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Accounting Analytics > Final Review > Flashcards

Final Review Flashcards

1
Q

What is Big Data?

A

Large & complex data sets

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2
Q

4 Vs

A

Volume
Variety (data sources of unstructured/structured data)
Velocity
Veracity (data quality - clean & credible)

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3
Q

Pros/Cons of Structured Data

A

Hard to collect
Limited Insights
Affordable
Active participation
Transparent

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4
Q

Pros/Cons of Unstructured Data

A

Easy to collect
Pricy
Unlimited Insights
Presence
Lack of transparency

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5
Q

What is Analytics?

A

ETL data to gain valuable insights to inform decision making
Requires critical thinking & judgement

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6
Q

IMPACT

A

I - Identify questions
M - Master the data
P - Perform the test plan
A - Address & refine results
C - Communicate insights
T - Track Outcomes

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7
Q

Descriptive

A

What happened?

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8
Q

Diagnostic

A

WHY did it happen? Root causes?

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9
Q

Prescriptive

A

What if scenarios
Optimize performance based on constraints

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10
Q

Predictive

A

What WILL happen (future outlook)? Probability? Forecasting

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11
Q

MASTER THE DATA

A

Appropriateness - can the data answer the questions
Accessibility - cost of acquisition, sources of data
Reliability - data integrity (accurate, valid, consistent)

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12
Q

Financial Accounting Data Sources

A
  • XBRL.gov
    -SEC EDGAR
    -Company websites/press releases
    -Fee based databases: Dow Jones, CRSP
    -Internal data - journal entries, general ledger, subledgers
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13
Q

Audit Data Sources

A
  • PCAOB (audit regulators)
  • Auditor Search
    -Audit Analytics (audit report, fees, restatement data)
  • Firm transparency reports - insight into audit culture
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14
Q

Managerial Accounting Data Sources

A
  • Budget Variance
  • Point of Sale Transaction
  • Potential cost drivers
  • Supply chain
  • CRM, HRM, ERM
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15
Q

Other Relevant Data Sources

A
  • Government Data (GDP, CPI, Census)
  • Sustainability Reports
  • Current & Historical Stock Prices
  • Earnings Forecast
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16
Q

Alternative Data

A
  • Social media
  • Cell phone location
  • Geospatial
  • Employee Sentiments (Glassdoor)
  • Foot traffic
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17
Q

What is Blockchain?

A

A decentralized digital ledger that records transactions

(Visibility for all parties on all transactions occurring on the same chain that is solidified by a hash - unable to go back to alter data)

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18
Q

Benefits of Blockchain

A
  • Verified transactions
  • Almost impossible to manipulate data
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19
Q

Limitations of Blockchain (Benefits of Relational Databases)

A

-Centralization of data
- Limitation of access to particular data tables
- Embedded checks through linking of tables with PK & FK

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20
Q

Delimiter

A

Smith | David or Smith, David

(Intentional separation of values for table column headings)

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21
Q

Qualifier

A

“Property, Plant and Equipment”
(Double quotes indicate keeping the text together)

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22
Q

Categorical Data

A

Data divided by grouping (composed of nominal and ordinal data)

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23
Q

Nominal Data

A

Gender, eye color, dates, account #

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24
Q

Ordinal data

A

Ranking (gold, silver, bronze)

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25
Q

Numerical data

A

Used for calculations (composed of interval & ratio)

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26
Q

Interval Data

A

No “absolute zero” –> temperature (0 degrees does not mean there is no temperature anymore)

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27
Q

Ratio Data

A

Defined zero value (sales, net income)

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28
Q

Skewed right

A

tail is to the right which is driven by outliers (mean > median)

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29
Q

Skewed left

A

tail is to the left (mean < median)

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30
Q

Correlation

A

measure relationship between 2 variables that ranges from -1 to 1

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31
Q

p-value > 0.05

A

fail to reject null hypothesis - not statistically significant

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32
Q

p-value < 0.05

A

reject null hypothesis - statistically significant

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33
Q

R^2

A

fit of data (increased R^2 = good fit)

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34
Q

Regression Analysis

A

Diagnostic Test (measure relationship) or Predictive Test (estimation of dependent variable value based on independent variable inputs)

35
Q

Linear Regression Formula

A

y = B1X1 + y-intercept + error

36
Q

Descriptive Analytics Tests

A
  • Sums, min/max, standard deviation, counts, difference in means
  • horizontal & vertical analysis
  • ratio analytics
  • Dupont analysis
  • IQR
37
Q

Diagnostic (Drill down or Root Cause) Tests

A
  • Hypothesis Testing (Regression)
  • Clustering
    -Grouping/Filtering in PivotTables
  • Benford’s Law
  • Fuzzy Matching
  • Identify outliers/anomalies through IQR
38
Q

Prescriptive Tests

A

-What-if scenarios
-Sensitivity analysis

39
Q

Predictive Tests

A

-Regression Analysis

40
Q

Where in the audit process could you use ADA?

A

-Risk assessment phase –> fraud risk/material misstatements
- Testing phase –> tests of controls, substantive tests

41
Q

Common Types of Analytics in Management Accounting

A

Descriptive: KPI, Clustering suppliers, customers, locations
Diagnostic: Comparing KPIs, Budget Variance, Regression (cost behavior)
Prescriptive: sensitivity, capital budgeting, goal-seek
Predictive: forecasting

42
Q

Cost Behavior (Regression Analysis)

A

Management’s attempt to understand how operating costs change in relation to a change in organizational activity

Variable (independent) + Fixed (y-intercept)

43
Q

Capital Budgeting

A

process of analyzing/deciding which long-term investment to make using NPV or IRR

44
Q

NPV

A

used to determine current value of all future cash flows generated by a project + initial capital investment

45
Q

IRR

A

time-adjusted rate of return for an investment

(NPV = 0 or IRR > r –> making $ or break even)

46
Q

In a balanced scorecard, what should be aligned with strategic goals of the organization?

A

Objectives

47
Q

Common Types of Analytics in Financial Accounting

A

Descriptive: horizontal, vertical & ratio analysis
Diagnostic: benchmarking/comparative analysis, DuPont
Prescriptive: sensitivity analysis
Predictive: bankruptcy predictions

48
Q

XBRL

A

tagging & reporting financial information in a computer readable format

49
Q

XBRL Taxonomy

A

defines/describes each key element & relationship between each element

50
Q

Strengths of XBRL

A

tagging allows data to be quickly transmitted & can extend taxonomy to include custom tags

51
Q

Weaknesses of XBRL

A

Concerns regarding data quality (accuracy, consistency, reliability?)

52
Q

All of the following are ways XBRL tags can be useful except for:

A

helping auditors assess where accounting errors may occur
helping financial analysts value a company
helping regulators determine if audit firms are in compliance with audit standards (correct)
helping regulators see if companies are in compliance with regulations

53
Q

Liquidity

A

measures short-term ability of company to pay its maturing obligations & meet unexpected needs of $

54
Q

Current ratio

A

short-term debt paying ability

55
Q

Cash debt coverage ratio

A

short-term debt paying ability on a cash basis

56
Q

Receivables Turnover Ratio

A

of times a company collects receivables a year

57
Q

Average Collection period

A

converts RTR into days

58
Q

Inventory Turnover ratio

A

of times a inventory was sold a year

59
Q

Days in Inventory

A

Avg # of days inventory is held

60
Q

Solvency

A

measures the ability of a company to survive for a long duration

61
Q

Debt to Assets Total Ratio

A

% of total assets provided by creditors

62
Q

Times Interest Earned Ratio

A

company’s ability to meet interest payments

63
Q

Cash Debt Coverage Ratio

A

long-term debt paying ability on a cash basis

64
Q

Free cash flow

A

cash available to pay dividends or expand operations

65
Q

Profitability

A

measure income or operating success of a company

66
Q

Return on Stockholder’s Equity

A

$ of net income earned/$ invested

67
Q

Return on assets

A

overall profitability of assets

68
Q

Profit Margin ratio

A

net income generated by each $ of sales

69
Q

Asset turnover

A

how efficiently assets are used to generate sales

70
Q

Gross Profit Rate

A

margin between selling price & COGS

71
Q

Earnings per Share

A

net income earned on each dollar of common stock

72
Q

P-E Ratio

A

increase means investors believe company future earnings will grow

73
Q

Payout Ratio

A

% of earnings distributed in cash dividends

74
Q

Basic DuPont Model

A

ROE = Profit Margin x Total Asset Turnover x Financial Leverage (higher value –> more risk as reliant on debt for funding)

75
Q

Increased ROE

A

increase in PM, TAT or FL

76
Q

Decreased ROE

A

decrease in PM, TAT, or FL

77
Q

Bankruptcy Classification (Altman’s Z-score)

A

Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5

78
Q

Variable Meanings

A

X1 = liquidity level
X2 = long-term profitability
X3 = short term profitability
X4 = solvency
X5 = asset efficiency (TAT)

79
Q

If Z < 1.80

A

Significant risk of bankruptcy - “distress zone”

80
Q

If 1.8 <= Z >= 3.0

A

At risk of bankruptcy - “gray zone”

81
Q

If Z > 3.0

A

Not at risk - “safe zone”

82
Q

A data point with which of the following z-scores would likely alert an auditor to the existence of a potential outlier that warrants further scrutiny?

A

3.5

83
Q

RPA

A

-Form of digital labor
-Software robots to automate repetitive tasks (scanning, reading docs, downloading/merging files, converting currency)
-Less flexible
-Does not make decisions
-Simple & quick implementations to deploy

84
Q

AI

A

-Tasks that require human intelligence
-Capable of tasks that require cognitive abilities, recognizing patterns, and making predictions
-Adapt and learn from new data
-Complex/resource intensive
-Make decisions based on data analysis/learned patterns

Accounting Analytics (1 decks)

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