Fundamental and Technical Analysis of the Stock Price

Spahija, Denis; Xhaferi, Seadin

doi:10.33807/monte.1.201904160

Cited by 4 publications

(4 citation statements)

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“…Investasi adalah suatu kegiatan seseorang atau organisasi mengeluarkan sejumlah uang di masa ini sebagai komitmen dengan harapan memperoleh manfaat di masa depan (Tandelilin, 2010a). Investasi di pasar modal khususnya dengan instrumen saham merupakan aktivitas yang menarik berbagai kalangan investor, baik investor sudah mahir sampai investor pemula (Spahija & Xhaferi, 2018). Harapan investor dalam berinvestasi adalah keuntungan.…”

Section: Pendahuluan Latar Belakangunclassified

Pengaruh volatilitas harga minyak, kurs, dan BI rate terhadap indeks harga saham gabungan 2018-2022

Yohanes,

Setyawan

2024

J. Manaj. Bisnis dan Kewirausahaan

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Macroeconomic factors that could influence IDX’s (JKSE) composite stock price index are crude oil prices, exchange rates from Rupiah to the US dollar, and BI rate. These are three independent variables that take into account several factors and situations in the world economy. The prices of crude oil are an indication of global demand. The exchange rate is based on the economic situation in Indonesia. In response to economic conditions in Indonesia, the Bank of Indonesia has a policy based on the BI Rate. These three variables indicate that when the economy is strong, the stock index also strengthens. The use of the TARCH method explains the volatilities of independent variables and their influence on dependent variables. The TARCH method is used when asymmetric volatility is present. The results of this research show crude oil prices and BI Rate positively and significantly affect the JKSE. This means when the economic condition is strong, the stock index will also be affected positively. While the Rupiah exchange rate has a negative and significant impact. This is because if the conversion rate decreases (Rupiah strengthens), it indicates that the Indonesian economy is strong. Simultaneous tests also show that all three variables influence the JKSE. These variables are examined to understand the impact of macroeconomic variables on the stock index, so readers are expected to have insights into the global economic situation before investing in stocks.

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Section: Pendahuluan Latar Belakangunclassified

Pengaruh volatilitas harga minyak, kurs, dan BI rate terhadap indeks harga saham gabungan 2018-2022

Yohanes,

Setyawan

2024

J. Manaj. Bisnis dan Kewirausahaan

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“…Stock prices are impacted by politics, economics, news, and investors use fundamental and technical analysis for predictions [27]. Fundamental analysis of a company's stock involves evaluating historical performance, anticipated future growth, and key factors such as profits, product quality, industry competition, financial balance, and cash flow projections [28]. Technical analysis involves predicting stock price trends through market trends and statistical data, addressing questions like optimal buying and selling times.…”

Section: Shouvik Banik Et Al Created An Lstm-based Decisionmentioning

confidence: 99%

“…Technical analysis involves predicting stock price trends through market trends and statistical data, addressing questions like optimal buying and selling times. It relies on tables, charts, and coefficients to make short-term and long-term predictions for specific stocks [28]. The review of existing studies and comparisons highlights the success of deep learning models like LSTM, ANN, RNN, SVM, SLSTM and BiLSTM in achieving accurate stock price predictions with minimal error.…”

Section: Shouvik Banik Et Al Created An Lstm-based Decisionmentioning

confidence: 99%

A Comparative Study of Deep Learning Algorithms for Forecasting Indian Stock Market Trends

Paul,

Das

2023

IJACSA

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This research underscores the vital significance of providing investors with timely and dependable information within the dynamic landscape of today's stock market. It delves into the expanding utilization of data science and machine learning methods for anticipating stock market movements. The study conducts a comprehensive analysis of past research to pinpoint effective predictive models, with a specific focus on widely acknowledged algorithms. By employing an extensive dataset spanning 27 years of NIFTY 50 index data from the National Stock Exchange (NSE), the research facilitates a thorough comparative investigation. The primary goal is to support both investors and researchers in navigating the intricate domain of stock market prediction. Stock price prediction is challenging due to numerous influencing factors, and identifying the optimal deep learning model and parameters is a complex task. This objective is accomplished by harnessing the capabilities of deep learning, thereby contributing to well-informed decision-making and the efficient utilization of predictive tools. The paper scrupulously examines prior contributions from fellow researchers in stock prediction and implements established deep learning algorithms on the NIFTY 50 dataset to assess their predictive accuracy. The study extensively analyzes NIFTY 50 data to anticipate market trends. It employs three distinct deep learning models-RNN, SLSTM, and BiLSTM. The results underscore SLSTM as the most effective model for predicting the NIFTY 50 index, achieving an impressive accuracy of 99.10%. It's worth noting that the accuracy of BiLSTM falls short when compared to RNN and SLSTM.

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“…The purpose of fundamental analysis is to buy an asset at a low price and sell it at a high price by determining the difference between the actual value of the asset and its market price. In technical analysis, future prices are predicted using historical securities data, assuming that stock prices are determined by market forces and history tends to repeat itself [ 6 ].…”

Section: Introductionmentioning

confidence: 99%