Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Abstract

Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...

Online Long Short-Term Memory Network for Stock Trading

“…Implemented trading strategy significantly outperformed the passive trading strategy and found many arbitrage opportunities and other inefficiencies. Obtained results coincide with the results from a more detailed study in [4]. Results show that it is possible to generate higherthan-normal returns by relying on the technical analysis only.…”

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

“…They compared the results of LSTM against SVM, RF, DNN, and the autoregressive integrated moving average model (ARIMA) and realized that LSTM is more suitable for financial time-series forecasting. Using LSTM, Fister et al [36] designed a model for automated stock trading. They argue that the performance of LSTM is remarkably higher than the traditional trading strategies, such as passive and rule-based trading strategies.…”

“…LSTM method has also been very popular for predicting financial time series. The DL applications to portfolio management [35], automated stock trading [36], and cryptocurrencies price prediction [88] have also been relatively popular.…”

Data Science in Economics

“…They compare the results of the LSTM against SVM, RF, DNN, and autoregressive integrated moving average model (ARIMA) and they realized that LSTM is more suitable for financial time-series forecasting in their study. Using LSTM, Fister et al [30] design a model for automated stock trading. They argue that the performance of LSTM is remarkably higher than the traditional trading strategies such as passive and rule-based trading strategies, in their case studies the German blue-chip stock and BMW in the period between 2010 and 2018 formed the data sources.…”

“…LSTM, CNN, and DNN are respectively the most applied DL models among the database of the study. LSTM is applied to stock price prediction [26][27][28], portfolio management [29], automated stock trading [30], and cryptocurrencies price prediction [82]. Among the reviewed papers, the LSTM method has only applied to find the patterns among financial time series data.…”