Fundamental Analysis of the Energy Market

Thomsett, Michael C.

doi:10.1057/9781137358479_12

Cited by 1 publication

(1 citation statement)

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“…Stock market forecasting combines fundamental and technical analysis, time series, and momentum investing strategies to predict market movements. Fundamental analysis evaluates a stock's value through economic indicators, company performance, and market demand [23,28,29]. Influential research has highlighted the importance of using financial ratios and accounting data for valuation, advocating for sector-specific studies [30][31][32][33].…”

Section: Stock Market Prediction Methodologiesmentioning

confidence: 99%

Enhancing Stock Market Forecasts with Double Deep Q-Network in Volatile Stock Market Environments

Papageorgiou,

Gkaimanis,

Tjortjis

2024

Electronics

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Stock market prediction is a subject of great interest within the finance industry and beyond. In this context, our research investigates the use of reinforcement learning through implementing the double deep Q-network (DDQN) alongside technical indicators and sentiment analysis, utilizing data from Yahoo Finance and StockTwits to forecast NVIDIA’s short-term stock movements over the dynamic and volatile period from 2 January 2020, to 21 September 2023. By incorporating financial data, the model’s effectiveness is assessed in three stages: initial reliance on closing prices, the introduction of technical indicators, and the integration of sentiment analysis. Early findings showed a dominant buy tendency (63.8%) in a basic model. Subsequent phases used technical indicators for balanced decisions and sentiment analysis to refine strategies and moderate rewards. Comparative analysis underscores a progressive increase in profitability, with average profits ranging from 57.41 to 119.98 with full data integration and greater outcome variability. These results reveal the significant impact of combining diverse data sources on the model’s predictive accuracy and profitability, suggesting that integrating sentiment analysis alongside traditional financial metrics can significantly enhance the sophistication and effectiveness of algorithmic trading strategies in fluctuating market environments.

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