Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market

Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.

Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market

This study examines herding behavior in the Pakistani Stock Market under different market conditions,focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014.Two regression models of Christie and Huang(Financ Analysts J 51:31-37,1995)and Chang et al.,(J Bank Finance 24:1651-1679,2000)are used for herding estimations.Results based on daily stock data reveal that there is an absence of herding behavior during rising(up)and falling(down)market as well as during high and low volatility in market.While herding behavior is detected during low trading volume days.Yearly analysis shows that herding existed during 2005,2006 and 2007,while it is not evident during rest of the period.However,herding behavior is not detected during Ramadan.Furthermore,during financial crisis of 2007-08,Pakistani Stock Market exhibits herding behavior due to higher uncertainty and information asymmetry.

Stock Market Reaction to Yearly Earnings Announcements for Firms in Financial Distress:Evidences from Italy

The purpose of this paper is to feed the debate regarding investor’s reaction to relevant financial information releases as yearly earnings announcements(EAs)with a specific focus on financial distressed firms.Using the event study methodology and adopting two well-known tests in the literature,we analyzed Italian listed companies in the period of 2008-2016,to detect whether there is a market reaction to EAs releases for firms in financial distress,adopting as a measure of financial distress the presence in the audit report of a going concern opinion(GCO).In the Italian legislation,the GCO must be communicated immediately to the market and this can be done before,simultaneously or after EAs.The achieved results shed light on the negative impact of EAs of distressed firms receiving a GCO.On the other hand,the possibility that negative abnormal returns are mainly due to the GCO release cannot be neglected.Hence,through additional tests,we found that effects of EAs are more persistent and significant than GCOs,in accordance with the prevailing literature,which sees,on average,EAs predominant information for investors.Our study is pioneering in disentangling possible effects of confounding events for the Italian stock market.The EAs superior effect confirms the dynamics characterizing weak and small equity markets as Italy where,before GCOs releases,some relevant and more precise information(such as earnings magnitude)is often held by shareholders because of the high percentage of family firms and/or concentrated ownership,demonstrating also the weakness of auditor profession if compared with other developed countries.

Stock Markets,Financial Depth,and Economic Growth in China:Evidence from ARDLModel

The relationship between financial development and economic growth in China is controversial.From this perspective,this article aims to identify this relationship using both capital market and banking intermediation indicators,which were rarely considered in the previous literature.An autoregressive model with staggered lags(ARDL)examines the long-run cointegration relationship between 1980 and 2020.The results suggest that the contribution of different subsectors of the Chinese financial system to economic growth differs.The development of the money market has a negative impact,whereas market capitalization has a positive impact on economic growth in China.Regarding the contribution of the banking system to China's economic growth,the two variables measuring the depth of financial institutions showed opposite impacts in both the short and long term.Regarding important policy implications,regulators need to ensure a pro-growth environment,effectively regulate the informal banking system,and prevent potential financial risks by revising policies.

Evaluation of forecasting methods from selected stock market returns

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

Analysis of Financial Derivatives by Mechanical Method (Ⅰ)——Basic Equation of Price of Index Futures

Similar to the method of continuum mechanics, the variation of the price of index futures is viewed to be continuous and regular. According to the characteristic of index futures, a basic equation of price of index futures was established. It is a differential equation, its solution shows that the relation between time and price forms a logarithmic circle. If the time is thought of as the probability of its corresponding price, then such a relation is perfectly coincided with the main assumption of the famous formula of option pricing, based on statistical theory, established by Black and Scholes, winner of 1997 Nobel’ prize on economy. In that formula, the probability of price of basic assets (they stand for index futures here) is assummed to be a logarithmic normal distribution. This agreement shows that the same result may be obtained by two analytic methods with different bases. However, the result, given by assumption by Black_Scholes, is derived from the solution of the differential equation.

Baidu index and predictability of Chinese stock returns

A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Accurate prediction of stock market behavior is a challenging issue for financial forecasting.Artificial neural networks,such as multilayer perceptron have been established as better approximation and classification models for this domain.This study proposes a chemical reaction optimization(CRO)based neuro-fuzzy network model for prediction of stock indices.The input vectors to the model are fuzzified by applying a Gaussian membership function,and each input is associated with a degree of membership to different classes.A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model.CRO was chosen because it requires fewer control parameters and has a faster convergence rate.Five statistical parameters are used to evaluate the performance of the model,and the model is validated by forecasting the daily closing indices for five major stock markets.The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior.We conducted the Deibold-Mariano test to check the statistical significance of the proposed model,and it was found to be significant.This model can be used as a promising tool for financial forecasting.

ARIMA and Facebook Prophet Model in Google Stock Price Prediction

We use the Autoregressive Integrated Moving Average(ARIMA)model and Facebook Prophet model to predict the closing stock price of Google during the COVID-19 pandemic as well as compare the accuracy of these two models’predictions.We first examine the stationary of the dataset and use ARIMA(0,1,1)to make predictions about the stock price during the pandemic,then we train the Prophet model using the stock price before January 1,2021,and predict the stock price after January 1,2021,to present.We also make a comparison of the prediction graphs of the two models.The empirical results show that the ARIMA model has a better performance in predicting Google’s stock price during the pandemic.

SIMPLEST DIFFERENTIAL EQUATION OF STOCK PRICE,ITS SOLUTION AND RELATION TO ASSUMPTION OF BLACK-SCHOLES MODEL

Two kinds of mathematical expressions of stock price, one of which based on certain description is the solution of the simplest differential equation (S.D.E.) obtained by method similar to that used in solid mechanics,the other based on uncertain description (i.e., the statistic theory)is the assumption of Black_Scholes's model (A.B_S.M.) in which the density function of stock price obeys logarithmic normal distribution, can be shown to be completely the same under certain equivalence relation of coefficients. The range of the solution of S.D.E. has been shown to be suited only for normal cases (no profit, or lost profit news, etc.) of stock market, so the same range is suited for A.B_ S.M. as well.

ANALYSIS OF FINANCIAL DERIVATIVES BY MECHANICAL METHOD (Ⅰ)-BASIC EQUATION OF PRICE OF INDEX FUTURES

Similar to the method of continuum mechanics, the variation of the price of index futures is viewed to be continuous and regular. According to the characteristic of index futures, a basic equation of price of index futures was established. It is a differential equation, its solution shows that the relation between time and price forms a logarithmic circle. If the time is thought of as the probability of its corresponding price, then such a relation is perfectly coincided with the main assumption of the famous formula of option pricing, based on statistical theory, established by Black and Scholes winner of 1997 Nobel' prize on economy. In that formula, the probability of price of basic assets (they stand for index futures here) is assummed to be a logarithmic normal distribution. This agreement shows that the same result may be obtained by two analytic methods with different bases. However, the result, given by assumption by Black-Scholes, is derived from the solution of the differential equation.

Exploring Apple’s Stock Price Volatility Using Five GARCH Models

The financial market is the core of national economic development,and stocks play an important role in the financial market.Analyzing stock prices has become the focus of investors,analysts,and people in related fields.This paper evaluates the volatility of Apple Inc.(AAPL)returns using five generalized autoregressive conditional heteroskedasticity(GARCH)models:sGARCH with constant mean,GARCH with sstd,GJR-GARCH,AR(1)GJR-GARCH,and GJR-GARCH in mean.The distribution of AAPL’s closing price and earnings data was analyzed,and skewed student t-distribution(sstd)and normal distribution(norm)were used to further compare the data distribution of the five models and capture the shape,skewness,and loglikelihood in Model 4-AR(1)GJR-GARCH.Through further analysis,the results showed that Model 4,AR(1)GJR-GARCH,is the optimal model to describe the volatility of the return series of AAPL.The analysis of the research process is both,a process of exploration and reflection.By analyzing the stock price of AAPL,we reflect on the shortcomings of previous analysis methods,clarify the purpose of the experiment,and identify the optimal analysis model.

Stock market liberalization and financial reporting quality

This study employs the Mainland-Hong Kong Stock Connect pilot program in China to investigate the influence of stock market liberalization on firm-level financial reporting quality(FRQ).First,through a staggered difference-indifference specification strategy,we find that eligible firms experience a significant improvement in FRQ,as measured by a composite proxy of accrual earnings management,real activities manipulation,and financial report restatement.Second,cross-sectional analyses suggest that the effect is stronger when firms are headquartered in regions with weaker institutional environments,characterized by lower judicial efficiency and less developed financial markets.We also show that the impact is more pronounced when firms face less external pressure and possess more effective corporate governance before stock market liberalization.Third,further evidence highlights that augmented FRQ is associated with a reduction in regulatory compliance costs,an improvement in stock price efficiency,and a mitigation of financing constraints.Collectively,we shed new light on the role of stock market liberalization in shaping firms’financial reporting behavior.