Stock Market Price Volatility Research Articles

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

Predicting Stock Market Volatility from Candlestick Charts: A Multiple Attention Mechanism Graph Neural Network Approach

As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments.

The Effect of Dividend Policy on Stock Market Price Volatility of Banks in the Gulf Cooperation Council (GCC) Markets:

This study examined the effect of dividend policy on the stock market price volatility of banks in the GCC markets. The study data were obtained from the annual financial reports and accounts of the selected sampled banks. The study used the panel data analysis fixed effect regression model. The findings revealed that dividend yield and dividends payout does not influence the market share price in the GCC market. Therefore, they do not affect the GCC banks’ market share price, thus the findings of the study support the dividend policy irrelevance theory. The study recommends that managers in the GCC banks should focus on and emphasize allocating more funds in investment rather than paying dividends. The findings have important practical implications for the policymakers on dividends in the GCC banks due to unstable and inconsistent trends of dividend payments and making it difficult for shareholders on dividend policy decisions predictions

An empirical study on effect of financial accounting indicators towards stock market price volatility

An empirical study on effect of financial accounting indicators towards stock market price volatility

An empirical study on effect of financial accounting indicators towards stock market price volatility

In the Indian stock market, the stock market price volatility is an unavoidable part of an investment decision of individual investors. It is essential to identify that share price of listed compan...

Factors affecting stock market index volatility: Empirical study

How is stock market price volatility affected, and what is the nature of the impact that macroeconomic variables do on the stock market price direction? The main objective of this study is to investigate the impact of some selected macroeconomic variables (inflation rate (INR), interest rate (IR), economic growth rate (EGR), and foreign investment (FI)) on Amman Stock Exchange (ASE) fluctuation for the period 1999–2018. The information is based on the annual data published by industrial companies listed at ASE. The study adopted a descriptive-analytical approach, also simple and multiple linear regression analysis was employed for the mentioned purpose (Nurfadilah & Samidi, 2017). The results revealed that there is no statistically significant impact of INR, IR, EGR, and FI collectively on ASE performance (Niewińska, 2020). Individually, the results indicated that there is a statistically significant impact of all variables (INR, IR, EGR, and FI) on ASE performance. Additionally, the results concluded that foreign investment, portrayed the highest impact factor on ASE performance, followed by a change in average interest rate, then inflation rate, and the least impact attributes to the economic growth rate. Finally, the research recommends that Jordanian banks should reduce the lending interest rate to enhance investment in securities and improve economic growth rate, also Jordanian authorities should encourage foreign direct and indirect investment and make more efforts to attract more foreign investment, either in the form of tax incentives or by extending finance at low-interest rates.

3% rules the market: herding behavior of a group of investors, asset market volatility, and return to the group in an agent-based model

This study presents an agent-based model of capital markets by adopting simple trading rules for bounded rational agents who maintain different expectations regarding a tipping point at which price starts to change its direction from rising (falling) to falling (rising). The effect of herding behavior on the volatility of stock market prices and the rate of return to the herding group are investigated by dividing agents into one or more groups. Herding behavior by a group of agents leads to high market volatility and high return for the agents in the group. Maximum rate of return is reached when the group size is approximately 3% of the total number of agents. This finding is consistent with the actual degree of herding behavior in markets found by empirical studies. However, the rates of return decrease when the group size exceeds 3%, and the premium of the herding group tends to disappear when the group size reaches a certain level (20%) compared with that of non-herding groups. Reducing the number of groups (or increasing the average size of the herding groups) leads to high price volatility.

Bank Risk Profiles and Business Model Characteristics

Abstract Current research, especially after the financial crisis, highlights different key determinants of high risk bank profiles. The main aim of this paper is to test, through an empirical model, the impact of various determinants of bank business models on the bank risk with the purpose of enabling early identification of signals of risk and timely application of prudential measures. There are two basic business models for banks: market-oriented wholesale bank business model and client-oriented bank business model. In the wholesale model, a significant share of the assets is comprised of securities in the trade portfolio, the bank is strongly involved in the international financial markets, while on the income side of the bank profile, a large part is related to non-interest income. In the client related business model, classical banking is dominant, which is visible in the high share of loan-related assets, a larger share of self-financing and a larger share of income from interest-operational income in the total income structure of the bank. In the panel analysis of the empirical data, as an indicator of the bank risk profile, the stock market price to stock market price volatility ratio was used with the presumption that the market price and its volatility, with sufficiently liquid shares listed on public stock exchanges, is representative of bank risk. The analysis is conducted on a homogenous example of 20 European banks in the period 2002-2017. Following the econometric analysis, the conclusion is that banks in which business model wholesale characteristics are dominant are more exposed to business risk in periods of market shocks and, as such, represent a danger for the long-term stability of the financial sector.

Oil Price Volatility, Exchange Rate Movements and Stock Market Reaction: The Nigerian Experience (1985-2017)

Given the observed volatility in crude oil prices in the international oil market and the role which oil and gas play in the Nigerian economy, this paper is an attempt to investigate the impact of crude oil prices and foreign exchange rate movements on stock market prices in Nigeria. In addition, the paper examined whether there is any volatility pass-through between the dollar price of Nigerian crude oil, foreign exchange rate of the Naira and stock market prices respectively. Data employed for the study are monthly values of the Nigerian Stock Exchange (NSE) All-Share Index (ASI), Dollar price of Nigerian Crude Oil (DPO) and the Official Exchange Rate of the Naira to the US Dollar (FXR) from January, 1985 to August, 2017. The methodology adopted for the study include the ADF unit root tests, Johansen co-integration tests, the ECM technique, Granger causality tests, variance decomposition as well as the GARCH(1,1) to model the volatility relationships among the variables. Findings reveal that there is one long-run dynamic co-integrating relationship among the variables ASI, DPO and FXR while the ECM results indicate that Crude oil price (DPO) significantly impact on Stock market prices. The Granger causality test reports a bi-directional causality relationship between ASI and DPO and a unidirectional causality running from FXR to ASI. The ARCH-GARCH volatility analysis demonstrates vividly that stock market prices in the NSE exhibit ARCH effect with a significant and positive first order ARCH term. The GARCH term is also positive and significant indicating that previous month’s stock market price volatility significantly influences current stock market volatility in the NSE. In addition, findings show that the volatility of dollar price of Nigerian oil (DPO) in the world oil market is significantly transmitted to the volatility of stock market prices in Nigeria. The pass-through effect of the volatility of exchange rate (FXR) to the volatility of stock market prices is also positive and significant. These findings offer significant informational signal to policy makers, portfolio managers/advisors and the investing public in achieving optimal asset and portfolio profile.

Beyond CAPM: estimating the cost of equity considering idiosyncratic risks

The present study proposes a new evaluation approach aimed at estimating the cost of equity through standardized models which consider an innovative set of firm-specific information on the main unsystematic risks which are typical of any business. Our objective is extending the Capital Asset Pricing Model (CAPM) by defining a standard formula for quantifying the premium for certain idiosyncratic risks as a function of a new set of firm-specific quantitative information. We define two econometric models, for listed and non-listed firms respectively, which consider five idiosyncratic risk factors: firm size, value factor, operating risks, financial structure and stock market price volatility. The models were tested on a sample of European non-financial companies. The empirical results show that while the CAPM systematically underestimates the cost of equity, the proposed models correctly estimate its expected value; furthermore, they show a slight improvement also in terms of estimates’ volatility. Due to their efficacy and ease of use, the proposed models represent a valid practical tool for investors, analysts and professional evaluators. This work contributes to the existing literature by proposing a typologically innovative extension of the CAPM set of explanatory variables, defining and testing new models for the estimation of the unsystematic risks’ spread of the cost of equity based on an original set of firm-specific accounting and market information.

Retraction: The Potential Impact of More Frequent Financial Reporting and Assurance: User, Preparer, and Auditor Assessments

The purpose of this study is to examine some of the potential impacts of more frequent financial reporting and concurrent assurance, as assessed by members of the assurer, preparer, and investor communities. Two hundred and fifteen participants (84 auditors, 30 controllers, 80 investors, as surrogated by MBA students, and 21 sell‐side analysts) took part in an experiment where they received a case situation involving a company that was planning to voluntarily change from quarterly to monthly (daily) external financial statement reporting, without (with) assurance. After reading the case materials, the participants assessed the likely effects of such changes on the decision usefulness of financial statements, quality of earnings, financial reporting behavior, stock market price volatility, analysts' consensus forecasts, and cost of capital. The results indicate that monthly reporting without assurance would significantly enhance the decision usefulness of financial statements, improve the quality of earnin...

DETERMINANTS OF COMMON FACTORS IN KOREAN BANKS’ CREDIT DEFAULT SWAP PREMIUMS

Using the panel analysis of non-stationarity in idiosycratic and common component method, we decompose Credit Default Swap (CDS) premium data of 11Korean banks into common factors and idiosyncratic shocks. We find that the CDS premium of all 11 banks is mostly explained by one common factor. We also find that the common factor of the banks’ CDS premium is mainly affected by the level and the volatility of stock market prices in developed markets and oil prices. It suggests that the Korean banking industry is susceptible to foreign shocks due to the heavy dependency of the Korean economy on export. We also find that a structural break in the common part of CDS premium occurred in mid-2007, implying that the exposure of credit risk in Korean banks jumped up after the 2007 financial crisis.

The Effect of Automation on Stock Market Price Volatility: A Case of Nairobi Securities Exchange

This study investigated the effect of the automation on stock market price volatility of the Nairobi Security Exchange (NSE). Two study periods were considered, pre-automation (January 2002 to June 2006) and post-automation periods (July 2008 to December 2012). This study provides empirical analysisof price volatility before and after automation at NSE. The study adopted a longitudinal research design and considered data on monthly NSE 20 Share Index and the average share closing prices on 37 NSE listed firms from January 2002 to December 2012. Secondary data was used in this analysis. Descriptive statistics were used for analysis together with a chi-square test and t-test was used to test the significance. The results indicate that the introduction of the ATS had no statistically significant effect on price volatility at the NSE.

Credit Crunch, Creditor Protection, and Asset Prices

In a Tobin's model with productivity and liquidity shocks, we study the mechanism through which strong creditor protection increases the level and lowers the volatility of stock market prices. There are two channels at work: (1) the Tobin's value under a credit crunch regime increases with creditor protection; and, (2) the probability of a credit crunch falls for given stochastic processes of underlying shocks when creditor protection improves. We test these predictions by using cross-country panel regressions of the stock market price level and volatility, in 40 countries, over the period from 1984 to 2004, at annual frequency. We create indicators for liquidity shocks based on quantity and price measures. Estimated probabilities of big shocks to liquidity are used as forecasts of credit crunch. We find broad empirical support for the hypothesis that creditor protection increases the stock market price level and reduces its volatility directly and via its negative effect on the probability of credit crunch. Our empirical findings are robust to multiple specifications.

The Potential Impact of More Frequent Financial Reporting and Assurance: User, Preparer, and Auditor Assessments (Retracted)

The purpose of this study is to examine some of the potential impacts of more frequent financial reporting and concurrent assurance, as assessed by members of the assurer, preparer, and investor communities. Two hundred and fifteen participants (84 auditors, 30 controllers, 80 investors, as surrogated by MBA students, and 21 sell-side analysts) took part in an experiment where they received a case situation involving a company that was planning to voluntarily change from quarterly to monthly (daily) external financial statement reporting, without (with) assurance. After reading the case materials, the participants assessed the likely effects of such changes on the decision usefulness of financial statements, quality of earnings, financial reporting behavior, stock market price volatility, analysts' consensus forecasts, and cost of capital. The results indicate that monthly reporting without assurance would significantly enhance the decision usefulness of financial statements, improve the quality of earnings, and reduce managements' aggressiveness with respect to discretionary accounting accruals, estimates, and principles; further, the findings suggest stock price volatility would be lower, analyst consensus of future earnings estimates would increase, and cost of capital would decrease. Assessments of daily reporting were consistent with monthly reporting, yet significantly stronger. The inclusion of concurrent auditor assurance resulted in directionally consistent yet significantly pronounced results in both monthly and daily reporting conditions on all measures. Additionally, participants agreed that providing monthly reports would be technically and economically feasible at this time, while daily reporting would not be feasible.

The Volatility of Stock Market Prices

If the volatility of stock market prices is to be understood in terms of the efficient markets hypothesis, then there should be evidence that true investment value changes through time sufficiently to justify the price changes. Three indicators of change in true investment value of the aggregate stock market in the United States from 1871 to 1986 are considered: changes in dividends, in real interest rates, and in a direct measure of intertemporal marginal rates of substitution. Although there are some ambiguities in interpreting the evidence, dividend changes appear to contribute very little toward justifying the observed historical volatility of stock prices. The other indicators contribute some, but still most of the volatility of stock market prices appears unexplained.