Abstract: Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.
Abstract: A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.
Abstract: The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.
Abstract: This paper discusses the reaction of investors in the Taiwan stock market to the most probable unknown earnings-related information and the most probable known earnings-related information. As compared with the previous literature regarding the effect of an official announcement of earnings forecast revision, this paper further analyzes investors’ cognitive bias toward the unknown and known earnings-related information, and the role of media during the investors' reactions to the foresaid information shocks. The empirical results show that both the unknown and known earnings-related information provides useful information content for a stock market. In addition, cognitive bias and disposition effect are the behavioral pitfalls that commonly occur in the process of the investors' reactions to the earnings-related information. Finally, media coverage has a remarkable influence upon the investors' trading decisions.
Abstract: How to detecting financial bubble? Addressing this simple question has been the focus of a vast amount of empirical research spanning almost half a century. However, financial bubble is hard to observe and varying over the time; there needs to be more research on this area. In this paper, we used abnormal difference between common stocks price and those preferred stocks price to explain financial bubble. First, we proposed the ‘W-index’ which indicates spread between common stocks and those preferred stocks in stock market. Second, to prove that this ‘W-index’ is valid for measuring financial bubble, we showed that there is an inverse relationship between this ‘W-index’ and S&P500 rate of return. Specifically, our hypothesis is that when ‘W-index’ is comparably higher than other periods, financial bubbles are added up in stock market and vice versa; according to our hypothesis, if investors made long term investments when ‘W-index’ is high, they would have negative rate of return; however, if investors made long term investments when ‘W-index’ is low, they would have positive rate of return. By comparing correlation values and adjusted R-squared values of between W-index and S&P500 return, VIX index and S&P500 return, and TED index and S&P500 return, we showed only W-index has significant relationship between S&P500 rate of return. In addition, we figured out how long investors should hold their investment position regard the effect of financial bubble. Using this W-index, investors could measure financial bubble in the market and invest with low risk.
Abstract: This study provides insight into the effects of investor sentiment, excess optimism, overconfidence, the disposition effect, and herding formation on momentum profits. This study contributes to the field by providing a further examination of the relationship between psychological factors and momentum profits. The empirical results show that there is no evidence of significant momentum profits in Taiwan’s stock market. Additionally, investor sentiment in Taiwan’s stock market significantly influences its momentum profits.
Abstract: Modern Portfolio Theory (MPT) according to
Markowitz states that investors form mean-variance efficient
portfolios which maximizes their utility. Markowitz proposed the
standard deviation as a simple measure for portfolio risk and the
lower semi-variance as the only risk measure of interest to rational
investors. This paper uses a third volatility estimator based on
intraday data and compares three efficient frontiers on the Croatian
Stock Market. The results show that range-based volatility estimator
outperforms both mean-variance and lower semi-variance model.
Abstract: This study examines the credibility of the signaling as
explanation for IPO initial underpricing. Findings reveal the initial
underpricing and the long-term underperformance of IPOs in Taiwan.
However, we only find weak support for signaling as explanation of
IPO underpricing.
Abstract: In this paper the core objective is to apply discrete wavelet transform and maximal overlap discrete wavelet transform functions namely Haar, Daubechies2, Symmlet4, Coiflet2 and discrete approximation of the Meyer wavelets in non stationary financial time series data from Dow Jones index (DJIA30) of US stock market. The data consists of 2048 daily data of closing index from December 17, 2004 to October 23, 2012. Unit root test affirms that the data is non stationary in the level. A comparison between the results to transform non stationary data to stationary data using aforesaid transforms is given which clearly shows that the decomposition stock market index by discrete wavelet transform is better than maximal overlap discrete wavelet transform for original data.
Abstract: This study explored the relationship between
psychological traits, demographics and financial behavioral biases for
individual investors in Taiwan stock market. By using questionnaire
survey method conducted in 2010, there are 554 valid convenient
samples collected to examine the determinants of three types of
behavioral biases. Based on literature review, two hypothesized
models are constructed and further used to evaluate the effects of big
five personality traits and demographic variables on investment biases
through Structural Equation Model (SEM) analysis. The results
showed that investment biases of individual investors are significantly
related to four personality traits as well as some demographics.
Abstract: This study1 holds for the formation of international financial crisis and political factors for economic crisis in Turkey, are evaluated in chronological order. The international arena and relevant studies conducted in Turkey work in the literature are assessed. The main purpose of the study is to hold the linkage between the crises and political stability in Turkey in details, and to examine the position of Turkey in this regard. The introduction part follows the literature survey on the models explaining causes and results of the crises, the second part of the study. In the third part, the formations of the world financial crises are studied. The fourth part, financial crisis in Turkey in 1994, 2000, 2001 and 2008 are reviewed and their political reasons are analyzed. In the last part of the study the results and recommendations are held. Political administrations have laid the grounds for an economic crisis in Turkey. In this study, the emergence of an economic crisis in Turkey and the developments after the crisis are chronologically examined and an explanation is offered as to the cause and effect relationship between the political administration and economic equilibrium in the country. Economic crises can be characterized as follows: high prices of consumables, high interest rates, current account deficits, budget deficits, structural defects in government finance, rising inflation and fixed currency applications, rising government debt, declining savings rates and increased dependency on foreign capital stock. Entering into the conditions of crisis during a time when the exchange value of the country-s national currency was rising, speculative finance movements and shrinking of foreign currency reserves happened due to expectations for devaluation and because of foreign investors- resistance to financing national debt, and a financial risk occurs. During the February 2001 crisis and immediately following, devaluation and reduction of value occurred in Turkey-s stock market. While changing over to the system of floating exchange rates in the midst of this crisis, the effects of the crisis on the real economy are discussed in this study. Administered politics include financial reforms, such as the rearrangement of banking systems. These reforms followed with the provision of foreign financial support. There have been winners and losers in the imbalance of income distribution, which has recently become more evident in Turkey-s fragile economy.
Abstract: This paper deals with heterogeneous autoregressive
models of realized volatility (HAR-RV models) on high-frequency
data of stock indices in the USA. Its aim is to capture the behavior of
three groups of market participants trading on a daily, weekly and
monthly basis and assess their role in predicting the daily realized
volatility. The benefits of this work lies mainly in the application of
heterogeneous autoregressive models of realized volatility on stock
indices in the USA with a special aim to analyze an impact of the
global financial crisis on applied models forecasting performance.
We use three data sets, the first one from the period before the global
financial crisis occurred in the years 2006-2007, the second one from
the period when the global financial crisis fully hit the U.S. financial
market in 2008-2009 years, and the last period was defined over
2010-2011 years. The model output indicates that estimated realized
volatility in the market is very much determined by daily traders and
in some cases excludes the impact of those market participants who
trade on monthly basis.
Abstract: This paper applies fuzzy clustering algorithm in classifying real estate companies in China according to some general financial indexes, such as income per share, share accumulation fund, net profit margins, weighted net assets yield and shareholders' equity. By constructing and normalizing initial partition matrix, getting fuzzy similar matrix with Minkowski metric and gaining the transitive closure, the dynamic fuzzy clustering analysis for real estate companies is shown clearly that different clustered result change gradually with the threshold reducing, and then, it-s shown there is the similar relationship with the prices of those companies in stock market. In this way, it-s great valuable in contrasting the real estate companies- financial condition in order to grasp some good chances of investment, and so on.
Abstract: Little attention has been paid to information
transmission between the portfolios of large stocks and small stocks in the Korean stock market. This study investigates the return and volatility transmission mechanisms between large and small stocks in
the Korea Exchange (KRX). This study also explores whether bad news in the large stock market leads to a volatility of the small stock
market that is larger than the good news volatility of the large stock market. By employing the Granger causality test, we found
unidirectional return transmissions from the large stocks to medium
and small stocks. This evidence indicates that pat information about
the large stocks has a better ability to predict the returns of the medium and small stocks in the Korean stock market. Moreover, by using the
asymmetric GARCH-BEKK model, we observed the unidirectional relationship of asymmetric volatility transmission from large stocks to
the medium and small stocks. This finding suggests that volatility in
the medium and small stocks following a negative shock in the large
stocks is larger than that following a positive shock in the large stocks.
Abstract: This paper is to explore the relationship and the level
of stock market integration of the Asian countries, primarily
concentrating on Malaysia, Thailand, Indonesia, and South Korea,
with the world from January 1997 to December 2009. The degree of
short-run and long-run stock market integration of those Asian
countries are analyzed in order to determine the significance of series
of regional and world financial crises, liberalization policies and
other financial reforms in influencing the level of stock market
integration. To test for cointegration, this paper applies coefficient
correlation, univariate regression analyses, cointegration tests, and
vector autoregressive models (VAR) by using the four Asian stock
markets main indices and the MSCI World index. The empirical
findings from this work reveal that there is no long-run stock market
integration for the four countries and the world market. However,
there is short run integration.
Abstract: The term private equity usually refers to any type of
equity investment in an asset in which the equity is not freely
tradable on a public stock market. Some researchers believe that
private equity contributed to the extent of the crisis and increased
the pace of its spread over the world. We do not agree with this.
On the other hand, we argue that during the economic recession
private equity might become an important source of funds for firms
with special needs (e.g. for firms seeking buyout financing, venture
capital, expansion capital or distress debt financing). However,
over-regulation of private equity in both the European Union and
the US can slow down this specific funding channel to the
economy and deepen credit crunch during global crises.