A Study on S&P 500 Index Stock Return and Volatility Using Arima and Garch Modeling

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.

As per Gordon’s model, the expected rate of return is 6.5%, but touching on the market review, this rate of return could

is stripped of some of the information relating to volatility, so therefore it will obviously do

Regulation of financial markets is very significant for investors in the stock markets, policy-makers, practitioners, and academicians who explore the topic very rigorously. This paper contributes to the field by examining the volatility among stock returns for firms in different segments of the financial services industry. My findings are useful to investors and have significant implication for policy-makers and federal regulators in the United States.

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.

Financial stocks are the shares issued by financial industries. With the development of global economics, the volatility of global stocks market reflects the economic situation across a number of countries. As a result, each nation’s fluctuation of stocks plays a significant role in its own economy. Furthermore, the financial stocks that act as an important sector of the whole stock market are caused fluctuating by a large number of factors. Without exception, there is a great number of reasons for the volatility of Australian financial stocks, which cause the whole financial share market fluctuating unsteadily in Australia as well. The purpose of this report is to discover the most influential cause of financial stocks volatility in Australia. Moreover, This report will analyse three main factors: a combination of three financial instruments, legal framework and inancial industry fundamentals, which influence the volatility of Australian financial stocks.

In the beginning of this investment period (9-9-2015), the igorpinto5 portfolio was properly distributed among 5 sectors: Services: Starbucks Corporation (30%), Banks: Goldman Sachs Group Inc. (28%), Industrials: JBS SA (23%), Technology: Amazon.com, Inc. (10%), and Homebuilding: D.R. Horton, Inc. (9%). The Igorpinto5 stock fund had close to no variation when compared to the market (9/15 until 11/13). Despite high volatility in the stock market in the period, the beta of the last four months of Igorpinto5 fund in relation to the S&P500 was 0.97. On 11/13/2015, the beta of Igorpinto5 represented

› Consider an investor who predicted in July 2001 that a set of stocks will

The key idea of vector error correction model is that there are the short term dynamic which generates stock prices deviations, and the long term equilibrium component that makes mean reverting. In the error correction model, the short term dynamics of stock prices changes are influenced by the deviation from the long term equilibrium. If the stock prices are integrated order of one, I(1), the model would be,

BSE Sensex is one of the flagship stock price indices in Indian stock market. In this paper, it has been tried to estimate how much BSE Sensex, as the form part of Indian capital market, has grown during the period 1986 to 2016 and after this it has been tried to forecast in which way BSE Sensex will be going to move in the near future holding period 2017-2020.

The 3-month T-bill rates and Dow Jones index are really close to the whole economic environment; the 3-month T-Bill rates are the preeminent default-risk-free rates in the US money market that is often used by

The aim of this article is to investigate the dynamic behavior of daily returns of Microsoft stock prices from 21/03/2005 to 21/03/2015, which are downloaded from the NASDAQ website and include 2514 observations

Relatively accurate prediction of multi-tiered, non-linear events has long been a difficult and time-consuming task to perform; forecasting the movement of securities on the stock market included. Stock prices fluctuate for innumerable reasons, so correctly forecasting a stock’s movement can be extremely difficult. There are two areas that have massive effect on stock pricing: the psychology, or sentiment of investors and the mathematical, or analytical standpoint. In order to effectively predict stock price movement, these two areas, more than anything else, must be factored in. Fundamental and technical analysis are easily included, but accounting for investor sentiment is more complicated. Using artificial neural networks (ANNs) coupled with the two schools of traditional analysis and regression analysis could prove useful in correctly forecasting the price movement of stocks on any exchange. How can ANNs be implemented with regression analysis to accurately predict the movement of stock prices? This report will be a semi-technical feasibility analysis of the different types of ANNs, predictive algorithms and financial analysis

Investors in volatility market should make no move waiting for the market to steady it self as volatility doesn’t change stocks valuations. Based on analyst’s opinion most of them recommend

Prior forecasting method is the prediction based on the growth rate of fundamental factors such as earning-per-share, book value, and invested capital. Another common method for forecasting is time series analysis. The traditional analysis uses regression models to discover market trends. However, financial time series are complex, noisy, and nonlinear pattern. As a result, later techniques have applied artificial intelligences; the most popular technique is neural networks (Zhang & Zhou, 2004). According to Refenes’s experiment (1994), the back-propagation networks, a type of neural network, predicts more accurately than regression models on the same datasets. Other techniques are ARMA and ARIMA model, genetic algorithm, and random walk. Pai and Lin (2005) introduces the A hybrid model ARIMA and support vector machine which give better result than single ARIMA. Edward Tsang et al. (1998) proposes EDDIE, a tool which constructs a genetic decision tree to evaluate whether a stock price increases in a specified time. Jar-Long and Shu-Hui (2006) proposes a two-layer bias decision tree with simple technical indicators. Typically, the input variables of prediction models are daily price, volume, rate of change, and technical indicators, for example, moving average (MA), relative strength index (RSI), and volatility (Vol).