An Improved BPNN Algorithm Based on Deep Learning Technology to analyze the Market Risks of A+H Shares

The backpropagation neural network (BPNN) algorithm of artificial intelligence (AI) is utilized to predict A+H shares price for helping investors reduce the risk of stock investment. First, the genetic algorithm (GA) is used to optimize BPNN, and a model that can predict multi-day stock prices is established. Then, the Principal Component Analysis (PCA) algorithm is introduced to improve the GA-BP model, aiming to provide a practical approach for analyzing the market risks of the A+H shares. The experimental results show that for A shares, the model has the best prediction effect on the price of Bank of China (BC), and the average prediction errors of opening price, maximum price, minimum price, as well as closing price are 0.0236, 0.0262, 0.0294 and 0.0339, respectively. For H shares, the model constructed has the best effect on the price prediction of China Merchants Bank (CMB). The average prediction errors of opening price, maximum price, minimum price and closing price are 0.0276, 0.0422, 0.0194 and 0.0619, respectively.

Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis

Equity traders are always looking for tools that will help them maximise returns and minimise risk, be it fundamental or technical analysis techniques. This research integrates tools used by equity traders and uses them together with machine learning and deep learning techniques. The presented work introduces a South African-based sentiment classifier to extract sentiment from new headlines and tweets. The experimental work uses four machine learning models for fundamental analysis and six long short-term memory model architectures, including a developed encoder-decoder long short-term memory model for technical analysis. Data used in the experiments is mined and collected from news sites, tweets from Twitter and Yahoo Finance. The results from 2 experiments show an accuracy of 96% in predicting one of the major telecommunication companies listed on the JSE closing price movement while using the linear discriminant analysis model and an RMSE of 0.023 in predicting a significant telecommunication company closing price using encoder-decoder long short-term memory. These findings reveal that the sentiment feature contains an essential fundamental value, and technical indicators also help move closer to predicting the closing price.

Profitability of technical trading strategies under market manipulation

Most technical trading strategies use the official closing price for analysis. But what is the effect when the official closing price is subject to market manipulation? This paper answers this question by testing the difference of profitabilities between using the official closing price and the last tick price. The results show a significant improvement of profitability by using the last tick price over the official closing price based on a data set in Hong Kong from 2011 to 2018.

Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

PERAMALAN VOLATILITAS DAN ESTIMASI VALUE AT RISK (VaR) SAHAM BLUE CHIP PADA SEKTOR PERBANKAN

Investment is an activity to invest an asset to obtain a greater profit. The investment there's in great demand by investors are stock investments. Based on market capitalization, stocks are classified into first-tier, second-tier, and third-tier stocks. Stocks that have the highest market capitalization are first-tier or blue-chip stocks. Blue-chip stocks are stocks that are classified as main shares on the listing board on the IDX. Before investing, it's important to know the level of investment risk in order to make the right investment decisions. The purpose of this study is to determine the risk of investing in blue-chip stocks namely BRI, BCA, and Bank Mandiri through volatility forecasting using the GARCH, EGARCH, or TGARCH models. The data used is the daily closing price of shares for the period of 25 May 2005 to 21 May 2021 which was obtained through the Yahoo Finance website. Based on the research results, it's known that Bank Mandiri has the highest investment risk and BCA has the lowest investment risk. Based on these results, it can be suggested that investors who like risk can choose to invest in Bank Mandiri shares, and those who don't like risk can invest in BCA shares.

A stock trading algorithm based on trend forecasting and time series classification

Algorithm trading relies on the automatic identification of buying and selling points of a given asset to maximize profit. In this paper, we propose the Trend Classification Trading Algorithm (TCTA) which is based on time series classification and trend forecasting to perform trade. TCTA first employs the K-means to cluster 5-days closing price segments and label them according to its trend. A deep learning classification model is then trained with these label sequences to estimate the next trend. Trading points are given by the alternation on trend estimates. Results considering 20 shares from Ibovespa show TCTA present higher profit than buy-and-hold and trading schemes based on Moving Average Converge Divergence (MACD) or Bollinger bands.