Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Nikou, Mahla; Mansourfar, Gholamreza; Bagherzadeh, Jamshid

doi:10.1002/isaf.1459

Cited by 188 publications

(119 citation statements)

References 47 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…and 2) practical: investment strategies [9], portfolio management [36], and beyond finance [29,72,78]. Financial models have previously relied only on numerical features [56,68] such as macroeconomic indicators [37]. This includes discrete (GARCH [11], rolling regression [71]), continuous (Markov chain [40] & stochastic volatility [2]), and neural approaches [48,55,58,68].…”

Section: Related Work 21 Multimodality In Financial Forecastingmentioning

confidence: 99%

“…Financial models have previously relied only on numerical features [56,68] such as macroeconomic indicators [37]. This includes discrete (GARCH [11], rolling regression [71]), continuous (Markov chain [40] & stochastic volatility [2]), and neural approaches [48,55,58,68]. Contemporary approaches: Newer work categorized under Fundamental Analysis [1] based on the Efficient Market Hypothesis [60] highlight the success of multimodal data in finance [53], as they capture a wider set of affecting knowledge and their interdependencies.…”

Section: Related Work 21 Multimodality In Financial Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Multimodal Multi-Task Financial Risk Forecasting

Sawhney

Mathur

Mangal

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

View full text Add to dashboard Cite

Stock price movement and volatility prediction aim to predict stocks' future trends to help investors make sound investment decisions and model financial risk. Companies' earnings calls are a rich, underexplored source of multimodal information for financial forecasting. However, existing fintech solutions are not optimized towards harnessing the interplay between the multimodal verbal and vocal cues in earnings calls. In this work, we present a multitask solution that utilizes domain specialized textual features and audio attentive alignment for predictive financial risk and price modeling. Our method advances existing solutions in two aspects: 1) tailoring a deep multimodal text-audio attention model, 2) optimizing volatility, and price movement prediction in a multi-task ensemble formulation. Through quantitative and qualitative analyses, we show the effectiveness of our deep multimodal approach. CCS CONCEPTS • Social and professional topics → Economic impact; • Computing methodologies → Natural language processing.

show abstract

Section: Related Work 21 Multimodality In Financial Forecastingmentioning

confidence: 99%

Section: Related Work 21 Multimodality In Financial Forecastingmentioning

confidence: 99%

Multimodal Multi-Task Financial Risk Forecasting

Sawhney

Mathur

Mangal

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

View full text Add to dashboard Cite

show abstract

“…Many researchers have focused their efforts on applying these new techniques on financial markets (Dixon, Halperin, & Bilokon, 2020;El-Bannany et al, 2020;Galeshchuk & Mukherjee, 2017;Hatefi Ghahfarrokhi & Shamsfard, 2020;Nikou, Mansourfar, & Bagherzadeh, 2019;Sarlin & Marghescu, 2011;Sreedharan et al, 2020a;2020b). Hatefi Ghahfarrokhi and Shamsfard (2020) investigated the impact of social media data in predicting the Tehran Stock Exchange variables.…”

Section: Common Statistical and ML Techniques: An Overviewmentioning

confidence: 99%

Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Khedr

Arif

et al. 2021

Intell Sys Acc Fin Mgmt

View full text Add to dashboard Cite

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

show abstract

“…However, LSTM has been used to predict the volatility of the S&P 500 (Xiong, Nichols, & Shen, 2015) and to forecast foreign exchange rates (Giles, Lawrence, & Tsoi, 2001). Nikou, Mansourfar, and Bagherzadeh (2019) predicted the daily close price data of a UK exchange-traded fund from January 2015 to June 2018 using four models of machine learning and indicated that the deep-learning method was better in prediction than the other methods. A hybrid forecasting system with evolutionary optimization and adaptive filtering was developed by Lahmiri (2020) to predict the price changes of S&P 500 data.…”

Section: Deep-learning Modelsmentioning

confidence: 99%