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Stock Price Prediction is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.

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Quantitative Finance > Statistical Finance

Title: stock price prediction using time series, econometric, machine learning, and deep learning models.

Abstract: For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learning-based models for stock price prediction. The data of Infosys, ICICI, and SUN PHARMA from the period of January 2004 to December 2019 was used here for training and testing the models to know which model performs best in which sector. One time series model (Holt-Winters Exponential Smoothing), one econometric model (ARIMA), two machine Learning models (Random Forest and MARS), and two deep learning-based models (simple RNN and LSTM) have been included in this paper. MARS has been proved to be the best performing machine learning model, while LSTM has proved to be the best performing deep learning model. But overall, for all three sectors - IT (on Infosys data), Banking (on ICICI data), and Health (on SUN PHARMA data), MARS has proved to be the best performing model in sales forecasting.

Comments:	This is the accepted version of our paper in the international conference, IEEE Mysurucon'21, which was organized in Hassan, Karnataka, India from October 24, 2021 to October 25, 2021. The paper is 8 pages long, and it contains 20 figures and 22 tables. This is the preprint of the conference paper
Subjects:	Statistical Finance (q-fin.ST); Machine Learning (cs.LG); Econometrics (econ.EM)
Cite as:	[q-fin.ST]
	(or [q-fin.ST] for this version)
	Focus to learn more arXiv-issued DOI via DataCite
Journal reference:	Proc. of IEEE Mysore Sub Section International Conference (MysuruCon), October 24-25, 2021, pp. 289-296, Hassan, Karnataka, India
:	Focus to learn more DOI(s) linking to related resources

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Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions.

1. Introduction

1.1. classical approaches for smp, 1.1.1. fundamental analysis, 1.1.2. technical analysis, 1.2. modern approaches for smp, 1.2.1. machine learning approach, 1.2.2. sentiment analysis approach, 2. research methodology, 3. generic scheme for smp, 4. types of data, 4.1. market data, 4.2. textual data, 5. data pre-processing.

References	Data	Type of Input	Prediction Duration
[ ]	S&P 500	Market data	Few days ahead
[ ]	NASDAQ index	Market data	Few days ahead
[ ]	DAX 30	broker house newsletters, RSS market feeds, and stock exchange data	Intraday
[ ]	Yahoo Finance	Financial News	Intraday
[ ]	DGAP, Euro-Adhoc	Corporate announcements financial new	Daily
[ ]	Yahoo finance (18 Stock Companies data)	Market data, yahoo finance message board data	Daily
[ ]	DJIA	Market data and Twitter	Daily
[ ]	BSE and NSE stocks	Market data, technical indicators, Twitter data	Intraday
[ ]	Nifty and Sensex	Market data and news	Intraday
[ ]	Yahoo Finance	Market data, Twitter data, and news data	Daily and monthly
[ ]	S&P, NYSE, DJIA	Market data, Technical Indicators, Social media data	Daily weekly
[ ]	Apple, yahoo	Market data, technical indicators	60 day and 90-day prediction
[ ]	Microsoft company	Twitter	Daily
[ ]	NASDAQ, DJIA, Apple Stock (AAPL)	Market data, technical indicators, news.	One-day ahead
[ ]	Google stock	Market data	Five days horizon
[ ]	Taiwan Stock Exchange CWI	Market data	High-frequency trading
[ ]	S&P 500	Market data	Daily
[ ]	Columbia Stock Market	Market data, Technical indicators	Next day
[ ]	S&P 500	Financial news from Noodle, Reuters	Intraday
[ ]	Enron Corpus	Sentiment data	Daily, weekly
[ ]	BSE, Tech Mahindra	Market data	Daily and weekly
[ ]	Apple stock data	Market data	Daily
[ ]	United States stock exchange	Market data, technical indicator	Daily
[ ]	KSE, LSE, Nasdaq, NYSE	Twitter, yahoo finance, Wikipedia	Weekly
[ ]	Google stock	Market data	Daily

References	Feature Selection	Order Reduction	Feature Representation
[ ]	Bag of Words	Stemming	Sentiment value
[ ]	Opinion Finder overall tone and polarity	Minimum Occurrence per document	Boolean
[ ]	Bag-of-words, noun phrases, word combinations, n-gram	Frequency for news, Chi2-approach and bi-normal separation (BNS) for exogenous-feedback-based feature selection, dictionary	TF-IDF
[ ]	Bag-of-words	WordNet to replace words	TF-IDF
[ ]	N-grams	Document frequency	Boolean
[ ]	Context based approach	SentiWordNet	Sentiment value
[ ]	Bag of words, LDA, JST, Aspect Based	-	TF-IDF
[ ]	Correlation	Lemmatization	Boolean
[ ]	Bag of Words	Chi2, Information Gain, Document Frequency, Occurrence	TF-IDF
[ ]	Bag-of-word, Word2vec		TF-IDF
[ ]	GA	PCA, FA, FO	-
[ ]	N-grams	SVM based Recursive Feature Elimination, PCA, KPCA, and XGB	-
[ ]	Bag-of-words	Occurrence	TF-IDF
[ ]	GA, Feature Ranking	PCA-SVM, DA-RNN	-

5.1. Feature Selection

5.2. order reduction, 5.3. feature representation, 6. machine learning methods.

• Artificial Neural Networks (ANN)
• Support Vector Machine (SVM)
• Naïve Bayes (NB)
• Genetic Algorithms (GA)
• Fuzzy Algorithms (FA)
• Deep Neural Networks (DNN)
• Regression Algorithms (RA)
• Hybrid Approaches (HA)

6.1. Artificial Neural Networks (ANN)

6.2. support vector machine (svm), 6.3. naïve bayes (nb), 6.4. genetic algorithms (ga), 6.5. fuzzy algorithms (fa), 6.6. deep neural networks (dnn), 6.7. regression algorithms (ra), 6.8. hybrid approaches (ha).

References	ANN	SVM	NB	DNN	FL	HA	GA	RA	EA
[ ]								✓
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[ ]		✓	✓					✓
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[ ]				✓			✓

7. Evaluation Metrics

Reference	Performance Measure	Prediction Type	Output
[ ]	MSE, MAD%	Few days ahead	MAD% (2.32) MLP
[ ]	Accuracy, trading return	Intraday	59.0%, 3.30%
[ ]	Accuracy	Daily	65.1%
Kalyanaraman, V., 2014	Accuracy	Daily	81.82%
[ ]	Accuracy	Daily	Average accuracy of 54.4%
[ ]	Accuracy, RMSE	Daily	59.6%
[ ]	Accuracy	Daily and monthly	DBT achieved better accuracy (76.9%) than SVM and LR
[ ]	Accuracy and correlation	Daily	Accuracy of around 70%
[ ]	Accuracy, RMSE	Long-term	99% accuracy for yahoo data (XGBoost)
[ ]	Error Rate, F-measure	Next Month, Next Week	0.85
[ ]	Accuracy, f-measure, precision, AUC	One day ahead	85%
[ ]	Log loss and accuracy	Daily, weekly	72% accuracy (LSTM)
[ ]	Accuracy, Return	Daily	58.1%
[ ]	Accuracy, MSE	Long short-term	56.7% (LSTM),57.2% (ELSTM)
[ ]	Accuracy	Daily, weekly	80%
[ ]	Accuracy, f-measure		0.84
[ ]	Accuracy	Daily	72%
[ ]	MSE, MAE, MAPE and R2	Daily	LR 0.73SVM 0.93
[ ]	MAPE	Daily	2.03–2.17
[ ]	training error, testing error	Daily	0.03, 0.072
[ ]	RMSE, Accuracy, AUC, R2, MAE	Monthly	>90%(Ensemble)
[ ]	Accuracy	Monthly	87.32
Seethalakshmi, R., 2020	R2, AIC	………	0.992 (R2)
[ ]	Accuracy	Next few days	90–96% (KNN Regression)
[ ]	Precision, recall, f-measure, accuracy	Weekly	76.5%
[ ]	MSE	Daily	0.0039(GA-LSTM)

8. Overfitting

9. comparative analysis, 10. challenges and open issues, 11. conclusions, author contributions, data availability statement, conflicts of interest, abbreviations.

SMP	Stock Market Prediction
SVM	Support Vector Machine
ANN	Artificial Neural Network
DNN	Deep Neural Network
RA	Regression Analysis
FA	Fuzzy Algorithm
NB	Naïve Bayes
GA	Genetic Algorithm
HA	Hybrid Approach
kNN	k- Nearest Neighbors
LDA	Latent Dirichlet Allocation
PCA	Principle Component Analysis
XGB	eXtreme Gradient Boost
FO	Firefly Optimization
TF-IDF	Term Frequency- Inverse Document Frequency
GARCH	Generalized Auto-regressive Conditional Heteroskedasticity
DAN	Deep Attention Neural Network
MLP	Multi-linear Perceptron
GFF	Generalized Feed Forward
NARX	Non-linear Auto-regressive Network with exogenous inputs
RBF	Radial Basis
MA	Moving Average
LPP	Locality Preserving Projection
FRPCA	Fast Robust Principle Component Analysis
KPCA	Kernel Principle Component Analysis
GRU	Gated Recurrent Unit
LSTM	Long Short Term Memory
ANFIS	Adaptive Neuro-Fuzzy Inference System
ABC	Ant Bee Colony
RNN	Recurrent Neural Network
RMSE	Root Mean Square Error
SVR	Support Vector Regression
CNN	Convolution Neural Network
DBN	Deep Belief Network
ARIMA	Auto Regressive Integrated Moving Average
VAR	Vector Auto-regression
AUC	Area Under Curve
MSE	Mean Square Error
MAE	Mean Absolute Error
R2	R-Squared
MAPE	Mean Absolute Percentage Error
POCID	Prediction of Change in Direction
DJIA	Dow Jones Industrial Average
S&P	Standard and Poor’s
GDP	Gross Domestic Product
NASDAQ	National Association of Securities Dealers Automated Quotations
DAX	Deutscher Aktien Index
KSE	Karachi Stock Exchange
LSE	London Stock Exchange
NYSE	New York Stock Exchange
BSE	Bombay Stock Exchange
AIC	Akaike Information Criterion

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Rouf, N.; Malik, M.B.; Arif, T.; Sharma, S.; Singh, S.; Aich, S.; Kim, H.-C. Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics 2021 , 10 , 2717. https://doi.org/10.3390/electronics10212717

Rouf N, Malik MB, Arif T, Sharma S, Singh S, Aich S, Kim H-C. Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions. Electronics . 2021; 10(21):2717. https://doi.org/10.3390/electronics10212717

Rouf, Nusrat, Majid Bashir Malik, Tasleem Arif, Sparsh Sharma, Saurabh Singh, Satyabrata Aich, and Hee-Cheol Kim. 2021. "Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions" Electronics 10, no. 21: 2717. https://doi.org/10.3390/electronics10212717

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Stock Market Prediction Techniques: A Review Paper

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1235))

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Due to the non-linear and highly volatile nature of the Stock market, it has become a very challenging task for researchers to make accurate predictions. Improving the efficiency of predictions has become the main goal of many researchers. From the traditional approach of working with historical dossiers to using the latest machine learning and deep learning techniques, researchers are busy finding out the best possible ways of accurate predictions. Many new models are suggested that can make good estimations of stock prices. Investors are interested in knowing both the immediate next-day prices and as well as future share prices in the long run. This paper inspects the algorithms and techniques that are useful for making accurate predictions.

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Sharma, K., Bhalla, R. (2022). Stock Market Prediction Techniques: A Review Paper. In: Luhach, A.K., Poonia, R.C., Gao, XZ., Singh Jat, D. (eds) Second International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-16-4641-6_15

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