  Machine learning is an increasingly popular tool with some success in
predicting stock prices. One promising method is the Trader-Company~(TC)
method, which takes into account the dynamism of the stock market and has both
high predictive power and interpretability. Machine learning-based stock
prediction methods including the TC method have been concentrating on point
prediction. However, point prediction in the absence of uncertainty estimates
lacks credibility quantification and raises concerns about safety. The
challenge in this paper is to make an investment strategy that combines high
predictive power and the ability to quantify uncertainty. We propose a novel
approach called Uncertainty Aware Trader-Company Method~(UTC) method. The core
idea of this approach is to combine the strengths of both frameworks by merging
the TC method with the probabilistic modeling, which provides probabilistic
predictions and uncertainty estimations. We expect this to retain the
predictive power and interpretability of the TC method while capturing the
uncertainty. We theoretically prove that the proposed method estimates the
posterior variance and does not introduce additional biases from the original
TC method. We conduct a comprehensive evaluation of our approach based on the
synthetic and real market datasets. We confirm with synthetic data that the UTC
method can detect situations where the uncertainty increases and the prediction
is difficult. We also confirmed that the UTC method can detect abrupt changes
in data generating distributions. We demonstrate with real market data that the
UTC method can achieve higher returns and lower risks than baselines.
