Understanding the nature of oil fluctuations using 1 neutral network moving average: A study on the returns of crude oil futures

This paper describes the profitability of technical trading rules which are enhanced by the use of neural networks on crude oil futures contracts traded on Chicago Merchantile Exchange and on Bursa Derivative Malaysia. The profitable returns on the futures contract on crude light oil futures traded from 2/1/2008 to 31/12/2014 offer a piece of evidence on the ability of technical trading rules using neural networks to outperform the threshold benchmark, buy and hold. The results here suggest that it is worthwhile to design, build and develop more robust, machine learning algorithms like neural networks enhanced moving average technical indicator to enhance portfolio returns. The conclusion drawn is that neural network can be used in technical analysis as a predictor for futures market prices

Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations

Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models

<div><p>The motivation behind this research is to innovatively combine new methods like wavelet, principal component analysis (PCA), and artificial neural network (ANN) approaches to analyze trade in today’s increasingly difficult and volatile financial futures markets. The main focus of this study is to facilitate forecasting by using an enhanced denoising process on market data, taken as a multivariate signal, in order to deduct the same noise from the open-high-low-close signal of a market. This research offers evidence on the predictive ability and the profitability of abnormal returns of a new hybrid forecasting model using Wavelet-PCA denoising and ANN (named WPCA-NN) on futures contracts of Hong Kong’s Hang Seng futures, Japan’s NIKKEI 225 futures, Singapore’s MSCI futures, South Korea’s KOSPI 200 futures, and Taiwan’s TAIEX futures from 2005 to 2014. Using a host of technical analysis indicators consisting of RSI, MACD, MACD Signal, Stochastic Fast %K, Stochastic Slow %K, Stochastic %D, and Ultimate Oscillator, empirical results show that the annual mean returns of WPCA-NN are more than the threshold buy-and-hold for the validation, test, and evaluation periods; this is inconsistent with the traditional random walk hypothesis, which insists that mechanical rules cannot outperform the threshold buy-and-hold. The findings, however, are consistent with literature that advocates technical analysis.</p></div

The determinants of capital structure: Evidence from public listed companies in Malaysia, Singapore and Thailand

We investigate the determinants of capital structure of public listed companies on Bursa Malaysia, Singapore Stock Exchange and Thailand Stock Exchange from 2004 to 2013. We also investigate how firm-specific factors such as profitability, firm size, tangibility of assets and depreciation to total assets along with the macroeconomic factor such as inflation influence the capital structure decisions of public listed companies. Our findings support capital structure theories such as trade-off and pecking order theories and are consistent with prior empirical studies. We find all the factors examined in this study provide strong explanatory power for the capital structure decisions of the sampled public listed companies across all three countries. We find profitability has a significant negative influence on capital structure for Malaysia and Singapore but insignificant for Thailand. While, firm size has a significant positive influence on capital structure for all countries. Our findings also suggest that tangibility of assets has a significant positive influence on capital structure for Malaysia and Singapore while insignificant for Thailand. The depreciation to total assets indicates a negative influence on capital structure across all the three countries. Our study should be of interest to top managers who wish to have optimal capital structure to improve the firm performance

Buy and hold returns versus mechanical trading Rules (SMA5, SMA20, MAC, MACD, KAMA, MA20,%, Portfolio’s OptMA19, Different Currencies OptMa, and AMA′): Returns for IDR, MYR, PHP, SGD, and THB from January 2, 2014 to December 31, 2014 after taking into account slippage costs of 1 tick per transaction.

<p>Buy and hold returns versus mechanical trading Rules (SMA5, SMA20, MAC, MACD, KAMA, MA20,%, Portfolio’s OptMA19, Different Currencies OptMa, and AMA′): Returns for IDR, MYR, PHP, SGD, and THB from January 2, 2014 to December 31, 2014 after taking into account slippage costs of 1 tick per transaction.</p

Chart of USD/MYR with AMA′ from 2005 to 2013.

<p>Chart of USD/MYR with AMA′ from 2005 to 2013.</p

Performance of the models, MAPE ratio of evaluation results for TAIEX futures.

<p>Performance of the models, MAPE ratio of evaluation results for TAIEX futures.</p

Descriptive statistics of the exchange rates of IDR, MYR, PHP, SGD, and THB against USD from 2005 to 2013 by the year.

<p>Descriptive statistics of the exchange rates of IDR, MYR, PHP, SGD, and THB against USD from 2005 to 2013 by the year.</p

Profit of the models, results for TAIEX futures.

<p>Profit of the models, results for TAIEX futures.</p

Profit of the models, results for SiMSCI futures.

<p>Profit of the models, results for SiMSCI futures.</p