RM-CVaR: Regularized Multiple β\beta-CVaR Portfolio

The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance). Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity. CVaR is defined as the expected value of the loss that occurs beyond a certain probability level ($\beta$). However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single $\beta$ and may output significantly different portfolios depending on how the $\beta$ is selected. We confirm even small changes in $\beta$ can result in huge changes in the whole portfolio structure. In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio. We perform experiments on well-known benchmarks to evaluate the proposed portfolio. Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.Comment: accepted by the IJCAI-PRICAI 2020 Special Track AI in FinTec

A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Statistics of strong explanative power, called "factor" have been proposed to summarize the essence of predictive stock returns. Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock returns is highly elusive, and still most investors, if partly, rely on their intuition to build a better decision making. The challenge here is to make an investment strategy that is consistent over a reasonably long period, with the minimum human decision on the entire process. To this end, we propose a new stock return prediction framework that we call Ranked Information Coefficient Neural Network (RIC-NN). RIC-NN is a deep learning approach and includes the following three novel ideas: (1) nonlinear multi-factor approach, (2) stopping criteria with ranked information coefficient (rank IC), and (3) deep transfer learning among multiple regions. Experimental comparison with the stocks in the Morgan Stanley Capital International (MSCI) indices shows that RIC-NN outperforms not only off-the-shelf machine learning methods but also the average return of major equity investment funds in the last fourteen years

Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock return in terms of the multi-factor model. Although these methods perform quite well, they have significant disadvantages such as a lack of transparency and limitations in the interpretability of the prediction. It is thus difficult for institutional investors to use black-box-type machine learning techniques in actual investment practice because they should show accountability to their customers. Consequently, the solution we propose is based on LSTM with LRP. Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM. Then, we approximate and linearize the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent factor model. Finally, we perform an empirical analysis of the Japanese stock market and show that our recurrent model has better predictive capability than the traditional linear model and fully-connected deep learning methods.Comment: In AAAI-19 Workshop on Network Interpretability for Deep Learnin

Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks

Living organisms can act autonomously because biological neural networks process the environmental information in continuous time. Therefore, living organisms have inspired many applications of autonomous control to small-sized robots. In this chapter, a small-sized robot is controlled by a hardware artificial neural network (ANN) without software programs. Previously, the authors constructed a multilegged walking robot. The link mechanism of the limbs was designed to reduce the number of actuators. The current paper describes the basic characteristics of hardware ANNs that generate the gait for multilegged robots. The pulses emitted by the hardware ANN generate oscillating patterns of electrical activity. The pulse-type hardware ANN model has the basic features of a class II neuron model, which behaves like a resonator. Thus, gait generation by the hardware ANNs mimics the synchronization phenomena in biological neural networks. Consequently, our constructed hardware ANNs can generate multilegged robot gaits without requiring software programs

Interactive Threshold Mercurial Signatures and Applications

Equivalence class signatures allow a controlled form of malleability based on equivalence classes defined over the message space. As a result, signatures can be publicly randomized and adapted to a new message representative in the same equivalence class. Notably, security requires that an adapted signature-message pair looks indistinguishable from a random signature-message pair in the space of valid signatures for the new message representative. Together with the decisional Diffie-Hellman assumption, this yields an unlinkability notion (class-hiding), making them a very attractive building block for privacy-preserving primitives. Mercurial signatures are an extension of equivalence class signatures that allow malleability for the key space. Unfortunately, the most efficient construction to date suffers a severe limitation that limits their application: only a weak form of public key class-hiding is supported. In other words, given knowledge of the original signing key and randomization of the corresponding public key, it is possible to identify whether they are related. In this work, we put forth the notion of interactive threshold mercurial signatures and show how they help to overcome the above-mentioned limitation. Moreover, we present constructions in the two-party and multi-party settings, assuming at least one honest signer. We also discuss related applications, including blind signatures, multi-signatures, and threshold ring signatures. To showcase the practicality of our approach, we implement the proposed constructions, comparing them against related alternatives

Present situation of Elaeocarpus zolloingeri tree planted in Mt. Shiroyama in 2006 : survival and infection status of Elaeocarpus yellows (Second Report)

This paper is an additional report of the 2016 survey report on the saplings of Elaeocarpus zollingeri (synonym: E. sylvestris) in Mt. Shiroyama in the Tokushima City. Since 1970s community of E. zollingeri in Mt. Shiroyama has continued to decline by Elaeocarpus yellows caused by phytoplasma. To prevent extinction 300 saplings were planted at the foot of the mountain by volunteers in 2006. In 2016, 10 years after planting, 40 trees had been alive, and the number has decreased to 29 in 2022. There were two other saplings near the planted area, presumably naturally occurring seedlings. The most grown tree was over 5 m in height, while there were 4 trees less than 1 m. These saplings were checked for phytoplasma infection by nested PCR, and 9 saplings were found to be infected. However, none of them shows symptoms of the disease in appearance, and there is no difference in tree height compared to the uninfected saplings. To prevent further loss in the future, poorly growing saplings should be transplanted into better conditions. Also, in areas with many surviving saplings, it is advisable to transplant several saplings to avoid overcrowding