Journal article Open Access

Stock Market Forecasting from Multi-Source Data using Tolerance Based Multi-Agent Deep Reinforcement Learning

C. Bhuvaneshwari; R.Beena

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="">
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Event extraction, Random forests, Restricted Boltzmann Machine, Sentiment analysis, Stock forecasting, Tolerance based multi-agent deep reinforcement learning.</subfield>
  <controlfield tag="005">20211023134845.0</controlfield>
  <controlfield tag="001">5593836</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Associate Professor, Department of Computer Science,  Kongunadu Arts and Science College, Coimbatore, India</subfield>
    <subfield code="a">R.Beena</subfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Publisher</subfield>
    <subfield code="4">spn</subfield>
    <subfield code="a">Blue Eyes Intelligence Engineering  &amp; Sciences Publication(BEIESP)</subfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">629339</subfield>
    <subfield code="z">md5:8334e75286d26821c8e54efb572dbadd</subfield>
    <subfield code="u"></subfield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-02-29</subfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o"></subfield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="c">3492-3499</subfield>
    <subfield code="n">3</subfield>
    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
    <subfield code="v">9</subfield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Research Scholar, Department of Computer  Science, Kongunadu Arts and Science College, Coimbatore, India</subfield>
    <subfield code="a">C. Bhuvaneshwari</subfield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Stock Market Forecasting from Multi-Source  Data using Tolerance Based Multi-Agent Deep  Reinforcement Learning</subfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u"></subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2"></subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">ISSN</subfield>
    <subfield code="0">(issn)2249-8958</subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">Retrieval Number</subfield>
    <subfield code="0">(handle)C6293029320/2020©BEIESP</subfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Analyzing and forecasting the future trends in stock market is challenging due to the ever increasing size of stock data. Modern techniques extract the stock indicators from the web data to forecast the stock movements. However, most previous studies were based on single source of data for extracting these indicators. This might not be effective in obtaining all the possible diverse factors that influence the market movements. Multi-source data has been rarely applied for stock prediction and even those techniques have limitations in handling larger data. In an attempt to utilize multi-source data more effectively for extracting stock indicators and improve the forecasting accuracy of stock movements, this paper developed a stock market forecasting model using Tolerance based Multi-Agent Deep Reinforcement Learning (TMA-DRL) model. The TMA-DRL model effectively combines the quantitative stock data with the indicators i.e. the events extracted from news data and sentiments extracted from tweets. This forecasting model utilizes Random forests to extract the twitter opinions and Restricted Boltzmann Machine (RBM) for event extraction from news data. Combining these indicators, the TMA-DRL model leads to improved data learning and provides highly accurate prediction of future stock trends. Datasets for evaluation were collected from three sources namely Twitter, Market News and Stock exchange, for 12 months period. Evaluation results illustrate the effectiveness of the proposed TMA-DRL stock market forecasting model which makes predictions with high accuracy and less time complexity.&lt;/p&gt;</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">issn</subfield>
    <subfield code="i">isCitedBy</subfield>
    <subfield code="a">2249-8958</subfield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.35940/ijeat.C6293.029320</subfield>
    <subfield code="2">doi</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
Views 41
Downloads 11
Data volume 6.9 MB
Unique views 37
Unique downloads 10


Cite as