I'm working on a proposal for my bachelor thesis and I am looking for areas to work on in the conjunction between Reinforcement Learning and Finance(I have some application domain knowledge in finance). I'm aware about the work being done in using RL for stock trading. I was considering using Advanced RL techniques like Rainbow etc for stock trading but don't know if it's a good enough proposal and whether it's been done before.
There are several DL and deep RL approaches, such as DNN, Autoencoder, RBM, LSTM-SVR, CNN, RNN, DDPG, DQN, where the advanced models improved prediction to extract better information and to find the optimal strategy mostly in complicated and dynamic market conditions.
You need to find a solid gap in literature in the last 3 years. Your work should represents the basic issue that all proposed approaches are mainly to fairly deal with the model complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability.
You may want to employ a variety of both DL and deep RL techniques, with the relevant strengths and weaknesses, that serve in economic problems to enable the machine to detect the optimal strategy associated with the market
The novel techniques in DNN, and recent interaction with reinforcement learning, so-called deep RL, have the potential to considerably enhance the model performance and accuracy. Look at recent approaches in both DL and deep RL perform better than the classical ML approaches.
You might want to go into a deeper understanding of the significant progress of efficient novel algorithms using deep neural architectures in conjunction with reinforcement learning concepts to detect the optimal strategy, namely, optimize the profit and minimize the loss while considering the risk parameters in a highly competitive market. You may want to explore these areas for future research, a survey of machine learning and deep learning in 5G, cloud computing, cellular network, and COVID-19 outbreak. Good luck.
Have a look at https://books.google.com.au/books?hl=en&lr=&id=oU9KDwAAQBAJ&oi=fnd&pg=PR21&dq=advances+in+financial+machine+learning+pdf&ots=7UDIX3AB6s&sig=7X56uhgTcYPtuIlOAlvSdmU_sJY#v=onepage&q=advances%20in%20financial%20machine%20learning%20pdf&f=false
https://mail.google.com/mail/u/2/#inbox
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?referer=http://scholar.google.com.au/&httpsredir=1&article=1103&context=soe_research
There are several DL and deep RL approaches, such as DNN, Autoencoder, RBM, LSTM-SVR, CNN, RNN, DDPG, DQN, where the advanced models improved prediction to extract better information and to find the optimal strategy mostly in complicated and dynamic market conditions.
You need to find a solid gap in literature in the last 3 years. Your work should represents the basic issue that all proposed approaches are mainly to fairly deal with the model complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability.
You may want to employ a variety of both DL and deep RL techniques, with the relevant strengths and weaknesses, that serve in economic problems to enable the machine to detect the optimal strategy associated with the market
The novel techniques in DNN, and recent interaction with reinforcement learning, so-called deep RL, have the potential to considerably enhance the model performance and accuracy. Look at recent approaches in both DL and deep RL perform better than the classical ML approaches.
You might want to go into a deeper understanding of the significant progress of efficient novel algorithms using deep neural architectures in conjunction with reinforcement learning concepts to detect the optimal strategy, namely, optimize the profit and minimize the loss while considering the risk parameters in a highly competitive market. You may want to explore these areas for future research, a survey of machine learning and deep learning in 5G, cloud computing, cellular network, and COVID-19 outbreak. Good luck.
Pradeep Paraman sir , can you tell me what exactly do you mean by "solid gap in literature"?
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