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

Time-course study of genetic changes in periodontal ligament regeneration after tooth replantation in a mouse model

Ohshima J., Abe S., Morita M., et al. Time-course study of genetic changes in periodontal ligament regeneration after tooth replantation in a mouse model. Scientific Reports 14, 15502 (2024); https://doi.org/10.1038/s41598-024-66542-8.This research focused on analyzing gene expression changes in the periodontal ligament (PDL) after tooth re-plantation to identify key genes and pathways involved in healing and regeneration. Utilizing a mouse model, mRNA was extracted from the PDL at various intervals post-replantation for RNA sequencing analysis, spanning from 3 to 56 days. The results revealed significant shifts in gene expression, particularly notable on day 28, supported by hierarchical clustering and principal component analysis. Gene ontology (GO) enrichment analysis highlighted an upregulation in olfactory receptor and G protein-coupled receptor signaling pathways at this time point. These findings were validated through reverse transcription-quantitative PCR (RT-qPCR), with immunochemical staining localizing olfactory receptor gene expression to the PDL and surrounding tissues. Moreover, a scratch assay indicated that olfactory receptor genes might facilitate wound healing in human PDL fibroblasts. These results underscore the importance of the 28-day post-transplant phase as a potential “tipping point” in PDL healing and regeneration. In conclusion, this research sheds light on the potential role of olfactory receptor genes in PDL regeneration, providing a foundation for developing new therapeutic approaches in tooth replantation and transplantation, with broader implications for regenerative medicine in oral health

指導と学習の振り返りを促す授業評価に関する基礎的研究 : 「アクティブ・ラーニング授業評価尺度」の作成

　アクティブ・ラーニングを促す授業作りと，客観的な授業の振り返りを実現するため，教師と生徒双方の視点に基づいた授業に関する質問項目を抽出し，信頼性・妥当性の高い指導と学習に関する授業評価尺度の作成を試みた。アクティブ・ラーニングを促す授業評価項目を収集するための調査を，研修を受講した県立高等学校の教諭を対象に行い，既存の授業評価に関する枠組みを参考に項目を整理し，48項目からなる質問項目（暫定尺度）を作成した。県立高等学校生徒81名を対象に質問紙調査を行い，因子分析（主因子法，プロマックス回転）を施した結果，「学びの見通しと振り返り」，「学習規律」，「安心と受容」，「主体的・能動的学び」の４因子が抽出された。Ｎ県立教育センターの職員らが望む評価の観点を踏まえて，これらの項目の妥当性を検討した。また，４因子について内的整合性を調べたところ，Cronbachのα係数は.71～.83の範囲にあり，信頼性も確認された。よって，４因子12項目で構成される「アクティブ・ラーニング授業評価尺度」が完成した。今後，授業評価尺度の改善を進め信頼性と妥当性を高めるとともに，生徒の学習意欲や学力などとの相関を分析していく必要がある

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