Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces

We consider the problem of short-time extrapolation of blue chips’ stocks indexes in the context of wavelet subspaces following the theory proposed by X.-G. Xia and co-workers in a series of articles [10], [11], [12], [13]. The idea is first to approximate the oscillations of the corresponding stock index at some scale by means of the scaling function which is part of a given multi-resolution analysis of $L^2\left(\mathbb{R}\right)$. Then, since oscillations at a finer scale are discarded, it becomes possible to extend such a signal up to a certain time in the future; the finer the approximation, the shorter this extrapolation interval. At the numerical level, a so-called Generalized Gerchberg–Papoulis (GGP) algorithm is set up which is shown to converge toward the minimum $L^2$ norm solution of the extrapolation problem. When it comes to implementation, an acceleration by means of a Conjugate Gradient (CG) routine is necessary in order to obtain quickly a satisfying accuracy. Several examples are investigated with different international stock market indexes.     

Nonlinearity effects in multidimensional scaling

When multidimensional scaling of n cases is derived from dissimilarities that are functions of p basic continuous variables, the question arises of how to relate the values of the variables to the configuration of n points. We provide a methodology based on nonlinear biplots that expresses nonlinearity in two ways: (i) each variable is represented by a nonlinear trajectory and (ii) each trajectory is calibrated by an irregular scale. Methods for computing, calibrating and interpreting these trajectories are given and exemplified. Not only are the tools of immediate practical utility but the methodology established assists in a critical appraisal of the consequences of using nonlinear measures in a variety of multidimensional scaling methods.     

中国股票市场的日历效应分析

本文以上证指数和深证指数为代表 ,对中国股票市场的日历效应进行实证分析 .主要从以下三个方面加以讨论 :收益率和交易量的均值及方差的日历特征 ;收益率日历特征的相关分析 ;收益率周内各日的转移概率特性     

Structure indices for multidimensional systems

The structure indices of a one-dimensional system are an importantset of invariants. In this paper we examine a generalizationof this concept to multidimensional linear systems, which correspondsto the algebraic concept of a Hilbert series. We use the standardtheory of the Hilbert series to explain some of the previousID system-theoretic results. We discuss the computation of nDstructure indices from an initial condition set, and the invariantswhich can be derived from these indices.     

Two level minimization in multidimensional scaling

Multidimensional scaling with city block norm in embedding space is considered. Construction of the corresponding algorithm is reduced to minimization of a piecewise quadratic function. The two level algorithm is developed combining combinatorial minimization at upper level with local minimization at lower level. Results of experimental investigation of the efficiency of the proposed algorithm are presented as well as examples of its application to visualization of multidimensional data.     

On the stochastic geometrical foundations of metric multidimensional scaling

This paper deals with the topological and measure theoretic probability foundations of metric multidimensional scaling. The first four sections develop minimal separation properties for a set of points or space to model a stimulus. The next three sections give measure theoretic probabilistic structure to random stimulus points and their random connecting paths.     

A behavioral stock market model

Stock exchanges are modeled as nonlinear closed-loop systems where the plant dynamics is defined by known stock market regulations and the actions of agents are based on their beliefs and behavior. The decision of the agents may contain a random element, thus we get a nonlinear stochastic feedback system. The market is in equilibrium when the actions of the agents reinforce their beliefs on the price dynamics. Assuming that linear predictors are used for prediction of the price process, a stochastic approximation procedure for finding market equilibrium is described. The proposed procedure is analyzed using the theory of Benveniste et al. (Adaptive algorithms and stochastic approximations. Springer, Berlin, 1990). A simulation result is also presented.     

Empirical Bayes stock market portfolios

We exhibit a compound sequential Bayes portfolio selection algorithm based solely on the past which not only lives off market fluctuations but follows the drift as well. In fact, this sequential portfolio performs as well (up to first order terms in the exponent) as the optimal portfolio based on advance knowledge of the n-period empirical distribution of the market. Moreover, to first order in the exponent, the capital resulting from this portfolio will be no less than the best of the available stocks. This is a result that holds for every sample sequence. Thus bull markets and bear markets can not fool the investor into over-committing or under-committing his capital to the risky alternatives available to him. The goal is accomplished by a choice of portfolio which is robust with respect to futures that may differ drastically from the past.     

A hybrid method for multidimensional scaling using city-block distances

The problem of multidimensional scaling with city-block distances in the embedding space is reduced to a two level optimization problem consisting of a combinatorial problem at the upper level and a quadratic programming problem at the lower level. A hybrid method is proposed combining randomized search for the upper level problem with a standard quadratic programming algorithm for the lower level problem. Several algorithms for the combinatorial problem have been tested and an evolutionary global search algorithm has been proved most suitable. An experimental code of the proposed hybrid multidimensional scaling algorithm is developed and tested using several test problems of two- and three-dimensional scaling.     

Making inefficient market indices efficient

This paper uses the concept of Marginal Conditional Stochastic Dominance and a generalization of the 50% Portfolio Rule to develop a tractable and parsimonious methodology for constructing a second degree Stochastic Dominance (SSD) efficient portfolio from a given, inefficient index. Because the SSD approach considers the entire probability distributions of asset returns, the resulting portfolios are efficient with respect to all risk-averse, utility-maximizing investors regardless of the form of their utility functions or the distributions of asset returns.     

Convergence of multidimensional random sequences with independent random indices

The paper present necessary and sufficient conditions for the weak convergence of R^m-valued random sequences with independent random indices under some additional assumptions. Operator normalization is considered. Supported by the Russian Foundation for Fundamental Research (grant No. 93-01-01446). Proceedings of the XVI Seminar on Stability Problems for Stochastic Models, Part I, Eger, Hungary, 1994.     

Weekday dependence of German stock market returns

The so‐called ‘Monday effect’ has been found for various stock markets of the world. The empirical finding that Monday returns are significantly smaller than returns measured for the remaining days of the week calls the efficiency hypothesis for pricing processes operating on stock markets into question. Investigating an index series measured at the Frankfurt stock exchange the paper compares estimation results of parametric and non‐parametric autoregressive models with respect to possible weekday dependence of return data. Allowing for heteroskedastic error distributions the wild bootstrap is used to infer against time‐varying means and correlation of return data in parametric models and to obtain confidence bands for non‐parametric estimates. It is shown that time dependence is an important feature describing the dynamics of German stock market returns in the period 1960–1979. Within two subsamples obtained from the period 1980–1997 the evidence in favour of such effects is mitigated substantially. Copyright © 2000 John Wiley & Sons, Ltd.     

Parallel branch and bound for multidimensional scaling with city-block distances

Multidimensional scaling is a technique for exploratory analysis of multidimensional data. The essential part of the technique is minimization of a multimodal function with unfavorable properties like invariants and non-differentiability. Recently a branch and bound algorithm for multidimensional scaling with city-block distances has been proposed for solution of medium-size problems exactly. The algorithm exploits piecewise quadratic structure of the objective function. In this paper a parallel version of the branch and bound algorithm for multidimensional scaling with city-block distances has been proposed and investigated. Parallel computing enabled solution of larger problems what was not feasible with the sequential version.     

Branch and bound algorithm for multidimensional scaling with city-block metric

A two level global optimization algorithm for multidimensional scaling (MDS) with city-block metric is proposed. The piecewise quadratic structure of the objective function is employed. At the upper level a combinatorial global optimization problem is solved by means of branch and bound method, where an objective function is defined as the minimum of a quadratic programming problem. The later is solved at the lower level by a standard quadratic programming algorithm. The proposed algorithm has been applied for auxiliary and practical problems whose global optimization counterpart was of dimensionality up to 24.     

Comparison of some techniques of multidimensional scaling in soil fauna ecology

The aim of this paper is to propose some alternative solutions to a problem proposed by El-Shishiny and Ghabbour, where the difficulty lies in vagueness rather than randomness. We first propose a city-block analysis, undoubtedly better adapted to this type of problem than a Euclidean analysis. In the second part we develop a technique consisting in the decomposition of every distance D_ij into two parts: the first, a star distance, considered as a personal effect; the second considered as a structure effect. These techniques are illustrated and compared in a real-life example.     

On spurious anti-persistence in the US stock indices

We reexamine the results of Serletis and Rosenberg [Serletis A, Rosenberg A. Mean reversion in the US stock market. Chaos, Solitons and Fractals 2009;40:2007–2015.] who claim that the returns of the most important US stock indices (DJI, NASDAQ, NYSE and S&P500) are strongly anti-persistent and thus mean reverting. We apply various methods to detect long-range dependence – detrending moving average, detrended fluctuation analysis, generalized Hurst exponent approach, classical rescaled range analysis and modified rescaled range analysis. We show that there are no signs of anti-persistence in any of the indices. Moreover, we discuss that the authors did not find any anti-persistence but rather showed returns of the said assets do not follow the scaling power law around their moving average with varying window length. Anti-persistence is thus spurious and due to wrong application of detrending moving average method.     

Multifractal structure in Latin-American market indices

We study the multifractal nature of daily price and volatility returns of Latin-American stock markets employing the multifractal detrended fluctuation analysis. Comparing with the results obtained for a developed country (US) we conclude that the multifractality degree is higher for emerging markets. Moreover, we propose a stock market inefficiency ranking by considering the multifractality degree as a measure of inefficiency. Finally, we analyze the sources of multifractality quantifying the contributions of two factors, the long-range correlations of the time series and the broad fat-tail distributions. We find that the multifractal structure of Latin-American market indices can be mainly attributed to the latter.     

Detecting stock market regimes from option prices

Equity market returns alternate between periods of calm and crises. Researchers commonly employ regime switching models to capture this behaviour. We show that forward-looking information extracted from option prices improves regime detection. In particular, horizon spreads in option-implied equity risk premia allow earlier detection of regime switches and improve prediction of the equity premium. This findings holds across recent disaster periods like the 2008/2009 financial crisis and the 2020 Covid pandemic outbreak, in both US and Emerging equity markets.