時間情報を考慮したトピックモデル(Online Mul-tiscale Dynamic Topic Model) を用い，時間情報を持ったニュース記事に対してトピックを割り当て，記事集合内のトピックの時間発展を推定する．推定したトピックの時系列変化と東証株価指数(TOPIX)のボラティリティとの関連を調べる．

We investigate the return predictability in news using a large sample of electronic news article diseminated from the Bloomberg. Using Naive-Bayes classifier, we create models that predict return of the Nikkei 225 futures. Our test reveals two models that beat the market by substantial margin. Based on the two selected models, we conduct out of sample money management simulation for the period between 2001 to 2011 and achieve 11.3% return per annum with the Sharpe ratio of 0.693. The result is better than the top 5% of the 1000 random models.

One is numerical information, including past stock prices, currency exchange rates, and interest rates. The other is textual information, mainly news stories covering statements of government dignitaries, consumer trends, and miscellaneous events. Although numerical information has been proven useful for predicting stock prices, its predictive power is limited in a sense that much information, such as the statements of government dignitaries mentioned above, resides only in textual information. Given a stock for which one would like to predict the future price, textual data provides different?but much wider coverage?of information from numerical information, which may be beneficial in prediction. This study exploits public Web news articles and attempts to estimate the residue that cannot be explained only by numerical information through a simple additive regression model. In addition, to distinguish between different types of news articles, such as those specific to particular companies or types of industry and more general top news, the framework of multiple kernel learning is adopted. The validity and effectiveness of the proposed approach is evaluated on the real-world data consisting of share prices of Nikkei 220 companies and 47 thousand Web news articles.

本研究ではツイッターデータから特徴量を抽出し、経済動向を表す指標との関連性を明らかにする、テキスト分析、グラフ表現分析、回帰分析からなる分析手法を開発した。この分析手法を実際のツイッターデータに適用し、経済指標の予測を行った。テキスト分析とグラフ表現分析、両者を組み合わせた分析のそれぞれの手法を用いた予測結果を比較することで、予測する経済指標によるテキストの特徴量とグラフ特徴量の影響の違いを明らかにした。経済全体を表す指標については、テキスト分析とグラフ表現分析の両者を用いることで予測精度が向上した。

The effect of option markets on their underlying market has been studied intensively since the first option contract was listed. Despite considerable effort including theoretical and empirical approaches, we still don't have a conclusive view toward that problem. We look the effect of option market, especially the effect of dynamic hedging, on underlying market by using artificial market. We proposed two market model in which option market and underlying market interacts. In this study, we could confim that dynamic heding increase or decrease the volatiliy of underlying market under some conditions.

リーマンショックや欧州危機など近年世界中の金融市場が不安定化している．金融市場の安定化を図るためにこれまでさまざまな対策がとられてきた．その方法の１つとして空売り規制やレバレッジ規制といった市場規制が挙げられる．本研究では，人工市場を用いてレバレッジ規制を行う際の鍵となる委託保証金率が変更された際株式市場にどのような影響を与えるのか検証を試みる．

The purpose of this research is to discuss some investment risk management issues for unexpected fluctuations such as "unintended consequences" in markets. "Unintended consequences" are illustrated as phenomena based on mutual-interaction between the macro and micro level, known as macro-micro linkage. From this perspective, I am interested in the changes of risk model methodology after the financial crisis and then tighter restriction by regulator in financial industry. I am focusing on how to generate better risk estimation based on the statistical model under the current circumstances.

This study presents an ANWSER model (asset network systemic risk model) to compute a bankruptcy reproductive ratio in analyzing the risk of bank failures in a financial crisis. We present the bankruptcy reproductive ratio as a function of the average number of interbank loans, the share of big banks, the risk exposure and diversity of investments which banks make.

本論文では，投資信託の組入比率上位10銘柄に基づいて，投資信託と投資先企業を頂点とした2部グラフを作成し，これをネットワーク構造分析の技術を用いて分析する方法を提案する．提案手法では，まず，Web ページのリンク関係を表すネットワーク構造から重要度が高いWeb ページを抽出するために開発されたHITS (Hypertext Induced Topic Selection) アルゴリズムを用いて投資信託と投資先企業間の投資関係を表すネットワーク構造を分析し，独自性が高い投資信託を抽出する．その後，投資信託を隣接ベクトルで表現し，ユークリッド距離に基づくk 平均法を用いてクラスタリングを行うことによって投資信託をk 個のグループに分ける．k = 5 として分析した結果，J-REIT，日経225，TOPIX，小型アクティブ，大型アクティブに分けることができた．

A method to build trading rules at algorithmic trading by Genetic Algorithm (GA) is presented. The trading rules are built by adapting parameters of technical indices. New fitness functions in GA, which aim at a robuster behavior to change of market trends, are proposed. Furthermore, We have found that the effectiveness of technical indices varies depending on market trend. We propose a hybrid dealing method which switches the technical indices by market trend. The result of experiment shows that profits through dealing are improved by our proposed method.