Comparing Technical and Fundamental Indicators in Stock Price Forecasting

Beyaz, Erhan; Tekiner, Firat; Zeng, Xiao‐Jun; Keane, John

doi:10.1109/hpcc/smartcity/dss.2018.00262

Cited by 38 publications

(27 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, stock price can fluctuate (Selvin et al, 2017) depending on political and economic conditions. Thus, investors have used fundamental and technical analysis simultaneously for the stock price prediction (Beyaz et al, 2018).…”

Section: Machine Learning For Stock Price Predictionmentioning

confidence: 99%

“…Fundamental analysis is a method to estimate intrinsic value of a stock by analyzing various internal and external factors that could have effects on the value of stock or company (Selvin et al, 2017). The fundamental factors include business environment, financial performance, economic data, and social and political behaviour (Beyaz et al, 2018).…”

Section: Machine Learning For Stock Price Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

View full text Add to dashboard Cite

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.

show abstract

Section: Machine Learning For Stock Price Predictionmentioning

confidence: 99%

Section: Machine Learning For Stock Price Predictionmentioning

confidence: 99%

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Obthong

Tantisantiwong

Jeamwatthanachai

et al. 2020

Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business

View full text Add to dashboard Cite

show abstract

“…This choice is made using technical or fundamental analysis [1,34,44]. There are many analytical approaches for decision making in stock market, which are categorized in two groups: technical analysis and fundamental analysis [8,34]. We shall discuss these two groups in the following paragraphs.…”

Section: Introductionmentioning

confidence: 99%

Analysis and classification of companies on tehran stock exchange with incomplete information

et al. 2021

View full text Add to dashboard Cite

Due to uncertainty and large number of companies in financial market, it has become difficult to choose the right stock to investments. Identifying and classifying stocks using fundamental criteria help investors to better understand the risks involved in selecting companies and better manage their own capital, thereby rapidly and accurately choose their preferred stock and make more secure profit. The main concern that capital market investors are facing difficulty to choosing the right stock despite the uncertainties in the market. Uncertainties in the market that lead to incomplete information are presented in this article to complete the reciprocal preference relation method. The purpose of this paper is to present a method for completing information to reduce the uncertainties in the market and finally classify companies in each industry based on fundamental criteria. The classification method used is acceptability / reject ability which is based on distance fuzzy analysis yields more accurate results. Finally, a case study on one of the most critical industries in Tehran Stock Exchange is presented to show the effectiveness of the proposed approach.

show abstract

“…Yıllardan beri, sermaye piyasasındaki oyuncular borsa üzerindeki yatırımlarında karı arttırmak için farklı yöntem ve teknikler kullanmaya çalışmışlardır. Bu hareketlerin analiz edilmesi teknik ve temel analiz olmak üzere iki ana başlık altta toplanmıştır: Teknik Analiz ve Temel Analiz [2,3]. Teknik analiz, geçmiş fiyat hareketlerine dayanan gelecekte oluşabilecek fiyatların tahmini olarak açıklanabilir.…”

Section: Introductionunclassified

Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini

Koçer

Önde

Şahingöz

2020

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

ÖzSon yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve "Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir. Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir. Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç, pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin zaman serisi değerleri kullanıldı. Spesifik olarakta IBM hisse senedi ile test çalışmaları yapıldı. Deneysel sonuçlar, hisse senedine ait son 5 günlük işlem verilerinin girdi olarak kullanıldığı BLSTM modeliyle yapılan eğitimin %63,54 lük bir yönsel doğruluk değerine ulaşıldığını göstermektedir. AbstractIn recent years, due to the technological advances in computer hardware and enhancements in machine learning techniques, there are two hot research areas in problem solving, the use of "Big Data" and "Parallel Processing". Many real-world problems can be solved with the use of different Deep Learning algorithms, which can be realized in parallel with multicore computing devices such as GPUs. Deep learning models show great success in applications such as classification of raw data, regression analysis and estimation in time series. One of the most active application areas of these models is "the financial market" which aims a good estimation of stock prices in the exchange market. In this paper, it is aimed to forecast the short or long term future value by looking at the previous log data of the stock on the process of change in the market. In this study, a price forecasting system was developed by using 3 different deep learning models named LSTM, GRU and BLSTM with a comparative analysis between them. The time series values of the stock were used from the New York Stock Exchange from 1968 to 2018 as a set of data to be free of speculative movements. Specifically, tests were conducted on IBM stock. Experimental results show that the directional accuracy of 63.54% was achieved with the BLSTM model where the last 5-day transaction data of the stock were used as input.

show abstract

Comparing Technical and Fundamental Indicators in Stock Price Forecasting

Cited by 38 publications

References 16 publications

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Analysis and classification of companies on tehran stock exchange with incomplete information

Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini

Contact Info

Product

Resources

About