The key objective in any data mining activity is to find as many unsuspected relationships between obtained data sets as possible to be able to achieve a better understanding on how the data and its relationships are useful to the data owner. The potential of knowledge discovery using data mining is huge and data mining has been applied in many different knowledge areas such as in large corporations to optimize their marketing strategies or even to smaller scale in medicinal research where data mining is used to find the relationship patient’s data with the corresponding medicinal prescription and symptoms. The various uses of data mining even extends to the possible forecasting of stock market for business analyst or investors in determining whether or not it is possible to combine 6 methods of analysing stocks and use them to automatically generate a prediction in increase or decrease of stock market prices by the end of the day. (K. Senthamarai Kannan et al, 2010) In his research paper, Kannan describes the use of the following 5 methods of analysing stocks. Among the methods described in the referred documentation were: • Typical Price • Chaikin Money Flow Indicator • Stochastic Momentum Index • Relative Strength Index • Bollienger Bands • Moving Average Kannan et al, 2010 predicts that by using the advantages of all the algorithms of the above, the buy and sell signal can be produced by using the Bollinger signal function. The Moving Average Crossover was used as a benchmark to determine how much effective is the new, combined technique as compared to the other methods. In his results, he documented that the profitable signal for Moving Average was 52.62% and that the new algorithm have a profitable signal of approximately 5... ... middle of paper ... ...ws the decision tree of MECE. Another article entitled: A Review: Application of Data Mining Tools for Stock Market by Kerti S. Mahajan et al, made a thorough review regarding the use of data mining tools such as decision trees, neural networks, association rules, clustering and factor analysis. In one of their excerpts, it is said that decision tree is an excellent tool for making financial or number based decisions where a number of complex information have to be taken into account. This statement further supports Qasem et al’s reasoning in using decision trees as a more suitable methodology to perform stock market analysis. Decision trees can also help in forming accurate and balanced picture of the risks and rewards involved that will particularly be a great interest to investors in finding out what the right time to buy is and how to find the right stocks.
Some Forex traders depend on fundamental analysis while others depend on technical analysis. However, many successful Forex traders use a combination of both strategies. However, the important point to remember here is that no one strategy or combination of strategies is 100%
The stock price of a company is one way to gauge the relative health of the company. The stock market, which includes the buyers, sellers, and investors, is always looking for ways to measure one company against another. By using stock price, dividends, earning per share, and bond rating, outsiders are able to gauge the overall health of a company against another one. In this method of looking at the trends of these indicators, comparisons can be made between Team Andrews and Team Baldwin over the course of rounds 4-6 in the Capsim simulation.
This innovative system takes you beyond your current comfort level as a Dud Trader to become a Stud Trader.
After evalutating both the Black-Scholes Model and the Brownian Motion, we have come to know that the Black-Scholes Model is quite predictive as it gets close to the observed price. We found that with the Brownian motion it may take on negative values which results certain modelling prices to be frowned upon , hence making Black-Scholes Model more realistic. As we ventured in this study, we found that there is still more research to be done since many of the modern option models stems from the Black-Scholes model. Thus making the modern option pricing models more
Before playing the stock market game, I honestly had no idea about how the stock market work. I, however, have learned so much about the process of the stock market. It was an advantage to learn how to buy and sell stocks without losing any thing, that will indeed enable me to invest in the real stock market without any concern. I learned that there is no certainty about wining or losing; however, there are many factors that we should consider before buying or selling stocks. One of theses factors is follow the daily news about the firm that you are willing to buy its stocks. Following the history of the firm transactions is also a significant factor that must be considered. The level of stability
The project is done to find out the impact of stock split on the stock market. In our project, we have made use of event study methodology to assess the accuracy of stock price reaction of 39 public listed Indian companies in National Stock Exchange (BSE) in the year 2006 and onwards. The abnormal returns (actual returns-returns from regression line) results were taken for 20 days before and after the announcement date to test whether the result is significant or not (Level of significance=5%). The project shows that there is no significance difference in the price level before the announcement date while after the announcement date, there was a significant difference in the price level for few days(level of significance being 5%) The project supports the hypothesis that Indian stock market is semi strong efficient.
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.
Chapter 11 closes our discussion with several insights into the efficient market theory. There have been many attempts to discredit the random walk theory, but none of the theories hold against empirical evidence. Any pattern that is noticed by investors will disappear as investors try to exploit it and the valuation methods of growth rate are far too difficult to predict. As we said before the random walk concludes that no patterns exist in the market, pricing is accurate and all information available is already incorporated into the stock price. Therefore the market is efficient. Even if errors do occur in short-run pricing, they will correct themselves in the long run. The random walk suggest that short-term prices cannot be predicted and to buy stocks for the long run. Malkiel concludes the best way to consistently be profitable is to buy and hold a broad based market index fund. As the market rises so will the investors returns since historically the market continues to rise as a whole.
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.
Following the trend of economy, it is important to investors to understand that strong economy creates strong stock market. To elaborate further, as stock prices are increased by current and future expectations of earnings, thus without a strong economy it would be difficult for the companies to increase and sustain their earnings (Kong 2013). The economy development is usually calculated using the gross domestic product of a countries. On the other hand, a change is the stock price can also cause a major impact to the consumers and investors directly. Hence, a loss in confidence by investors can cause a downturn in consumer spending in the long term, which will also affect the economy’s output (Aysen 2011). The graph below shows the relationship of stock market price (KLCI) and the GDP of Malaysia in 2009. Thus, it can be concluded that the economy and the stock market has a positive relationship.
Stock market prediction is the method of predicting the price of a company’s stock. It is believed that stock price is lead by random walk hypothesis. Random walk hypothesis states that stock market price matures randomly and hence can’t be predicted. Pesaran (2003) states that it is often argued that if stock markets are efficient then it should not be possible to predict stock returns. In fact, it is easily seen that stock market returns will be non-predictable only if market efficiency is combined with risk neutrality. On the other hand it is also been concluded that using variance ratio tests long horizon stock market returns can be predicted....
It has reached the day and age where accurate and real time prediction tools are needed in modern clinics and hospitals. To utilize predictive medicine it is important to use the right trends of data mining methodologies to get accurate results (Paramasivam et al. 2014).
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.
I am currently majoring in Finance Management. Most of the time people think of finance as just managing money. However, finance is needed for so much more! The finance industry deals with starting businesses, developing new products, expanding markets, as well as everyday things like saving for retirement, purchasing a home, and even insurance. The stock market, asset allocation, portfolio analysis, and electronic commerce are all key aspects in finance. In this paper, I will explain how these features play a vital role in the industry, along with the issues that come with these factors.