Q2\"}, {\"value\": 1672000.0, \"fin_quarter_viz\": \"2013-Q3\"}, {\"value\": 10924000.0, \"fin_quarter_viz\": \"2014-Q4\"}, {\"value\": 415000.0, \"fin_quarter_viz\": \"2012-Q1\"}, {\"value\": 922000.0, \"fin_quarter_viz\": \"2012-Q3\"}, {\"value\": 475000.0, \"fin_quarter_viz\": \"2012-Q2\"}, {\"value\": 1929000.0, \"fin_quarter_viz\": \"2013-Q4\"}, {\"value\": 15818000.0, \"fin_quarter_viz\": \"2015-Q3\"}, {\"value\": 13348000.0, \"fin_quarter_viz\": \"2015-Q2\"}, {\"value\": 1278000.0, \"fin_quarter_viz\": \"2012-Q4\"}, {\"value\": 11939000.0, \"fin_quarter_viz\": \"2015-Q1\"}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"6d0af126-67d2-4762-8bac-aa8be3449c7a\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x119184828>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#6d0af126-67d2-4762-8bac-aa8be3449c7a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#6d0af126-67d2-4762-8bac-aa8be3449c7a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(inventory.loc[:,['value', 'fin_quarter_viz']]).mark_line().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('value', axis=alt.Axis(title='Inventory in USD')),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ar_inv = pd.merge(ar_os, inventory, how='left', on=['adsh', 'fin_quarter_viz'], \\\n",
    "               suffixes=('_ar', '_inv')).sort_values('fin_quarter_viz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ar_inv['ratio']=ar_inv['value_inv']/ar_inv['value_ar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-84ca797fc6f30cfea6ca4a51390c7d63\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"Financial Quarter\"}, \"field\": \"fin_quarter_viz\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Inventory in USD\"}, \"field\": \"ratio\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-84ca797fc6f30cfea6ca4a51390c7d63\": [{\"adsh\": \"0001104659-12-035988\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2011\", \"coreg_ar\": null, \"ddate_ar\": 20120331, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 997000.0, \"footnote_ar\": null, \"fs_year_ar\": 2012, \"fs_quarter_ar\": 2, \"period_ar\": 20120331, \"fin_quarter_viz\": \"2012-Q1\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2011\", \"coreg_inv\": null, \"ddate_inv\": 20120331, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 415000.0, \"footnote_inv\": null, \"fs_year_inv\": 2012, \"fs_quarter_inv\": 2, \"period_inv\": 20120331, \"ratio\": 0.41624874623871616}, {\"adsh\": \"0001104659-12-056210\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2012\", \"coreg_ar\": null, \"ddate_ar\": 20120630, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 1393000.0, \"footnote_ar\": null, \"fs_year_ar\": 2012, \"fs_quarter_ar\": 3, \"period_ar\": 20120630, \"fin_quarter_viz\": \"2012-Q2\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2012\", \"coreg_inv\": null, \"ddate_inv\": 20120630, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 475000.0, \"footnote_inv\": null, \"fs_year_inv\": 2012, \"fs_quarter_inv\": 3, \"period_inv\": 20120630, \"ratio\": 0.34099066762383345}, {\"adsh\": \"0001104659-12-076280\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2012\", \"coreg_ar\": null, \"ddate_ar\": 20120930, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 2231000.0, \"footnote_ar\": null, \"fs_year_ar\": 2012, \"fs_quarter_ar\": 4, \"period_ar\": 20120930, \"fin_quarter_viz\": \"2012-Q3\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2012\", \"coreg_inv\": null, \"ddate_inv\": 20120930, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 922000.0, \"footnote_inv\": null, \"fs_year_inv\": 2012, \"fs_quarter_inv\": 4, \"period_inv\": 20120930, \"ratio\": 0.4132675930076199}, {\"adsh\": \"0001047469-13-002788\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2012\", \"coreg_ar\": null, \"ddate_ar\": 20121231, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 3063000.0, \"footnote_ar\": null, \"fs_year_ar\": 2013, \"fs_quarter_ar\": 1, \"period_ar\": 20121231, \"fin_quarter_viz\": \"2012-Q4\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2012\", \"coreg_inv\": null, \"ddate_inv\": 20121231, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 1278000.0, \"footnote_inv\": null, \"fs_year_inv\": 2013, \"fs_quarter_inv\": 1, \"period_inv\": 20121231, \"ratio\": 0.41723800195886385}, {\"adsh\": \"0001104659-13-039896\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2012\", \"coreg_ar\": null, \"ddate_ar\": 20130331, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 5346000.0, \"footnote_ar\": null, \"fs_year_ar\": 2013, \"fs_quarter_ar\": 2, \"period_ar\": 20130331, \"fin_quarter_viz\": \"2013-Q1\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2012\", \"coreg_inv\": null, \"ddate_inv\": 20130331, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 1611000.0, \"footnote_inv\": null, \"fs_year_inv\": 2013, \"fs_quarter_inv\": 2, \"period_inv\": 20130331, \"ratio\": 0.30134680134680136}, {\"adsh\": \"0001104659-13-062017\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2012\", \"coreg_ar\": null, \"ddate_ar\": 20130630, \"qtrs_ar\": 0, \"uom_ar\": 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\"fs_year_inv\": 2014, \"fs_quarter_inv\": 3, \"period_inv\": 20140630, \"ratio\": 0.3280047365304914}, {\"adsh\": \"0001104659-14-078750\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2014\", \"coreg_ar\": null, \"ddate_ar\": 20140930, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 21446000.0, \"footnote_ar\": null, \"fs_year_ar\": 2014, \"fs_quarter_ar\": 4, \"period_ar\": 20140930, \"fin_quarter_viz\": \"2014-Q3\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2014\", \"coreg_inv\": null, \"ddate_inv\": 20140930, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 8840000.0, \"footnote_inv\": null, \"fs_year_inv\": 2014, \"fs_quarter_inv\": 4, \"period_inv\": 20140930, \"ratio\": 0.4121980788958314}, {\"adsh\": \"0001047469-15-002532\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2014\", \"coreg_ar\": null, \"ddate_ar\": 20141231, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 24307000.0, \"footnote_ar\": 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\"fs_quarter_inv\": 2, \"period_inv\": 20150331, \"ratio\": 0.3728374242708138}, {\"adsh\": \"0001104659-15-057928\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2014\", \"coreg_ar\": null, \"ddate_ar\": 20150630, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 38598000.0, \"footnote_ar\": null, \"fs_year_ar\": 2015, \"fs_quarter_ar\": 3, \"period_ar\": 20150630, \"fin_quarter_viz\": \"2015-Q2\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2014\", \"coreg_inv\": null, \"ddate_inv\": 20150630, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 13348000.0, \"footnote_inv\": null, \"fs_year_inv\": 2015, \"fs_quarter_inv\": 3, \"period_inv\": 20150630, \"ratio\": 0.3458210269962174}, {\"adsh\": \"0001558370-15-002707\", \"tag_ar\": \"AccountsReceivableNetCurrent\", \"version_ar\": \"us-gaap/2015\", \"coreg_ar\": null, \"ddate_ar\": 20150930, \"qtrs_ar\": 0, \"uom_ar\": \"USD\", \"value_ar\": 37857000.0, \"footnote_ar\": null, \"fs_year_ar\": 2015, \"fs_quarter_ar\": 4, \"period_ar\": 20150930, \"fin_quarter_viz\": \"2015-Q3\", \"tag_inv\": \"InventoryNet\", \"version_inv\": \"us-gaap/2015\", \"coreg_inv\": null, \"ddate_inv\": 20150930, \"qtrs_inv\": 0, \"uom_inv\": \"USD\", \"value_inv\": 15818000.0, \"footnote_inv\": null, \"fs_year_inv\": 2015, \"fs_quarter_inv\": 4, \"period_inv\": 20150930, \"ratio\": 0.41783553900203396}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"2baa204e-0466-4ffa-84de-add54733eff3\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11919d470>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#2baa204e-0466-4ffa-84de-add54733eff3"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#2baa204e-0466-4ffa-84de-add54733eff3"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(ar_inv).mark_line().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('ratio', axis=alt.Axis(title='Inventory in USD')),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assume that inventory valuation and prices remained stable over the period of time, we see a sharp reversal of the relationship of inventory and accounts receivable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also look at the cash flow. We would expect an unusual cash flow because of the various deals that were predated to 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the free cash flow by subtracting the cash flow from the previous quarters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf = num_os[(num_os['tag'].str.contains('NetCashProvidedByUsedInOperatingActivitiesContinuingOperations'))].copy().sort_values('fin_quarter_viz')\n",
    "cf['previous'] = cf['value'].shift(1)\n",
    "cf['previous'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.732086Z",
     "start_time": "2018-05-25T19:34:53.715270Z"
    }
   },
   "outputs": [],
   "source": [
    "cf['value_check'] = cf['value']-cf['previous']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:53.794530Z",
     "start_time": "2018-05-25T19:34:53.740669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
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-8667000.0, \"value_check\": -4615000.0}, {\"adsh\": \"0001047469-15-002532\", \"tag\": \"NetCashProvidedByUsedInOperatingActivitiesContinuingOperations\", \"version\": \"us-gaap/2014\", \"coreg\": null, \"ddate\": 20141231, \"qtrs\": 4, \"uom\": \"USD\", \"value\": -18325000.0, \"footnote\": null, \"fs_year\": 2015, \"fs_quarter\": 1, \"period\": 20141231, \"fin_quarter_viz\": \"2014-Q4\", \"previous\": -13282000.0, \"value_check\": -5043000.0}, {\"adsh\": \"0001104659-15-036502\", \"tag\": \"NetCashProvidedByUsedInOperatingActivitiesContinuingOperations\", \"version\": \"us-gaap/2014\", \"coreg\": null, \"ddate\": 20150331, \"qtrs\": 1, \"uom\": \"USD\", \"value\": -359000.0, \"footnote\": null, \"fs_year\": 2015, \"fs_quarter\": 2, \"period\": 20150331, \"fin_quarter_viz\": \"2015-Q1\", \"previous\": -18325000.0, \"value_check\": 17966000.0}, {\"adsh\": \"0001104659-15-057928\", \"tag\": \"NetCashProvidedByUsedInOperatingActivitiesContinuingOperations\", \"version\": \"us-gaap/2014\", \"coreg\": null, \"ddate\": 20150630, \"qtrs\": 2, \"uom\": \"USD\", \"value\": -904000.0, \"footnote\": null, \"fs_year\": 2015, \"fs_quarter\": 3, \"period\": 20150630, \"fin_quarter_viz\": \"2015-Q2\", \"previous\": -359000.0, \"value_check\": -545000.0}, {\"adsh\": \"0001558370-15-002707\", \"tag\": \"NetCashProvidedByUsedInOperatingActivitiesContinuingOperations\", \"version\": \"us-gaap/2015\", \"coreg\": null, \"ddate\": 20150930, \"qtrs\": 3, \"uom\": \"USD\", \"value\": -9251000.0, \"footnote\": null, \"fs_year\": 2015, \"fs_quarter\": 4, \"period\": 20150930, \"fin_quarter_viz\": \"2015-Q3\", \"previous\": -904000.0, \"value_check\": -8347000.0}]}};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"b1a25eeb-b259-4215-8d85-7e6da0336768\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x1191f9a20>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#b1a25eeb-b259-4215-8d85-7e6da0336768"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#b1a25eeb-b259-4215-8d85-7e6da0336768"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(cf).mark_line().encode(\n",
    "    x=alt.X('fin_quarter_viz', axis=alt.Axis(title='Financial Quarter')),\n",
    "    y=alt.Y('value_check', axis=alt.Axis(title='Inventory in USD')),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is an unusual spike at the beginning of the year 2015, which corresponds to the predated revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use quandl to obtain the closing stock prices for Osiris Therapeutics. If you want to replicate this notebook, you need to fill in your own API key for quandl. You can obtain your own API key [here](https://www.sec.gov/litigation/complaints/2017/comp-pr2017-207.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quandl\n",
    "quandl.ApiConfig.api_key = 'exPxfKxWk4Hz_L2E9nQy'\n",
    "\n",
    "os = quandl.get_table('WIKI/PRICES', ticker = ['OSIR'], \n",
    "                        qopts = { 'columns': ['date', 'close'] }, \n",
    "                        date = { 'gte': '2012-12-31', 'lte': '2016-12-31' }, \n",
    "                        paginate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the stock price and include the beginning of the fraudulent periods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-25T19:34:56.924637Z",
     "start_time": "2018-05-25T19:34:56.587410Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"0e03448a-e966-475e-8e5f-d283d46e052a\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x1183f82e8>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#0e03448a-e966-475e-8e5f-d283d46e052a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#0e03448a-e966-475e-8e5f-d283d46e052a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock = alt.Chart(os).mark_line().encode(\n",
    "    x=alt.X('date', axis=alt.Axis(title='Date')),\n",
    "    y=alt.Y('close', axis=alt.Axis(title='Closing Stock Price')),\n",
    ").properties(width = 600)\n",
    "\n",
    "fraud = pd.DataFrame([\n",
    "    {\n",
    "        \"start\": \"2014-01-01\",\n",
    "        \"end\": \"2015-12-31\",\n",
    "    },\n",
    "    {\n",
    "        \"start\": \"2015-01-01\",\n",
    "        \"end\": \"2015-12-31\",\n",
    "    },\n",
    "    {\n",
    "        \"start\": \"2015-11-16\",\n",
    "        \"end\": \"2017-11-02\",\n",
    "    },\n",
    "])\n",
    "\n",
    "f = alt.Chart(fraud).mark_rule().encode(\n",
    "    x = 'start:T'\n",
    ")\n",
    "\n",
    "alt.layer(\n",
    "    stock, f,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Osiris announced an improved [equity compensation plan on October 3, 2014](https://www.sec.gov/Archives/edgar/data/1360886/000110465914069872/0001104659-14-069872-index.htm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>2014-10-03</td>\n",
       "      <td>12.8500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   close\n",
       "None                   \n",
       "565  2014-10-03 12.8500"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os[os['date']=='2014-10-03']"
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   "source": [
    "Osiris announced its results for Q1 2015 on May 11, 2015."
   ]
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   "execution_count": 42,
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       "      <td>2015-05-11</td>\n",
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       "           date   close\n",
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    "os[os['date']=='2015-05-11']"
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    "The highest stock price was on July 16, 2015."
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   "cell_type": "code",
   "execution_count": 43,
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    {
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       "      <td>2015-07-16</td>\n",
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       "           date   close\n",
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       "369  2015-07-16 22.6400"
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    "os[os['close']==os[os['date']>'2014-12-31']['close'].max()]"
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   "metadata": {},
   "source": [
    "Selling some of the new shares in May (as one of the executives did), would result in a profit of USD 4.37 per share."
   ]
  },
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   "metadata": {},
   "source": [
    "## Final thoughts"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "The reported frauds are often mentioned in the literature. For instance, [channel stuffing](https://en.wikipedia.org/wiki/Channel_stuffing) is very similar to the methods used by Osiris executives. The complaint also shows that some of Osiris' distributors played along. \n",
    "\n",
    "More interesting, the quarterly data from the financial statement show unusual developments (e.g., the decrease in the accounts receivable turnover rate). While it is easy to tell in hindsight, an automatic analysis of red flags may have highlighted the unusual pattern. \n",
    "\n",
    "As such, this case makes a strong argument [in favor of quarterly reporting](). "
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project (including the data) is available at [https://github.com/mschermann/forensic_accounting](https://github.com/mschermann/forensic_accounting)."
   ]
  }
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