Price Data Research Articles

Adaptive Market Efficiency Hypothesis in ASEAN Stock Markets: A Variance Ratio Analysis Across Market Capitalization Segments

This study examines the efficiency of the ASEAN stock market and investigates the dynamic adaptability of this efficiency over time. Utilizing advanced methodologies, including the Multiple Variance Ratio (MV) and Wild Bootstrapped Variance Ratio (WBVR) tests, in conjunction with the Rolling Window technique, the research assesses market efficiency across different periods from March 2009 to March 2024. The analysis is conducted using daily price data, segmented into the overall market and three sub-groups based on market capitalization, to explore the potential impact of company size on market efficiency. The findings indicate that market efficiency is not static; it fluctuates, particularly during periods of economic crises or significant events, when all market segments exhibit inefficiency, deviating from the Random Walk theory. This suggests that during such periods, stock prices become more predictable, contrary to the expectations of an efficient market. Additionally, the study finds that changes in market efficiency are consistent across different company sizes, suggesting that market or company size does not significantly influence efficiency.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Comparative Analysis of Financial Forecasting Techniques in Machine Learning for Netflix Stock Price Prediction

Abstract - In the stock market, predicting the price of stocks is still a hot research issue. One of the most challenging tasks in the field of computation is prediction of the stock market. The prediction depends on a number of variables, including physiological vs. Physical elements, rational vs. irrational investor behavior, investor attitude, market rumors, etc. All these factors work together to make stock values unpredictable and very challenging to predict accurately. Supervised Machine Learning (SML) algorithms is used to predict future value by using past stock price data. ML approaches have the ability to reveal trends and knowledge that hadn't previously noticed, and they can be used to predict that are incredibly accurate. The primary objective of this project is to predict stock prices of Netflix for analyzing profit on day closing. Data was gathered between the years of 2002 and 2022, and separated into two parts: training set and testing set. Only the testing portion is to be used for the final forecast. Four machine learning algorithms such as linear regression, decision tree, MLP, and KNN are used to forecast the close value of the Netflix stock price. This project's output adds to the body of knowledge on predicting stock prices and offers investors some new information for accurate prediction of stock prices.

Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings

Amidst the backdrop of rural revitalization and cultural renaissance, there is a surge in the construction demand for replica traditional vernacular dwellings. Traditional cost estimation methods struggle to meet the need for rapid and precise estimation due to the complexity inherent in their construction. To address this challenge, this study aims to enhance the accuracy and efficiency of cost estimation by innovatively developing an Adaptive Self-Explanatory Convolutional Neural Network (ASCNN) model, tailored to meet the specific cost estimation needs of replica traditional vernacular dwellings in the Huizhou region. The ASCNN model employs a Random Forest model to filter key features, inputs these into the CNN for cost estimation, and utilizes Particle Swarm Optimization (PSO) to optimize parameters, thereby improving predictive accuracy. The decision-making process of the model is thoroughly interpreted through SHAP value analysis, ensuring credibility and transparency. During the construction of the ASCNN model, this study collected and analyzed bidding control price data from 98 replica traditional vernacular dwellings. The empirical results demonstrate that the ASCNN model exhibits outstanding predictive performance on the test set, with a Root Mean Square Error (RMSE) of 9828.06 yuan, a Mean Absolute Percentage Error (MAPE) of 0.6%, and a Coefficient of Determination (R2) as high as 0.989, confirming the model’s high predictive accuracy and strong generalization capability. Through SHAP value analysis, this study further identifies key factors such as floor plan layout, roof area, and column material coefficient that are central to cost prediction. The ASCNN model proposed in this study not only significantly improves the accuracy of cost estimation for Huizhou replica traditional vernacular dwellings, but also enhances its transparency and credibility through model interpretation methods, providing a reliable basis for related investment decisions. The findings of this study also offer valuable references and insights for rapid and precise cost estimation of replica buildings in other regions worldwide.

Mimicking crypto portfolios in sustainable investment

In this paper, considering the difference in the energy demand level, we utilize the daily pricing data from 10/01/2019 to 06/30/2023 to construct mimicking crypto portfolios with 12 clean cryptocurrencies to replace the dirty cryptocurrency, Bitcoin (BTC). With a monthly rebalancing strategy, the mimicking portfolio closely matches the exposures to the risk factors of the BTC but with fewer specific risks. Furthermore, relying on the bivariate dynamic conditional correlation (DCC-) GARCH model, we compare the hedging capability of BTC and the corresponding mimicking crypto portfolio against movements of returns of sustainable assets. The empirical results show that the mimicking crypto portfolio provides ESG investors with a cheaper hedge tool of higher hedge effectiveness compared to the BTC. Moreover, we find that the mimicking crypto portfolio can act as a strong safe haven for the S&P Global Clean Energy Index and S&P Latin America Emerging LargeMidCap ESG Index during periods of market stress. Therefore, the mimicking crypto portfolio is a more attractive option for ESG investors due to its superior hedging efficiency and the added environmental advantages.

Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models

PurposeThe goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023.Design/methodology/approachThe data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test.FindingsUsing the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1).Originality/valueThe study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

Railways Tender Price Prediction Using Machine Learning and Deep Learning Algorithms

The idea of determining the optimum bid amount in any railway tender competition has been a complex and critical task. Reasonable pricing is the main gateway in the process of winning contracts. The approach presented here is based on data-driven pricing to maximize tender wins and reduce potential profit loss. The model shall make use of historical tender data, competitor pricing data, and market indicators in order to predict the optimal bid amounts. This advanced ML model runs advanced machine learning algorithms, including regression models and ensembles, to study the intricate relationship between factors that would affect the successful execution of a bid. Deep learning models are integrated into the model to provide it with better handling of temporal dependencies and other hidden patterns in data, hence yielding accurate and robust predictions. The key objective of the work is to increase the winning rate of tender contracts by at least 10%, while competitive profitability is ensured. Because of precise competitor price predictions, business success criteria are oriented to reaching this higher win rate on tenders. The Machine learning success criteria target a price prediction accuracy of at least 90%. Another important set of economic success criteria that should be targeted is an improvement in profit margins of at least 5% through more accurate pricing strategies and a reduction in the number of rejected bids. It will offer great value to the businesses operating in the railway industry in making proper decisions on operations and strategic planning. The paper develops a fusion of traditional statistical methodologies with advanced ML and DL techniques in order to provide a robust solution for competitive advantage and increased profitability in the dynamic and competitive railway tender market.

LSTM-Based Gasoline Price Prediction - An Example of Gasoline 95# in Beijing

Gasoline, as one of the indispensable energy sources in the global economy, the stability of its price has an important impact on national energy security and economic development. This study aims to improve the accuracy of gasoline price prediction through the use of Long Short-Term Memory Network (LSTM). By collecting and analyzing the historical price data of No. 95 gasoline in Beijing, the author constructed a prediction model based on LSTM, which is particularly suitable for dealing with long-term dependence in time series data. By comparing their performance to the traditional Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), LSTM demonstrates its advantages in processing complicated time series data. The validity of the model is verified by several evaluation metrics, including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results of the study indicate that the LSTM model shows high accuracy and reliability in predicting gasoline prices. In addition, future work will explore the introduction of multivariate analysis and hybrid network models to further the accuracy of the predictions and its generalization ability.

An Approach to Analyzing Migration Flow and House Price Data: Exploring Spatial Linkages between Flow, Price, Distance, Age at the Local Level

This paper is intended to elucidate the efficacy of employing a Geographic Information Systems (GIS) methodology to investigate the spatial interrelations among migration flows, housing prices, age demographics, and geographical distances. Utilizing data on migration flows and housing prices from Greater Manchester, UK, in 2011, in this study, we performed statistical analyses and generated a suite of visual mappings to illustrate the spatial dynamics between migration patterns, distances, age brackets, and the segmentation of migration destinations by housing price tiers. The findings reveal pronounced spatial associations among migration destinations, underscored by the interplay of housing price levels with age categories, distance intervals, or the origination points tied to specific housing price ranges. This GIS-based analytical framework offers policy-makers and urban planners a powerful tool for discerning distinct migration trends and locales, thereby facilitating more informed decision-making processes.

Comparison of Linear Regression and LSTM (Long Short-Term Memory) in Cryptocurrency Prediction

Abstract Cryptocurrency, particularly Bitcoin, has become a major topic in the financial and digital trading sectors due to its ability to facilitate direct transactions without intermediaries and the transparency offered by blockchain technology. However, the high volatility of Bitcoin prices necessitates accurate prediction methods to support better investment decisions. This research aims to compare the accuracy of Linear Regression and Long Short-Term Memory (LSTM) methods in predicting Bitcoin prices using historical data from Yahoo Finance. The research process begins with the collection of historical Bitcoin price data from September 17, 2014, to July 15, 2024, followed by data processing that includes cleaning and splitting the dataset into training and test data. Linear Regression and LSTM models are applied to the training data and tested to evaluate their performance in price prediction. The research findings show that the LSTM model significantly outperforms the Linear Regression model in terms of prediction accuracy. The LSTM model records much lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), as well as perfect R² scores on both datasets, demonstrating its high precision in prediction. In contrast, the Linear Regression model shows higher errors and lower explanatory power of data variability. These findings indicate that LSTM is more effective in capturing temporal patterns and Bitcoin price fluctuations, offering better accuracy and potentially being more suitable for future cryptocurrency price analysis, providing better guidance for investors in this highly dynamic market.

The Predictive Ability and Profitability of Moving Average Rules in the Saudi Stock Market

Abstract Following the implementation of capital market reforms, Saudi Arabia has opened up its stock market to international investors. This liberalisation has led to the inclusion of the stock exchange into leading global indices, which is expected to lead to an influx of foreign investment. Thus, it is important and timely to examine the efficiency of the Saudi stock market. To that end, this paper conducts a comprehensive investigation of the predictive ability and profitability of a popular trading rule, the moving average rule, using firm-level data for the Saudi stock market over the period 1st January 2008 to 31st December 2017. The results show that the moving average rule has predictive ability; the active rules for a majority of the sample companies outperformed the corresponding passive strategy. Furthermore, the results were economically significant as the rules were profitable even after the consideration of transaction costs. The results also showed that the most profitable moving average rules were those with short-moving average lengths, and that the introduction of a bandwidth, which serves to eliminate weak trading signals, had a positive impact on rule profitability. Importantly, the analysis showed that the most profitable component of the rule related to short selling as these trades resulted in higher profits than the long positions. A disaggregated analysis of sectors showed that the trading rules outperformed the buy-and-hold strategy in all seven industries considered, while the analysis of covariance revealed the importance of careful selection of filter size. Overall, the analysis documents significant inefficiencies in the Saudi stock market and suggests that the employment of a simple trading rule, based on past price data, can yield substantial profits across companies and sectors even in a costly trading environment. These findings suggest that the recent reforms that were implemented to improve the efficiency of the Saudi stock market have been suboptimal, and that further regulatory reform is required.

The Impact of Pig Futures on the Price Transmission in the Pig Industry Chain during Market Shocks

In recent years, frequent external emergencies have continuously impacted China’s pig industry chain. As the scale and standardization of pig farming in China have increasingly improved, pig futures have met the conditions for good operation and were listed for trading on the Dalian Commodity Exchange on 8 January 2021. To study the impact and influence of African swine fever, COVID-19, and the listing of pig futures on the price transmission mechanism at various stages of China’s pig industry, weekly price data from the pig industry from January 2015 to June 2023 were selected to construct an SV-TVP-VAR model for analysis. The empirical results showed that the shocks of African swine fever and COVID-19 caused price fluctuations at various stages of the pig industry chain, while price fluctuations significantly decreased after the listing of pig futures. Therefore, the introduction of pig futures effectively alleviated the price fluctuations at various stages of the pig industry chain following the shocks of African swine fever and COVID-19, and relevant policy recommendations are proposed accordingly.

Does Islamic Mutual Fund Bear Higher Risk than Conventional Mutual Funds? An Empirical Analysis from Bangladesh

This study aims to identify whether Islamic mutual funds bear higher risk exposure than conventional mutual funds due to implementing their shariah screening criteria to select assets on the portfolio. For this purpose, monthly closing price data of Islamic and conventional funds operated in Bangladesh are collected from January 2016 to August 2023. A total of 2320 observations of each risk measure (e.g., standard deviation, semi-standard deviation, semi-standard deviation, and beta) are computed using a 12-month rolling window method to compare risk exposure between Islamic and conventional funds using univariable and multivariate analysis. The univariate analysis is conducted by performing an independent samples t-test, which confirms that overall, Islamic funds bear lower risk exposure than conventional funds. In multivariate analysis, the Feasible Generalized Least Square (FGLS) method, a dynamic panel data analysis model, is applied where the effects of macroeconomic variables such as deposit rate, GDP growth rate, exports, imports, broad money, and remittances are controlled. The results of the multivariate analysis also confirm that Islamic mutual fund risk exposure is lower than that of conventional mutual funds. This study will be helpful for practitioners and institutional investors in decision-making.

Effect of Macroeconomic Factors on Mutual Funds Risk and Return: An Empirical Study from Bangladesh

This study aims to identify the influence of macroeconomic factors on the risk and return of mutual funds in Bangladesh. Monthly closing price data of 27 mutual funds from Bangladesh are collected from December 2015 to December 2022. Additionally, monthly data of 6 macroeconomic variables, i.e., deposit rate, export, import payments, remittance, broad money (M2) and GDP growth rate, are gathered for this study. This study utilized standard deviation and beta as risk measures, and the Sharpe and Treynor ratios are applied as risk-adjusted return (RAR) measures. All the risk and risk-adjusted return measures are computed using 12-months rolling window method. The random effect model of panel data analysis is applied to find the influence of macroeconomic variables on the risk and return of mutual funds. Overall findings indicate that macroeconomic factors significantly influence mutual fund risk exposure. On the other hand, risk-adjusted return (RAR) is also significantly influenced by the macroeconomic variables. This empirical evidence helps practitioners and institutional investors in their decision-making in the situation of market asymmetric. Keywords: Risk-Adjusted Return, Bangladesh, Mutual Fund, Standard Deviation, Beta, Sharpe Ratio, Treynor Ratio

A novel trading system for the stock market using Deep Q-Network action and instance selection

Stock trading is a complex decision-making process that involves predicting market price movements. Many investors attempt to buy at low prices and sell at high prices, which can be difficult due to numerous factors that can influence stock prices. Consequently, researchers have developed trading systems designed to generate high stock market returns using machine learning or deep learning techniques. However, building a trading system using supervised learning can be challenging in real-time due to noisy stock price data. Reinforcement learning trading systems can play an active role in the stock market and their performance depends heavily on how the learning environment is constructed. Therefore, this study proposes a Deep Q-Network (DQN) Action Instance Selection Trading System (DAIS) to improve the limitations of both supervised learning and reinforcement learning trading systems. DAIS learns effective trading timing by labeling the behavior of DQN models and using instance selection to effectively handle noise in stock price data. Our proposed model is evaluated by testing 72 companies listed on Korean Composite Stock Price Indexes (KOSPI) and comparing the results with those obtained in previous studies using supervised and reinforcement learning trading systems. The results indicate that the DAIS trading system is more efficient than the compared systems.

Comparison of Fuzzy Grey Markov Model (1,1) and Fuzzy Grey Markov Model (2,1) in Forecasting Gold Prices in Indonesia

Currently, gold investment is considered promising despite the ever-changing price of gold. However, obtaining optimal profits is a challenge for investors. Therefore, a proper forecasting method is needed to forecast the gold price so investors can know the best transaction time. This study used two forecasting methods: the Fuzzy Grey Markov Model (1,1) and a new, never-before-used approach, the Fuzzy Grey Markov Model (2,1). The Fuzzy Grey Markov Model (2,1) approach is interesting because it can be considered for forecast data that shows varying increases and decreases, such as the gold price data used in this study. Both methods are combined models that utilize fuzzy logic to handle uncertainty in data; the Grey model forms a forecasting model, and the Markov chain determines the state transition probability matrix. Next, the error rates of the two methods are compared based on the Mean Absolute Percentage Error (MAPE) value to obtain the best forecasting method. As a result of this study, the Fuzzy Grey Markov Model (1,1) was chosen as the best forecasting method with a MAPE value of 0.28%.

Online Portfolio Selection of Fuzzy Mean Regression Strategy Considering Investor Sentiment Based on Text Data

Investors are often affected by emotion, cognition, and other psychological factors in stock trading when making decisions. At present, people can use machine learning and other technologies to obtain a massive amount of text data from the Internet to mine information related to investor behavior and sentiment. Building intelligent online portfolio trading strategies that consider investor sentiment has become an important topic and key challenge in the financial field. Therefore, this paper explores how to use text data to depict investor sentiment, fuzzifies historical stock price data, designs a new weight transfer equation, and finally obtains a novel fuzzy mean regression strategy that considers investor sentiment based on text data. We conduct empirical tests on this strategy by using the stock price data selected from CSI300 constituent stocks, as well as the text data of investors’ opinions on the internet. The results show that the strategy proposed in this study has a higher Calmar ratio than other mean regression strategies previously studied.

Integration of Price Data in Wind Power Sales Systems Using Association Rule Mining Combined with Deep Learning Algorithms

With the growing attention of the global population towards renewable energy sources such as wind power derived from natural resources such as Solar, Geothermal, and Wind energy, there is a growing need for genuine estimation of wind energy. Wind energy is important in the supply of electricity in the global energy markets because of its enormous importance in the delivery of renewable energy. As such, it is crucial for accurate appreciation of the power of the wind to ably respond to issues that relate to the trading of power while at the same time addressing issues to do with planning, scheduling and strategic positioning of wind power generation. Therefore, the present work aims to develop a new model known as the Association Rule with DL-based Wind Power Generation and Price Prediction (ARDL-WPGPP). It utilizes two datasets: An Energy dataset includes various columns like tie, wind onshore forecast, and price actual, whereas the weather features dataset includes only wind speed. These datasets are combined to create a dataset where each transaction represents 2 hours of wind generation. A data mining approach is employed to uncover hidden patterns, rules, concepts, and correlations within these datasets, operating on various types of data including quantitative, textual, and multimedia formats. To efficiently extract rules from the dataset, an improved Apriori algorithm is introduced. Subsequently, the generated rules incorporating wind speed are passed into an improved LSTM model, which learns by comparing the label data. The price actual value serves as the label data, assigning labels to data points based on their actual price value. High price actual values indicate high wind power prices, while low values indicate low wind power prices.

Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia

Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.

Volatility of Fish Prices in Manado City Using Multivariate GARCH Model

Time series data modeling is generally performed using the assumption of homoscedasticity or residual variance that is constant over time. However, the homoscedasticity assumption cannot answer the problem of volatility in economic and business time series data, because generally data on economics and business have residual variances that always change over time. Therefore, a model developed with the assumption of not constant variance is known as heteroskedasticity model. Multivariate GARCH model is a development of the univariate GARCH model. The multivariate GARCH model can be viewed as a conditional heteroskedasticity model in a multivariate time series. This paper discusses the parameterization of covariance matrices such as Vech model representation, BEEK model and Constant Correlation model. For parameter estimation the maximum likelihood method is used. Furthermore, multivariate GARCH model application is applied for multivariate model. In this research, the data used are data on the prices of fresh fish in Manado. The fish price data is the price of Tuna, Skipjack (Cakalang), Cobe (Tongkol), Kite (Malalugis) and Selar fish (Tude). The ARCH effect test results show that the data meet the assumption of non-constant variance (heteroscedasticity). GARCH mutivariate modeling in the form of a VEC diagonal model gives the results of the price volatility of fresh fish in Manado