• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.741-749
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• 2021

The research paper endeavors to investigate the presence of seasonal anomalies in the Indian equity market. It also aims to verify the notion that equity markets are for long-term investors. The study employs daily index data of Sensex, Bombay Stock Exchange, to understand its volatility for the period ranging from January 2001 to August 2020. To analyze the seasonal effects in the stock market of India, multiple regression techniques along with descriptive analysis, graphical analysis and various statistical tests are used. The study also employs the rolling returns at different time intervals in order to understand the underlying risks and volatility involved in equity returns. The results from the analysis reveal that daily and monthly seasonality is not present in Sensex returns i.e., investors cannot earn abnormal returns by timing their investment decisions. Hence, the major finding of this study is that the Indian stock market performance is random, and the returns are efficient. The other major conclusion of the research is that the equity returns are profitable in the long run providing investors a hope that they can make gains and compensate for the loss in one period by a superior performance in some other periods.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.167-183
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• 2022

Purpose - The purpose of this study is to show the need to use the past long-term returns for investment decisions in U.S. equity funds and to suggest an investment strategy using long-term returns. Design/methodology/approach - This study solves the problem of high return volatility in long-term returns and proposes new investment portfolios based on the behavior of fund investors according to past returns. For the investment portfolio of this study, 60 months are divided into several periods and the average of the performance ranks for each period is used. Findings - First, funds with high average returns over multiple periods have lower future outflows and higher future returns than funds with high 60-month cumulative returns. Second, funds with low average returns over multiple periods have lower future inflows and lower future returns than funds with low 60-month cumulative returns. The findings mean that when making decisions based on past long-term returns, it is a smarter investment choice to buy funds with high average returns over multiple periods and sell funds with low average returns over multiple periods. Research implications or Originality - This study shows that it is necessary to use long-term returns in fund investment by analyzing the characteristics of the portfolio based on past returns. In addition, the study is meaningful in that it suggests a way to use long-term returns more efficiently based on the behavior of fund investors and shows that such investments lead to higher returns in the future.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.127-135
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• 2021

We analyze the information asymmetry in the capital market by examining the long-term performance by the insider's trading behavior in the companies that made investment announcements for the construction of the new office building. The results are summarized as follows. On average, the long-term abnormal returns on share prices of sample firms represent a significant positive value. The regression analysis confirmed that there is a statistically significant positive correlation between the factor of the change in equity of large shareholders and the long-term performance. On the other hand, negative correlation was observed between change in equity of small individual investors and long-term performance. These results mean that an insider can determine the authenticity of a manager's private intention. In other words, it supports that the insider is in a position of information superiority. In addition, it is expected to provide practical usefulness to investors in that the change in equity can be used as a predictor of long-term performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.311-324
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• 2021

Investors have increasingly become interested in macroeconomic antecedents in order to better understand the investment environment and estimate the scope of profitable investment in equity markets. This study endeavors to examine the interdependency between the macroeconomic antecedents (international oil price (COP), Domestic gold price (GP), Rupee-dollar exchange rates (ER), Real interest rates (RIR), consumer price indices (CPI)), and the BSE Sensex and Nifty 50 index return. The data is converted into a natural logarithm for keeping it normal as well as for reducing the problem of heteroscedasticity. Monthly time series data from January 1992 to July 2019 is extracted from the Reserve Bank of India database with the application of financial Econometrics. Breusch-Godfrey serial correlation LM test for removal of autocorrelation, Breusch-Pagan-Godfrey test for removal of heteroscedasticity, Cointegration test and VECM test for testing cointegration between macroeconomic factors and market returns,] are employed to fit regression model. The Indian market returns are stable and positive but show intense volatility. When the series is stationary after the first difference, heteroskedasticity and serial correlation are not present. Different forecast accuracy measures point out macroeconomics can forecast future market returns of the Indian stock market. The step-by-step econometric tests show the long-run affiliation among macroeconomic antecedents.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.535-556
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• 2024

Measuring stock market volatility and its determinants is critical for stock market participants, as volatility spillover effects affect corporate performance. This study adopted a novel approach to analysing and implementing GARCH-MIDAS modelling methods. The classical GARCH as a benchmark and the univariate GARCH-MIDAS framework are the GARCH family models whose forecasting outcomes are examined. The outcome of GARCH-MIDAS analyses suggests that inflation, interest rate, exchange rate, and oil price are significant determinants of the volatility of the Johannesburg Stock Market All Share Index. While for Nigeria, the volatility reacts significantly to the exchange rate and oil price. Furthermore, inflation, exchange rate, interest rate, and oil price significantly influence Ghanaian equity volatility, especially for the long-term volatility component. The significant shock of the oil price and exchange rate to volatility is present in all three markets using the generalized autoregressive conditional heteroscedastic-mixed data sampling (GARCH-MIDAS) framework. The GARCH-MIDAS, with a powerful fusion of the GARCH model's volatility-capturing capabilities and the MIDAS approach's ability to handle mixed-frequency data, predicts the volatility for all variables better than the traditional GARCH framework. Incorporating these two techniques provides an innovative and comprehensive approach to modelling stock returns, making it an extremely useful tool for researchers, financial analysts, and investors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.4
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• pp.45-70
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• 2009

This article investigates which types of the strategies announced by the listed firms contribute to enhancing the long-term performance of the companies. Since 2002, Korean Exchange adopted the "faire disclosure policy" which mandates that all publicly traded companies must disclose material information to all investors at the same time. Thanks to the policy, Korean investors can, now, easily access the board's decision on management strategies on the same day the decision is made. If the companies trustfully carry out their announced strategies, we can decide which types of strategies actually enhance or deteriorate the long-term performance, simply by comparing the announced strategies and the firm's performance. The sample companies are confined to 60 firms that became listed in the KOSDAQ market through back-door listing from 2003 to 2005. Using only the newly listed companies, we can avoid the interference on the long-term performance of the strategies pursued before the event date. This often holds true, for many companies radically modify their strategies after the listing. Furthermore, the back-door listing companies serve our purpose better than IPO companies do, because the former tend to have a variety of announcement within a given period of time beginning the listing date. Using these sample companies, this article analyzes the effect on one year buy-and-hold returns and abnormal buy-and-hold returns after the listing of the various types of strategies announced during the same period of time. The results show that those evidences of restructuring such as 'reduction of capital' and 'resignation of incumbent board members', actually contribute to the increase in adjusted long-term stock returns. Those strategies which can be view as evidence of new investment such as 'increase in tangible assets', 'acquisition of other companies', do also helps the stockholders better off. On the contrary, 'increase in bank loans', 'changes of CEO' and 'merger' deteriorate the equity value. The last findings let us to presume that the back-door listing companies appear to use the bank loans for value-reducing activities; the change in CEO is not a sign of restructuring, but rather a sign of failure of the restructuring; another merger carried out after back-door listing itself is also value-reducing activity. This article's findings on reduction of capital, merger and bank loans oppose the results of the former empirical studies which analyze only the short-term effect on stock price. Therefore, more long-term performance studies on public disclosures are in order.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.81-90
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• 2016

This study aims to investigate the association between stock performance and credit ratings, and credit rating changes using a sample of 1,691 KRX firm-years that acquire equity in the form of long-term bonds from 2002 to 2013. Previous U.S. literature is mixed with regard to the relation between credit ratings and stock price. On one hand, there is evidence of a positive relation between credit ratings and stock prices, an anomaly established in U.S. studies. On the other hand, the CAPM model suggests a negative relation between stock prices and credit ratings, implying that investors expect financial rewards for bearing additional risk. To our knowledge, we are the first to examine the relationship between stock price and default risk proxied by credit ratings in period t+1. We find a negative (positive) relation between credit ratings (risk) in period t+1 and stock returns in period t, suggesting that credit rating agencies do not consider stock returns as a metric with the potential to influence default risk. Our results suggest that market participants may prefer firms with higher credit risk because of expected higher returns.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.