IRJET- Analysis of Various Machine Learning Algorithms for Stock Value Prediction
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This document analyzes and compares the performance of various machine learning algorithms for stock value prediction, including linear regression, logistic regression, k-nearest neighbors (kNN), decision trees, and support vector machines (SVM). The algorithms are tested on stock market data from five companies. SVM, kNN, and decision trees are found to have the best performance based on mean squared error, with kNN and decision trees being the most accurate predictors of stock market value.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1151
Analysis of Various Machine Learning algorithms for Stock Value
Prediction
Abhit Sudhakar Sawwalakhe1, Sukhada Shrirang Kulkarni2
1,2Student, Department of CSE, Government College of Engineering Amravati, Maharashtra India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Accuracy plays an important role in stock
market prediction. Although many algorithms are available
for this purpose, selecting the most accurate one continues to
be the fundamental task in getting the best results. In order to
achieve this, in this paper we have comparedandanalyzed the
performance of various available algorithms such as Linear
regression, SVM, kNN, etc. This involves training the
algorithms, executing them, getting the results, comparing
various performance parameters of these algorithms and
finally obtaining the most accurate one.
Key Words: Machine learning, Stock Market prediction
1. INTRODUCTION
We are always intrigued by any work which will provide us
opportunity to peeks in the future and if there is some kind
of financial benefit involved in this prediction then it
becomes lot more exciting. Stock market is one of the
examples of such an opportunity of gaining money by
investing in the right stock so as to reap maximum benefit.
However, it is kind of a gamble aswehumanscannotprocess
such large data of stocks and predict the future of one
particular stock. Hence many researchers have worked in
this field in an attempt to guess the price of stock using
various methods. Stock market prediction is the task aimed
at guessing the future value of a company's stock. The
successful prediction will maximize the benefit of the
customer. In this paper we have discussed various
algorithms to predict the same.
In this paper we used stock data of five companies from the
Huge Stock market dataset consisting of data ranging from
2011 to 2017 to train different machine learningalgorithms.
Hence we compared the accuracy of different machine
learning algorithms.
2. RELATED WORK
There is a wide variety of machine learning algorithms used
for stock prediction. In [1] Logistic regression model is used
to predict stock market. In [2] it has beenillustratedthathow
linear regression outperforms polynomial regression due to
over fitting problem of former one in some cases. In [3], the
efficiency of SVM as powerful predictive tool has been
discussed. In [4], decision tree algorithm has beenusedtofor
the prediction. In [5], kNN has been stated as stable and
robust and the prediction were similar to actual value.
3. STOCK VALUE DATASET
The Stock Market dataset [6] has stock value of various
companies over decades. Each company stock has five
attributes listed .They are Date, Open, High, Low, Close,
Volume, OpenInt which are further described in the table 1.
We have selected five companies namely IBM(IBM), Coca-
Cola Co (KO), American Airlines Group Inc (AAL) , American
Water Works Company Inc (AWK) , Walmart Inc (WMT) to
apply various machine learning algorithms.
Table -1: Sample Table format
Sr. No Attribute Description
1 Date Date of the stock value
2 High Highest point of the price of a stock
at the exchange
3 Low Lowest point of the price of a stock
at the exchange
4 Open Opening price of a stock at the
exchange
5 Close Closing price of a stock at the
exchange
6 Volume Volume of stock is average of total
traded stocks at the exchange over a
period of time.
4. Performance analysis of algorithms
In this research paper we compared machine learning
algorithms using stock market dataset. We performed
experimentswithvariousalgorithmsonstockmarketdataset
and observed the mean square error to predict accuracy
using four algorithms namely Linear regression, Logistic
regression, k-Nearest neighbor, and Decision tree. We used
Python libraries such as pandas, numpy to load the dataset
and to perform mathematical calculations respectively and
we used sklearn to model different machine learning
algorithms. We used 0.20 of our whole dataset to test our
model. We calculated mean square values for each of five
companies using each algorithm which is illustrated in table
2. From table 2, we can conclude that SVM, Decision tree and
kNN have better accuracies. We have plotted three of five
best performing algorithms’ mean square error for each
company to compare performance of each algorithm. SVM,
kNN and Decision tree yielded best result. The result can
further be enhanced by processing data properlyasthereare
large fluctuations in stock market.](https://image.slidesharecdn.com/irjet-v5i11221-181206102312/85/IRJET-Analysis-of-Various-Machine-Learning-Algorithms-for-Stock-Value-Prediction-1-320.jpg)
IRJET- Analysis of Various Machine Learning Algorithms for Stock Value Prediction
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1151 Analysis of Various Machine Learning algorithms for Stock Value Prediction Abhit Sudhakar Sawwalakhe1, Sukhada Shrirang Kulkarni2 1,2Student, Department of CSE, Government College of Engineering Amravati, Maharashtra India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Accuracy plays an important role in stock market prediction. Although many algorithms are available for this purpose, selecting the most accurate one continues to be the fundamental task in getting the best results. In order to achieve this, in this paper we have comparedandanalyzed the performance of various available algorithms such as Linear regression, SVM, kNN, etc. This involves training the algorithms, executing them, getting the results, comparing various performance parameters of these algorithms and finally obtaining the most accurate one. Key Words: Machine learning, Stock Market prediction 1. INTRODUCTION We are always intrigued by any work which will provide us opportunity to peeks in the future and if there is some kind of financial benefit involved in this prediction then it becomes lot more exciting. Stock market is one of the examples of such an opportunity of gaining money by investing in the right stock so as to reap maximum benefit. However, it is kind of a gamble aswehumanscannotprocess such large data of stocks and predict the future of one particular stock. Hence many researchers have worked in this field in an attempt to guess the price of stock using various methods. Stock market prediction is the task aimed at guessing the future value of a company's stock. The successful prediction will maximize the benefit of the customer. In this paper we have discussed various algorithms to predict the same. In this paper we used stock data of five companies from the Huge Stock market dataset consisting of data ranging from 2011 to 2017 to train different machine learningalgorithms. Hence we compared the accuracy of different machine learning algorithms. 2. RELATED WORK There is a wide variety of machine learning algorithms used for stock prediction. In [1] Logistic regression model is used to predict stock market. In [2] it has beenillustratedthathow linear regression outperforms polynomial regression due to over fitting problem of former one in some cases. In [3], the efficiency of SVM as powerful predictive tool has been discussed. In [4], decision tree algorithm has beenusedtofor the prediction. In [5], kNN has been stated as stable and robust and the prediction were similar to actual value. 3. STOCK VALUE DATASET The Stock Market dataset [6] has stock value of various companies over decades. Each company stock has five attributes listed .They are Date, Open, High, Low, Close, Volume, OpenInt which are further described in the table 1. We have selected five companies namely IBM(IBM), Coca- Cola Co (KO), American Airlines Group Inc (AAL) , American Water Works Company Inc (AWK) , Walmart Inc (WMT) to apply various machine learning algorithms. Table -1: Sample Table format Sr. No Attribute Description 1 Date Date of the stock value 2 High Highest point of the price of a stock at the exchange 3 Low Lowest point of the price of a stock at the exchange 4 Open Opening price of a stock at the exchange 5 Close Closing price of a stock at the exchange 6 Volume Volume of stock is average of total traded stocks at the exchange over a period of time. 4. Performance analysis of algorithms In this research paper we compared machine learning algorithms using stock market dataset. We performed experimentswithvariousalgorithmsonstockmarketdataset and observed the mean square error to predict accuracy using four algorithms namely Linear regression, Logistic regression, k-Nearest neighbor, and Decision tree. We used Python libraries such as pandas, numpy to load the dataset and to perform mathematical calculations respectively and we used sklearn to model different machine learning algorithms. We used 0.20 of our whole dataset to test our model. We calculated mean square values for each of five companies using each algorithm which is illustrated in table 2. From table 2, we can conclude that SVM, Decision tree and kNN have better accuracies. We have plotted three of five best performing algorithms’ mean square error for each company to compare performance of each algorithm. SVM, kNN and Decision tree yielded best result. The result can further be enhanced by processing data properlyasthereare large fluctuations in stock market.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1152 Table -2: Mean Square Error of different companies for different algorithms Fig -1: American Airlines Group Inc (AAL) Fig -2: American Water (AWK) Fig -3: International Business Machines Corporation (IBM) Fig -4: The Coca-Cola Co. (KO) Fig -5: Walmart Inc (WMT) Algorithms IBM KO AAL AWK WMT Linear Regression 222.24 2.30 45.9 33.87 84.419 Logistic Regression 409.04 127.71 146.01 862.94 1141.73 k-Near Neighbors 5.84 0.248 2.07 0.489 0.522 Decision Tree 5.98 0.248 2.07 0.49 0.54 Support Vector Machine 13.45 0.398 7.50 1.243 1.890
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 11 | Nov 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1153 3. CONCLUSION Support Vector Machine (SVM), DecisionTreeandk-Nearest Neighbor (kNN) give the most accurate predictions while Linear regression and Logistic regression cannotbeusedfor prediction as they have shown prediction values very far from actual values for most of the data. We can infer that k- Nearest Neighbor and Decision Tree are most precise algorithms to predict the stock market value. REFERENCES 1) Jibing Gong, B. Gawali, Shengtao Sun, “A New Approach of Stock Price Trend Prediction Based on Logistic Regression Model,” New Trends in Information and Service Science, 2009. NISS '09. 2) Lucan Nunno, “Stock Market Price Prediction Using Linear and Polynomial Regression Models,” 3) Z. Hu, J. Zhu and K. Tse, "Stocks market prediction using Support Vector Machine," 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, Xi'an, 2013, pp. 115-118. 4) Use Decision Trees in Machine Learning to Predict Stock Movements 5) Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi “Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm”, International Journal of Business, Humanities and Technology Vol. 3 No. 3; March 2013 6) https://www.kaggle.com/borismarjanovic/price- volume-data-for-all-us-stocks-etfs