Opening Price Research Articles

Daily Forecasting Trend Jakarta Composite Index (JCI) Using Multivariate Long Short Term Memory

The need to be able to predict stock price movements is one of the problems that is difficult for investors to solve.Forecasting price movements serves as a signal to buy and sell. Forecasting stock price movements can provide us with a reference in making investment decisions. This study aims to predict the daily trend JCI. The datasets used in this research are JCI, NASDAQ, and NYSE with a range of 21 years (01/01/2000 to 31/12/2021). The features used as input are the opening prices of the JCI, NASDAQ, and NYSE. The amount of data used is 5211 lines. The deep learning method used is multivariate LSTM. optimization of the model used using Adam. There are 4 LSTM models made using loss metrics MSE and MAE, using 2 epochs of 100, and 300 epochs. The results showed that the 4 LSTM models could predict the daily trend (d+1) of the JCI. The most optimum model is the model made using MSE with 300 epochs.

The Global Transmission of Stock Market: A Spatial Analysis

The stock markets, exhibiting complex self-correlation or cross-correlation over a broad range of time scales, are correlated not only in time but also in space. The conventional spatial weight matrix in the econometric analysis is short of economic relation between nonadjacent economic entities. Therefore, this paper applies the detrended cross-correlation analysis coefficient and partial correlation coefficient to analyze the global spatial interaction. This study computes the spatial Moran’s I value by the two types of weight matrix for the 15 typical stock indices around the world, to explore the spatial agglomeration phenomenon. Then, the Spatial Durbin Model is applied to investigate the transmission of the stock market. The result from the Moran’s I value indicates that the 15 typical stock indices are spatially correlated. The result of the Spatial Durbin Model gives the relationship among the closing price, the opening price, the highest price, and the lowest price.

Opening price manipulation and its value influences

We investigate how opening price manipulation influences market behaviors and investors' returns. Analyzing direct evidence comprising 87 opening price manipulation cases, and indirect evidence consisting of 19,003 suspected cases detected by an opening price manipulation identification model that we construct, we examine the impact of manipulation on mispricing, investors' welfare, trading activity and price volatility. Our results indicate that manipulated stocks experience significantly lower returns and a higher probability of price reversal after manipulation. Investors who purchase manipulated stocks at their opening price, or the volume-weighted average price, on the manipulation day make losses on their investments. Further, manipulation increases market trading activity and price volatility due to the influx of retail investors. Our additional analysis demonstrates that enhancing the intensity of external supervision and internal governance can mitigate mispricing caused by opening price manipulation. Our study provides novel evidence of the economic consequences of open market manipulation and policy implications for governments and regulators to develop effective supervisory processes to reduce manipulation and mitigate its impact on efficient markets.

IMPLEMENTASI METODE LONG SHORT TERM MEMORY UNTUK MEMPREDIKSI PERGERAKAN NILAI HARGA EMAS

Investment is an activity that is invested or financed with the hope of profit or return in the future. One form of investment is to use gold. Many people are interested in investing in gold. Apart from being quite affordable, investing in gold is also very easy to do and flexible. Gold is included in the low risk investment class because its price movements tend to follow the rate of inflation. This study uses the Long Short Term Memory (LSTM) method to predict the movement of gold prices which will visualize the data in the form of a Line Chart. This study aims to provide decision support material regarding the movement of buying and selling prices of gold through the presentation of data aimed at people who invest in gold. However, real gold price movements cannot be separated from external factors such as the exchange rate of the rupiah against foreign currencies, import export policies and other government policies. There are many influencing factors such as opening price, closing price, adjust close, volume, highs, lows recorded over a certain period of time. Another factor is the Daily Return which can be positive (Grown In Value) and negative (Lost In Value).

Construction of Development Momentum Index of Financial Technology by Principal Component Analysis in the Era of Digital Economy

The purpose is to study applying mathematical analysis in financial technology (FinTech) development in the era of digital economy. An Evaluation Index System (EIS) for the current situation of Chinese FinTech enterprises is established by considering the impact of the era of the digital economy on the development of FinTech. Specifically, the Principal Component Analysis (PCA) is introduced to construct the principal component prediction model based on functional data. Then, six Chinese State-owned Enterprises (SOEs) are selected. Their stock prices are predicted using the proposed model through an empirical study. The results show that selecting three principal components to evaluate the financial situations of six SOEs is reasonable. The accumulated variance values of the first three principal components of the stock's closing price and opening price are all greater than 85%. Thus, the selected three principal components can obtain the potential information of the original data. The gap between the actual value and the proposed model-predicted value of the stocks of the six SOEs is relatively small. The Root Mean Square Error (RMSE) of China National Petroleum Corporation (CNPC) is 0.105, more than 10%. The predicted values of Huadian Energy and China Shenhua are 9.4% and 8.5%, respectively, second only to CNPC. Therefore, the proposed principal component prediction model based on functional data can predict the closing price of stocks well. The accuracy is relatively high and matches well with financial data analysis. This research has important implications for the development of FinTech.

Role of Business Intelligence and Knowledge Management in Solving Business Problems

The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

A price tag on species

Species have intrinsic value but also partake in a long range of ecosystem services of major economic value to humans. These values have proved hard to quantify precisely, making it all too easy to dismiss them altogether. We outline the concept of the species stock market (SSM), a system to provide a unified basis for valuation of all living species. The SSM amalgamates digitized information from natural history collections, occurrence data, and molecular sequence databases to quantify our knowledge of each species from scientific, societal, and economic points of view. The conceptual trading system will necessarily be very unlike that of the regular stock market, but the looming biodiversity crisis implores us to finally put an open and transparent price tag on symbiosis, deforestation, and pollution

Book Building vs Fixed Price Revisited: The Case of Indonesia

We revisited the evidence on how IPO methods affect return (opening, close and initial return) and why does return affects the volatility in Indonesia. As one of the emerging countries, Indonesia had a changing regulation regarding the IPO method from the fixed price to the book building method since October 2000. Using a clustering regression analysis method, we find that the opening price and initial price in the book building period are significantly higher than that in the fixed price period. Furthermore, there is no effect of the opening return on the volatility. In contrast, closing return affects the volatility positively, while the relationship between initial return and volatility shows somewhat mixed results.

Prediction of stock purchase decisions using artificial neural network method

The difficulty of determining a stock purchase decision is a problem to benefit from stock transactions. This study aims to give a person's decision to buy one of the issuer's company shares to get a profit on the same day. The dataset used in this study came from the investing.com website in the form of daily data shares of PT Indofood CBP Sukses Makmur Tbk with ICBP stock code for the period January - September 2019. The attributes used in this study were the opening price, highest price, lowest price, closing price, transaction volume, day representation, and decisions. The dataset that has been collected was normalized using the Min-Max method to facilitate data processing. This research used the backpropagation neural network method and used the 10-Fold Cross Validation and Confusion Matrix for validation. The results of this study indicate that the backpropagation neural network method uses the bipolar activation function with training cycles of 2000 and learning rate of 0.03 has the best performance namely 69.35% of accuracy, 67.65% of precision, 74.19% of recall and 30.65% of error rate for prediction of stock purchase decisions per day in the form of buy or not.

Volatility forecasting using intraday information with the CARR models for the China stock markets

ABSTRACT This study mainly focuses on utilizing the range-based CARR and CARRX models to explore the non-trading effects during the lunch break and overnight periods in volatility changes forecasting for the two stock markets in China. We find that the price difference in absolute values between the close price in day t-1 and the open price in day t are able to predict the volatility change in the morning trading session significantly. The price range during the lunch break and the trading volume change percentage in the morning session appear positive correlation with the volatility in the afternoon trading session.

Stock Prediction Based on Twitter Sentiment Extraction Using BiLSTM-Attention

A profitable stock price prediction will yield a large profit. According to behavioural economics, other people's emotions and viewpoints have a significant impact on business. One of them is the rise and fall of stock prices. Previous studies have shown that public sentiments retrieved from online information can be very valuable on market trading. In this paper, we propose a model that works well in predicting future stock prices by using public sentiments from social media. The online information used in this research is financial tweets collected from Twitter and the stock prices values retrieved from Yahoo! Finance. We collected tweets related to Netflix Company stocks and the stock prices for the same period which is 5 years from 2015 to 2020 as the dataset. We extracted the sentiment value using VADER algorithm. In this paper, we apply a Bidirectional Long Short-Term Memory (BiLSTM) architecture to achieve our goal. Moreover, we created seven different experiments with different stock price parameters and selected sentiment values combinations and investigated the model by adding an attention layer. We experimented with two different sentiment values, tweet’s compound value and tweet’s compound value multiplied by favorites count. We considered the favorites count as one representation of public sentiments. From the seven experiments, the experiment with Bidirectional Long Short-Term Memory (BiLSTM) - attention model combined with our selected stock price parameters namely close price, open price, and using Twitter sentiment values that are multiplied with the tweet’s favorites count yields a better RMSE result of 2.482e-02 in train set and 2.981e-02 in the test set.

Can Use Simple Messages Information to Make a Remuneration Profit? –Evidence from Taiwan Stocks

Purpose: The goal of this research is to use the correlation analysis method (CAM) to find out the investors can invest with simple messages.
 Results: This research's empirical results indicate the following: (1) Based on our empirical results obtained from using the correlation analysis method (CAM) method, the highest price, lowest price, and closing price can affect the opening price, and there is a significant and positive relationship. Moreover, the stock trading volume is an insignificant positive correlation, and the rate of price spread is an insignificant negative correlation. (2) This research assumes no external interference and government protection. And that stock investors do not have any technical analysis and other conditions. Whether the company can make a profit can reflect the value of bonds and stocks through public information. Therefore, investors can invest based on simple information. The grey relational analysis (GRA) research shows that that both the highest price and the lowest price displays were significant. The closing price was strong. In contrast, the study found that the stock trading volume and price spread displays are weak, and the analysis results showed almost no effect.
 Unique contribution to theory, policy and practice: Therefore, if you play day trader it is feasible to use the high and low stock prices to make a remuneration profit.

Research and Forecast Analysis of Financial Stability for Policy Uncertainty.

The instability of financial market will have a great impact on money, bonds, and stocks and affect the economic development of society and people's lives. Therefore, it is very necessary for us to study and predict the financial stability. According to the forecast results, we will analyze and make a series of preparatory measures. First, we make a series of analyses on the structure and significance of policy uncertainty and financial stability. This paper introduces the advantages and disadvantages of the P/L model, the KLS signal method, and the BP neural network model for financial stability early warning, It is clearly pointed out that the BP neural network is more reliable and accurate, Then, the BP neural network, the ant colony algorithm, and the genetic algorithm are used to predict the opening price, closing price, highest price, and lowest price of KDJ index of Cathay Pacific Group's 5-day data. Compared with the real value, we find that the BP neural network is almost the smallest in forecasting the opening price and closing price, or the lowest price and the highest price, and has good stability, which once again proves the feasibility of applying the BP neural network to the research and prediction of financial stability.

Evidence for round number effects in cryptocurrencies prices

This paper analyses the relationship between price clustering and trade volume in the Ether, Ripple and Litecoin cryptocurrencies. We examine at which digits price clustering exists and study the behaviour at different price levels and time frames. By using recent data to provide an updated view of price clustering in the cryptocurrency market, we find a remarkable level of price clustering at round prices: 5.29%, 2.84% and 2.97% for Ether, Ripple and Litecoin for every one minute at open prices, respectively. This paper reaffirms the negotiation hypothesis by finding that price clustering appears at prices at which traded volume is higher.

Does systematic risk change when markets close? An analysis using stocks’ beta

Investors' behavior and news or events occurring during the market closure affect open price variation. For these reasons, daytime and overnight returns and volatilities move differently. In light of these effects, it is natural to ask whether systematic risk varies between trading and non-trading periods. We answer this question by evaluating the US stocks’ beta from 1992 to 2020. Computing daily, daytime, and overnight betas using a proper market index for each trading period, we show how market risk varies over time and is influenced by market shocks. We observe a high overnight risk concentrated within small and large stocks. We find that daytime systematic risk is generally higher, especially between 2001 and 2019. Furthermore, we show how the outbreak of the Covid-19 pandemic led to an increase in overnight risk implying that US stocks became more sensitive to the market closure.

Research on the Feasibility of Applying GRU and Attention Mechanism Combined with Technical Indicators in Stock Trading Strategies

The vigorous development of Time Series Neural Network in recent years has brought many potential possibilities to the application of financial technology. This research proposes a stock trend prediction model that combines Gate Recurrent Unit and Attention mechanism. In the proposed framework, the model takes the daily opening price, closing price, highest price, lowest price and trading volume of stocks as input, and uses technical indicator transition prediction as a label to predict the possible rise and fall probability of future trading days. The research results show that the proposed model and labels designed by this research can effectively predict important stock price fluctuations and can be effectively applied to financial commodity trading strategies.

Contracting to (dis)incentivize? An integrative transaction‐cost approach on how contracts govern specific investments

AbstractResearch SummaryBuyer–supplier collaborations are plagued by multiple frictions—haggling, non‐contractible adaptation, and resource appropriation. This article examines how contracts govern relationship‐specific investment in the face of these frictions. In our model, investment increases the value the supplier creates for the buyer ex post by adapting a component to her needs. At the same time, specific investment exposes the supplier to haggling, while providing him with knowledge needed to appropriate the buyer's preexisting resources. By muting both haggling and adaptation incentives, “closed price” contracts elicit higher investment than “open price” contracts when adaptation is unimportant, and lower investment otherwise. Moreover, an optimal price format seeks to incentivize investment when resource appropriation is unimportant, and to disincentivize investment otherwise. Our evidence on component procurement contracts supports both predictions.Managerial SummaryThis study offers new insights on how OEMs govern their collaborations with suppliers. We examine settings where the supplier invests in producing a dedicated component, and ask under which contractual form she is more/less motivated to invest. We find that “closed price” contracts decrease supplier's investment when the component has a complex interface with the OEM's product, and hence is more subject to post‐contractual adaptation. We also find that OEMs choose the contract that discourages investment when they possess unique resources that too‐closely‐involved suppliers may copy or appropriate. Managers at OEMs that possess proprietary technologies and customer bases are exposed to the appropriation downside of suppliers' dedicated investment, and will thus benefit from learning through our study how other professionals address this issue.

Analysis of Stock Price Forecasts-Take Midea Group as an Example

The price of a stock is affected by many factors. On the one hand, it is influenced by various external factors, such as macro factors, microeconomics factors, and on the other hand, it is also affected by internal factors, such as the actual operation of listed companies, at the same time, the impact of domestic and international news will also have an impact on the stock price and stock market, so the prediction of the stock price is very important. Based on the analysis of the technical aspects that affect the stock price, this paper chooses the white horse stock of Midea Group as the research object, from January 1, 2022 to May 27, 2022 on the stock’s opening price, the highest price, the lowest price, quantity, turnover as independent variables; The price of the stock at the close of the next day is a dependent variable. The multivariate linear regression model of dependent variable about independent variable is established, and the model is improved by factor analysis, and the final stock price prediction model is obtained.

Deep learning of predicting closing price through historical adjustment closing price

In recent years, deep learning has shown its power in many fields such as computer vision and natural language processing. However, there is not much research on stock prediction using deep learning, so we explored and proposed a deep learning model based on LSTM to predict stock closing prices. The model can predict the closing price of the stock on the following day based on the previous day’s total volume, the opening price, and the adjustment closing price that can reflect the peripheral information of the stock. To improve the accuracy of the prediction, we invoke the attention module on the model. Experiments show that our model performs well on two datasets, the CSI 300 and the Hang Seng Index, with very little error between the predicted closing price and the actual closing price.

Compare CNN and LSTM Model to Forecast the Stock Price

The prediction of stock price has been one of the hot research fields in recent years. Although the trend of stock price is non-linear and complex, and affected by the global situation, corporate decisions and other factors, it is difficult to accurately predict the stock price. However, since the change of stock price directly affects the interests of investors, stock price prediction can help people to provide a reference information for future decision-making. In previous studies, neural networks and deep learning have shown good results in stock price prediction. In this paper, two models of LSTM and CNN are used to forecast MRF stock price respectively, and the predicted results are analyzed and compared. We used stock data from January 1, 2013 to May 18, 2018, including four characteristic values: the highest price, the lowest price, the opening price and the closing price. The data of the first 1060 trading days were used to train the two models respectively, and the data of the last 265 trading days were used to test the models. The results show that the prediction effect of CNN model is more accurate than that of LSTM model.