Algorithmic Machine Learning for Prediction of Stock Prices

Beg, Mirza Omer; Awan, Mubashar Nazar; Ali, Syed Shahzaib

doi:10.4018/978-1-5225-7805-5.ch007

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…However, existing predictive models struggle to adapt swiftly to unforeseen market events, influenced by intricate external factors such as economic trends, market dynamics, firm growth, consumer prices, and industry-specific shifts. These factors impact stock prices, leading to unpredictable outcomes [7,8]. Hence, a fundamental analysis integrating economic factors and the ability to analyze financial news and events is imperative.…”

Section: Introductionmentioning

confidence: 99%

Stock Market Prediction Using Deep Reinforcement Learning

Awad,

Elkaffas,

Fakhr

2023

ASI

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Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.

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Section: Introductionmentioning

confidence: 99%

Stock Market Prediction Using Deep Reinforcement Learning

Awad,

Elkaffas,

Fakhr

2023

ASI

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“…If the output falls within the overlapping zone of two membership functions, ambiguity is generated [12]. NL can handle ambiguity by defining a confidence value for the truth component according to Equation (5). If the truth value surpasses the confidence value (i.e., 0.5), the final output is the truth component value and the falsity and indeterminacy components are not significant; otherwise, the ambiguous results are generated [12,29].…”

Section: Ps Ns Outputmentioning

confidence: 99%

“…Figure 7 illustrates how to determine the confidence value. i| f = significant, t < 0.5 insignificant, t ≥ 0.5 (5) where t, i, and f are the truth, indeterminacy, and falsity components, respectively. The final polarity is calculated from the neutrosophic output according to polarity classes in [34].…”

Section: Ps Ns Outputmentioning

confidence: 99%

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Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Abdelfattah,

Darwish,

Elkaffas

2024

JTAER

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Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.

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“…The information verification system is divided into three stages. The Document Retrieval and sentence selection [17] stage have been used from [30] as they have the current best method which performs well on the FEVER task [65]. The claim verification stage will be improved upon.…”

Section: Proposed Approachmentioning

confidence: 99%

Few Shot Learning for Information Verification

Khalid,

Beg

2021

Preprint

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Information verification is quite a challenging task, this is because many times verifying a claim can require picking pieces of information from multiple pieces of evidence which can have a hierarchy of complex semantic relations. Previously a lot of researchers have mainly focused on simply concatenating multiple evidence sentences to accept or reject claims. These approaches are limited as evidence can contain hierarchical information and dependencies. In this research, we aim to verify facts based on evidence selected from a list of articles taken from Wikipedia. Pretrained language models such as XLNET are used to generate meaningful representations and graph-based attention and convolutions are used in such a way that the system requires little additional training to learn to verify facts.

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Algorithmic Machine Learning for Prediction of Stock Prices

Cited by 6 publications

References 8 publications

Stock Market Prediction Using Deep Reinforcement Learning

Stock Market Prediction Using Deep Reinforcement Learning

Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis

Few Shot Learning for Information Verification

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