示例#1
0
 def call_fraud(self, other_args: List[str]):
     """Process fraud command."""
     parser = argparse.ArgumentParser(
         add_help=False,
         formatter_class=argparse.RawTextHelpFormatter,
         prog="fraud",
         description=
         ("M-score:\n------------------------------------------------\n"
          "The Beneish model is a statistical model that uses financial ratios calculated with"
          " accounting data of a specific company in order to check if it is likely (high"
          " probability) that the reported earnings of the company have been manipulated."
          " A score of -5 to -2.22 indicated a low chance of fraud, a score of -2.22 to -1.78"
          " indicates a moderate change of fraud, and a score above -1.78 indicated a high"
          " chance of fraud.[Source: Wikipedia]\n\nDSRI:\nDays Sales in Receivables Index"
          " gauges whether receivables and revenue are out of balance, a large number is"
          " expected to be associated with a higher likelihood that revenues and earnings are"
          " overstated.\n\nGMI:\nGross Margin Index shows if gross margins are deteriorating."
          " Research suggests that firms with worsening gross margin are more likely to engage"
          " in earnings management, therefore there should be a positive correlation between"
          " GMI and probability of earnings management.\n\nAQI:\nAsset Quality Index measures"
          " the proportion of assets where potential benefit is less certain. A positive"
          " relation between AQI and earnings manipulation is expected.\n\nSGI:\nSales Growth"
          " Index shows the amount of growth companies are having. Higher growth companies are"
          " more likely to commit fraud so there should be a positive relation between SGI and"
          " earnings management.\n\nDEPI:\nDepreciation Index is the ratio for the rate of"
          " depreciation. A DEPI greater than 1 shows that the depreciation rate has slowed and"
          " is positively correlated with earnings management.\n\nSGAI:\nSales General and"
          " Administrative Expenses Index measures the change in SG&A over sales. There should"
          " be a positive relationship between SGAI and earnings management.\n\nLVGI:\nLeverage"
          " Index represents change in leverage. A LVGI greater than one indicates a lower"
          " change of fraud.\n\nTATA: \nTotal Accruals to Total Assets is a proxy for the"
          " extent that cash underlies earnings. A higher number is associated with a higher"
          " likelihood of manipulation.\n\n\n"
          "Z-score:\n------------------------------------------------\n"
          "The Zmijewski Score is a bankruptcy model used to predict a firm's bankruptcy in two"
          " years. The ratio uses in the Zmijewski score were determined by probit analysis ("
          "think of probit as probability unit). In this case, scores less than .5 represent a"
          " higher probability of default. One of the criticisms that Zmijewski made was that"
          " other bankruptcy scoring models oversampled distressed firms and favored situations"
          " with more complete data.[Source: YCharts]"
          "McKee-score:\n------------------------------------------------\n"
          "The McKee Score is a bankruptcy model used to predict a firm's bankruptcy in one year"
          "It looks at a companie's size, profitability, and liquidity to determine the probability."
          "This model is 80% accurate in predicting bankruptcy."),
     )
     parser.add_argument(
         "-e",
         "--explanation",
         action="store_true",
         dest="exp",
         default=False,
         help="Shows an explanation for the metrics",
     )
     ns_parser = parse_known_args_and_warn(parser, other_args,
                                           EXPORT_ONLY_RAW_DATA_ALLOWED)
     if ns_parser:
         av_view.display_fraud(self.ticker, ns_parser.exp)
示例#2
0
def test_display_fraud(mocker, df):
    mocker.patch(
        "gamestonk_terminal.stocks.fundamental_analysis.av_view.av_model.get_fraud_ratios",
        return_value=(df),
    )
    av_view.display_fraud(ticker="TSLA")