This document contains Discounted cash flow (DCF) analysis of NTPC which tells future free cash flow projections and discounts them (most often using the weighted average cost of capital) to arrive at a present value, which is used to evaluate the potential for investment. If the value arrived at through DCF analysis is higher than the current cost of the investment, the opportunity may be a good one.
Note:
1) The figures of Balance Sheet, Profit and Loss and Cash Flow Statements are in crores.
2) For reference XL sheet is attached in this document ,where it included all the calculations to arrive Discounted Cash Flow of NTPC.
This document contains Discounted cash flow (DCF) analysis of NTPC which tells future free cash flow projections and discounts them (most often using the weighted average cost of capital) to arrive at a present value, which is used to evaluate the potential for investment. If the value arrived at through DCF analysis is higher than the current cost of the investment, the opportunity may be a good one.
Note:
1) The figures of Balance Sheet, Profit and Loss and Cash Flow Statements are in crores.
2) For reference XL sheet is attached in this document ,where it included all the calculations to arrive Discounted Cash Flow of NTPC.
Presentation on Private Equity Valuation of Bkash. This presentation was performed for a National Financial Modeling Competition called " Blueprints," organized by NSU Finance Club
This document contains Discounted cash flow (DCF) analysis of NTPC which tells future free cash flow projections and discounts them (most often using the weighted average cost of capital) to arrive at a present value, which is used to evaluate the potential for investment. If the value arrived at through DCF analysis is higher than the current cost of the investment, the opportunity may be a good one.
Note:
1) The figures of Balance Sheet, Profit and Loss and Cash Flow Statements are in crores.
2) For reference XL sheet is attached in this document ,where it included all the calculations to arrive Discounted Cash Flow of NTPC.
Presentation on Private Equity Valuation of Bkash. This presentation was performed for a National Financial Modeling Competition called " Blueprints," organized by NSU Finance Club
This document contains Discounted cash flow (DCF) analysis of NTPC which tells future free cash flow projections and discounts them (most often using the weighted average cost of capital) to arrive at a present value, which is used to evaluate the potential for investment. If the value arrived at through DCF analysis is higher than the current cost of the investment, the opportunity may be a good one.
Note:
1) The figures of Balance Sheet, Profit and Loss and Cash Flow Statements are in crores.
2) For reference XL sheet is attached in this document ,where it included all the calculations to arrive Discounted Cash Flow of NTPC.
Financial InformationIn this worksheet, you will recreate both theChereCheek752
Financial InformationIn this worksheet, you will recreate both the company's balance sheet and income statement for the past 3 yearsDon't forget to note your references for your data in the last TABMicrosoftMicrosoftIncome StatementBalance SheetFor the Years ended 2018 through 2020For the Years ended 2018 through 2020(Amounts in millions)(Amounts in millions)Fiscal Year:202020192018Fiscal Year:202020192018Total Revenue143,015125,843110,360 Cash, Cash Equivalents and Short Term Investments136,527133,819133,768Cost of Revenue46,07842,91038,353 Inventories1,8952,0632,662Gross Profit96,93782,93372,007 Trade and Other Receivables, Current32,01129,52426,481 Selling, General and Administrative Expenses24,70923,09822,223 Other Current Assets11,48210,1466,751 Research and Development Expenses19,26916,87614,726 Total Current Assets181,915175,552169,662Operating Income/Expenses43,97839,97436,949 Deferred Costs/Assets, CurrentTotal Operating Profit/Loss52,95942,95935,058 Total Non-Current Assets119,396111,00489,186Non-Operating Income/Expenses, Total777291,416 Net Property, Plant and Equipment52,90443,85636,146Pretax Income53,03643,68836,474 Net Intangible Assets50,38949,77643,736Provision for Income Tax8,7554,44819,903 Total Long Term Investments2,9652,6491,862Net Income from Continuing Operations44,28139,24016,571 Other Non-Current Assets13,13814,7237,442Total Assets301,311286,556258,848 Financial Liabilities, Current3,7495,5163,998 Provisions, Current7,8746,8306,103 Deferred Liabilities, Current36,00032,67628,905 Other Current Liabilities10,0279,3518,744 Total Current Liabilities72,31069,42058,488 Long Term Debt59,57866,66272,242 Capital Lease Obligations, Non-Current7,6716,1885,568 Tax Liabilities, Non-Current204233541 Deferred Income/Customer Advances/Billings in Excess of Cost, Non-Current3,1804,5303,815 Payables and Accrued Expenses, Non-Current29,43229,61230,265 Other Non-Current Liabilities10,6327,5815,211 Total Non-Current Liabilities110,697114,806117,642Total Liabilities183,007184,226176,130 Equity Attributable to Parent Stockholders118,304102,33082,718 Paid in Capital80,55278,52071,223 Retained Earnings/Accumulated Deficit34,56624,15013,682 Reserves/Accumulated Comprehensive Income/Losses3,186(340)(2,187)Total Equity118,304102,33082,718Total Equity and Liabiltiies301,311286,556258,848
3-Horizontal Analysis ISMicrosoftIncome StatementFor the Years ended 2018 through 2020(Amounts in millions)2019201820202019$ Change% Change20192018$ Change% Change2082018$ Change% ChangeTotal Revenue143,015125,84317,17213.6%125,843110,36015,48314.0%110,360110,360- 00.0%Cost of Revenue46,07842,9103,1687.4%42,91038,3534,55711.9%38,35338,353- 00.0%Gross Profit96,93782,93314,00416.9%82,93372,00710,92615.2%72,00772,007- 00.0% Selling, General and Administrative Expenses24, ...
Financial Modelling Videos (Fimovi) enables its customers to develop flexible and robust financial plans and cashflow models based on Microsoft Excel. Fimovi provides a broad range of financial modelling related services for clients across many industry sectors. http://www.excel-financial-model.com/
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. WIPRO Prediction of development and bankruptcy
By using ; Z-score model
--------------------------0------------------------
SUBMITTED TO
------------------------0----------------------
Dr. SAIYAD KHWAJA SAFIUDDIN
ASSISTANCE PROFESSOR
DEP. MANAGEMENT STUDIES
MANUU
===============0===============
PRESENTED BY
=============0=============
SHAHBAZ ALAM
MBA 2nd SEM
ROLL NO. 17MMBA008HY
SUBJECT . FINANCIAL MANAGEMEN
1
2. Z-Score model
The Z-score formula for predicting bankruptcy was published
in 1968 by Edward I. Altman, who was, at the time, an
Assistant Professor of Finance at New York University. The
formula may be used to predict the probability that a firm will
go into bankruptcy within two years.
Z-scores are used to predict corporate defaults and an
easy-to-calculate control measure for the financial
distress status of companies in academic studies. The Z-score
uses multiple corporate income and balance sheet values to
measure the financial health of a company.
2
3. Cont….
Z-Score mentions three status of financial
stress of a company.
1. Safe zone :- If the final Z-score generated in this
model is higher than 2.99.
2. Grey zone :- if the final Z-score, generated, is
between 1.81 to 2.99.
3. Distress zone :- If the final Z-score is
less than 1.81.
3
4. ABOUT WIPRO
1. The company was incorporated on 29 December 1945 in Amalner,
Maharashtra by Mohamed Hasham Premji as 'Western India Vegetable
Products Limited', later abbreviated to 'Wipro‘ .
2. Wipro Limited (Western India Palm Refined Oils Limited or more
recently, Western India Products Limited).
3. Services : digital strategies, business consulting, BPO and IT services
4. Chairman : Azim premji
5. CEO : Abid Ali Neemuchwala.
6. Number of employees: 166790.
7. It’s headquartered in Bengaluru, India.
8. the company shifted its focus to new business opportunities in the IT
and computing industry 1980s.
4
5. Balance sheet
Mar '17 Mar '16 Mar '15 Mar '14 Mar '13
12 mths 12 mths 12 mths 12 mths 12 mths
Sources Of Funds
Total Share Capital 486.10 494.10 493.70 493.20 492.60
Equity Share Capital 486.10 494.10 493.70 493.20 492.60
Reserves 46,219.50 40,411.10 34,127.90 28,862.70 23,736.90
Networth 46,705.60 40,905.20 34,621.60 29,355.90 24,229.50
Secured Loans 116.10 120.10 114.30 106.00 50.40
Unsecured Loans 6,048.80 6,575.90 5,919.30 4,404.30 3,995.60
Total Debt 6,164.90 6,696.00 6,033.60 4,510.30 4,046.00
Total Liabilities 52,870.50 47,601.20 40,655.20 33,866.20 28,275.50
Mar '17 Mar '16 Mar '15 Mar '14 Mar '135
6. Application Of Funds
Gross Block 10,970.30 10,297.50 9,451.20 9,034.60 8,312.50
Less: Accum. Depreciation 6,608.10 6,108.80 5,412.80 5,059.60 4,403.10
Net Block 4,362.20 4,188.70 4,038.40 3,975.00 3,909.40
Capital Work in Progress 0.00 0.00 361.20 275.10 378.90
Investments 35,146.10 18,463.00 10,768.50 11,036.00 10,904.20
Inventories 355.90 526.20 479.40 228.30 320.50
Sundry Debtors 8,129.90 8,704.80 8,144.20 8,550.90 8,499.40
Cash and Bank Balance 3,516.60 12,007.80 15,667.50 10,554.90 7,800.40
Total Current Assets 12,002.40 21,238.80 24,291.10 19,334.10 16,620.30
Loans and Advances 10,952.10 14,960.90 13,949.30 11,116.70 8,893.80
Total CA, Loans & Advances 22,954.50 36,199.70 38,240.40 30,450.80 25,514.10
Current Liabilities 8,607.00 8,776.90 8,364.70 7,992.60 8,792.80
Provisions 1,679.40 2,798.40 4,388.60 3,878.10 3,638.30
Total CL & Provisions 10,286.40 11,575.30 12,753.30 11,870.70 12,431.10
Net Current Assets 12,668.10 24,624.40 25,487.10 18,580.10 13,083.00
Total Assets 52,176.40 47,276.10 40,655.20 33,866.20 28,275.50
Contingent Liabilities 3,753.70 4,385.10 3,022.20 2,793.40 2,657.80
Book Value (Rs) 192.13 165.56 140.22 119.03 98.38
6
7. Profit & loss
Mar 17 Mar 16 Mar 15 Mar 14 Mar 13
12 mths 12 mths 12 mths 12 mths 12 mths
INCOME
Revenue From Operations
[Gross]
45,639.60 44,684.60 41,210.00 38,765.10 33,229.60
Less: Excise/Sevice
Tax/Other Levies
0.00 0.00 0.20 7.90 3.10
Revenue From Operations
[Net]
45,639.60 44,684.60 41,209.80 38,757.20 33,226.50
Other Operating Revenues 408.20 0.00 0.00 0.00 0.00
Total Operating Revenues 46,047.80 44,684.60 41,209.80 38,757.20 33,226.50
Other Income 2,570.00 2,771.50 2,499.00 1,611.20 1,325.30
Total Revenue 48,617.80 47,456.10 43,708.80 40,368.40 34,551.80
EXPENSES
Cost Of Materials Consumed 0.00 0.20 3.40 205.30 354.20
Purchase Of Stock-In Trade 2,186.90 2,655.50 2,456.40 2,285.80 2,347.20
Changes In Inventories Of
FG,WIP And Stock-In Trade
164.00 -53.10 -254.30 0.90 -18.20
Employee Benefit Expenses 21,854.40 21,379.70 19,726.30 18,337.50 15,904.20
Finance Costs 392.10 527.80 362.90 374.70 352.40
Depreciation And
Amortisation Expenses
1,047.70 868.80 778.40 736.70 701.30
Other Expenses 12,285.60 11,595.10 10,078.70 8,819.30 7,705.60
Total Expenses 37,930.70 36,974.00 33,151.80 30,760.20 27,346.70
7
8. Profit/Loss Before
Exceptional, ExtraOrdinary
Items And Tax
10,687.10 10,482.10 10,557.00 9,608.20 7,205.10
Profit/Loss Before Tax 10,687.10 10,482.10 10,557.00 9,608.20 7,205.10
Tax Expenses-Continued Operations
Current Tax 2,430.40 2,452.30 2,376.60 2,056.30 1,530.00
Less: MAT Credit
Entitlement
0.00 0.00 0.00 0.00 71.90
Deferred Tax 95.00 -69.20 -12.70 52.40 10.00
Tax For Earlier Years 0.00 0.00 0.00 112.10 86.80
Total Tax Expenses 2,525.40 2,383.10 2,363.90 2,220.80 1,554.90
Profit/Loss After Tax And
Before ExtraOrdinary
Items
8,161.70 8,099.00 8,193.10 7,387.40 5,650.20
Profit/Loss From
Continuing Operations
8,161.70 8,099.00 8,193.10 7,387.40 5,650.20
Profit/Loss For The Period 8,161.70 8,099.00 8,193.10 7,387.40 5,650.20
Mar 17 Mar 16 Mar 15 Mar 14 Mar 13
12 mths 12 mths 12 mths 12 mths 12 mths
OTHER ADDITIONAL INFORMATION
EARNINGS PER SHARE
Basic EPS (Rs.) 33.61 32.97 33.00 30.09 23.03
Diluted EPS (Rs.) 33.51 32.91 33.00 30.01 22.99
VALUE OF IMPORTED AND INDIGENIOUS RAW
MATERIALS
Imported Raw Materials 0.00 0.00 2.60 141.60 242.60
Indigenous Raw Materials 0.00 0.00 0.80 63.70 111.60
STORES, SPARES AND LOOSE TOOLS
DIVIDEND AND DIVIDEND PERCENTAGE
Equity Share Dividend 729.10 1,482.30 2,963.60 1,973.60 1,724.70
Tax On Dividend 148.50 308.50 592.40 335.30 289.20
Equity Dividend Rate (%) 200.00 300.00 600.00 400.00 350.00
8
9. FINANCIAL HELTH(2017)
Particular Amount (in cror)
Retain earning
Rs 44910.50
Working capital Rs 4061.10
EBIT Rs 10687.10
Net worth Rs 46705.60
Total assets Rs 63156.90
Sales Rs 46047.80
9
12. Conclusion
The Z-score value of wipro limited is 3.58,
which is higher than 2.99. so the company is
in Safe zone for the year of 2017.
and this is a secure situation for investment in
wipro limited .
there is very less chance of company going
bankrupt in the next two year.
12
