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Essay on data mining in decision making
Essay on data mining in decision making
Essay on data mining in decision making
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Every day, almost every moment, we are making decisions. The decision-making process is extremely important in our life. Since as long as you made a decision, we will contribute most of our capital, time, focus and energy on the direction you selected. We believe that better decision can make life better. Different decision results come from, sometimes, different knowledge set or preferences people have. Before doing this project, we have reached a consensus that knowledge is power. And data mining can give us better knowledge to make better decision. This project study will introduce the detail of our working, including data-preprocessing, exploratory data analysis, predictive model construction, result analysis. The report is designed to have 4 sections. Section 1 will be a brief project introduction. Section 2 is about data description and data preprocessing. The data mining methodologies we employed is detailed in Section 3. Section 4 shows the results of this data mining project. Problem Description and Objective Venture capital is financial capital provided to some young, high-potential, high risk companies. The venture capital fund makes money by owning equity in the companies it invests in, which usually have a novel technology or business model in high technology industries, such as biotechnology, IT and software. In addition, venture capital is attractive for new companies with limited operating history that are too small to raise capital in the public markets and are hardly qualified to secure a bank loan. The typical venture capital investment happens when venture capitalists show strong interests on the targeted start-ups and expect high returns at the time when they exit, which is usually with the company going IPO or being acquired. The initial idea we have seems irrational at first. We supposed that we are the investment manager of a venture capital fund. We have 10 million in hand. Generally, if we do nothing with this money, our money keeps watered due to the inflation. In effect, the existence of a huge amount of historical data shows that data mining can provide a competitive advantage over human inspection of these data. Even though, economics theory named the Efficient Market Hypothesis suggests that the markets adapt so rapidly in terms of price adjustments that there is no space to gain profits in a consistent way. However, this theory does not always align with the reality in the financial market, which leaves the investors some space for speculation. The general goal of venture capital is to maintain a portfolio of equities of some early-stage, and high-potential companies.
The efficient market, as one of the pillars of neoclassical finance, asserts that financial markets are efficient on information. The efficient market hypothesis suggests that there is no trading system based on currently available information that could be expected to generate excess risk-adjusted returns consistently as this information is already reflected in current prices. However, EMH has been the most controversial subject of research in the fields of financial economics during the last 40 years. “Behavioural finance, however, is now seriously challenging this premise by arguing that people are clearly not rational” (Ross, (2002)). Behavioral finance uses facts from psychology and other human sciences in order to explain human investors’ behaviors.
The case study is about an interview, conducted to four venture capitalists from four of the most prominent VC Silicon Valley firms, Kleiner Perkins Caufield & Byers (KPCB), Menlo Ventures, Trinity Ventures and Alta Partners. These firms invest both in seed as well as in later-stage companies, which operate mostly in the information technology sector. However, each VC has developed different sector portfolio depending on the expertise of the venture capitalists, the partner network and other factors. Professor Mike Roberts and Lauren Barley a senior research associate, both from Harvard Business School, have made a series of seven questions to their interviewees to understand how they evaluate potential venture opportunities and what they look at in order to decide if they will fund them and in which way. The questions were dealing with how VC’s evaluate potential venture opportunities, how they conduct due diligence, what process id followed for the decision making, what financial analyses is performed, the role of risk in the evaluation and how they think of potential exit routes. These questions were asked individually and revealed several similarities as well as differences in the strategy and the criteria that are used for the evaluation.
The following introductory sections describe the problem to be investigated and the goal to be achieved. The introduction also provides an analysis of the relevance and significance of the research and a discussion of barriers and issues related to achieving the goal. In addition, the approach and resources to be used in accomplishing the goal are discussed. Finally, a brief summary is provided.
In particular, startups conform to a set of formalized, ritualistic practices in order to obtain venture capital (VC) funding during the “seed” phase. Almost paradoxically, new companies are regarded as a kernel of innovation and invention in the economy and yet they seem to emulate each others’ routines in the pursuit of early investment, decoupled from the actual products or services they plan to sell to the
Big Data, Predictive Analytics and Data Mining have other important applications that do not embody direct impact over managerial strategy in a company; nonetheless, they represent a significant tool in society. These include the successful use of Big Data in astronomy (e.g., the Sloan Digital Sky Survey of telescopic information), politics (e.g., a political campaign focused on people most likely to support a candidate based on social networks or web searches) (Murdoch and Detsky, 2013), and education, where Data Mining offers educational institutions additional approaches to improve graduation rates of students, students' success and learning outcomes, through prediction, cluster analysis, association and classification by info-data informatics tools (Beikzadeh, Phon-Amnuaisuk, and Delavari, 2008).
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
In the beginning, businesses used information technology for automating the processes primarily to reduce labor costs. Subsequently, information technology is used for delivering information with speed and accuracy.
There is a sense of complexity today that has led many to believe the individual investor has little chance of competing with professional brokers and investment firms. However, Malkiel states this is a major misconception as he explains in his book “A Random Walk Down Wall Street”. What does a random walk mean? The random walk means in terms of the stock market that, “short term changes in stock prices cannot be predicted”. So how does a rational investor determine which stocks to purchase to maximize returns? Chapter 1 begins by defining and determining the difference in investing and speculating. Investing defined by Malkiel is the method of “purchasing assets to gain profit in the form of reasonably predictable income or appreciation over the long term”. Speculating in a sense is predicting, but without sufficient data to support any kind of conclusion. What is investing? Investing in its simplest form is the expectation to receive greater value in the future than you have today by saving income rather than spending. For example a savings account will earn a particular interest rate as will a corporate bond. Investment returns therefore depend on the allocation of funds and future events. Traditionally there have been two approaches used by the investment community to determine asset valuation: “the firm-foundation theory” and the “castle in the air theory”. The firm foundation theory argues that each investment instrument has something called intrinsic value, which can be determined analyzing securities present conditions and future growth. The basis of this theory is to buy securities when they are temporarily undervalued and sell them when they are temporarily overvalued in comparison to there intrinsic value One of the main variables used in this theory is dividend income. A stocks intrinsic value is said to be “equal to the present value of all its future dividends”. This is done using a method called discounting. Another variable to consider is the growth rate of the dividends. The greater the growth rate the more valuable the stock. However it is difficult to determine how long growth rates will last. Other factors are risk and interest rates, which will be discussed later. Warren Buffet, the great investor of our time, used this technique in making his fortune.
The efficient market hypothesis has been one of the main topics of academic finance research. The efficient market hypotheses also know as the joint hypothesis problem, asserts that financial markets lack solid hard information in making decisions. Efficient market hypothesis claims it is impossible to beat the market because stock market efficiency causes existing share prices to always incorporate and reflect all relevant information . According to efficient market hypothesis stocks always trade at their fair value on stock exchanges, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by purchasing riskier investments . In reality once cannot always achieve returns in excess of average market return on a risk-adjusted basis. They have been numerous arguments against the efficient market hypothesis. Some researches point out the fact financial theories are subjective, in other words they are ideas that try to explain how markets work and behave.
Big data is a concept that has been misunderstood therefore I will be writing this paper with the intentions of thoroughly discussing this technological concept and all its dimensions with regard to what constitutes big data and how the term came about. The rapid innovations in Information Technology have brought about the realisation of big data. The concept of big data is complex and has different connotations but I intend to clarify its functions. Big data refers to the concept of a collection of large and complex amounts of data that are found extremely difficult to notate or even process by most on-hand devices and database technologies.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
Studying Banking and Finance at University of St.Gallen will help me further increase my proficiency in corporate finance and financial markets. The in-depth research of specific topics, as well as a comprehensive curriculum, is a possibility for me to focus on my topic of interest – the mechanisms and institutions involved in providing venture capital and identifying angel investors as means to encourage innovation.... ... middle of paper ... ...