Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.
problem Understanding asymmetry's impact on modeling and forecasting realized volatility in Japanese stock markets.
method Employed heterogeneous autoregressive (HAR) models with three types of asymmetry: positive and negative realized semivariance, asymmetric jumps, and leverage effects.
result Leverage effects significantly influence realized volatility modeling and forecast performance in Japanese stock markets.
This study explores the time-varying structure of market efficiency in the prewar and wartime Japanese stock market using a new market capitalization-weighted stock price index, the equity performance index. We examine whether the adaptive market hypothesis (AMH) is supported in that era. First, we find that the degree…
We investigated the network structures of the Japanese stock market through the minimum spanning tree. We defined grouping coefficient to test the validity of conventional grouping by industrial categories, and found a decreasing in trend for the coefficient. This phenomenon supports the increasing external influences …
Study on time-varying APT validity in Japanese stock market.
problem Validity of Arbitrage Pricing Theory (APT) in Japanese stock market over time.
method Rolling window method applied to Fama and MacBeth's two-step regression and Kamstra and Shi's generalized GRS test.
result APT validity is unstable over time in Japanese stock market, influenced by monetary policy and business cycle.
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
We investigated the temporally evolving network structures of the Japanese and Korean stock markets through the minimum spanning trees composed of listed stocks. We tested the validity of conventional grouping by industrial categories, and found a common trend of decrease for Japan and Korea. This phenomenon supports t…
Quantum neural network and tensor network models outperform classical models in Japanese stock market predictions.
problem Improving stock return predictions using quantum and quantum-inspired machine learning.
method Evaluation of quantum neural network and tensor network models against classical models like linear and neural networks.
result Tensor network model outperforms classical models in Japanese stock market, including linear and neural network models.
The persistence phenomenon is studied in the Japanese financial market by using a novel mapping of the time evolution of the values of shares quoted on the Nikkei Index onto Ising spins. The method is applied to historical end of day data from the Japanese stock market during 2002. By studying the time dependence of th…
In this study, we have investigated empirically the effects of market properties on the degree of diversification of investment weights among stocks in a portfolio. The weights of stocks within a portfolio were determined on the basis of Markowitz's portfolio theory. We identified that there was a negative relationship…
We present an interacting-agent model of speculative activity explaining bubbles and crashes in stock markets. We describe stock markets through an infinite-range Ising model to formulate the tendency of traders getting influenced by the investment attitude of other traders. Bubbles and crashes are understood and descr…
In this paper, we quantitatively investigate the properties of a statistical ensemble of stock prices. We focus attention on the relative price defined as X(t)=S(t)/S(0), where S(0) is the initial price. We selected approximately 3200 stocks traded on the Japanese Stock Exchange and formed a statistical ensem…
In this study, we attempted to determine how eigenvalues change, according to random matrix theory (RMT), in stock market data as the number of stocks comprising the correlation matrix changes. Specifically, we tested for changes in the eigenvalue properties as a function of the number and type of stocks in the correla…
Study explores financial market linkages between Japan and US markets.
problem Inconsistency in empirical studies regarding financial market causal linkages.
method Causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge.
result VAR-LiNGAM reveals causal influences among financial markets, while LPCMCI identifies potential latent confounders.
This study examines the adaptive market hypothesis (AMH) in Japanese stock markets (TOPIX and TSE2). In particular, we measure the degree of market efficiency by using a time-varying model approach. The empirical results show that (1) the degree of market efficiency changes over time in the two markets, (2) the level o…
Quantitative analysis of order-splitting behavior in Japanese stock market.
problem Understanding and quantifying the order-splitting behavior of traders in the Japanese stock market.
method Analysis of a large dataset of trading accounts over nine years, clustering traders into order-splitting and random traders, and applying statistical methods to analyze metaorder length and sign correlation.
result The metaorder length distribution follows power laws with exponent α, and the sign correlation exponent γ is approximately α-1, supporting the LMF model.
We introduce a new measure for the capital market efficiency. The measure takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension). The efficiency measure is taken as a distance from an ideal efficient market situation. Methodolog…
Study shows activist board representation improves Japanese companies' performance.
problem Lack of innovation and improvement in Japanese companies.
method Examined two Japanese companies with activist board representation, analyzing performance metrics.
result Companies with activist board representation experienced significant improvements in stock returns and operational metrics.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
We investigate scaling and memory effects in return intervals between price volatilities above a certain threshold q for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the …
In this paper we examine inefficiencies and information disparity in the Japanese stock market. By carefully analysing information publicly available on the internet, an `outsider' to conventional statistical arbitrage strategies--which are based on market microstructure, company releases, or analyst reports--can never…
Study compares ML algorithms for predicting stock market directional bias.
problem Predicting the direction of stock market movements.
method Examined and contrasted logistic regression, decision tree, random forest, and a deep neural network.
result All models consistently reach above 50% in directional bias forecasting.
We investigated financial market data to determine which factors affect information flow between stocks. Two factors, the time dependency and the degree of efficiency, were considered in the analysis of Korean, the Japanese, the Taiwanese, the Canadian, and US market data. We found that the frequency of the significant…
Deep learning predicts cross-sectional stock prices for practical investment.
problem Predicting stock prices using cross-sectional factors.
method Deep learning model for daily stock price prediction.
result Profitable investment framework demonstrated in Japanese stock market.
In this paper, we quantitatively investigate the statistical properties of a statistical ensemble of stock prices. We selected 1200 stocks traded on the Tokyo Stock Exchange, and formed a statistical ensemble of daily stock prices for each trading day in the 3-year period from January 4, 1999 to December 28, 2001, corr…
This study evaluates prewar Japanese financial market efficiency using time-varying models.
problem Determining when prewar Japanese financial market lost its price formation function.
method Time-varying parameter model, generalized least squares-based time-varying vector autoregressive model.
result The prewar Japanese financial market lost its price formation function in 1932.
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper im…
We analyze realized volatilities constructed using high-frequency stock data on the Tokyo Stock Exchange. In order to avoid non-trading hours issue in volatility calculations we define two realized volatilities calculated separately in the two trading sessions of the Tokyo Stock Exchange, i.e. morning and afternoon ses…
In order to figure out and to forecast the emergence phenomena of social systems, we propose several probabilistic models for the analysis of financial markets, especially around a crisis. We first attempt to visualize the collective behaviour of markets during a financial crisis through cross-correlations between typi…
We perform an extended analysis of the distribution of drawdowns in the two leading exchange markets (US dollar against the Deutsmark and against the Yen), in the major world stock markets, in the U.S. and Japanese bond market and in the gold market, by introducing the concept of ``coarse-grained drawdowns,'' which all…
This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distributio…
This study conducts a comprehensive analysis of time series segmentation on the Japanese stock prices listed on the first section of the Tokyo Stock Exchange during the period from 4 January 2000 to 30 January 2012. A recursive segmentation procedure is used under the assumption of a Gaussian mixture. The daily number …
This paper examines the integration process of the Japanese major rice markets (Tokyo and Osaka) from 1881 to 1932. Using a non-Bayesian time-varying vector error correction model, we argue that the process strongly depended on the government's policy on the network system of the telegram and telephone; rice traders wi…
Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.
problem Understanding stock market dynamics and predicting market bubbles.
method Non-linear analysis using time-dependent Tsallis statistics and Generalized Hurst Exponents.
result Temporal trends of q-triplet values differ before and after market bubbles, indicating significant market dynamics changes.
We investigate the probability distribution of the volatility return intervals τ for the Chinese stock market. We rescale both the probability distribution Pq(τ) and the volatility return intervals τ as Pq(τ)=1/τˉf(τ/τˉ) to obtain a uniform scaling curve for different threshold value q. The scali…
Study examines market reactions and spillovers in Japanese bank mergers using multiple methods.
problem Understanding valuation and spillover effects of bank mergers in the Japanese banking sector.
method Combines event study, VAR models, IRFs, and PSM to analyze two M&A events.
result Significant positive market reaction and prolonged positive spillovers detected.
This paper introduces a new correction scheme to a conventional regression-based event study method: a topological machine-learning approach with a self-organizing map (SOM).We use this new scheme to analyze a major market event in Japan and find that the factors of abnormal stock returns can be easily can be easily id…
Previous analyses of a large ensemble of stock markets have demonstrated that a log-periodic power law (LPPL) behavior of the prices constitutes a qualifying signature of speculative bubbles that often land with a crash. We detect such a LPPL signature in the foreign capital inflow during the bubble on the US markets c…
Network geometry measures predict market instability.
problem Predicting financial market instability using network geometry.
method Discrete Ricci curvatures to capture network fragility.
result Different geometric measures distinguish normal and crash periods.
We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversi…
Stock prediction has always been attractive area for researchers and investors since the financial gains can be substantial. However, stock prediction can be a challenging task since stocks are influenced by a multitude of factors whose influence vary rapidly through time. This paper proposes a novel approach (Word2Vec…
We present a general methodology to incorporate fundamental economic factors to our previous theory of herding to describe bubbles and antibubbles. We start from the strong form of Rational Expectation and derive the general method to incorporate factors in addition to the log-periodic power law (LPPL) signature of her…
A diversified portfolio is created by solving the MIS problem in large market graphs, outperforming conventional methods.
problem Finding the maximum independent set (MIS) in large-scale market graphs is computationally challenging.
method Solved the MIS problem using a quantum-inspired algorithm (Simulated Bifurcation) and a combinatorial optimization solver.
result The SB-based solver optimized MIS portfolios, achieving a Sharpe ratio of 1.16 and outperforming major indices.
Our society has been computerised and globalised due to emergence and spread of information and communication technology (ICT). This enables us to investigate our own socio-economic systems based on large amounts of data on human activities. In this article, methods of treating complexity arising from a vast amount of …
Option pricing formulas are derived from a non-Gaussian model of stock returns. Fluctuations are assumed to evolve according to a nonlinear Fokker-Planck equation which maximizes the Tsallis nonextensive entropy of index q. A generalized form of the Black-Scholes differential equation is found, and we derive a martin…
Study proposes a new method for deep portfolio optimization using residual factors.
problem Non-stationary financial market makes traditional machine learning methods ineffective.
method Predict distribution of residual factors using a novel neural network architecture with financial inductive biases.
result Demonstrated improved performance on U.S. and Japanese stock market data.
A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out.…
We propose to represent a return model and risk model in a unified manner with deep learning, which is a representative model that can express a nonlinear relationship. Although deep learning performs quite well, it has significant disadvantages such as a lack of transparency and limitations to the interpretability of …
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 re…