Michiharu Kitano's research while affiliated with Japan Research Institute and other places

Publications (7)

Article
Full-text available
We use a sparse variational dropout Bayesian neural network (SVDBNNs) to propose an investment strategy that gives consideration to predictive uncertainty. The proposed method is validated through simulation on historical orderbook data from the Tokyo Stock Exchange. Our results were found to outperform other standard non-Bayesian approaches on ris...
Article
Full-text available
While exchanges and regulators are able to observe and analyze the individual behavior of financial market participants through access to labeled data, this information is not accessible by other market participants nor by the general public. A key question, then, is whether it is possible to model individual market participants’ behaviors through...
Conference Paper
Financial markets are known to have difficulties in predicting, such as huge elements involved, unsteady internal structure, and existence of the market impact. Even when machine learning and deep learning methods are applied, predictions must include uncertainty, and investment decision making using uncertain prediction may cause large losses and...
Article
Full-text available
Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep...
Chapter
Forecasting financial market trends is challenging. Predicting financial market trends always involves uncertainty because the economy is a complex system with a wide variety of interactions. Thus, to consider uncertainty, trends must be estimated stochastically. Conventional machine learning and deep learning methods cannot learn prediction uncert...
Conference Paper
Full-text available
Accurate prediction of financial markets is considered one of the most difficult problems due to the nature of its complexity, influenceability, and nonstationarity. Recent financial forecasting applications using neural networks typically have not taken the predictive uncertainty into consideration. Without proper consideration of predictive uncer...
Conference Paper
In recent years, predictions by machine learning and deep learning methods are utilized in various scenes of society. A model trained with deep learning methods can predict the target with high accuracy, but can not consider the predictive confidence sufficiently, and may predict high confident for extrapolated data which is hard to predict. In thi...

Citations

... That way, we do not have to find complicated rules for agent behaviour, rather a utility function that agents want to optimize, using the actions available to them. This combination of methods was used successfully for various different systems [28][29][30][31][32][33][34][35]. A generic framework that makes use of this synergy was developed in [36] and expanded with an iterative learning approach in [37]. ...