Forecasting the Movement of Renewables Stocks Using BSE Energy Index
Ankita Nandy
Department of Computing, Asia Pacific University, Kuala Lumpur, Malaysia.
7-18
Vol: 12, Issue: 1, 2022
Receiving Date:
0021-12-10
Acceptance Date:
2022-02-09
Publication Date:
2022-02-10
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http://doi.org/10.37648/ijrst.v12i01.002
Abstract
Coincident to the dip in the demand of conventional sources of energy like coal, oil and gas as the pandemic progressed has been a surge in the global demand for environment friendly practices, putting the spotlight on energy generated from renewable sources. The Renewables sector has found favor and is witnessing steady rise on a global level. Though a minor contributor to the power generation in India, this sector is deemed to grow in the coming years as India strives to reduce its CO2 emissions, making the related instruments lucrative investment options. Stock exchanges are critical to the economic health of a nation and the pandemic led to major crashes in several exchanges around the world. Investment firms can employ deep learning models to forecast the movement of the market and thus assure their customers of high returns in the high-risk environment, cutting through the general pessimism pervading the investment sphere post-pandemic. This work builds forecasting models for two such stocks using neural networks. Selecting the BSE as the universe of study, two companies are selected and modelled across two techniques: LSTM and Bidirectional LSTM, employing three different feature sets. The inclusion of BSE Energy Index in the models alongside the historical prices enables capturing the influence of external elements on the energy market.
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Keywords:
Stock market; COVID; March market crash; LSTM; Renewables
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