Analyzing Stock Selection in the Energy Sector: A Python-Based Capital Asset Pricing Model Approach
DOI:
https://doi.org/10.24246/persi.v8i1.p1-13Keywords:
Stock Selection, CAPM, Python, Energy SectorAbstract
Abstract. This study explores the complex aspects of stock valuation and decision-making on the Jakarta Stock Exchange (JKSE), focusing on the Energy Sector, particularly within the Sub-Industry of Oil, Gas, & Coal Equipment & Services. The primary objective is to ascertain "Buy" and "Sell" recommendations grounded in anticipated returns and stock performance. This classification stems from a meticulous examination of each stock's alignment with both its intrinsic value and the overarching market value of the JKSE. Seven stocks (DEWA, DOID, MYOH, PKPK, PTRO, UNIQ, and WINS) merit "Buy" endorsements, as their projected returns surpass their present market valuations, potentially indicating undervaluation. Conversely, "Sell" recommendations are assigned to four companies (ITMA, RUIS, SICO, and TAMU) in cases where projected returns fall short, suggesting either underperformance or overvaluation. Incorporating Python and the Capital Asset Pricing Model (CAPM), this analysis yields a noteworthy insight. Companies like WINS and DOID display a substantial association with the JKSE market value, as evidenced by their notably low P-values of 0.014 and 0.038, respectively. In contrast, the P-values for other companies fall below the 0.05 threshold, implying that the market value of JKSE lacks a substantial impact. This research highlights the complex relationships involving market valuations, stock performance, and investor choices. By discerning the various degrees of correlation and comprehending the factors that influence "Buy" and "Sell" recommendations, investors can adeptly navigate the intricacies of the market and optimize their investment returns.
Keywords: Stock Selection, CAPM, Python, Energy Sector
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