Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

Abstract

The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies

“…The technical indicators imported in this research include stochastic oscillator (KD), relative strength index (RSI), bias ratio (BIAS), William's oscillator (W%R), and moving average convergence/divergence (MACD). Since the range of each technical indicator value is different from the meaning of stock market momentum, this study adopts the concept of Lee et al [28] and normalizes the technical indicators as the following:…”

“…Based on the above literature, most studies have not conducted in-depth discussions on the application of technical indicators to sequential deep networks. In our previous research [28], we explored the effectiveness and practicality of various financial analysis technical indicators in time series deep learning networks. In financial commodity trading, it is common to analyze and discuss prices at various levels, such as fundamental, technical, and chip analysis.…”

“…This study designed feature selection methods for famous technical indicators, such as 9 K-9D, 16 K-16D, RSI, MACD, and Williams%R, and measured it with a four-layer LSTM. The main contribution of [28] is to define the regularization of mainstream technical indicators applied to sequential neural networks.…”

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

“…Subsequently, more in-depth network models were applied to different trading markets and financial commodity price predictions [29][30][31][32][33][34]. In our previous research [35], we explored the effectiveness and practicality of various financial analysis technical indicators in the time series deep learning network. This research uses well-known Moving Average (MA) technical indicators to design the feature selection algorithm, and uses the four-layer LSTM to conduct the actual measurement.…”

Time Series Forecasting with Data Transform and Its Application in Sport

“…In a time series forecasting problem, the primary mission is to predict the next value in the series based on the values in the past. Time series forecasting [2] [3] is very useful in many realworld applications including economic, management, environment, healthcare, stock prediction [4] [5], etc. This problem means constructing a model from available data to predict the next values in the near future.…”