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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
Numerical data
Used for calculations (composed of interval & ratio)
26
Interval Data
No "absolute zero" --> temperature (0 degrees does not mean there is no temperature anymore)
27
Ratio Data
Defined zero value (sales, net income)
28
Skewed right
tail is to the right which is driven by outliers (mean > median)
29
Skewed left
tail is to the left (mean < median)
30
Correlation
measure relationship between 2 variables that ranges from -1 to 1
31
p-value > 0.05
fail to reject null hypothesis - not statistically significant
32
p-value < 0.05
reject null hypothesis - statistically significant
33
R^2
fit of data (increased R^2 = good fit)
34
Regression Analysis
Diagnostic Test (measure relationship) or Predictive Test (estimation of dependent variable value based on independent variable inputs)
35
Linear Regression Formula
y = B1X1 + y-intercept + error
36
Descriptive Analytics Tests
- Sums, min/max, standard deviation, counts, difference in means - horizontal & vertical analysis - ratio analytics - Dupont analysis - IQR
37
Diagnostic (Drill down or Root Cause) Tests
- Hypothesis Testing (Regression) - Clustering -Grouping/Filtering in PivotTables - Benford's Law - Fuzzy Matching - Identify outliers/anomalies through IQR
38
Prescriptive Tests
-What-if scenarios -Sensitivity analysis
39
Predictive Tests
-Regression Analysis
40
Where in the audit process could you use ADA?
-Risk assessment phase --> fraud risk/material misstatements - Testing phase --> tests of controls, substantive tests
41
Common Types of Analytics in Management Accounting
Descriptive: KPI, Clustering suppliers, customers, locations Diagnostic: Comparing KPIs, Budget Variance, Regression (cost behavior) Prescriptive: sensitivity, capital budgeting, goal-seek Predictive: forecasting
42
Cost Behavior (Regression Analysis)
Management's attempt to understand how operating costs change in relation to a change in organizational activity Variable (independent) + Fixed (y-intercept)
43
Capital Budgeting
process of analyzing/deciding which long-term investment to make using NPV or IRR
44
NPV
used to determine current value of all future cash flows generated by a project + initial capital investment
45
IRR
time-adjusted rate of return for an investment (NPV = 0 or IRR > r --> making $ or break even)
46
In a balanced scorecard, what should be aligned with strategic goals of the organization?
Objectives
47
Common Types of Analytics in Financial Accounting
Descriptive: horizontal, vertical & ratio analysis Diagnostic: benchmarking/comparative analysis, DuPont Prescriptive: sensitivity analysis Predictive: bankruptcy predictions
48
XBRL
tagging & reporting financial information in a computer readable format
49
XBRL Taxonomy
defines/describes each key element & relationship between each element
50
Strengths of XBRL
tagging allows data to be quickly transmitted & can extend taxonomy to include custom tags
51
Weaknesses of XBRL
Concerns regarding data quality (accuracy, consistency, reliability?)
52
All of the following are ways XBRL tags can be useful except for:
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
Liquidity
measures short-term ability of company to pay its maturing obligations & meet unexpected needs of $
54
Current ratio
short-term debt paying ability
55
Cash debt coverage ratio
short-term debt paying ability on a cash basis
56
Receivables Turnover Ratio
of times a company collects receivables a year
57
Average Collection period
converts RTR into days
58
Inventory Turnover ratio
of times a inventory was sold a year
59
Days in Inventory
Avg # of days inventory is held
60
Solvency
measures the ability of a company to survive for a long duration
61
Debt to Assets Total Ratio
% of total assets provided by creditors
62
Times Interest Earned Ratio
company's ability to meet interest payments
63
Cash Debt Coverage Ratio
long-term debt paying ability on a cash basis
64
Free cash flow
cash available to pay dividends or expand operations
65
Profitability
measure income or operating success of a company
66
Return on Stockholder's Equity
$ of net income earned/$ invested
67
Return on assets
overall profitability of assets
68
Profit Margin ratio
net income generated by each $ of sales
69
Asset turnover
how efficiently assets are used to generate sales
70
Gross Profit Rate
margin between selling price & COGS
71
Earnings per Share
net income earned on each dollar of common stock
72
P-E Ratio
increase means investors believe company future earnings will grow
73
Payout Ratio
% of earnings distributed in cash dividends
74
Basic DuPont Model
ROE = Profit Margin x Total Asset Turnover x Financial Leverage (higher value --> more risk as reliant on debt for funding)
75
Increased ROE
increase in PM, TAT or FL
76
Decreased ROE
decrease in PM, TAT, or FL
77
Bankruptcy Classification (Altman's Z-score)
Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5
78
Variable Meanings
X1 = liquidity level X2 = long-term profitability X3 = short term profitability X4 = solvency X5 = asset efficiency (TAT)
79
If Z < 1.80
Significant risk of bankruptcy - "distress zone"
80
If 1.8 <= Z >= 3.0
At risk of bankruptcy - "gray zone"
81
If Z > 3.0
Not at risk - "safe zone"
82
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?
3.5
83
RPA
-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
AI
-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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