provide the details Conclusions

2. FINANCIAL FORECASTING AND ALGORITHMS FOR PREDICTION Regression analysis is one of the most widely used techniques for analyzing multifactor data. Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data where the relationship between the variables can be described with a linear model. Linear regression is a predictive model that uses training and scoring data sets to generate numeric predictions in data. It is important to remember that linear regression uses numeric data types for all of its attributes. For long time linear regression was very common algorithm for prediction tasks. But, now are more typical methods such as neural networks, support vector machines (SVM) (North, 2012). Advantages/limitations of linear regression model: 1. Linear regression implements a statistical model that, when relationships between the independent variables and the dependent variable are almost linear, shows optimal results 2. Linear regression is often inappropriately used to model non-linear relationships 3. Linear regression is limited to predicting numeric output 4. A lack of explanation about what has been learned can be a problem (Douglas C. Montgomery, 2013) 2.1. Neural Networks Several linear and nonlinear statistical models were proposed in the literature to solve the problem of financial phenomena forecasting. (Clements, 2004)Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best Al techniques for optimization and parameter estimation (Hadavandi E., 2010). Neural networks have become increasingly popular in finance as financial services organizations have been the second largest sponsors of research in neural network application. An accurate forecast into the future can offer tremendous value in areas as diverse as financial market price movements, financial expense budget forecasts, website click through likelihoods, insurance risk, and drug compound efficacy, to name just a few. Many algorithm techniques, ranging from regression analysis to ARIMA for time series, among others, are regularly used to generate forecasts. A neural network approach provides a forecasting technique that can operate in circumstances where classical techniques cannot perform or do not generate the desired accuracy in a forecast. Neural networks offer a modeling and forecasting approach that can accommodate circumstances where the existing data has useful information to offer, but it might be clouded by several of the factors mentioned above (Omidi A., 2011). Neural networks can also account for mixtures of continuous and categorical data. These attributes make neural networks an excellent tool to potentially take the place of one or more traditional methods such as regression analysis and general least squares. Thus, neural networks can generate useful forecasts in situations where other techniques would not be able to generate an accurate forecast. In other situations, neural networks might improve forecasting accuracy dramatically by taking into account more information than traditional techniques are able to synthesize. Finally, the use of a neural network approach to build a predictive model for a complex system does not require a statistician and domain expert to screen through every possible combination of variables. Thus, the neural network approach can dramatically reduce the time required to build a model (Edward R. Jones, 2004). Artificial neural networks (ANNs) have been popularly applied for stock market prediction, since they offer superlative learning ability. They often result in inconsistent and unpredictable performance in the prediction of noisy financial data due to the problems of determining factors involved in design (Kyoung-jae Kim, 2012). 2.2 Support Vector Machines SVMs were developed by Cortes & Vapnik for binary classification. SVMs represent a powerful technique for general (nonlinear) classification, regression and outlier detection with an intuitive model representation (Hamel, 2011). Support Vector Machine (SVM) is a relatively new learning algorithm that has the desirable characteristics of the control of the decision function, the use of the kernel method, and the sparsity of the solution (Meyer, 2012). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm at the moment as Artificial Neural Networks used to do before. SVMs nowadays have become a popular technique in flexible modelling. There are some drawbacks, though: SVMs scale rather badly with 40 the data size due to the quadratic optimization algorithm and the kernel transformation. Furthermore, the correct choice of kernel parameters is crucial for obtaining good results (Yuan, 2011). 2.3 Bonds and Financial Forecasting A bond is a fixed interest financial asset issued by governments, companies, banks, public utilities and other large entities. Bonds pay the bearer a fixed amount a specified end date. A discount bond pays the bearer only at the ending date, while a coupon bond pays the bearer a fixed amount over specified interval (month, year, etc.) as well as paying a fixed amount at the end date. A bond that provides a standard against which the performance of other bonds can be measured. Government bonds are almost always used as benchmark bonds. Also referred to as "benchmark issue" or "bellwether issue". More specifically, the benchmark is the latest issue within a given maturity. For a comparison to be appropriate and useful, the benchmark and the bond being measured against it should have a comparable liquidity, issue size and coupon (Investopedia, 2014). The bond price prediction can help banks and financial institutions to build their portfolio in diversified manner. Using the trade price, the investor can assume not only the price of that bond, but can also the interest rates, and hence, has a very useful tool in his hand for investment purpose, thus making decisions about whether to invest or not, and if invest then when to invest (Diebold, 2006). 3. DATA AND METHODOLOGY Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions (Bramer, 2013). Data Mining methodologies have been evolving over time. In today's ever-changing economic environment, there is ample opportunity to leverage the numerous sources of financial data now readily available to the savvy business decision maker. This data can be used for business gain if the data is converted to information and then into knowledge (Kantardzic, 2011). Data mining processes, methods and technology oriented to transactional-type data have grown immensely in the last quarter century. There is significant value in the interdisciplinary notion of data mining for forecasting when used to solve bond price problems. The intention of this talk is to describe how to get the most value out of the myriad of available data by utilizing data mining techniques specifically oriented to data collected over time (Hui Li, 2012), Investors use predicted bond trade prices to inform their trading decisions throughout the day. In this paper we want to show how linear regression can be used to predict the next trading price of a US corporate bond. We use bond price data provided through Benchmark Solutions and Kaggle.com', which includes variables such as current coupon, time to maturity, and details of the previous 10 trades, among others. Regression models are useful and understandable models which are used for prediction and data fitting