• Title/Summary/Keyword: multiple returns

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The Way to Use Information on Long-term Returns: Focus on U.S. Equity Funds (장기 수익률 정보의 활용 방안: 미국 주식형 펀드를 대상으로)

  • Ha, Yeon-Jeong;Oh, Hae-June
    • 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.

Seasonality and Long-Term Nature of Equity Markets: Empirical Evidence from India

  • SAHOO, Bibhu Prasad;GULATI, Ankita;Ul HAQ, Irfan
    • 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.

Determinants of the Prices and Returns of Preferred Stocks (우선주가격 및 수익률 결정요인에 관한 연구)

  • Kim, San;Won, Chae-Hwan;Won, Young-Woong
    • Asia-Pacific Journal of Business
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    • v.11 no.2
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    • pp.159-172
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    • 2020
  • Purpose - The purpose of this study is to investigate economic variables which have impact on the prices and returns of preferred stocks and to provide investors, underwriters, and policy makers with information regarding correlations and causal relations between them. Design/methodology/approach - This study collected 98 monthly data from Korea Exchange and Bank of Korea. The Granger causal relation analysis, unit-root test and the multiple regression analysis were hired in order to analyze the data. Findings - First, our study derives the economic variables affecting the prices and returns of preferred stocks and their implications, while previous studies focused mainly on the differential characteristics and related economic factors between common and preferred stocks. Empirical results show that the significant variables influencing the prices and returns of preffered stocks are consumer sentiment index, consumer price index, industrial production index, KOSPI volatility index, and exchange rate between Korean won and US dollar. Second, consumer sentiment index, consumer price index, and industrial production index have significant casual relations with the returns of preferred stocks, providing market participants with important information regarding investment in preferred stocks. Research implications or Originality - This study is different from previous studies in that preferred stocks themselves are investigated rather than the gap between common stocks and preferred stocks. In addition, we derive the major macro variables affecting the prices and returns of preferred stocks and find some useful causal relations between the macro variables and returns of preferred stocks. These findings give important implications to market participants, including stock investors, underwriters, and policy makers.

Industry Stock Returns Prediction Using Neural Networks (신경망을 이용한 산업주가수익율의 예측)

  • Kwon, Young-Sam;Han, In-Goo
    • Asia pacific journal of information systems
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    • v.9 no.3
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    • pp.93-110
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    • 1999
  • The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

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The Gains To Bidding Firms' Stock Returns From Merger (기업합병의 성과에 영향을 주는 요인에 대한 실증적 연구)

  • Kim, Yong-Kap
    • Management & Information Systems Review
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    • v.23
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    • pp.41-74
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    • 2007
  • In Korea, corporate merger activities were activated since 1980, and nowadays(particuarly since 1986) the changes in domestic and international economic circumstances have made corporate managers have strong interests in merger. Korea and America have different business environments and it is easily conceivable that there exists many differences in motives, methods, and effects of mergers between the two countries. According to recent studies on takeover bids in America, takeover bids have information effects, tax implications, and co-insurance effects, and the form of payment(cash versus securities), the relative size of target and bidder, the leverage effect, Tobin's q, number of bidders(single versus multiple bidder), the time period (before 1968, 1968-1980, 1981 and later), and the target firm reaction (hostile versus friendly) are important determinants of the magnitude of takeover gains and their distribution between targets and bidders at the announcement of takeover bids. This study examines the theory of takeover bids, the status quo and problems of merger in Korea, and then investigates how the announcement of merger are reflected in common stock returns of bidding firms, finally explores empirically the factors influencing abnormal returns of bidding firms' stock price. The hypotheses of this study are as follows ; Shareholders of bidding firms benefit from mergers. And common stock returns of bidding firms at the announcement of takeover bids, shows significant differences according to the condition of the ratio of target size relative to bidding firm, whether the target being a member of the conglomerate to which bidding firm belongs, whether the target being a listed company, the time period(before 1986, 1986, and later), the number of bidding firm's stock in exchange for a stock of the target, whether the merger being a horizontal and vertical merger or a conglomerate merger, and the ratios of debt to equity capital of target and bidding firm. The data analyzed in this study were drawn from public announcements of proposals to acquire a target firm by means of merger. The sample contains all bidding firms which were listed in the stock market and also engaged in successful mergers in the period 1980 through 1992 for which there are daily stock returns. A merger bid was considered successful if it resulted in a completed merger and the target firm disappeared as a separate entity. The final sample contains 113 acquiring firms. The research hypotheses examined in this study are tested by applying an event-type methodology similar to that described in Dodd and Warner. The ordinary-least-squares coefficients of the market-model regression were estimated over the period t=-135 to t=-16 relative to the date of the proposal's initial announcement, t=0. Daily abnormal common stock returns were calculated for each firm i over the interval t=-15 to t=+15. A daily average abnormal return(AR) for each day t was computed. Average cumulative abnormal returns($CART_{T_1,T_2}$) were also derived by summing the $AR_t's$ over various intervals. The expected values of $AR_t$ and $CART_{T_1,T_2}$ are zero in the absence of abnormal performance. The test statistics of $AR_t$ and $CAR_{T_1,T_2}$ are based on the average standardized abnormal return($ASAR_t$) and the average standardized cumulative abnormal return ($ASCAR_{T_1,T_2}$), respectively. Assuming that the individual abnormal returns are normal and independent across t and across securities, the statistics $Z_t$ and $Z_{T_1,T_2}$ which follow a unit-normal distribution(Dodd and Warner), are used to test the hypotheses that the average standardized abnormal returns and the average cumulative standardized abnormal returns equal zero.

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Does a Firm's IPO Affect Other Firms in the Same Conglomerate?

  • Bhadra, Madhusmita;Kim, Doyeon
    • Asia-Pacific Journal of Business
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    • v.12 no.3
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    • pp.37-50
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    • 2021
  • Purpose - This study aimed to examine the behavior surrounding the Initial Public Offering (IPO) event of firms within the same conglomerate and the impact of under-pricing and Return on Equity(ROE) on a firm's abnormal stock returns. Design/methodology - This study collected data from 166 South Korean Chaebols, consisting of 355 firms distributed as 202 listed on Korea Composite Stock Price Index (KOSPI) and 153 firms listed on Korean Securities Dealers Automated Quotations (KOSDAQ) from 2000 to 2020. The Capital Asset Pricing Model (CAPM) and the multiple regression analysis were hired to analyze the data. Findings - First, we found an adverse price reaction of IPO listing in the same chaebol group, and firms with higher under-pricing affect other firms' stock prices more adversely within the conglomerate. Next, we explored a negatively significant relation between ROE and the chaebol firms' stock returns during IPO events. Research implications - The novelty of this study is there are not many empirical studies on the impact of IPO within a conglomerate. So, the findings of this study contribute to the literature for analyzing stock's abnormal returns within a conglomerate.

Robustness of Cash Flow Value: Investment in ASEAN

  • LAU, Wei Theng;MAHAT, Fauziah Binti
    • The Journal of Asian Finance, Economics and Business
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    • v.6 no.2
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    • pp.247-255
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    • 2019
  • This study examines the different roles of cash flow in assessing investment returns in the Association of Southeast Asian Nations (ASEAN). The analysis covers over 900 listed firms across Malaysia, Indonesia, Philippines, Singapore and Thailand for the period post the Asian financial crisis of 2001-2017. Firm-level panel data analysis shows that cash flow factors are important in all contexts of cash return on assets, earnings quality and market value multiple across the region even after controlling for typical measures of profitability. The results suggest that firms should manage cash flow prudently in considerations of firm value from the shareholder's perspective, measured directly using stock return. Cash profitability on assets should become an important firm performance indicator, whilst higher cash component over reported earnings is preferred. The market also tends to respond favourably to cash flow yield as a price multiple in valuation, outpacing the role of earnings yield. Such findings are robust across the pre and post subprime crisis periods, across estimation methods pertaining to finance panel standard errors, as well as across static and dynamic considerations of returns. It is hence sensible to consider cash flow factors in the research pertaining to asset pricing and factor investing in the ASEAN region.

THE OVERPAYMENT IN MULTIPLE BIDDING (기업합병: 다수경쟁에서의 과잉지분에 대한 연구)

  • Lee, You-Tay
    • The Korean Journal of Financial Management
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    • v.14 no.3
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    • pp.319-339
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    • 1997
  • This paper provides an empirical analysis of the winner's curse in the context of corporate takeovers. The study analyzes conditions which make overpayment likely. For a sample of corporate takeovers completed between 1982 and 1993, the analysis shows that the volatility of targets relative to that of acquirers (not the uncertainty of the target or acquirer alone) has a definitive impact on the magnitude of the winner's curse. Also, the incidence is more pronounced in multiple-bidder than in single-bidder contests. Specifically, white knights are more likely to overpay than other acquirers in multiple bidding situations. Furthermore, the study finds that the process of competitive bidding is a zero sum game since the greater returns to the shareholders of target firms in multiple-bid contests come at the expense of the acquiring companies, Overall, the evidence suggests that the bidders need to become more conservative, particularly as the relative uncertainty of the target's 'true' value and the number of bidders increase.

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Price Discovery in the Korean Treasury Bond Futures Market (한국국채선물시장에서의 가격발견기능에 관한 연구)

  • Seo, Sang-Gu
    • Management & Information Systems Review
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    • v.30 no.2
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    • pp.257-275
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    • 2011
  • The price relationship between the futures market and the underlying spot market has attracted the attention of academics, practitioners, and regulators due to their roles during periods of turbulence in financial markets. The purpose of this paper is to investigate the dynamic of price relationship(or lead-lag relationship) between Korean Treasury Bond futures market and spot market. To examine the nature of the price relationship, descriptive statistics, serial correlation, and cross-correlation are used as a preliminary statistics in the Korean Treasury Bond spot and futures market. Next, following Stoll-Whaley(1990) and Chan(1992), the multiple regression method is used to examine the lead-lag patterns between the two markets. The empirical results are summarized as follows. The mean returns of spot markets and future markets are positive(+) and negative(-) respectively and the standard deviation of both stock and futures returns increase through the sub-periods. For the most periods, there is negative skewness in the both markets. The zero excess kurtosis due to the heavy tails of the distribution are relatively large. The autocorrelations in the spot returns for the sample periods are positive in time lag 1, but the autocorrelations in the future returns shows no significant evidence. The results of the daily cross-correlations between the KTB spot and futures returns indicate that a lead-lag relationship don't exist for price changes of futures and spot markets as a preliminary analysis. Finally, empirical results of regression analysis for both market indicate that there is no evidence that the KTB futures lead the KTB spot market, or the KTB spot market lead the KTB futures market. These results are robust for all sub-periods.

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System Trading using Case-based Reasoning based on Absolute Similarity Threshold and Genetic Algorithm (절대 유사 임계값 기반 사례기반추론과 유전자 알고리즘을 활용한 시스템 트레이딩)

  • Han, Hyun-Woong;Ahn, Hyun-Chul
    • The Journal of Information Systems
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    • v.26 no.3
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    • pp.63-90
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    • 2017
  • Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.