Financial Data analysis using MS Excel Innovation
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Wikipedia
A financial analyst is a professional, undertaking financial analysis for external or internal clients as a core feature of the job. The role may specifically be titled securities analyst, research analyst, equity analyst, investment analyst, or ratings analyst. The job title is a broad one: in banking, and industry more generally, various other analyst-roles cover financial management and (credit) risk management, as opposed to focusing on investments and valuation; these are also discussed in this article.
Financial analysts are employed by mutual- and pension funds, hedge funds, securities firms, banks, investment banks, insurance companies, and other businesses, helping these companies or their clients make investment decisions. In corporate roles, financial analysts perform budget, revenue and cost modelling and analytics as part of their responsibilities; credit analysis is likewise a distinct area.
Financial analysts invariably use spreadsheets (and statistical software packages) to analyze financial data, spot trends, and develop forecasts. The analyst often also meets with company officials to gain a better insight into a company’s prospects and to determine the company’s managerial effectiveness.
Analysts specializing in advanced mathematical modeling and programming are referred to as “quants”; see Finance § Quantitative finance for an overview, and Quantitative analysis (finance) § Types for the various roles.
In a stock brokerage house or investment bank (discussed below), the analyst will read company financial statements and analyze commodity prices, sales, costs, expenses, and tax rates in order to determine a company’s value and project future earnings. On the basis of their results, they write reports and make presentations, usually making recommendations to buy or sell a particular investment or security.
Typically, at the end of the assessment, an analyst would provide a rating recommending or investment action: to buy, sell, or hold the security. Senior analysts may actually make the decision to buy or sell for the company or client if they are the ones responsible for managing the assets. Other, “junior” analysts use the data to model and measure the financial risks associated with making a particular investment decision. See Securities research § Career path.
Usually, financial analysts study a specific industry – “sector specialists” – assessing current trends in business practices, products, and industry competition. Among the industries with the most analyst coverage are biotechnology, financial services, energy, mining / resources, and computer hardware, software and services. Analysts must keep abreast of new regulations or policies that may affect the industry, as well as monitor the economy to determine its effect on earnings. A 1999 paper by Ezra Zuckerman found that, as equity analysts divide securities by discrete sectors, companies which fall outside or across multiple sectors are punished in the ratings of analysts
Analysts also specialize in fixed Income. Similar to Equity Analysts, Fixed Income Analysts assess the value and analyze the risks of various securities, here focusing on interest rate- and fixed income securities, particularly bonds. They may further specialize, but here by issuer-type, i.e. municipal bonds, government bonds, and corporate bonds; the latter specialization is often decomposed into convertible bonds, high yield bonds, and distressed bonds; some cover syndicated bank loans. The reporting focuses on the ability of the issuer to make payments – similar to the credit analysis described below – but also on the relative value of the security in question, and in context of the overall market and yield curve. See Fixed income analysis.
Analysts are generally divided into ‘sell-side’ and ‘buy-side’. The buy-side is sometimes considered more prestigious, professional, and scholarly, while the sell-side may be higher-paid and more like a sales and marketing role. It is common to begin careers on the sell-side at large banks then move to the buy-side at a fund.
- A sell-side analyst’s work is not used by its employer to invest directly, rather it is sold either for money or for other benefits by the employer to buy-side organisations. Sell-side research is often used as ‘soft money’ rather than sold directly, for example provided to preferred clients in return for business. Writing reports or notes expressing opinions is always a part of “sell-side” (brokerage) analyst job and is often not required for “buy-side” (investment firms) analysts. It is sometimes used to promote the companies being researched when the sell-side has some other interest in them, as a form of marketing, which can lead to conflicts of interest.
- A buy-side analyst, such as a fund manager, works for a company which buys and holds stocks itself, on the analyst’s recommendation. As they gain experience, analysts often move from buy-side research, concerning individual securities and sectors, into portfolio management itself, selecting the mix of investments for a company’s portfolio. They may also become fund managers and manage large investment portfolios for individual investors.
Typically, analysts use fundamental analysis principles, but technical analysis and tactical evaluation of the market environment are also routine. Analysts obtain information by studying public records and filings by the company, as well as by participating in public earnings calls where they can ask direct questions to the management. Additional information can be also received in small group or one-on-one meetings with senior members of management teams. However, in many markets such information gathering became difficult and potentially illegal due to legislative changes brought upon by corporate scandals in the early 2000s. One example is Regulation FD (Fair Disclosure) in the United States. Many other developed countries also adopted similar rules.
Analyst performance is ranked by a range of services such as StarMine owned by Thomson Reuters or Institutional Investor magazine. Research by Numis found that small companies with the most analyst coverage outperformed peers by 2.5 per cent — while those with low coverage underperformed by 0.7 per cent.
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses.Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.
Analysis, refers to dividing a whole into its separate components for individual examination. Data analysis, is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Data, is collected and analyzed to answer questions, test hypotheses, or disprove theories.
Statistician John Tukey, defined data analysis in 1961, as:
“Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases. The CRISP framework, used in data mining, has similar steps.
Microsoft Excel is a spreadsheet developed by Microsoft for Windows, mac OS, Android and iOS. It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA). Excel forms part of the Microsoft Office suite of software.
Microsoft Excel has the basic features of all spreadsheets, using a grid of cells arranged in numbered rows and letter-named columns to organize data manipulations like arithmetic operations. It has a battery of supplied functions to answer statistical, engineering, and financial needs. In addition, it can display data as line graphs, histograms and charts, and with a very limited three-dimensional graphical display. It allows sectioning of data to view its dependencies on various factors for different perspectives (using pivot tables and the scenario manager). A Pivot Table is a tool for data analysis. It does this by simplifying large data sets via Pivot Table fields. It has a programming aspect, Visual Basic for Applications, allowing the user to employ a wide variety of numerical methods, for example, for solving differential equations of mathematical physics, and then reporting the results back to the spreadsheet. It also has a variety of interactive features allowing user interfaces that can completely hide the spreadsheet from the user, so the spreadsheet presents itself as a so-called application, or decision support system (DSS), via a custom-designed user interface, for example, a stock analyzer, or in general, as a design tool that asks the user questions and provides answers and reports. In a more elaborate realization, an Excel application can automatically poll external databases and measuring instruments using an update schedule, analyze the results, make a Word report or PowerPoint slide show, and e-mail these presentations on a regular basis to a list of participants. Excel was not designed to be used as a database.
Excel 2016 has 484 functions. Of these, 360 existed prior to Excel 2010. Microsoft classifies these functions in 14 categories. Of the 484 current functions, 386 may be called from VBA as methods of the object “Worksheet Function” and 44 have the same names as VBA functions.
The Windows version of Excel supports programming through Microsoft’s Visual Basic for Applications (VBA), which is a dialect of Visual Basic. Programming with VBA allows spreadsheet manipulation that is awkward or impossible with standard spreadsheet techniques. Programmers may write code directly using the Visual Basic Editor (VBE), which includes a window for writing code, debugging code, and code module organization environment. The user can implement numerical methods as well as automating tasks such as formatting or data organization in VBA and guide the calculation using any desired intermediate results reported back to the spreadsheet.
VBA was removed from Mac Excel 2008, as the developers did not believe that a timely release would allow porting the VBA engine natively to Mac OS X. VBA was restored in the next version, Mac Excel 2011, although the build lacks support for ActiveX objects, impacting some high level developer tools.
A common and easy way to generate VBA code is by using the Macro Recorder. The Macro Recorder records actions of the user and generates VBA code in the form of a macro. These actions can then be repeated automatically by running the macro. The macros can also be linked to different trigger types like keyboard shortcuts, a command button or a graphic. The actions in the macro can be executed from these trigger types or from the generic toolbar options. The VBA code of the macro can also be edited in the VBE. Certain features such as loop functions and screen prompt by their own properties, and some graphical display items, cannot be recorded but must be entered into the VBA module directly by the programmer. Advanced users can employ user prompts to create an interactive program, or react to events such as sheets being loaded or changed.
Macro Recorded code may not be compatible with Excel versions. Some code that is used in Excel 2010 cannot be used in Excel 2003. Making a Macro that changes the cell colors and making changes to other aspects of cells may not be backward compatible.
VBA code interacts with the spreadsheet through the Excel Object Model, a vocabulary identifying spreadsheet objects, and a set of supplied functions or methods that enable reading and writing to the spreadsheet and interaction with its users (for example, through custom toolbars or command bars and message boxes). User-created VBA subroutines execute these actions and operate like macros generated using the macro recorder, but are more flexible and efficient.
From its first version Excel supported end-user programming of macros (automation of repetitive tasks) and user-defined functions (extension of Excel’s built-in function library). In early versions of Excel, these programs were written in a macro language whose statements had formula syntax and resided in the cells of special-purpose macro sheets (stored with file extension .XLM in Windows.) XLM was the default macro language for Excel through Excel 4.0. Beginning with version 5.0 Excel recorded macros in VBA by default but with version 5.0 XLM recording was still allowed as an option. After version 5.0 that option was discontinued. All versions of Excel, including Excel 2010 are capable of running an XLM macro, though Microsoft discourages their use.