An Effective Investment Strategy Using Deep Graph Reinforcement Learning: Evidence from China's Stock Market

2022 ◽  
Author(s):  
ziran zhao ◽  
Hongduo Cao ◽  
ying li
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Investment Strategy

scholarly journals Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 1506 ◽  
Author(s):  
Otabek Sattarov ◽  
Azamjon Muminov ◽  
Cheol Won Lee ◽  
Hyun Kyu Kang ◽  
Ryumduck Oh ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Historical Data ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Specific Knowledge ◽  
Right Action ◽  
Net Profit ◽  
Market Needs ◽  
The Right

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

scholarly journals Mean-variance investment strategy applied in emerging financial markets: Evidence from the Colombian stock market

2015 ◽  
Vol 9 (1) ◽  
pp. 22-29 ◽  
Author(s):  
Fernando García ◽  
Jairo Alexander González-Bueno ◽  
Javier Oliver
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Investment Strategy ◽  
Mean Variance

Does the financial crisis influence the month and the trading month effects?

2015 ◽  
Vol 32 (2) ◽  
pp. 181-203 ◽  
Author(s):  
Evangelos Vasileiou ◽  
Aristeidis Samitas
Keyword(s):  
Stock Market ◽  
Methodological Approach ◽  
Investment Strategy ◽  
January Effect ◽  
Research Approach ◽  
Content Type ◽  
Calendar Effects ◽  
Empirical Results ◽  
Financial Growth ◽  
Calendar Anomalies

Purpose – This paper aims to examine the month and the trading month effects under changing financial trends. The Greek stock market was chosen to implement the authors' assumptions because during the period 2002-2012, there were clear and long-term periods of financial growth and recession. Thus, the authors examine whether the financial trends influence not only the Greek stock market’s returns, but also its anomalies. Design/methodology/approach – Daily financial data from the Athens Exchange General Index for the period 2002-2012 are used. The sample is separated into two sub-periods: the financial growth sub-period (2002-2007), and the financial recession sub-period (2008-2012). Several linear and non-linear models were applied to find which is the most appropriate, and the results suggested that the T-GARCH model better fits the sample. Findings – The empirical results show that changing economic and financial conditions influence the calendar effects. The trading month effect, especially, completely changes in each fortnight following the financial trend. Regarding the January effect, which is the most popular month effect, the results confirm its existence during the growth period, but during the recession period, we find that it fades. Therefore, by examining the aforementioned calendar effects in different periods, different conclusions may be reached, perhaps because the financial trends’ influence is ignored. Research limitations/implications – The empirical results confirm the authors' assumption that a possible explanation for the controversial empirical findings regarding the calendar anomalies may be the different financial trends. However, these are some primary results that are confirmed only for the Greek case. Further empirical research for deeper stock markets and/or a group of countries may be useful to reach conclusions regarding the financial trends’ influence on the calendar anomalies patterns. Practical implications – The findings are helpful to anyone who invests and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, proving useful for investors who take these anomalies into account when they plan their investment strategy. Originality/value – This paper contributes to the literature by presenting an alternative methodological approach regarding the calendar anomalies study and a new explanation for the calendar effects existence/fade through time by examining the calendar anomalies patterns under a changing economic environment and financial trends.

scholarly journals BUILDING AN ARTIFICIAL STOCK MARKET POPULATED BY REINFORCEMENT‐LEARNING AGENTS

2009 ◽  
Vol 10 (4) ◽  
pp. 329-341 ◽  
Author(s):  
Aleksandras Vytautas Rutkauskas ◽  
Tomas Ramanauskas
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Self Regulation ◽  
Market Model ◽  
Emergent Properties ◽  
Q Learning ◽  
Evolutionary Selection ◽  
Learning Agents ◽  
Artificial Stock Market

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement-learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision-making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

scholarly journals MAPS: Multi-Agent reinforcement learning-based Portfolio management System.

2020 ◽  
Author(s):  
Jinho Lee ◽  
Raehyun Kim ◽  
Seok-Won Yi ◽  
Jaewoo Kang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Portfolio Management ◽  
Management System ◽  
Investment Strategy ◽  
Sharpe Ratio ◽  
Training Procedure ◽  
Learning Methods ◽  
Multi Agent ◽  
Diversified Portfolio

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.

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