scholarly journals Stock price forecast of macro-economic factor using recurrent neural network

2021 ◽  
Vol 10 (1) ◽  
pp. 74
Author(s):  
M. Reza Pahlawan ◽  
Edwin Riksakomara ◽  
Raras Tyasnurita ◽  
Ahmad Muklason ◽  
Faizal Mahananto ◽  
...  
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Stock Market ◽  
Foreign Exchange ◽  
Recurrent Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Foreign Exchange Rate ◽  
Economic Sectors ◽  
Corporate Funding

<span id="docs-internal-guid-a29e641b-7fff-1dc7-f2b4-6f5488c7c0a5"><span>The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.</span></span>

scholarly journals Relationship between Foreign Exchange Rate and Stock Price of Commercial Joint Stock Banks: Evidence from Vietnam

2016 ◽  
Vol 8 (7) ◽  
pp. 193 ◽  
Author(s):  
Tran Mong Uyen Ngan
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Foreign Exchange Rate ◽  
Series Data ◽  
Causality Test ◽  
Using Data ◽  
The Relationship

The relationship between foreign exchange rate and stock price is one popular topic that is interested by not only board managers of banks but also stock investors. By using data about foreign exchange rate between Vietnam Dong (VND) and United State Dollar (USD), stock prices data of nine commercial joint stock banks in Vietnam from the first day of 2013 to the last day of 2015, this paper try to answer the question “Does foreign exchange rate impact on stock price and vice verse?”. Applying Dickey Fuller test and Var Granger Causality test for the time series data, the results show that there is an impact of foreign exchange rate on stock price. Although the fluctuation in foreign exchange rate VND/USD causes the change in stock prices of commercial joint stock banks in Vietnam, however, the vector of this impact is not clearly. On the opposite way, the change in stock price does not cause the change in foreign exchange rate, this relation is one-way relation.

Interest Rate, Foreign Exchange Rate, and Stock Market Development in Nigeria

2018 ◽  
Vol 9 (3) ◽  
pp. 247-253 ◽  
Author(s):  
Edward Adedoyin Adebowale ◽  
Akindele Iyiola Akosile
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Stock Market ◽  
Foreign Exchange ◽  
The United States ◽  
Ordinary Least Squares ◽  
Foreign Exchange Rate ◽  
Market Development ◽  
National Bureau ◽  
Stock Market Development

This research investigated the effect of interest rate and foreign exchange rate on stock market development in Nigeria. This research was centered on two research problems. First, it was whether interest rate had a significant effect on stock market development in Nigeria. Second, it was whether foreign exchange rate had a significant impact on stock market development in Nigeria. The scope of the research covered the period from 1981 to 2017. Data for this period were chosen because it covered pre and post-liberalization periods of Nigerian financial system. This research made use of ex post facto research design. Secondary data were sourced from Nigerian Stock Exchange reports, Central Bank of Nigeria statistical bulletins, and National Bureau of Statistics publications. Data were collected on Stock Market Capitalization (SMC), Prime Lending Rate (PLR) and Real Exchange Rate (RER) (Nigerian Naira in relation to American Dollars of the United States). Data analysis was carried out with Ordinary Least Squares (OLS) and Cochrane-Orcutt Iterative techniques. The findings reveal that interest rate has a significant negative effect, and foreign exchange rate has a significant positive effect on Nigerian stock market development during the period covered. It is suggested that monetary authorities should strive to formulate policies that will make interest and foreign exchange rates stable, competitive, and at a level that will stimulate the investment of funds in the stock market.

scholarly journals Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks

2020 ◽  
Vol 6 (2) ◽  
pp. 137-148
Author(s):  
J. Oliver Muncharaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Stock Market ◽  
Recurrent Neural Network ◽  
Stock Prices ◽  
Arima Model ◽  
Time Series Models ◽  
Prediction Errors ◽  
The Creation

In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

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