This presentation discusses Saad Suhail's portfolio investment of 10 billion rupees across debt and equity markets. 60% or 6 billion was invested in the debt market through treasury bills, PIBs and TFCs. 40% or 4 billion was invested in the equity market through MTS, future contracts and shares of various companies. The portfolio aims to generate regular income, preserve capital, and achieve a return higher than the benchmark of 7.25%.
Financial statements analysis of bank of communications, ChinaPrem Thapamagar
We used common sized analysis, CAMELS framework and Du Pont analysis to analyze financial statements of Bank of Communications, a state owned bank and a fortune 500 company in China. As financial statements of banks are different from that of a manufacturing company, analysis techniques are used in a different manner as well.
Trifid Research is a consistent Share advice-building firm in India, which are working last four years in share advice. Trifid Research offers finest Currency Tips, Commodity Tips and Forex Tips so on.
Financial statements analysis of bank of communications, ChinaPrem Thapamagar
We used common sized analysis, CAMELS framework and Du Pont analysis to analyze financial statements of Bank of Communications, a state owned bank and a fortune 500 company in China. As financial statements of banks are different from that of a manufacturing company, analysis techniques are used in a different manner as well.
Trifid Research is a consistent Share advice-building firm in India, which are working last four years in share advice. Trifid Research offers finest Currency Tips, Commodity Tips and Forex Tips so on.
Fund raising basics by Vipul Thakkar- Haribhakti (Jan 2012)GetEvangelized
This deck was presented by Vipul Thakkar (Haribhakti) at the first module of the funding Clinic series initiated by TiE Mumbai's Investor Forum in Jan 2012
Best in Class Finance Transformation - Best Practices for the Finance FunctionProformative, Inc.
The evolution of the CFO role from controlling and reporting to strategy and support for the exec team now includes responsibility to deliver value for key stakeholders, such as investors. Top finance organizations are capable in multiple components of enterprise performance management (EPM), including strategic planning, execution, cost visibility, driver behavior, forecasting, planning, predictive analytics, ERM, and process productivity improverment (lean and Six Sigma). This workshop covers effective EPM frameworks, optimal organizational structure, talent management, leveraging technology to improve processes, and best practices for process change.
Speaker:
Birgit Starmanns, Senior Director, Solution Marketing, SAP
Presentation delivered at CFO Dimensions 2013
Workshop
The Bombay Stock Exchange is an Indian stock exchange located at Dalal Street, Mumbai. Established in 1875, the BSE is Asia’s first stock exchange. More than 5000 Companies are registered under this exchange.
Total M. Cap of the company is around 4K Cr. SBI Ltd, LIC Ltd & Small Cap World Fund Inc, Bajaj Holdings & Investment Ltd are the strategic investors. CMP as on 23/03/2018 is 738.
Recommended Strategies and Long-Term ObjectivesUpon review .docxdanas19
Recommended Strategies and Long-Term Objectives
Upon review of the data provided within the appendices, in conjunction with the substantive strategic analyses noted above, there seems to be a clear strategy for iRobot to take to gain a competitive advantage in the near future. Furthermore, this strategy will ensure the company’s financial security and exponential growth for the next decade. Within three years, iRobot will have fully absorbed the new strategy’s initial costs and will provide substantial increases in net income and cash flows, which in turn will result in impressive financial statements to appeal to investors, as well as improved operating efficiency within the company to allow it to expand to new markets.
iRobot has an increasing number of competitors within its market, and its current market share is relatively small, despite the company’s continual growth over the past several years. The company has put little effort into its marketing campaigns, and has also placed few resources to research and development. However, the recommended strategy for the company will be to use considerable capital in research and development, to create innovative robots designed for the retail industry. Major retail companies, such as Walmart, are beginning to invest in robots to facilitate a great number of tasks, both in physical retail locations, as well as manufacturing and distribution centers. With the commitment from companies such as these to continue integrating robotics into their operations, a new lucrative market is available for iRobot. If the company could develop a robot to facilitate the needs of these retail giants, iRobot could recognize massive profits, and also capitalize on relatively untouched market, quickly grabbing up the majority of the market share.
The suggested strategy is for iRobot to invest $70 million in 2019 in the R & D department, to design and produce a retail-specific robot within 6 months. The company currently has more than enough liquid assets to cover this investment without putting the company in financial stress. Once the robots are developed, iRobot will invest $25 million in a marketing campaign geared specifically for the retail industry, to gain the attention of retailers and supply chain companies worldwide. In 2019, iRobot will purchase 5,000 robots, with the goal of selling 2,500 in the first year. The average cost for such a robot will be around $15,500 per robot for production, while the average sales price that iRobot could charge to retailers is around $50,000 per robot.
For the first year, iRobot will incur and additional $172.5 million in the initial investment and production of the first run of 5,000 robots. However, the company will also recognize an additional $125 million in revenues from the 2,500 robots to be sold in 2019. This will result in a decrease in net income of around 44%, which still nets the company nearly $50 million in net income. Although the first year represents .
Capitalstars is as stock advisory company, we generate intraday trading calls in Stock cash and F&O in NSE & BSE, Commodities including bullions, metals.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
4. To Construct A Optimum Return Investment
Portfolio
To Provide Regular Stream Of Income At
Comparative Rate Of Return
To Preserve The Capital To Extent Possible By
Investing
5. Alternative to a portfolio against which
performance is measured.
Our company’s benchmark return is 7.25%.
9. Government Securities (3.6 Billions)
40% in TBILLS: Having amount of 1.44 Billion,
we Invested in 3 months, 6 months and 12
months T-Bills at 6.17%, 6.18% and 6.21%
respectively.
60% in PIB’s: Having amount of 2.16 billions,
we invested 5 years & 10 years PIB’s at 7.75%
and 8.75% respectively
11. Private Securities (2.4 Billions)
Invested 20%, 35% & 55% of 2.4 Billions in
TFC’s of Bank Al-habib, Bank Alfalah & HBL at
the rate 4.98%, 5.95% and 8.71% respectively.
12.
13. Invested 4 Billion in Capital Market. In MTS
and Future Contract, we invested 0.8 Billion
respectively, And 2.4 Billions Rs in Outer
investments/shares.
Margin Trading System (20%)
Future Contracts (20%)
Shares (60%)
16. Invested in 20% in each industry. Purchased
following companies shares
Cements
439488 shares of LUCKY Cement
937500 shares of KOHAT Cement
Fertilizers
334006 shares of Engro Fert. & 781275 shares of
Engro Corp.
Automobile Accessories
334006 shares of EXIDE and 400300 shares of Atlas
Battery
18. Having Investment of 0.8 Billions, We invested
PIOC-APR, ATRL-MAY and EFOOD-APR at
return of 0.45%, 1.32% and 1.68%.
19. The purchase of securities in ready market by
equity participation is known as MTS. We
invested. The maximum mark up rate in MTS
Market is KIBOR + 8%.
We have invested 0.8 Billion Rs in MTS to get
good return.
20. Return from investment in equity is
210167161 and
Return from debt is 330,314,448.
21. SSCMC
provide consultancy to the investors for
better investment decisions
preserve the capital of the investors and
provides the good return on investments