News Release

Nomura’s Joint Research Papers with Preferred Networks Selected byAAAI and AAMASTokyo, December 21, 2020—Nomura Holdings, Inc. and Nomura Asset Management Co., Ltd.,the core company within the Asset Management Division of Nomura Group, today announced thatsome of the findings of their joint research with Preferred Networks, Inc. (PFN) on investment
methods have been selected by prestigious AI academic conferences AAAI (Association for theAdvancement of Artificial Intelligence) and AAMAS 1 (International Conference on Autonomous
Agents and Multiagent Systems).
The three companies have been conducting research on advanced investment methods sinceOctober 2019. Nomura aims to leverage the findings of this joint research to launch a fund.
The asset management industry has been actively pursuing research on new investment methods
using machine learning and data science. Despite the tremendous amount of research presented
at academic conferences each year, very few of these methods have been implemented in theasset management business. To improve investment performance, it is critical that investment
methods provide practical solutions in addition to being cutting edge.
Nomura Group has for many years developed various quantitative methods to address investment
business issues based on its knowledge of financial engineering and data science. PFN develops
practical applications using machine learning, deep learning and other advanced technologies tohelp solve issues specific to various industries, such as automated driving and cancer treatment.
To provide clients with more investment opportunities and added value, Nomura has started
discussions with PFN towards creating a fund that makes full use of AI and other technologies,
including the results of their joint research.
Reference
1. Abstract from AAAI submission
Title: Deep Portfolio Optimization via Distributional Prediction of Residual Factors
Recent developments in deep learning techniques have motivated intensive research on machinelearning-aided stock trading strategies. However, since the financial markets have a highly nonstationary nature hindering the application of typical data-hungry machine learning methods,
leveraging financial inductive biases is important to ensure better sample efficiency androbustness. In this study, we propose a novel method of constructing a portfolio based on
predicting the distribution of a financial quantity called residual factors, which is known to begenerally useful for hedging the risk exposure to common market factors.

1AAAI and AAMAS receive submissions from distinguished AI researchers from world-renowned universities and research
institutions each year, and have a very high standard for accepting submissions. Presentations at these conferences by Japanesefinancial institutions are rare, demonstrating the academic merit of Nomura’s joint research with PFN. Paper submitted to AAMAS
was accepted as Full Paper.

The key technical ingredients are twofold. First, we introduce a computationally efficient extraction
method for the residual information, which can be easily combined with various prediction
algorithms. Second, we propose a novel neural network architecture that allows us to incorporate
widely acknowledged financial inductive biases such as amplitude invariance and time-scale
invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data.
Through ablation experiments, we also verify that each individual technique contributes toimproving the performance of trading strategies. We anticipate our techniques may have wide
applications for various financial problems.
2. Abstract from AAMAS submission
Title: Trader-Company Method: A Metaheuristics for Interpretable Stock Price Prediction
Investors try to predict returns of financial assets to make successful investment. Many
quantitative analysts have used machine learning-based methods to find unknown profitable
market rules from large amounts of market data. However, there are several challenges in financial
markets hindering practical applications of machine learning-based models. First, in financial
markets, there is no single model that can consistently make accurate predictions because tradersin markets quickly adapt to newly available information. Instead, there are a number of ephemeraland partially correct models called “alpha factors.” Second, since financial markets are highly
uncertain, ensuring interpretability of prediction models is quite important to make reliable trading
strategies.
To overcome these challenges, we propose the Trader-Company method, a novel evolutionarymodel that mimics the roles of a financial institution and traders belonging to it. Our method
predicts future stock returns by aggregating suggestions from multiple weak learners called
Traders. A Trader holds a collection of simple mathematical formulae, each of which represents a
candidate of an alpha factor and would be interpretable for real-world investors. The aggregation
algorithm, called a Company, maintains multiple Traders. By randomly generating new Traders
and retraining them, Companies can efficiently find financially meaningful formulae whilst avoiding
overfitting to a transient state of the market. We show the effectiveness of our method byconducting experiments on real market data.

ends
NomuraNomura is a global financial services group with an integrated network spanning over 30 countries. By connecting
markets East & West, Nomura services the needs of individuals, institutions, corporates and governments through its
four business divisions: Retail, Asset Management, Wholesale (Global Markets and Investment Banking), and MerchantBanking. Founded in 1925, the firm is built on a tradition of disciplined entrepreneurship, serving clients with creative
solutions and considered thought leadership. For further information about Nomura, visit www.nomura.com.