Comparative Predictive Modeling On Cnx Nifty With Artificial Neural Network

Page 3: Introduction to the Financial System Page 7: Commercial Banks Page 12: The Share Market and the Corporation Page 15: Corporations Issuing Equity into the Share Market Page 19: Investors in the Share Market Page 24: Short-term Debt Page 28: Medium- to Long-term Debt Page 32: Interest Rate Determination and Forecasting Page 37: The Foreign Exchange Market Page 40: Factors that Influence the Exchange Rate Page 42: Futures Contracts and Forward Rate Agreements Page 47: Options

In the paper Nguyen et al \cite{Micro:Ng}, the model makes use of the Micro-reviews\cite{Micro:Ng} generated by the users through various social media sites about any particular entity. Micro-Reviews \cite{Micro:Ng} are the reviews that are not too long , easy to comprehend and also considered as the most appropriate feedback of the customer. But it is starting to get complicated as the number of Micro-Reviews \cite{Micro:Ng} are increasing and is hard to go through several thousands of the user reviews to find the best review suitable to the user preferences. In order to overcome this, these reviews are categorised in to either positive or negative feedbacks. Then this Micro-Reviews \cite{Micro:Ng} are associated and

One of the biggest advantages of Neural Network is that it can actually learn from observing data sets. This way it uses a random function approximation tool, which helps to estimate the most efficient and ideal solution while defining all the computing functions and distributions. Neural Networks takes data samples instead of entire data sets to arrive to a solution, which saves a lot of time and money. Neural Networks are considered as simple mathematical models to enhance existing data analysis technology.

Instead we use the original predictors to predict the response. The original dataset was split into a training set that consists of 75% of the total observations and a test set that consists of 25% of the total observations. Observations were chosen randomly. Supervised learning methods was conducted on the training set to obtain a model, then the model was used on the test set to assess the prediction performance. The values for “K” in KNN were tuned via cross-validation. Due to the volume of the data, the “cost” parameter in the SVM was chosen somewhat ad hoc and the “mtry” parameter in the random forest was chosen as default. The error rates are as

Factors affecting forecast error and accuracy- Time horizon, Level of detail – product, geography, time.

There is a chance that further volatility in global markets, either caused by concerns over the health of China’s economy or the US Federal Reserve’s plans to raise interest rates, could influence the RBA’s economy forecasts.

More accuracy can be achieved by refining our data and knowing the significance of different attributes in the data, or the role of training or transfer functions. The role of neural networks and its working is still a mystery and is gradually evolution

The first law of forecasting is to assume the future will behave like the past .Event at that, there is a limit to how accurate the forecast can be even when using pass data. This research paper aims to forecast Walmart stock prices over a two year period. But because stock prices are more substantial when they are presented either in monthly or weekly data, the paper will be forecasting over two years but on a monthly basis i.e. Walmart stock prices is forecasted over a 24 month period.

By summarizing the data, we can interpret that the cars (cumulative abnormal return) has a mean of 0.313% with a standard deviation of 4.358%. Meanwhile, the gim, bcf, lnta and tv variables have high standard deviation compared to other variables. To further study about the gim and bcf, we have run the

We shall identify statistical tools and methods to collect data and also analyze data to determine the appropriate decision for the identified problem in A-CAT CORP forecasting.

Companies forecast in different ways and for different reasons. For the sake of my current employer, some of the reasons to why they forecast is to ensure that there are plenty of cellular phones in stock or even to make sure that the company has enough numbers to assign to customers when they purchase wireless service from us. The following paper will compare and contrast various forecasting methods while also elaborating on the method that my current employer use for forecasting sales and mobile identification number (MDN) requirements.

Forecasters use these mental maps to organize their observations of directional information. Since innovations rarely apply to the entire marketplace, information must be tagged for the appropriate price point, category and classification. In this way, forecasters turn random bits of data into useful information for decision support, points and style directions.

This is useful like why this thing is going. Then what next. This predictive ability us dependent on the goodness of fit of the statistical model.

Forecasting mode has become a necessity for the organization to lean on. Borousan claimed that implementation of an accurate forecasting process helps companies is trying to obtain financing from investors. This method is a prediction based on previous sales performance and analyse of expected market conditions. There is important thing for

We are trying to develope the back propagation (BP) neural network to form a prediction model for prediction of various shares in stock market. I. PROJECT DESCRIPTION The stock market is predictable or not predictable is still a question without an answer. Most scientists and economists believe in stock is