top down investing advantages of computer
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Top down investing advantages of computer forex hotel in samara

Top down investing advantages of computer

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I started my career as a programmer. We did big, boring but necessary implementations for large companies. I started by doing billing systems. In billing we literally started thinking about all of the types of bills that would be generated for customers: full payment, partial payment, split payment, senior discount, student discount, level pay plan, etc. This is bottom-up planning. It is useful in many situations and was useful to me in this situation.

I next moved into system design where I designed computer systems to deal with large industrial natural gas customers and telecommunication companies. I had to understand their business requirements and document them all. Post MBA I went into strategy consulting where my job was to problem solve for clients.

Often I had a very limited time. This is a problem for a strategy consultant because you are, by definition, a generalist that is thrown into new problems again and again. I struggled on my first few assignments.

It took too long for me to get around to speak to every departement, get data, assemble it, analyze the data to formulate conclusions and then communicate it to executive staff. The wisest mentor I ever had was Ameet Shah , my partner on several projects. He taught me much that I know about critical thinking. He coached me that I had to start with the answers. How can that be effective?

The key is not to be wedded to our original answer. We can also begin to socialize our answers with people in the company to get their reactions. That alone will help us solve the problems. I found this book very useful but still a little bit hard to implement. It is about structuring your thoughts, presentations and communications. You group items into component parts in a top-down manner.

The book is expensive but for me personally this helped me enormously. This and frankly a lot of practice. But I approach problems in a different way now. I start with answers and structure what I think the organization of the problem is. For more information, please email us or contact your J. Morgan client advisor. You are about to leave the site Close.

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It is an evolving and comprehensive platform for cloud computing offered by Amazon. Have you ever wondered why so many businesses are moving towards it? It is simply because there are several advantages of cloud computing. It helps in improving the cash flow, increases the efficiency and numerous others.

Have a look at these top 10 advantages of cloud computing. Less Costs The services are free from capital expenditure. There are no huge costs of hardware in cloud computing. You just have to pay as you operate it and enjoy the model based on your subscription plan. The workers can get onto the applications needed basically from anywhere. Some of the applications even function off-line. Flexibility in Capacity It offers flexible facility which could be turned off, up or down as per the circumstances of the user.

For instance, a promotion of sales is very popular, capacity can be immediately and quickly added to it for the avoidance of losing sales and crashing servers. When those sales are done, the capacity can also be shrunk for the reduction of costs. All over Functioning Cloud computing offers yet another advantage of working from anywhere across the globe, as long as you have an internet connection.

Even while using the critical cloud services that offer mobile apps, there is no limitation of the device used. Automated Updates on Software In cloud computing, the server suppliers regularly update your software including the updates on security, so that you do not need to agonize on wasting your crucial time on maintaining the system.

Security Cloud computing offers great security when any sensitive data has been lost. As the data is stored in the system, it can be easily accessed even if something happens to your computer. Out of the different investment options in India , Stoxbox also contains quality stocks that are selected using both fundamental and technical analysis. Moreover, through Stoxbox, you can also benefit from diversification as Stoxbox has a combination of the best investments in India.

Furthermore, before investing in stocks , you need a complete understanding of the stock market. Stocks are not safe investments as their value fluctuates continuously. Also, be knowledgeable about the rules of stock trading to generate profits on your investments.

Assess and analyse stocks on different parameters to make an informed investment. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Articles Videos Research. July 9, Share on Facebook Share on Twitter. Introduction When investing money in stocks , you need to pick the right stocks that can generate the maximum returns on your investments.

Do you know what they are? Important ratios include the Price-Earnings Ratio, return on capital employed, profit margins, current ratio, etc. Current and expected earnings. Revenue of the company. Cash flow analysis of the company. The products offered by the company, their demand, market share, and its USP The management of the company.

Then, stocks of companies belonging to the top-performing sectors are chosen. The fundamental and technical analysis of a particular stock is conducted and then compared with stocks of other companies. Then, the best stock is picked. Thus, investors can pick stocks of any company in the specific industry and expect growth.

If the stocks of a company perform well, it does not guarantee that the overall sector would also perform well. Investors try to pick stocks of such companies that are expected to outperform their competitors. You can find out and understand the monetary policy adopted by the country. This can help you earn attractive returns on your investments. You can have a holistic look at a company and its stock and then make an informed decision.

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Generally, when facing a problem or equation, you can find a solution in one of two ways. You can either process the problem in your head by matching it to previous information or you can let the equation present its variables to you without adding any context. The first describes a top-down approach, used by those who prefer applying previous knowledge to educate their perception. The opposite approach, bottom-up, is based on the belief that development should part from a stimulus.

In other words, what drives our perception is that which we sense. For example, a call center chat bot is based on a defined set of options and, depending on the answers, it guides the caller through a tree of options. These are applications of AI that provide systems with the ability to automatically learn and improve from experience without being explicitly programmed.

Generally, both Top-down and Bottom-up AI can offer good results. The question is rather when to use the one and when to use the other. You should always be mindful of the problem you are trying to solve and the data you have available when deciding what tool or methodology to apply. A great example in this regard was shared with me by an expert who formerly worked for one of the leading hard disk manufacturers.

They were running a cloud-based, big data research program. They decided to set-up their own Hadoop cluster and then spent a lot of time trying to figure out how to tune the system. Apparently, this process was extremely cumbersome, so in parallel they also tried a top-down approach based on genetic algorithms. It turned out that the results of top-down vs. The application of bottom-up AI may encounter 3 typical issues: 1 the necessary data may not be available, 2 the necessary data may be too expensive to generate, and 3 edge cases or unknowns.

An example for the latter is autonomous cars and the rare scenarios that bottom-up AI cannot handle. For example, when your autonomous car encounters a snowplow for the first time it will simply not know what to do and you better switch over to manual or stop. To counter this issue, developers are trying to capture as many miles as possible to increase learning weather, traffic scenarios, …. Data availability and cost are the key drivers in deciding whether to go for top-down vs.

Bottom-up systems require lots of often costly data, while top-down AI is data-efficient which may give it an edge over data-hungry bottom-up AI. If this ratio is high, it is better to apply non-parametric models bottom-up AI. Once the ratio gets very small, you want to go for parametric methods top-down AI.

To learn this capability, it was only necessary to learn the 10 digits. But then you may also need to sequence the data, etc. Bottomline in this case: you can easily spend tens of millions of dollars on data, but you would not learn much using bottom-up AI. In this context, it needs to be highlighted that a major fraction of data cost is often associated with data cleansing and labeling.

As we will discuss below, in addition to considerations on data availability and cost, properly framing the problem is key. Only once you have described your objectives, anticipated cost and effort required, and the expected outcome, you can make the best possible decision on whether to go for top-down or bottom-up AI or maybe even an approach that is not AI-based.

In a business context this is often a major issue. As a result, trust in bottom-up AI is often limited — even if the results are positive. This challenge is already relevant during the development stage. I tried different methods, I looked for shortcuts, but with only very limited success. I heard similar examples and opinions from across many sectors.

This is particularly true in strongly regulated environments, such as Finance and Health Care. Here, transparency of decision making is typically a must which often excludes bottom-up AI. Hence, a human operator can very much make sense of the results, can understand the decision-making process, and can also interfere, if necessary. In addition, even if a top-down approach is algorithmically difficult and you run it in the cloud, the required computing performance is often equal to that of a laptop.

The key prerequisite for data-efficient AI, however, is the availability of high-quality data. In the series of interviews that I conducted various experts shared the opinion that we will hear much more about data-efficient AI in the coming years. A former VP at Google stated that he was expecting that in the immediate future many will be adjusting their expectations and understand that not everything is a deep learning or bottom-up AI problem.

After all, decision trees can still do wonderful things today. IoT devices are typically tiny and hence very limited in terms of processing power , yet in many cases you want to incorporate the capability to learn. Looking at the characteristics and capabilities of both top-down AI and bottom-up AI, it may not come as a surprise that many experts mentioned the huge potential of hybrid systems.

These combine key business logic elements of top-down AI with the machine or deep learning capabilities of bottom-up AI to facilitate decision-making. Applying top-down AI as simple as a decision tree may result, for example, in fewer escalations to supervisors. Today, any case with a bit of complexity is often escalated and then, ultimately, customer wait time increases, along with customer frustration. Another example from the commercial space that one interviewee shared was from a shipping company that, amongst other things, often had to ship thousands of pipes.

Counting all the pipes manually would be an extremely time-consuming effort. A simple answer to the problem seemed to be to simply take photos and use bottom-up AI to develop an algorithm that counts the pipes. The problem, however, was that there were no standard sizes and dimensions for the pipes. So even if you captured all shapes and size today, there might be new types of pipes tomorrow. Also, obtaining the necessary training data would have been relatively expensive.

The bottom-up analysis takes a completely different approach. Generally, the bottom-up approach focuses its analysis on specific characteristics and micro attributes of an individual stock. In bottom-up investing concentration is on business-by-business or sector-by-sector fundamentals. Bottom-up investing begins its research at the company level but does not stop there. These analyses weigh company fundamentals heavily but also look at the sector, and microeconomic factors as well.

As such, bottom-up investing can be somewhat broad across an entire industry or laser-focused on identifying key attributes. Most often, bottom-up investors are buy-and-hold investors who have a deep understanding of a company's fundamentals. Fund managers may also use a bottom-up methodology.

For example, a portfolio team may be tasked with a bottom-up investing approach within a specified sector like technology. They are required to find the best investments using a fundamental approach that identifies the companies with the best fundamental ratios or industry-leading attributes.

They would then investigate those stocks in regards to macro and global influences. Metric focused smart-beta index funds are another example of bottom-up investing. Generally, while top-down and bottom-up can be very distinctly different they are often used in all types of financial approaches like checks and balances.

For example, while a top-down investment fund might primarily focus on investing according to macro trends, it will still look at the fundamentals of its investments before making an investing decision. Vice versa, while a bottom-up approach focuses on the fundamentals of investments, investors still want to consider systematic effects on individual holdings before making a decision.

Charles Schwab. Advisors Asset Management. Technical Analysis Basic Education. Investing Essentials. Fundamental Analysis. Your Money. Personal Finance. Your Practice. Popular Courses. Top-Down vs. Bottom-Up: An Overview Top-down and bottom-up approaches are methods used to analyze and choose securities. Key Takeaways Top-down usually encompasses a vast universe of macro variables while bottom-up is more narrowly focused. Top-down investing strategies typically focus on exploiting opportunities that follow market cycles while bottom-up approaches are more fundamental in nature.

While top-down and bottom-up can be very distinctly different they are often each used in all types of financial approaches like checks and balances. Article Sources. Investopedia requires writers to use primary sources to support their work.

These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy.

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