Price Movements Research Articles

Combining Autoregressive Integrated Moving Average Model and Gaussian Process Regression to Improve Stock Price Forecast

Stock market performance is one key indicator of the economic condition of a country, and stock price forecasting is important for investments and financial risk management. However, the inherent nonlinearity and complexity in stock price movements imply that simple conventional modeling techniques are not adequate for stock price forecasting. In this paper, we present a hybrid model (ARIMA + GPRC) which combines the autoregressive integrated moving average (ARIMA) model and Gaussian process regression (GPR) with a combined covariance function (GPRC). The proposed hybrid model can account for both the linearity and nonlinearity in stock price movements. Based on daily data on three stocks listed on the Shanghai Stock Exchange (SSE), it is found that GPRC outperforms GPR with a single covariance function. Further, the proposed hybrid model is compared with the ARIMA model, artificial neural network (ANN), and GPRC model. Based on the forecasting trend and the statistical performance of the four models, the ARIMA + GPRC model is found to be the dominant model for stock price forecasting and can significantly improve forecasting performance.

Examining the Random Walk Hypothesis: An investigation of the Indian stock market

Using the Efficient Market Hypothesis (EMH) as the theoretical foundation, this research study explores the Random Walk Hypothesis in the context of the Indian stock market. By using the Runs Test to analyse the daily closing prices of eight well-known Indian companies between January 1, 2018, and June 30, 2021, the study finds that patterns in stock prices in the Indian stock market show weak-form efficiency and are consistent with the Random Walk Hypothesis. The analysis indicates that stock price movements are largely unpredictable and devoid of discernible patterns, aligning with the notions posited by proponents of the Efficient Market Hypothesis. The research underscores significant implications, validating the presence of weak-form efficiency and emphasizing the need for alternative analytical approaches in investment strategies. Despite inherent limitations, this study contributes to ongoing discourse on market efficiency and financial dynamics, advocating for interdisciplinary approaches and global perspectives to refine understanding and facilitate more informed investment decisions in the Indian capital market.

IMPACT OF META VERSE ANNOUNCEMENT DATE ON STOCK PRICES

Purpose-The developing metaverse, a virtual world poised to transform online interaction, has raised an important question: how does a company's participation influence its financial standing? This study focuses on the e-commerce behemoth Amazon and its prospective entry into the metaverse. Our major goal is to determine whether a statement by Amazon on their metaverse plans causes a significant impact in their stock price. By examining past stock data with any potential metaverse announcement dates, we hope to determine whether Amazon's excitement about the metaverse translates into an actual financial benefit. This study dives deeper by examining the specifics of the announcement (assuming it occurs). Does the level of commitment and investment mentioned affect the stock price reaction? Finally, this study aims to bridge the gap between the hoopla surrounding the metaverse and its real-world ramifications for corporations such as Amazon. Approach: Event Definition: The event of interest is the particular day on which our selected companies makes a public declaration about entering the metaverse. This date will be critical to our analysis. Event Window: We'll set a timetable around the announcement date. This window will most likely consist of two parts: Estimation Window: The period preceding the announcement, we have selected 110 days, helps set a baseline for Amazon's stock price performance and eliminates the impact of unrelated market movements. Event Window: This time period, we have selected as 10 days before and after following the announcement allows us to monitor any potential changes in stock price due to metaverse news. Findings: Stock Price Change: The investigation may discover a statistically significant boost in companies stock price following a metaverse announcement. This suggests that investors reacted positively to the announcement, maybe seeing the metaverse as a new growth prospect. No Change or Negative Change: Alternatively, the investigation may discover no substantial change, or even a decline in stock price following the announcement. This could suggest investor concern about companies capacity to capitalize on the metaverse, as well as a general market correction during the event window. Impact of Announcement Specificity: The study may find that the level of depth and commitment conveyed in the announcement influences stock price movement. A more thorough and ambitious proposal may elicit a better positive response from investors. Industry-Specific Effects: The research design might be expanded to compare company's stock price movement to those of other companies in different industries following metaverse announcements. This could reveal whether particular industries have a stronger stock market reaction because of their deeper relationship to the metaverse. Limitation: For starters, the metaverse is a very new concept, so any announcement by worldwide companies may focus on future projects. This makes it difficult to estimate the long-term financial impact on the company's stock price. Furthermore, the specifics of the announcement itself (assuming it occurs) are critical. A vague announcement with few specifics may have a lower impact on the stock price than a well-defined plan with realistic investment figures. These constraints underscore the need for additional research as the metaverse and corporate participation in it evolve.

Equity Analysis on IT Sector Stocks

The investment in share market is highly rewarding but highly risky. The concept of analysis comes into picture when decision must be made on choosing a particular company's shares for investment. Analysis includes fundamental analysis. Fundamental analysis is a method of forecasting the future price movements of a financial instrument based on economic, political, environmental, and other relevant factors and statistics that will affect the basic supply and demand. This paper examined fundamental analysis of IT sector of the selected companies from the Information Technology sector, since IT sector is one of the most upcoming sectors in the Indian Market Secondary Data of last 5 year (2019-2023). The sample of the stocks for the purpose of collecting secondary data has been selected based on Random Sampling. For analysis the monthly share prices of the last 5 financial years is collected for ten leading IT companies. for the study through fundamental analysis is carried out by tools like Economic analysis, Industrial analysis and Company Analysis Key ratios include ROA, ROI, ROE, EPS, DPS, dividend payout ratio, dividend yield, and P/E ratio. The stocks are chosen in an unbiased manner and each stock is chosen independent of the other stocks chosen. The major findings are all IT sector companies performing very well and their fundamentals are very strong based on P/E ratio, EPS and Payout ratio. It is recommended to the investor that to invest in IT sector company for long term will be benefited. Key Words: IT Sector, Fundamental Analysis, Investment, Equity, Market [1] Student, B. V. Patel Institute of Management, Uka Tarsadia University [2] Associate Professor, B. V. Patel Institute of Management, Uka Tarsadia University

Consensus Analysts Target Price and Stock Price Returns in Nigeria

This study focuses on the effect of an Investment Valuation technique (Consensus Analysts Target Price) on Stock Price Return of listed companies in the Nigerian Exchange between the period of 2011 and 2023. The ex-post facto research design was employed in this research due to the use of historic (secondary) data from the selected fifteen (15) companies across 3 major sectors on the Nigeria Exchange (NGX). Panel autoregressive distribution lag model was the estimation techniques and inference made on 5% significance level. Prior to the data analysis, a number or pre-estimation test were carried out such as co-integration, homogeneity, and cross-sectional dependence in addition to basis correlation and descriptive analysis. Findings from the short run reveals a significant negative coefficient (-0.273) for lagged Stock Price Return (L.STR), indicating a substantial short-term correction effect. Conversely, the lagged Consensus Analyst Target Price (L.CATP) shows a positive coefficient (0.141) without statistical significance, suggesting a modest positive relationship without an immediate impact on Stock Price Return. Transitioning to the long run, the highly significant negative coefficient (-1.273) for long-run Stock Price Return (lr_STR) emphasizes a robust and persistent negative adjustment effect. Over an extended period, Consensus Analyst Target Price (lr_CATP) introduces a marginally significant positive coefficient (0.077), implying a modest positive impact on Stock Price Return. Based on the results, one recommendation concerning Consensus Analyst Target Price (CATP) and Stock Price Return is to exercise caution in relying solely on CATP for short-term decision-making. Investors should be aware that, in the short term, deviations from the lagged return may not be significantly influenced by CATP. Therefore, considering additional factors or indicators alongside CATP could enhance the accuracy of short-term decision-making in understanding stock price movements.

Deep Reinforcement Learning Based Optimization and Risk Control of Trading Strategies

Deep reinforcement learning (DRL) based optimization leverages advanced machine learning techniques to solve complex decision-making problems in various domains. Deep learning in trading involves the application of sophisticated neural network architectures to analyze financial data and make predictions in stock markets, forex, commodities, and other trading domains. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), traders can extract valuable insights from vast amounts of historical market data, including price movements, trading volumes, and market sentiment. These models can learn complex patterns and relationships within the data, enabling them to forecast future market trends, identify potential trading opportunities, and manage risks more effectively. Deep learning in trading has shown promise in improving decision-making processes, enhancing trading strategies, and achieving higher returns, although challenges such as data scarcity, model interpretability, and overfitting remain areas of ongoing research and development. In this paper evaluated the stock market to achieve the financial gain to achieve the significant improvement in prediction with the stock trading. The data related to the stock market are optimized with stock trend data of 7,000 stocks situated in the United States. The estimated stock trade data were computed and processed with the Multiagent Q-learning model for the data detection and classification. A deep Q-network is developed to adaptively pick distinct action subsets and train the market making agent based on the inventory states. The experimental findings demonstrate the effectiveness of our suggested methodology in feature learning and superiority in accuracy improvement when compared to deep learning-based modelling of frequency trading patterns and standard signal processing approaches for stock trend prediction.

Testing bitcoin’s safe-haven property and the correlation between Bitcoin, gold, oil, stock markets, and Google trends

Since its public introduction in 2009, Bitcoin has grown to be the most well-known cryptocurrency worldwide. There is still debate as to whether Bitcoin may be used as a hedge against other assets. The purpose of this study is to investigate the correlation between Bitcoin and conventional commodity markets such as gold, crude oil, stock markets, and investor interest (quantified via Google Trends). In addition, the paper also tests Bitcoin’s safe haven role compared to other commodity markets. The Vector Autoregression model using daily database collected during the period 2013–2021 is employed to investigate the relationship between Bitcoin and traditional commodity markets. The impulse response function is used to analyze Bitcoin price movements against economic shocks from gold, oil prices, and the Dow Jones Industrial Average. In addition, the value-at-risk (VaR) model is used to test Bitcoin’s safe-haven property compared to other conventional commodity markets. The research results show that Bitcoin has negative impacts on gold, crude oil prices, and the stock market. Besides, Bitcoin responds negatively to a sharp decline in investor interest. Furthermore, the results of the VaR model show that Bitcoin is the second most volatile and risky asset, only after the crude oil market, and much riskier than gold. This result proves that Bitcoin cannot yet be considered a safe-haven instrument. These findings have several implications for investors and policymakers to minimize the risks associated with this cryptocurrency. AcknowledgmentThe authors would like to send their sincere thanks to the Reviewers and Editorial Board of the Journal. Their valuable comments and helpful support helped improve the paper’s quality. No funding was granted for this study.

Navigating the green transition: the influence of energy volatility on green and sustainable ETFs

ABSTRACT This study explores the interaction between extreme energy commodity price fluctuations and green and sustainable Exchange Traded Funds (ETFs) returns. Empirical findings indicate that the re- turns of green ETFs exhibit variation in the sign and magnitude of their response to different energy commodities. Specifically, we observe significant influences from crude oil and natural gas price movements, whereas carbon emissions products show no evidence of a substantial interaction.

Pengaruh Straits Times Index, Harga Emas Dunia, Dan Inflasi Terhadap Ihsg Di Bursa Efek Indonesia Periode 2018 – 2023

The aims of this research is to determine and analyze the influence of the Straits Times Index (STI), world gold prices, and inflation on the Indonesia Composite Index on the Indonesia Stock Exchange during the period 2018 - 2023. The population in this research is time series data for 60 months from the ICI price movement on the Indonesia Stock Exchange during the period July 2018 – June 2023. The sampling technique used saturated samples so that the samples obtained were 60. The research method used was multiple linear regression analysis. The results of this research show that 1) the Straits Times Index (STI), World Gold Prices and Inflation simultaneously have a significant effect on the ICI on the IDX for the period 2018 – 2023, 2) the Straits Times Index (STI) partially has a positive effect and significantly to the ICI on the IDX for the period 2018 – 2023, 3) World gold prices partially have a positive and significant effect on the ICI on the IDX for the period 2018 – 2023, 4) Inflation partially has a positive and significant effect on the ICI on the IDX for the period 2018 – 2023 Keywords: Straits Times Index, World Gold Prices, Inflation, ICI

Reconciling Open Interest with Traded Volume in Perpetual Swaps

Perpetual swaps are derivative contracts that allow traders to speculate on, or hedge, the price movements of cryptocurrencies. Unlike futures contracts, perpetual swaps have no settlement or expiration in the traditional sense. The funding rate acts as the mechanism that tethers the perpetual swap to its underlying with the help of arbitrageurs. Open interest, in the context of perpetual swaps and derivative contracts in general, refers to the total number of outstanding contracts at a given point in time. It is a critical metric in derivatives markets as it can provide insight into market activity, sentiment and overall liquidity. It also provides a way to estimate a lower bound on the collateral required for every cryptocurrency market on an exchange. This number, cumulated across all markets on the exchange in combination with proof of reserves, can be used to gauge whether the exchange in question operates with unsustainable levels of leverage, which could have solvency implications. We find that open interest in Bitcoin perpetual swaps is systematically misquoted by some of the largest derivatives exchanges; however, the degree varies, with some exchanges reporting open interest that is wholly implausible to others that seem to be delaying messages of forced trades, i.e., liquidations. We identify these incongruities by analyzing tick-by-tick data for two time periods in 2023 by connecting directly to seven of the most liquid cryptocurrency derivatives exchanges.

Mortgage rates and credit risk: Evidence from mortgage pools

AbstractIn the 1990s, securitised subprime loans supported the growth of mortgage lending. We study the evolution of initial mortgage rates as a function of loan and borrower characteristics during 1992–2015. We compare the evolution of initial rates on securitised subprime mortgages with rates of prime privately securitised mortgages, mortgages securitised by government‐sponsored enterprises, and nonsecuritised mortgages. Starting in 2003 the risk premium on subprime loans decreases until it disappears at the onset of the Global Financial Crisis. We find that loading factors on subprime rates are cointegrated with delinquencies and house price movements, providing evidence of the important role of the subprime market.

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.

The Intraday Dynamics Predictor: A TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units for High-Frequency Price Movement Forecasting

This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limit order book (LOB) and order flow (OF) microstructure data and improving prediction accuracy over current state-of-the-art models. The proposed deep learning model, TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units (TFF-CL-GRU), takes LOB and OF features as input and consists of convolutional layers splitting into three channels before rejoining into a Gated Recurrent Unit. Key innovations include a tailored input representation incorporating LOB and OF features across recent timestamps, a hierarchical feature-learning architecture leveraging convolutional and recurrent layers, and a model design specifically optimised for LOB and OF data. Experiments utilise a new dataset (MICEX LOB OF) with over 1.5 million LOB and OF records and the existing LOBSTER dataset. Comparative evaluation against the state-of-the-art models demonstrates significant performance improvements with the TFF-CL-GRU approach. Through simulated trading experiments, the model also demonstrates practical applicability, yielding positive returns when used for trade signals. This work contributes a new dataset, performance improvements for microstructure-based price prediction, and insights into effectively applying deep learning to financial time-series data. The results highlight the viability of data-driven deep learning techniques in algorithmic trading systems.

Insights into Bitcoin and energy nexus. A Bitcoin price prediction in bull and bear markets using a complex meta model and SQL analytical functions

Cryptocurrencies are in the center of attention of investors, public authorities and researchers, but the interest has shifted from purely financial aspects regarding the way of trading, lack of regulation and supervision of transactions, volatility, correlation with other assets to aspects related to sustainability taking in account the high energy consumption generated by the mining process and the impact on environmental pollution. Bitcoin was chosen for the research considering the dominance that this financial asset has on the cryptocurrency market and its position as alpha currency.The article focuses on the relationship between Bitcoin transactions and energy consumption, for period 1st January 2019—31st of May 2022, this interval having significant price movements. The authors made a prediction of the Bitcoin price using a complex meta-model and SQL analytical functions. The analysis is based on 15 fundamental variables in order to forecast the price: Bitcoin data (prices and volume), electricity price and traded quantity on day-ahead market (DAM), gas price and traded quantity on DAM, inflation in EU, EU-ETS emissions certificates and oil prices. The study reveals the importance of the relationship Bitcoin—energy—carbon emissions, elements that capture the impact of the mining process on the environment from the perspective of energy consumption. Investors on the Bitcoin market must be aware not only of the importance of financial aspects on the price of cryptocurrencies (inflation, demand, offer), but also of other elements related to the evolution of energy prices (electricity, oil, gas, renewable energy) and the evolution of emissions certificates prices. Considering the promotion of the principles of sustainable development on the capital market, portfolio investors have become increasingly attentive to the social and environmental performance of financial assets. This study aims to make financial market players aware of the non-financial implications of their transactions. In addition, the energy transition and the reconfiguration of the energy mix are elements of impact on the cryptocurrency market through the technical levers involved in the mining process.

Exploring the Relationship Between Google Trends and Cryptocurrency Metrics

Abstract Bitcoin and Ethereum are the two largest cryptocurrencies in the world by market capitalization and trading volume and the most popular despite high price fluctuations. This paper analyzes the relationship between Bitcoin and Ethereum metrics and the internet search interest on cryptocurrencies. As the literature shows, Google searches signal investor attention and Google Trends has proven useful for nowcasting economic and financial indicators. We aim to find the impact of Google Trends on Bitcoin and Ethereum prices, trading volumes and market capitalization since 2015 and discuss the potential correlations and patterns that may exist between these metrics and Google search interest. Through correlation and time-series analysis, we provide insights into the dynamics of this relationship and its implications for understanding cryptocurrency market behavior. The interest in cryptocurrencies tracked by Google Trends is a good indicator of measuring the social interest in the cryptocurrency market that drives a price movement. On the other hand, the price fluctuations of Bitcoin and Ethereum generate media and social attention and increase the interest in these cryptocurrencies. We also observe a positive effect of Google Trends values on trading volumes. The findings could help investors to understand the cryptocurrencies dynamics and build their trading strategies and could be of special interest to policymakers.

Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.

Pengaruh Rasio Keuangan, Pertumbuhan Laba Terhadap Harga Saham LQ-45 Indonesia

This research aims to find empirical evidence of the influence of several financial ratios, namely Current Ratio, Return on Assets, Earnings Per Share, Debt to Equity Ratio, and Total Assets Turnover, as well as profit growth on the stock prices of companies listed in the LQ-45 index on the Indonesia Stock Exchange (IDX) during the 2018-2022 period. The method used is quantitative causality with data analysis using multiple linear regression with the Stata application. The research sample was obtained from the OSIRIS database and selected using a purposive sampling method with the following criteria: (1) companies that are consistently listed in the LQ-45 index on the Indonesia Stock Exchange for the 2018-2022 period; (2) companies that issue financial reports in the 2018-2022 time period, which contain all the information required in the research variables; (3) and companies that publish stock prices at the close of 2018-2022. This process resulted in 23 companies, and the final sample of observations obtained was 115 data points. The research results show that simultaneously, financial ratio variables and profit growth significantly influence stock prices. This means that the combination of these variables has a strong impact on company share price movements in the LQ-45 index. However, when analyzed partially, only the Earning Per Share (EPS) variable has a significant influence on stock prices. This shows that EPS has a more dominant role than other variables in influencing company stock prices in the LQ-45 index.

PENGARUH RETURN ON ASSET (ROA), EARNING PER SHARE (EPS), DAN DEBT TO EQUITY RATIO (DER) TERHADAP HARGA SAHAM PADA PERUSAHAAN YANG TERDAFTAR DALAM INDEKS IDX30 DI BEI PERIODE 2018-2022

The movement of stock prices, reflected by company fundamental factors, can provide significant signals about the overall economic condition. Understanding company fundamentals is crucial for the public to make informed investment decisions, thereby maximizing returns through stock price movements. This research aims to discover the influence of Return on Assets (ROA), Earnings Per Share (EPS), and Debt to Equity Ratio (DER) on stock prices in companies listed in the IDX30 index on the Indonesia Stock Exchange (BEI) during the 2018-2022 period. The population used in this research accumulates to a total of 54 companies. The sample was determined using purposive sampling method, resulting in 19 companies. This research employs classic assumption tests and multiple linear analysis, asxwell as F-test and T-test in data analysis. The findings conclude thatxsimultaneously, Return on Assets (ROA), Earnings Per Share (EPS), and Debt To Equity Ratio (DER) have a positive and significant effect on stock prices. Partially, Returnxon Assets (ROA) has a positive and significantxeffect on stock prices, while Earnings PerxShare (EPS) and Debt To EquityxRatio (DER) do not have a significantxpartial effect onxstock prices.

Stock Market Price Prediction and Forecasting Using Stacked LSTM

Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. Therefore, data scientists and analysts found that Deep Learning outperformed Machine Learning which is also proofed by all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain. This paper explores different stacked LSTM Models for non-stationary financial time series in stock price prediction. This study is to predict stock market prices to make more acquaint and precise investment decisions. The experimental result will show that, this method can get quite accurate result, particularly effective in stock prediction. The proposed LSTM model will be designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, The Model will have a comparative analysis between different LSTM models and integrating the model based on the result obtained from the analysis. The results will suggest that the developed stacked LSTM produces better predictive power and generalization. Key Words: stock market prediction, stacked LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Neural Networks, Data Visualization, Training and Testing Set, Time series analysis, Predictive models, Future Forecasting.

Application of the Support Vector Regression Method with the Grid Search Algorithm to Predict Movement Gold Price

Gold is an investment with the smallest risk because it can be sold anytime and anywhere. In Indonesia, gold bullion as an investment product is known for its purity level of 99.99%, namely gold bullion produced by PT. Aneka Tambang (Antam) through its Precious Metals business unit. Apart from its pure production, Antam gold bullion is easier to resell anytime and anywhere because it has an official certificate from the international gold standardisation institution, namely LBMA (London Bullion Market Association), to more easily estimate the value of gold bullion when sold. To overcome this, predictions of future gold prices are needed. In this research, one of the prediction methods is Support Vector Regression with the Grid Search Algorithm. In this method this method will be used to predict the price of gold, which aims to predict and find out the price of gold one year in the future to produce a level accuracy (MAPE) of 5.43% and the prediction of gold prices increasing from 2023-June-01 to 2024-March-23 while experiencing a decline starting in 2024-March-24. Research by examining the relationship between variables, which emphasises data consisting of numbers so that it is analysed based on statistical procedures using the Support Vector Regression method with data sourced from the daily price of gold bullion through PT. Gallery 24 Pawnshops, North Sumatra. Where this method is very well used in predicting by choosing the best kernel used is the linear kernel because, from these three kernels, the best hyperparameters were obtained for predicting gold price movements using a linear kernel with a division for training and testing data of 60: 40. The MAPE value obtained was 5.43.