Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark

• URL: http://arxiv.org/abs/2201.12283v1
• Date: Wed, 19 Jan 2022 10:22:33 GMT
• Title: Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark
• Authors: Taylan Kabbani (1 and 2), Fatih Enes Usta (3) ((1) Ozyegin University, (2) Huawei Turkey R&D Center, (3) Marmara University)
• Abstract summary: Different features are given to help the machine learning model predict the label of a given day. The overall sentiment score on a given day is created from all news released on that day. Random Forest was the best performing model with a 63.58% test accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend feature as our label to be predicted. Different features are given to help the machine learning model predict the label of a given day; whether it is an uptrend or downtrend, those features are technical indicators generated from the stock's price history. In addition, as financial news plays a vital role in changing the investor's behavior, the overall sentiment score on a given day is created from all news released on that day and added to the model as another feature. Three different machine learning models are tested in Spark (big-data computing platform), Logistic Regression, Random Forest, and Gradient Boosting Machine. Random Forest was the best performing model with a 63.58% test accuracy.

Related papers

• Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
  This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market. It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. The results show a buy/sell signal accuracy of over 92%.
  arXiv  Detail & Related papers  (2024-10-09T14:29:50Z)
• Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
  We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
  arXiv  Detail & Related papers  (2024-06-10T11:42:30Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms [3.2732602885346576]
  This research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer.
  arXiv  Detail & Related papers  (2023-05-17T07:04:32Z)
• TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data [1.5749416770494706]
  We will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach.
  arXiv  Detail & Related papers  (2023-03-13T18:55:41Z)
• Stock Price Prediction Under Anomalous Circumstances [81.37657557441649]
  This paper aims to capture the movement pattern of stock prices under anomalous circumstances. We train ARIMA and LSTM models at the single-stock level, industry level, and general market level. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98%.
  arXiv  Detail & Related papers  (2021-09-14T18:50:38Z)
• Stock price prediction using BERT and GAN [0.0]
  This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. It uses a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP) After, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores.
  arXiv  Detail & Related papers  (2021-07-18T18:31:43Z)
• FitVid: Overfitting in Pixel-Level Video Prediction [117.59339756506142]
  We introduce a new architecture, named FitVid, which is capable of severe overfitting on the common benchmarks. FitVid outperforms the current state-of-the-art models across four different video prediction benchmarks on four different metrics.
  arXiv  Detail & Related papers  (2021-06-24T17:20:21Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)
• A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models [0.0]
  We present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
  arXiv  Detail & Related papers  (2020-04-17T19:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.