Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine

Mohanty, Dipana Jyoti; Parida, A.K.; Khuntia, Shelly Suman

doi:10.1016/j.asoc.2020.106898

Cited by 60 publications

(23 citation statements)

References 45 publications

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“…Various research works have been conducted to assess the fluctuating financial market behavior within a deep learning framework with a prime focus on predicting the stock price and thereby minimizing the losses of stockholders [23]. The authors of [24] combined both stock data and news headline sentiments to devise a better trading system.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Trading Based on Market Sentiments and Reinforcement Learning

Suhail¹,

Sankar²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Stock market is a place, where shares of different companies are traded. It is a collection of buyers' and sellers' stocks. In this digital era, analysis and prediction in the stock market have gained an essential role in shaping today's economy. Stock market analysis can be either fundamental or technical. Technical analysis can be performed either with technical indicators or through machine learning techniques. In this paper, we report a system that uses a Reinforcement Learning (RL) network and market sentiments to make decisions about stock market trading. The system uses sentiment analysis on daily market news to spot trends in stock prices. The sentiment analysis module generates a unified score as a measure of the daily news about sentiments. This score is then fed into the RL module as one of its inputs. The RL section gives decisions in the form of three actions: buy, sell, or hold. The objective is to maximize long-term future profits. We have used stock data of Apple from 2006 to 2016 to interpret how sentiments affect trading. The stock price of any company rises, when significant positive news become available in the public domain. Our results reveal the influence of market sentiments on forecasting of stock prices.

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Section: Related Workmentioning

confidence: 99%

Stock Market Trading Based on Market Sentiments and Reinforcement Learning

Suhail¹,

Sankar²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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show abstract

“…In the modern era, the most trendy pattern prediction technique is artificial intelligence. ere is a lot of research analysis of pattern prediction using AI algorithm [15][16][17][18][19]. Each model has its own advantages and limitations associated with it and also shows an error when we execute it.…”

Section: Objectivementioning

confidence: 99%

Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

2022

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For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.

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“…It outperforms the well-known TIs and macroeconomic variables. Mohanty et al (2020) believe that TIs under a deep learning framework can improve financial market forecasts' quality. However, there is no consensus on whether the TI analysis is effective in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%