Multiscale Event Study of Private Placement Announcement Effect

Empirical mode decomposition (EMD) is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression (LLQ) is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps

Accurate clustering of time series is a challenging problem for data arising from areas such as financial markets, biomedical studies, and environmental sciences, especially when some, or all, of the series exhibit nonlinearity and nonstationarity. When a subset of the series exhibits nonlinear characteristics, frequency domain clustering methods based on higher-order spectral properties, such as the bispectra or trispectra are useful. While these methods address nonlinearity, they rely on the assumption of series stationarity. We propose the Bispectral Smooth Localized Complex EXponential (BSLEX) approach for clustering nonlinear and nonstationary time series. BSLEX is an extension of the SLEX approach for linear, nonstationary series, and overcomes the challenges of both nonlinearity and nonstationarity through smooth partitions of the nonstationary time series into stationary subsets in a dyadic fashion. The performance of the BSLEX approach is illustrated via simulation where several nonstationary or nonlinear time series are clustered, as well as via accurate clustering of the records of 16 seismic events, eight of which are earthquakes and eight are explosions. We illustrate the utility of the approach by clustering S&P 100 financial returns.

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

Non-stationary time series plays a prominent role in the analysis of performance time series of many real-world systems. Recently, fuzzy time series models have been extended to forecast non-stationary time series. Over different phases of time, performance time series may show drastic changes. Therefore, a non-stationary time series is partitioned according to different phases of time. These phases may be taken as weeks, months, or years. Over different phases of time, the universe of discourse, knowledge base, and rule base may vary. A common constraint in the modelling of time-variant fuzzy time series is their incapability to address the phase change of time in the model and accordingly incorporate the necessary changes in the universe of discourse, knowledge base, and rule base over different time phases. To address this issue, a Phase-cum-Time Variant Fuzzy Time Series Model (PTVFTS) is presented in this paper. The PTVFTS model is developed so that it will address the problem of phase change as well as the time variations within each phase simultaneously. The concept of the model rebuilding process is applied to handle changes over each phase and the modified parameter adaptation technique is used for the time variations within each phase. The developed model is applied to the daily closing price time series for the years 2017, 2018, 2019, 2020, 2021, and 2022 separately of stock market indices, NASDAQ, S&P 500, Dow Jones, and TAIEX. The comparison of the developed model is made with the time-variant fuzzy time series model known as the non-stationary fuzzy time series model (NSFTS), Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) model, Long Short-Term Memory (LSTM) model, and the classical Auto-Regressive Integrated Moving Average (ARIMA) model. The efficiency of the developed PTVFTS model is tested using forecasting metrics and statistical tests. The comparison shows that the developed model is more efficient in forecasting phase-cum-time variant non-stationary time series.

This paper presents an Empirical mode decomposition (EMD) approach for achieving high-speed frequency-tagged steady state visual evoked potential (SSVEP) brain computer interface (BCI) system. Empirical mode decomposition (EMD) has been demonstrated as a local and fully data-driven technique for the data processing of nonlinear and non-stationary time-series. It allows the frequency and amplitude of a time-series to be evaluated with excellent time resolution. The proposed system utilized flickering sources with high flickering rate acting as cursor system for the purpose of alternatively communication system. Induced EEG signal were decomposed into different scale of oscillation components namely intrinsic mode functions. In order to find out the SSVEP-related IMFs, instantaneous frequency were computed by generalized zero crossing (GZC). After identification of the frequency power by quadrature detection, the result shows the proposed system achieved 51.46 bits/min of average ITR. Compare with FFT approaches, EMD method provide better temporal and frequency sensitivity in detection of cognitive state of neuron activities.KeywordsBrain computer interface (BCI)Empirical mode decomposition (EMD)Steady-state visual evoked potential (SSVEP)

The Fourier transform of a nonstationary time series has the difficulty of providing a proper physical interpretation of the signal as the physical attributes and dynamics associated with the time series may consist of several transients that are different in their nature. The time series analysis method developed by NASA [N. Huang et al., ‘‘The Empirical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis’’ (to be published)] has a unique approach to adaptively break down the nonstationary time series into the sum of several simple mode functions. Each single mode function can then be expressed in terms of an analytical signal whose amplitude and phase can both be varied with time. Those time variations can be related to the abrupt changes associated with dynamic systems. Such a time variation characteristic can be used to observe the occurrence of a transient event even in a nonstationary process. Applications of this technique to observe various types of acoustic signals in noisy environments are discussed.

The electric power load is inherently nonlinear and nonstationary time series, which has periodicity and randomicity. It is difficult to accurately forecast with a single method. So a hybrid method based on empirical mode decomposition (EMD) and support vector machines (SVMs) was presented in this paper. First, load time series was adaptively decomposed into a series of smooth intrinsic mode functions (IMFs) with different scales via EMD; second, tendency of each IMF was forecasted with different SVM respectively according to its change and weather factors were taken account in forecasting; finally, final results were obtained by summing up these forecasting results of each IMF together. The simulation results show that the hybrid method has higher precision and greater generalization ability.

IN MY judgment," maintained William McChesney Martin, Jr., Chairman of the Board of Governors, Federal Reserve System, "we can never under the present margin regulations have the same result that occurred in terms of a financial crash in 1929 through undermargined accounts, low margins resulting in a financial debacle."' A widely used elementary economics textbook claimed, "Margin requirements have exercised an important restrictive influence on security speculation, as contrasted with the uncontrolled period of the late 1920's... Nearly everyone agrees that Federal Reserve margin requirements exercise a healthy restraint on speculative stock purchases in a boom" (Bach, 1963, p. 116). But what evidence is there that margin requirements have the effects claimed? At the December meetings of the American Economic Association, George J. Stigler, in the Presidential address, called on economists to test their assumptions that state regulation of private activity has indeed been successful (Stigler, 1965). This article brings together some data on the efficacy of margin requirements. While the figures can only be considered preliminary, and more data should be collected to test margin requirements, the data that are available and presented here indicate that not one of the aims of the legislation establishing margin requirements has been accomplished.2

This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

Assessment of spatiotemporal characteristics of drought under climate change is significant for drought mitigation. In this study, the standardized precipitation evapotranspiration index (SPEI) calculated at different timescales was adopted to describe the drought conditions in the Heihe River Basin (HRB) from 1961 to 2014. The period characteristics and spatiotemporal distribution of drought were analyzed by using the extreme-point symmetric mode decomposition (ESMD) and inverse distance weight interpolation methods. Four main results were obtained. (1) The SPEI series of the upper reaches of the HRB at different timescales showed an upward trend (not significant) during 1961–2014. In the middle and lower reaches, the SPEI series exhibited significant downward trends. (2) The annual SPEI series of the lower reaches was decomposed through the ESMD method and exhibited a fluctuating downward trend as a whole. The oscillation showed quasi-3.4-year and quasi-4.5-year periods in the interannual variation, while a quasi-13.5-year period occurred in the interdecadal variation. The interannual period plays a leading role in drought variation across the HRB. (3) The entire research period was divided into three subperiods by the Bernaola–Galvan segmentation algorithm: 1961–1966, 1967–1996, and 1997–2014. The spring drought frequency and autumn drought intensity arrived at their maxima in the lower reaches during 1997–2014, with values of 72.22% and 1.56, respectively. The high frequency and intensity areas of spring, summer, and autumn drought moved from the middle-upper reaches to the middle-lower reaches of the HRB during 1961–2014. (4) Compared to the wavelet transform, the ESMD method has self-adaptability for signal decomposition and is more accurate for drought period analysis. Extreme-point symmetric mode decomposition is a more efficient decomposition method for nonlinear and nonstationary time series and has important significance for revealing the complicated change features of climate systems.

According to daily and weekly data of account open rate as well as stock market returns, we set a vector auto-regression model to make a study on the effect of investor sentiment change on Shanghai and Shenzhen stock markets. The result shows: investor sentiment has greater effect on Shenzhen stock market than on that of Shanghai, and its effect on stock market is greater in the long run than in short term. Besides, volatility of account open rate is the Granger reason of the yield volatility of Shanghai and Shenzhen 300 index, Shanghai A-share index, and Shenzhen A-share index; while vice versa, that depends on different time limits.KeywordsInvestor sentimentStock returnsVAR (Vector Auto-Regression)

Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed SEMD algorithm and presents some applications to biomedical time series recorded during neuromonitoring.

The theory and practice of the Hilbert-Huang transform (HHT) provide another way of analyzing nonlinear and nonstationary time series. The central idea of HHT is the empirical mode decomposition (EMD), which is then decomposed into a set of intrinsic mode functions (IMFs) and analyzed by using the Hilbert spectrum. This chapter is primarily a summary of current applications of HHT and suggests areas for future research based on my personal views. 13.