A Proposed Prediction Model for Forecasting the Financial Market Value According to Diversity in Factor

Transcription

1 A Proposed Prediction Model for Forecasting the Financial Market Value According to Diversity in Factor Ms. Hiral R. Patel, Mr. Amit B. Suthar, Dr. Satyen M. Parikh Assistant Professor, DCS, Ganpat University, Assistant Professor, DCS, Ganpat University, Dean, FCA, Ganpat University 131

2 Abstract The objective of the proposed work to study and improve the supervised learning algorithms to predict the effect of various kind of government, particular sector s or the stock price. This report shows the proposed research work flow to fulfill the objective of same. The main aim of this study is to provide the best prediction model based on past histories and the financial news. Stock prices are strong-minded by furnish and requirement of investors. The demand supply gap has affected by the financial news. But it is a hard and time consuming task to read and analyze a lot of news published on several sources. So, investors have not enough time to review all financial news those affect stock price. The financial Market behavior is also based on the financial news so the news impact analysis guides more accurate predictions and gives more profitable trade so proposed models are considering the news impact in financial market prediction.. 1. Introduction Lots of researchers have done the research to know more about the future of market movement on various parameters. Different hypothesis has already been released like Effective Market Hypothesis (EMH). But still there is a scope of further research to identify the activities like insider trading, minimize the effect of overreaction etc The research has already been done to understand the factors that cause the market to rise and fall, but still further research is possible to predict further efficient financial product value based on cumulative effect of general market, corporate wages and news, and political news sentiment responsible for the movement of market. The overreaction of financial product prices always due to the series of good or bad news, the Government policies, economical and political news etc... are also the reasons for the financial market fluctuations. Consequently, the main goal of this study is to get the clear cut initiative to captivating conclusion for devoting the money. The different approaches are used for financial market prediction like data mining techniques, Machine Learning Techniques and Artificial Intelligence. Prediction can help investors as an advice or can be used as a component inside automatic agents. Sometimes prediction systems indirectly help the investors by providing supportive information such as the future market direction. The main objective of the proposed model is to to examine the effect of technical factor as well as fundamental factors like the kinds of government, different sectors for financial products and forecast the up and down trends. 2. Financial Models Financial, Political, Economical and Global Event Information, that moves financial markets in the world. This rich variety of on-line information and news in term of RSS feed, blog, alerts make it an attractive resource from which to mine the knowledge [1]. Financial data are available in different variety of size, shape and forms. They give the snapshot of trade where price and other parameters are recorded. [2] As per the econometrics, the classification of the financial data is Time Series Data, Cross Sectional Data, and Panel Data. With the technical analysis, Fundamental analysis posits that companies that do well in their line of work, be it by having high profits, a good managerial structure, a successful focus on research and innovation, or any other similar factors, will do well in analyzing the financial market. The following figure shows the methodology available for financial models. Figure 1. Methodologies for Financial Models 3. Forecasting Methodology There are various methodologies available for market forecasting. The lots of investor invest them money in financial market. Financial market is too dynamic in nature. As per latest growth of technology the dynamic market situation is also predictable. So the forecasting methods are required. The following figure shows the different available methodology for market forecasting. 132

3 This model requires the prior and expert knowledge to predict the value. Figure 2. Methodologies for Financial Forecasting The stock market have non linear data in nature so now a day for forecasting purpose non linear models are used more and give effective results. As per Flash Crash 2010, On May 6, US stock markets opened down and moved down most of the day on uncertainties about the debt crisis in Greece. The market was fall rapidly in 5 minutes. So after at most 5 months of investigations led by Gregg E. Berman, the U.S. Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) mattered a joint report dated September 30, 2010 and titled "Findings Regarding the Market Events of May 6, 2010" discovering the sequence of events leading to the Flash Crash. [14] This one shows the effects of news or events affection on the financial market. Financial research summarizes Fundamental and Technical approaches as two essential trading philosophies. Many researchers have attempted to predict the markets with these philosophies and utilizing artificial intelligent base techniques using Linear Regression (LR), Neural Networks (NNs), Genetic Algorithms (Gas), Support Vector Machines (SVMs) and Case-based Reasoning (CBR). [4][5][6] Barberis, Nicholas, and Richard Thaler emphasized psychological and behavioral elements of market value determination and propose sentimental analysis philosophies. Market value movement is depending on sentiment and opinion over news contents and global events. [7]Many studies have used news and event information (qualitative factors) as well as quantitative data in predicting financial markets. Hong and Han, introduced an automated system model that acquires event-knowledge from the Internet for the prediction of interest rates. The system is designed to adopt a prior-knowledge base, which is seen as expert knowledge as a foundation and then to apply the information to a neural network model for interest rate prediction. Mittermayer proposed a system to categorize financial news articles into pre-defined categories and then derive appropriate trading strategies based on these categories. Shihavuddin & his co-authors have proposed data mining algorithms which have been tested on the available information to learn the useful trends about the behavior of the stock market. So lots of financial and computational techniques are available for to predict the financial products value. The composition of financial and computational techniques will helpful to providing best accuracy based model. 4. Objective of Proposed Model The main objective of this study is to provide the prediction model for financial products with the premier accuracy. To accomplish the objective following activities will helpful. The objective of the proposed work to do study, improvement in the supervised learning algorithms to predict the effect of various kind of government, particular sector or the stock price. Stock prices are determined by supply and demand of investors. The demand supply gap has affected by the financial news. But it is a hard and time consuming task to read and analyze a lot of news published on several sources. So, investors have not enough time to review all financial news those affect stock price. [14] Considering the news impact in analyzing the stock market behavior, leads to more precise predictions and as a result more profitable trades. So far various prototypes have been developed which consider the impact of news in stock market prediction. Lots of researchers have done the research to know more about the future of market movement on various parameters. Different hypothesis has already been released like Effective market Hypothesis. But still there is a scope of further research to identify the activities like insider trading, minimize the effect of overreaction etc Shihavuddin, Masuna Venkateshwarlu & many other authors gives conclusion that there will be more precious research work extendible in this direction. [11] The composition of technical and fundamental analysis provides the way to improve the accuracy of proposed prediction model. 133

4 5. Proposed Model for Financial Market Forecasting Financial research encapsulates two elemental trading philosophies; Fundamental and Technical approaches [3]. Many researchers have attempted to predict the markets with these philosophies and utilizing artificial intelligent base techniques using [4][5] Linear Regression (LR) Neural Networks (NNs) Genetic Algorithms (Gas) Support Vector Machines (SVMs) Case-based Reasoning (CBR) Barberis, Nicholas, and Richard Thaler emphasized psychological and behavioural elements of market value determination and propose sentimental analysis philosophies. [6] on the Effect of Financial News using Advance computational techniques will be developed. The following figure shows the proposed research model. So as per the survey of different approaches proposed by authors the following model is proposed. Market value movement is depend on sentiment and opinion over news contents and global events. Many studies have used news and event information (qualitative factors) as well as quantitative data in predicting financial markets. [10] Hong and Han introduced an automated system model that acquires event-knowledge from the Internet for the prediction of interest rates. The system is designed to adopt a prior-knowledge base, which is seen as expert knowledge as a foundation and then to apply the information to a neural network model for interest rate prediction. [7] Kloptchenko represented mining techniques that analyzed quantitative and qualitative data from annual financial reports, in order to see if the textual part of the report contains some indication about future financial performance. They predict the movement of five major global stock indices based on current news. They addressed the problem of extracting, analyzing and synthesizing valuable information from continuous text streams covering financial information. [10] Mittermayer proposed a system to categorize financial news articles into pre-defined categories and then derive appropriate trading strategies based on these categories. [14] Figure 3. Financial Forecasting Model The proposed study will provides the effective prediction model with accuracy. To develop the model following concepts will helpful to achieve the objective. 6. Conclusion As per the current scenario lots of research work has been carried out as well as on going for the prediction of stock market. To full fill the objective of this research work, different models have studied and tried to find out the significant pros and cons of them. It's also trying to find out which one gives the best accurate results among them. So as per proposed model, the agent based system is developed which is helpful to gathering the financial data from online sources. After this the current work is ongoing to implement the TFIDM and semantic analysis on gathered data. This will helpful to mapping the relations among financial market product values and financial news or events information using computational intelligence techniques so the main objective of preparing prediction model is fulfill. Shihavuddin & his co-authors represents data mining algorithms which has been tested on the available information to learn the useful trends about the behaviour of the stock market. [19] As per the research objective an Automated Prediction Model for financial products value based 134

5 7. References 1. Azadeh Nikfarjam, Ehsan Emadzadeh & Saravanan Muthaiyah(2010), Text mining approaches for stock market prediction.ieee Volume 4 2. Sapankevych, N (2009), Time Series Prediction Using Support Vector Machines: A Survey IEEE Wojciech Gryc (2011), Neural Network Predictions of Stock Price Fluctuations 4. Technical-Analysis (2005), The Trader's Glossary of Technical Terms and Topics. 5. W. Huang, Y. Nakamori, and S. Wang (2005), "Forecasting stock market movement direction with support vector machine," Computers & Operations Research, vol. 32, 2005, pp Ingo Steinwart and Andreas Christmann (2008), Support Vector Machines. Springer-Verlag, New York, ISBN A. Mahajan, L. Dey, and S.M. Haque (2008), "Mining Financial News for Major Events and Their Impacts on the Market," 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Ieee, 2008, pp R.P. Schumaker and H. Chen (2009), "Textual analysis of stock market prediction using breaking financial news:the AZFin Text system," ACM Transactions on Information Systems, vol. 27, 2009, pp M. I. Yasef Kaya & M. Elif Karsligil (2010), Stock Price Prediction using Financial News Articles IEEE 2010, ISBN Thimmaraya Ramesh & Masuna Venkateshwarlu (2011), A New Quantitative behavioural Model for Financial Prediction rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore 11. A. S. M Shihavuddin (2010), Prediction of Stock Price analyzing the online financial news using Naïve Bayes classifier & local economic trends IEEE 2010, ISBN Zabir khan (2011), Price Prediction of Share Market using ANN, IJCA 13. Chiris Brooks, Introductory Econometrics for Finance. Cambridge University Press Crash 15. Dr. Yashpal Singh, Alok Singh Chauhan (2009) Neural Networks In Data Mining Journal of Theoretical and Applied Information Technology 16. Pratap Kishore Padhiary (2011) Development of Improved Artificial Neural Network Model for Stock Market Prediction IJEST 2011 ISSN : Vol. 3 No Mehul N Vora (2011) Genetic Algorithm for Trading Signal Generation International Conference on Business and Economics Research Vol I (2011) IACSIT Press, Kuala Lumpur, Malaysia 18. Zabir Haider Khan, Tasnim Sharmin Alin and Md. Akter Hussain (2011) Price Prediction of Share Market using Artificial Neural Network (ANN) International Journal of Computer Applications ( ) Volume 22 No.2, May Gabriel h i Cheong Fung, Jeffrey Xu Yu and Wai Lam (2003), Stock Prediction: Integrating Text Mining Approach using Real-Time News /03/$ IEEE 135