Neural Networks Are Used For Forecasting

Finally we got all our number and determine the slope, and the intercept in order to find out the forecast for the next

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

Given the event date and stock price data, the EP and TP can be constructed in order to estimate the normal returns and abnormal returns respectively.

In this project we first checked consistency and seasonality of S&P500 index stock performance by splitting its recent twenty years historical data into ten two year data and built ARIMA and GARCH models for each sub-period. We found that the models are considerably consistent before 2007-2008 sub-period, and there exists some minor seasonality in several subperiods, but no particular pattern can be identified for the whole period. We then tried to predict future return, volatility and VaR using the model we built for the last sub-period based on rolling forecast procedure. Though the fitted values of 10th sub-period model are

The previous reports have already approached the industry and financial analysis of Myer. This report will analyze the forecast, valuation and application of Myer, including forecasting the major data, valuating share price under four model and discussing the opportunity and challenge of Myer.

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.

To start with, the 1st model used is regression line method. According to this method, the technique fits a trend line to a series of historical data point and the projects the line into the future for medium to long range forecasts

With the arising of the capital market in the United Kingdom in the 18th century, the stock market becomes an essential approach for investors to invest. The price of stock fluctuating enormously, there are usually two kinds of invest in the stock market: long-run invest and short –run invest. In order to conduct a long-time invest more rationally, information about the company such as its operating situation, financial situation are required to judge if it is worthwhile to invest. As for short-time investment, stock prices are more dynamic and susceptible to quick change. There exist more unpredicted factors such as other investors’ behavior on this stock, the influence of social media, the social influence and etc. Markov Chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event (Markov Chain, n.d.). The Fluctuation of Stock Price can be regarded as Markov Process (Vasanthi, 2011), there are a lot of researches in this field and the prediction model had already well established. According to the situation of different countries these models are adjusted when applied in different countries and different market.

Multilayer Perceptron (MLP) is an artificial neural network that learns nonlinear function mappings. An MLP can be viewed as a logistic regression classifier where the input is first transformed using a learned non-linear transformation. This transformation projects the input data into space where it becomes linearly separable. This intermediate layer is referred to as a hidden layer. A single hidden layer is sufficient to make MLPs a universal approximator.

Choose one of the forecasting methods and explain the rationale behind using it in real life.

The Random Walk Model is a different paradigm of the Hypothesis of Efficient Markets. This model was initially examined by Kendall (1953). The model states that the price fluctuations of stocks do not depend on each other and have the same probability distribution. The theory explains that stock price change randomly and investors are unable to predict stock prices. The model is linked to the belief that markets are extremely efficient and that investors cannot beat or predict the market because stock prices reflect all available information and as the new information

Forecasting is often defined as the estimation of the value of a variable (or set of variables) at some future point in time (Goodier, 2010). It can be applied to a number of different situations when there is uncertainty about the future and the data collected can aid in decisions that need to be made (Armstrong, 2001). In relation to healthcare, forecasting models have been used to aid their sector’s departments to plan staff rota schedules, ensuring that a sufficient amount of senior staff are available at any given time throughout the day, week, month and year. As explained previously, a fundamental factor that causes overcrowding is a limited supply of resources to treat patients, leading to a longer time spent in an Emergency

The least square technique based on linear, exponential, asymptotic, curvilinear and logarithmic equations has been applied on the available data to produce the estimated data. The error analysis has been made to produce estimated error. It has been observed that average error based on least square technique based on linear equation has shown the minimum error (2.25%) as compared to the other models according to table 2. Therefore least square technique based linear equation has been chosen as the best known solution.

With the penetration of the research on the securities market, scholars have found many “abnormal phenomenon”, which is contrary to the Efficient Market Hypothesis(EMH) proposed by Eugene Fama, a professor of Finance at the university of Chicago Booth School of Business. The hypothesis was based on the efficient markets model after the proof of theoretical and empirical literature. Fama argued that stock prices has fully reflected all available information (mainly historical information about price changes, such as the previous stock price) in the efficient market and new information is unpredictable which makes the stock price changes follow a random walk. However, in weak-form efficiency market, investors can not rely on the analysis of the trend of historical changes of stock price to get the so-called law of change in the stock price and consistently obtain excess profits by it. If the weak-form Efficient Market Hypothesis is set up, the technical analysis of the share price become unhelpful anymore, and the basic analysis may also help the investors to gain extra profits. Numerous experimental tests on weak-form efficiency market hypothesis show that the market is basically inefficient after deducting trading costs. For

Extreme learning machine proposed by\cite{elm,elms} is a feed forward neural network classifier with single hidden layer in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer\cite{elm} ,which