Research article · 2018 · Full text
International Journal of Statistics and Applied MathematicsStock market prediction using time series analysis
Volume 3(1) · Pages 465–469
Research areasStock Market Forecasting MethodsTime Series Analysis and ForecastingForecasting Techniques and Applications
Abstract
Author-writtenStock market is a market that enables seamless exchange of buying and selling of company stocks. Every Stock Exchange has their own Stock Index value. Index is the average value that is calculated by combining several stocks. This helps in representing the entire stock market and predicting the market’s movement over time. The Equity market can have a profound impact on people and the country’s economy as a whole. Therefore, predicting the stock trends in an effective manner can minimize the risk of investing and maximize profit. In our paper, we are using the Time Series Forecasting methodology for predicting and visualizing the predictions. Our focus for prediction will be based on the technical analysis using historic data and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively in the field of finance and economics as it is known to be robust, efficient and has a strong potential for short-term share market prediction.
Summary
Source-checkedPlain-language reading of the full paper, prepared with AI and checked against the text. Each point links to the authors' own words.
Why it matters
- Predicting stock market trends is important because the equity market significantly impacts individuals and the national economy, and effective forecasting can reduce investment risk while increasing profit. View evidence ↗
- Forecasting stock trends is considered one of the most challenging tasks in finance due to the intricate nature of the stock market. View evidence ↗
- The ARIMA model is widely used in finance and economics because it is robust, efficient, and effective for short-term share market predictions. View evidence ↗
The question
- The study aims to evaluate the accuracy of forecasting stock values across various sectors to help new investors make informed investment decisions. View evidence ↗
What they did
- The researchers used technical analysis based on historical data and the ARIMA model for their predictions. View evidence ↗
- The study utilized the R programming language and R-Studio environment to implement the ARIMA model. View evidence ↗
- The QuantMod package in R was used to retrieve stock data, specifically for Apple (AAPL), for the period from 2016-01-01 to 2017-01-01. View evidence ↗
- The analysis used closing prices from October 2015 to October 2017, converted into a time series using the ts() function. View evidence ↗
- The forecast package was used to predict stock prices for the next 2 years (h=24) using the ARIMA model with a 95% confidence level. View evidence ↗
What they found
- Referencing prior research, the authors note that the ARIMA model has achieved over 85% accuracy in predicting stock prices for various Indian stock sectors. View evidence ↗
- The study identifies the MACD as a useful tool for analyzing the momentum, strength, and direction of a security's price movement. View evidence ↗
- The authors find that when a divergence between price and the MACD line is spotted, there is an increased probability of price retracement. View evidence ↗
What it means
- The authors conclude that the ARIMA model is a key tool for predicting short-term stock market trends. View evidence ↗
- The authors recommend comparing the MACD line with the signal line for accuracy, noting that while price divergence signals are rare, they are the most effective for precise analysis. View evidence ↗
Visual summary
1 visualsIllustrations explain the paper; they do not show study data.
Study overview
AI-generated conceptual illustration (computer monitor displaying a financial candlestick chart). It explains the idea and does not show study data.
View source evidence ↗Introduction
The stock market is a general term which refers to the collection of markets where the issuing and trading of equities, bonds and other sorts of securities takes place through various physical and electronic exchanges and over the counter market. The stock market is one of the most important components of a market economy, because it provides companies with access to capital by allowing investors to buy shares of ownership in a company. The arena of stock market is constantly developing under the process of refinements. Considering the variations it brings every day, investors need to plan intensively to make profit. Forecasting the stock exchange data includes an assumption that the information publicly available at present has some predictive relationships to the future stock returns. Stock trend forecasting is one of the most difficult tasks to achieve in finance market because of the difficulty in the highly intricate world of stock market. The investors in the stock market always find a technique that can guarantee easy profiting by forecasting the stock trends and minimize the risk of investing. This motivates the researchers in the domain field to develop new forecasting models. Stock prices are not randomly generated values rather they can be treated as a discrete time series model which is based on a set of well-defined numerical data items collected at successive points at regular intervals of time. Since, it is essential to identify a model to analyze trends of stock prices with adequate information for decision making, it recommends that transforming the time series using ARIMA is a better algorithmic approach than forecasting directly, as it gives more authentic results. Autoregressive Integrated Moving Average (ARIMA) Model converts a non-stationary data to a stationary data before working on it. It is one of the most popular model to predict linear time series data. R is a programming language and environment for statistical processing and graphics. The R dialect is generally utilized by data analysts for statistical programming and data analysis. R language was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand and is presently maintained by the R development core team. R-Studio is a free and powerful integrated development environment for R language which allows the user to implement ARIMA model. In this paper, we have mainly focused on the amount of accuracy of forecasting stock values for various sectors which will aid new investors understand the market and make a wise decision to invest in the stock market.
By importing data set in R
We have downloaded the historical data of APPLE from Yahoo Finance. The historical data of any company can be downloaded. A csv file is generated which we will import in R studio for our analysis.
3.2.1
Importing the data aapl=read.csv("C:\\Users\\Laptop\\Downloads\\AAPL.csv",he ader=T) head (aapl)
Creating a time series function
In our data the closing prices are listed in the 5 th column, we use the ts() function to create time series. We have taken data from October 2015 to October 2017. aapl.ts=ts(rev(aapl) [5] , start=c (2015, 10), end=c (2017, 10), freq=12) aapl. ts In addition, we also take the log of values and place them both in an accessible data frame. We have saved the closing values and the log values in a separate file. >aapl=data. frame (closing=aapl. ts, lclosing=log(aapl. ts)) > save (aapl, file="aapl. df. RData") > load ("aapl. df. RData")
Plotting the stock values
Plot the data frame x-axis year and y-axis closing price plot (aapl$closing, main="AAPLE Stock Prices(APPLE)", lwd=2,sub="October 2015 to October 2017",ylab="Closing Price")
Decomposition
The function stl() removes the seasonal component from the data set and the trend is found after smoothening the remainder. aapl. stl=stl (aapl $closing, s. window="periodic") plot (aapl. stl, main="APPLE Stock decomposition")
Forecasting using stl()
We use the forecast package to predict the stock price over the next 2 years. Prediction method=ARIMA Model h=24 (Predict 2 years into the future) level =95 (95% confidence level) install. packages ('forecast') library (forecast) aapl. f=forecast (aapl. stl, method="arima", h=24, level=95) plot (aapl. f, ylab="Stock Price", xlab="Year", sub="Forecast from October 2017 to October 2019")
Conclusion
In this paper we have predicted the share market trends by considering a company's historical time series data. The ARIMA Model played a key role in predicting the short-term trends of a stock market. The investors can do a thorough analysis of the share market by visualizing the graphical plots. There are various technical ways in which the plots can be visualized. For accurate prediction the MACD line should be compared with the signal line indicator. The divergence with price signal is generated very rarely but is the most effective technique for precise analysis. This can help the investors make a profitable decision on whether to buy/sell/hold a stock.
References
7 in the original paper- Banerjee D. Forecasting of Indian stock market using time-series ARIMA model, 2nd IEEE International Conference on Business and Information Management (ICBIM), 2014, 131-135.
- Ayodele A Adebiyi, Aderemi O Adewumi, Charles K Ayo. Stock price prediction using the ARIMA model, 16th IEEE International Conference on Computer Modelling and Simulation (UKSim), 2014, 106 -112.
- Uma Devi B, Sundar D, Alli P. An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50., International Journal of Data Mining & Knowledge Management Process (IJDKP) 2013, 3.
- Jarrett E Jeffrey, Kyper E. ARIMA modeling with intervention to forecast and analyze chinese stock prices, International Journal of Engineering Business Management, 2011; 3(3)
- Contreras JR, Espinola FJ, Nogales AJ. Conejo, ARIMA models to predict next-day electricity prices, IEEE Transactions on Power Systems, 2003.
- Mondal P, Shit L, Goswami S. Study of effectiveness of time series modeling (ARIMA) in for casting stock prices, International Journal of Computer Science, Engineering and Applications, 2014.
- Angadi M, Kulkarni A. Time Series Data Analysis for Stock Market Prediction using Data Mining Techniques with R International Journal of Advanced Research in Computer Science, 2015.
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