Does investor sentiment create value for asset pricing? An empirical investigation of the <scp>KOSPI</scp>‐listed firms

This paper proposes the development of an improved investor sentiment index (ISI) to apply on the Korea Composite Stock Price Index (KOSPI) and assess the vitality of sentiment-based factor for explaining critical equity market anomalies in asset pricing in Korea. We follow the methodology of Huang et al. (2015), the align sentiment index, and employ the partial least squares method to overcome the drawbacks of the pioneering BM index of Baker and Wurgler (2006, 2007). Based on the daily trading and price data for individual companies from 2006 to 2021, we construct a novel ISI, which has robust predicting ability for the aggregate stock market return, in comparison to other popular measures of sentiment in the contemporary finance literature. Furthermore, the sentiment-based factor in this paper captures the small firm effect that the asset pricing modelling, containing the more topical Fama–French five factor modelling (5F-FF), has struggled to illuminate completely. Given that our results have shown Korean stock market as fairly well-organised in terms of the availability of the market intelligence, we speculate our results to have important managerial implications for financial regulators in Korea and countries holding similar economic features

Analysis of the Relationship Between Stock Prices and Their Volatilities

In this thesis, the main purpose is to test the relationship between the stock price index and their volatilities by using the returns from daily stock prices. Generally, the volatility of stock prices is shown by its standard deviation or variance, and the value of variance is chosen to represent volatility in this paper. Consequently, the relationship between the conditional variance, which is the volatility, and the stock price index is analyzed by applying different forecasting models to the log returns of the stock price indexes to perform correlation and regression tests and testing the conditional variance and residual terms from the models. For the chapter 2 of this paper, the portfolio of basic financial markets will be introduced. And there are the basic information about the 8 stock price indexes selected in Asia stock markets and their exchanged market. In the next section chapter 3, firstly there is the introduction the volatility of stock price index and the methodologies we used for analyzing the relationship between the indexes and volatilities. The main selected models are the time series models including the Exponentially Weighted Moving Average (EWMA), the Autoregressive conditional heteroskedasticity model (ARCH), Generalized Autoregressive ConditionalHeteroskedasticity model (GARCH), GARCH-in-mean model and the exponential general autoregressive conditional heteroskedastic model (EGARCH). Furthermore, by using an econometrics Software which is Econometrics Views (EViews) to calculate the selected models, it is observed that the relationships mentioned in chapter 3 have asymmetry and leverage effectIn this thesis, the main purpose is to test the relationship between the stock price index and their volatilities by using the returns from daily stock prices. Generally, the volatility of stock prices is shown by its standard deviation or variance, and the value of variance is chosen to represent volatility in this paper. Consequently, the relationship between the conditional variance, which is the volatility, and the stock price index is analyzed by applying different forecasting models to the log returns of the stock price indexes to perform correlation and regression tests and testing the conditional variance and residual terms from the models. For the chapter 2 of this paper, the portfolio of basic financial markets will be introduced. And there are the basic information about the 8 stock price indexes selected in Asia stock markets and their exchanged market. In the next section chapter 3, firstly there is the introduction the volatility of stock price index and the methodologies we used for analyzing the relationship between the indexes and volatilities. The main selected models are the time series models including the Exponentially Weighted Moving Average (EWMA), the Autoregressive conditional heteroskedasticity model (ARCH), Generalized Autoregressive ConditionalHeteroskedasticity model (GARCH), GARCH-in-mean model and the exponential general autoregressive conditional heteroskedastic model (EGARCH). Furthermore, by using an econometrics Software which is Econometrics Views (EViews) to calculate the selected models, it is observed that the relationships mentioned in chapter 3 have asymmetry and leverage effect154 - Katedra financídobř

A Network Analysis of the Asia-Pacific and Other Developed Stock Markets: Pre and Post Global Financial Crisis

This paper examines the volatility spillover and connectedness between Asia-Pacific, US, UK, and eurozone stock markets. A spillover index is built using forecast error variance decomposition in a vector autoregression framework and the spillover index is used to build network diagrams. It shows evidence of how the increase in risk transfer (volatility spillover) between the markets led to the global financial crisis and of the higher level of connectedness since. Network diagrams show the direction and strength of the connectedness. The network strength estimation enables us to understand the risk associated with connectedness across the markets in the event of a trigger and its influence in portfolio management decisions of international funds. The Chinese market appears to be the most insulated, while the South Korean, Hong Kong, and Singapore stock markets dominate in terms of risk transfer. The US, UK, EU, Singapore and Hong Kong are the top five volatility spillover recipient markets, both during pre and post global financial crisis periods. We find the market size to be irrelevant in the determination of the level of connectedness, whereas the role of geographical proximity cannot be ruled out. The findings are relevant to multinational investment strategies and in understanding the relative risk of investment in the Asia-Pacific region

Emerging stock markets in the Pacific Basin : An empirical analysis with particular reference to the Korean stock market.

This thesis investigates emerging stock markets in the Pacific Basin with particular reference to the Korean stock market, which is representative of typical, fast-growing emerging markets. Using a broader range of econometric models, the short-run and long-run behaviour of stock prices, the impact of changes of a price limit system, and derivatives trading on the stock market are investigated. In the first two chapters, recent performance of emerging stock markets in the Pacific Basin and the development of the Korean stock market are examined. Chapter 3 investigates the behaviour of Korean stock market volatility is investigated. The results show that the GARCH(1,1)-AR(1) and the GARCH(1,1)-MA(1) seemed to be the best fit models among the Autoregressive Conditional Heteroscedasticity (ARCH) class models. The nexus between Korean stock market returns and macroeconomic variables is investigated in Chapter 5. The evidence suggests that changes in the exports/imports ratio is the most important determinant in forecasting the variance of stock returns in the Korean export-oriented economy. Chapter 6 provides tests of long-run equilibrium among Pacific-Basin stock markets for a period spanning the Asian financial market crises. Using unit root tests, which allow for a possible crash, the results find that four of the series are trend stationary. Among the remaining I(1) series, little evidence of cointegration is found. In Chapter 7, the consequences of price limits for weak-form efficiency is investigated for the first time. The evidence suggests that the stock market as a whole approaches a random walk as price limits are relaxed. Chapter 8 investigates the impact on the spot market of trading in KOSPI 200 futures. Empirical results show that futures trading increases the speed at which information is impounded into spot market prices. The lead-lag relation is asymmetric with stronger evidence that the stock index futures market leads the spot market

Effects Of Voluntary Disclosure Of The Schedule Of Manufacturing Cost On Analysts’ Earnings Forecasts: Evidence From Korea

We provide the effects of voluntary disclosure of the schedule of manufacturing cost on analysts’ earnings forecasts. We set up and analyze the disclosure of the schedule of manufacturing cost as a proxy for voluntary disclosure. Specifically, we examine the associations between voluntary disclosure of it and the accuracy of analysts’ earnings forecasts and bias in earnings forecasts. The results of our study are as follows. First, the relationship between voluntary disclosure of the schedule of manufacturing cost and the accuracy of analysts’ earnings forecasts is significant in the positive (+) direction. This means that the accuracy of analysts’ earnings forecasts is higher in the case of the firms that voluntarily disclosed the schedule of manufacturing cost, as compared to other firms. Second, the relationship between voluntary disclosure of the schedule of manufacturing cost and analysts’ bias in earnings forecasts is significant in the negative (-) direction. This means that analysts underestimate earnings in the case of the firms that voluntarily disclose the schedule of manufacturing cost, as compared to other firms. Since the schedule of manufacturing cost is still an interesting item and useful information in the capital market, the results of our study provide important implications not only to managers, but also to investors and supervisory authority. Limitations of our study include the fact that not all diverse variables that affect voluntary disclosure and analysts’ forecasts are considered.

Evolving integrated multi-model framework for on line multiple time series prediction

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel Integrated Multi-Model Framework (IMMF) that combined models developed at three di erent levels of data granularity, namely the Global, Local and Transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three di erent levels of data granularity. Our experimental results indicate that IMMF signi cantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem

Market efficiency, nonlinearity and technical analysis in the global market

In this thesis we investigate market efficiency from a different perspective. Instead of traditional approach to one market in specific, this time around we study market efficiency from a global perspective. See the global market indices as one single market. We used both nonlinear methods and technical analysis in order to accomplish our purpose. We used BDS to test for nonlinearity in the return series, as expected the results conformed with the general view, which is market returns exhibit nonlinear dependence. We trace the cause of the dependence as a result of the ARCH type process. We also used technical trading strategy to test whether profit can be made through trading in stock indices around the world. We investigate the simple moving averages, weighted moving averages and exponential moving averages with different allocation of resources, we found all techniques to be profitable when 1% and 2% commission are considered. For the 50 day simple moving average, the average daily return is 0,0009%, compared with the - 0,669% of the buy and hold strategy. These results were also confirmed using bootstrap methodology in which we considered the random walk model as return generating process. These rules are profitable after accounting for commission fees.Nesta dissertação analisou-se a eficiência dos mercados numa perspectiva diferente. Em vez da abordagem tradicional a um mercado específico, estudou-se a eficiência de uma forma global. Considerou-se que os índices globais dos mercados formavam um mercado único integrado. Utilizaram-se simultaneamente métodos não lineares e a análise técnica para testar a eficiência do mercado global. Utilizou-se a BDS para testar a não linearidade na série de rendibilidades dos índices, tal como esperado, os resultados confirmaram os estudos anteriores, ou seja, os mercados têm uma dependência não linear. Esta dependência resultará de um processo de tipo ARCH. Utilizaram-se regras de “trading” baseadas na análise técnica para testar se é possível obter uma rendibilidade anómala com os referidos índices de acções. Consideraram-se médias móveis simples, ponderadas e exponenciais, ensaiando várias afectações diferentes de recursos (ponderação igual e proporcional), detectou-se que todas as estratégias eram rentáveis mesmo depois de considerar comissões de 1% e até de 2%. Para a média móvel simples de 50 dias, a rendibilidade media diária é de 0,0009%, comparável com -0,669% para a estratégia “buy and hold”. Estes resultados também foram confirmados através da metodologia de “bootstrap”, em que se considerou o modelo “random walk” como um processo gerador das rendibilidades. Estas estratégias são rentáveis mesmo depois de consideradas as comissões