Forecasting Stock Price

Introduction

The stock market is one of the most traded markets in the world. It is one of the markets where huge transactions up to trillions of dollars change hands every day. People in this market are therefore obliged to provide accurate and credible information in order to help the traders determine the prices of their stocks (Meade & Islam 1995).

 

Investors place their money in selected stocks so as to sell them when they increase their prices and make profits. These investors must, therefore, ensure that they buy the right stocks so that they can make their profits. If they fail to identify the stock that will increase in price, they fall at risk of losing their money or not making their profits as they desire. Volatile stocks are best to invest in, though the investor must be aware of the stock with the highest likelihood of increasing (Cutler, Poterba & Summers 1991). The investor must buy the stock when it is at its low, then sell it at its high so that they can make their profits.

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The Rationale of the Assignment

Investment in company stock is usually guided by many factors. Key among them is the predicted future performance of the stock (Cameron, Windmeijer, Gramajo, Cane & Khosla 1997). Investors would always want to buy a stock that will increase in price in the future to ensure that their money would be valued higher after a certain period. An investor will always want to put their money in a strong and stable stock that would not lose its value because every unit of money the stock loses reflects negatively on the investment and makes a loss to the investor.

It is therefore important that investors develop tools that would help them determine the possibilities of decline or otherwise, in the value of the stock. Some investors would want to sell their stock when it reaches its peak and seemingly starts to decline, while they would look to buy the stock at the lowest possible price (Cameron, Windmeijer, Gramajo, Cane & Khosla 1997).

Further, accompany would know its performance through its share prices. The managers are able to predict and determine their performance with regard to the industry through the use of the shares. Since the finance market is determined by many other external factors, such as regional macroeconomics, rather than just its internal factors, an increase in the price of a stock means that the corporation is performing well within its industry and that it is coping with the turbulent macroeconomics (Preis, Moat & Stanley 2013). By using predictions, a company would, therefore, be ready to deal with tough times ahead, though the conditions might change and reduce the correctness of the predicted figures (Cutler, Poterba & Summers 1991).

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Challenges

In determining the future price of a stock, there are several challenges that one faces. One, current and future trends may be as a result of a certain existing factor that may be short-lived. The time series model assumes that there would be no external factors that could affect the price in the future. It assumes that the present conditions would prevail even in the future. In financial markets, many microfinance factors affect trends. They change with time and most of them follow a certain seasonal trend. In this case, the researcher needs to have a long period that would include different seasons so that they can have a concrete regression model that can adequately predict the future.

Secondly, it is agreed that predictions are not accurate in any real-world (Preis, Moat & Stanley 2013). A predictor will use their tools today and give a verdict that would be overturned the day that follows. A company will try to ensure that its price remains stable, but this feat is hard to achieve because it requires restructuring that could change the course of its share price. With this in mind, the company managers are only able to predict, but with little certainty that the company would follow the predicted path. Therefore, decisions made may not be as concrete as they are supposed to be. More analysis is needed to adequately determine the future price of a stock.

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Literature Review

Before trading in a financial market, a company has to change ownership and include other people in its ownership. Most companies do this to raise a certain amount of money to meet their daily expansions or salvage their performance when the business performs dismally. Many companies would not agree to sell a percentage of their shares to admit new members in the board, because the more san investor buys into a company, the higher the stakes they get to make decisions. Therefore, many companies have adopted a way of increasing the number of their shares and designating prices. Experts who consider many factors before they fix a certain figure as the worth of the company carry out this process (Preis, Moat & Stanley 2013).

After carrying out a regression and time series analysis, analysts get an idea of the possible price of the stock under the current situation. They then try to add more variables that they would make to the model to determine the swing the new factors would bring to the prices of the stock. Regression and time series analysis, therefore, becomes a very fundamental method of predicting the future under the prevailing market and industrial conditions.

Time series analysis and prediction is at times insufficient and fail to adequately address the issues because of the seasonality of the financial market. It is usually hard to adequately determine the period that the predictor requires in order to make the most near accurate results. After the entry into the market, some stocks take time before they stabilize in their prices. There are also issues like financial and industrial bubbles that could lead to the wrong valuation of company stocks (Preis, Moat & Stanley 2013).

In this case, if a statistician tries to predict the price using this past data, there is a high likelihood that they would miss the desired point since the share is very volatile and fluctuating. To get the best period that would not be biased remains a very hard task because the researcher could take a very long period that may not give the accurate state of affairs, or take a very short period that may not reflect the performance cycles adequately.

Terminology

Financial Market

This is a term used to describe any place where sellers and buyers are involved in the process of trading assets that include bonds, equities, currencies, derivatives. Variable Y is known as a dependent variable while x are independent variables (Florian, Ralph, Christian & Klaus 2007).

Stock Market Prediction

This refers to the act of attempting to predict the future value of the stock of a company in any financial exchange.

Regression

This is a statistical measure that tries to derive the relationship between one constant (usually denoted as y) variable and other changing variables (denoted as x). Regression can either be linear or multiple (Florian, Ralph, Christian & Klaus 2007).

Time Series Analysis and Forecasting

This is a tool that, determines the present trends and uses regression to predict the possible figures in the future (Fisher 1922).

Research Article

Financial markets are present in every country in the world. They differ in size and activity and some have strong effects on the national and regional economic balance of their location (Chatfield 1993). Some of these financial markets trade millions and even trillions of dollars every day. Financial markets vary in demand and activity. They have their trends that are driven by the demand for securities and stock (Christoffersen & Diebold 2006). This leads to an increase of the traded securities per unit time while the converse is also true.

In other seasons, the demand for securities may reduce thus reduced trade activities and eventually lower the value of stocks. Some of these downturns are brought about by the macroeconomic forces of the region or country the markets are found. Factors such as unemployment, taxation trends, production among others will affect the performance of the market. In order to make sound investments, investors must have adequate information regarding the markets in order to determine the levels of risks they face by buying certain stock. They need to evaluate the most possible movement of the stock price in the future.

The clarity and transparency of the information derived from the markets are therefore vital in helping investors buy or sell their stock (Chatfield 1993). In most of these financial markets, information is very accurate, making them some of the justest trading places.

One of the most effective prediction tools is Time Series Analysis and Prediction. The tool uses past and current data to determine future trends of the price. It observes a specific variable and makes periodic observations at regular intervals. In this method, one needs to create a model for data trends. It is from the model that future values can be predicted. The tool uses regression in predicting. The regression line gives the researcher a model that helps them insert figures and determine the most likely price of the stock at the moment in the future (Christoffersen & Diebold 2006).

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In linear regression, the regression line determines the relationship between the dependent variable and the independent variable (Nagelkerke 1991). When using time series analysis to determine the price of a given stock in the future, the dependent variable is the price, while the independent variable is time. The model is usually in the form of

y = ax + c, where

y - denotes the dependent variable;

x - the independent variable;

a - is the slope of the regression line;

c - the y-intercept where the regression line crosses the y-axis (Fisher 1922).

After the data has been analyzed and a model created, one can use it to determine the future values of y, by inserting the value of x at that time. Further, it derives another figure, coefficient of determination denoted as R2 that determines the level of accuracy of the adopted model (Nagelkerke 1991).

Methodology

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Sample Data and Period

Data were obtained from the Bloomberg website for Dubai Market Dubai Islamic Insurance & Reinsurance Co as it traded in Dubai Financial Market. The data used showed the stock price between 2nd January 2014, which was the first trading day of the year, and 29th May 2014. Daily closing prices were preferred to opening prices. The data was entered on a daily basis, and on days where there was no trading in the market, the closing price of the previous trading day was used. This was to ensure that there was continuity in the data, as there was no regular trading pattern. There were weeks that the market opened for only 4 days, while there were others when trading was done for five days.

Analytical Approach

The collected data were analyzed using MS Excel and the following graph plotted. On the graph, the number of days to be predicted were added to determine the position of the linear regression curve at that time, based on the data of the past.

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Conclusion

The method used for forecasting the future price of the stock was adequate. The coefficient of determination stood at 0.476, which meant that 47.6% of all the points used in the analysis touched the line of fit. This means that the model could be used to determine future prices with the certainty of 47.5%. In the second data summary, there was no forecast applied which gave a different value for R-squared of 0.4345. This figure means that the regression model can be used for inferences regarding the data. From the Analysis of Variance (ANOVA), the regression model is significant and the model fits the data. Thirdly, both intercepts are significant since both p- values are less than 0.05 (Nikoli, Muresan, Feng, & Singer 2012).

From the data observed and analysis, it is evident that the model obtained is highly significant and can be used to effectively predict the future prices of the stock in the company. The data can also be used for other inferences. Before an investor puts money in the stock, they can look at the possible future performance through this method, which will help them make the right decisions.

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