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Tytuł artykułu

Return dispersion and price volatility: a moderated analysis on portfolio management strategies

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Dyspersja zwrotów i zmienność cen: analiza moderowana strategii zarządzania portfelem produktów
Języki publikacji
EN
Abstrakty
EN
The is study aims to analyze the effect of return dispersion on price return volatility and to analyze the moderator role of book-to-market that can weaken the causal effect based on the portfolio management framework. This paper specifically examines the causal effect at sub-group level of value and growth stocks portfolios. The sample observed are stocks covered in the index SSE-50 in China, DJI-30 in the United States, LQ-45 in Indonesia, and KLCI-30 in Malaysia. The observation period was during the covid-19 pandemic from 1 April 2020 to 30 March 2021. The analytical approaches applied are the GARCH(p,q) model, the hierarchical moderated regression analysis (HMRA) procedure, and the ordinary least squared technique. The findings of the investigation show that when the estimation models are not separated into sub-groups, return dispersion positively influences return volatility. However, when the return dispersion is grouped based on the magnitude of BMR, the estimation results on the causality effect from dispersion of return to price return volatility show an insignificant effect for all sub-groups of value, neutral, and growth stocks. Specifically, when a company has a higher BMR, increased dispersion of return on such value stock does not change in its return volatility. As an implication, portfolio managers and market participants could minimize the uncertainty of price movements and eliminate share trading delays by implementing a strategy of style investing and selecting shares to form a value-type portfolio. Moreover, the companies should manage the position of their book value to remain classified as the value stocks segment, which could maintain the interest of market participants and lower the cost of capital.
PL
Celem niniejszego badania jest analiza wpływu dyspersji zwrotów na zmienność cen zwrotu oraz zbadanie roli wskaźnika księgowej wartości rynkowej (BMR) jako moderatora, który może osłabiać efekt przyczynowy w ramach zarządzania portfelem. Artykuł ten szczególnie bada efekt przyczynowy na poziomie podgrup portfeli akcji wartościowych i wzrostowych. Próba badawcza obejmuje akcje z indeksów SSE-50 w Chinach, DJI-30 w Stanach Zjednoczonych, LQ-45 w Indonezji i KLCI-30 w Malezji. Okres obserwacji obejmował pandemię COVID-19 od 1 kwietnia 2020 r. do 30 marca 2021 r. Zastosowane podejścia analityczne to model GARCH(p,q), procedura hierarchicznej moderowanej analizy regresji (HMRA) oraz technika najmniejszych kwadratów (OLS). Wyniki badania pokazują, że gdy modele estymacyjne nie są podzielone na podgrupy, dyspersja zwrotów pozytywnie wpływa na zmienność zwrotów. Jednakże, gdy dyspersja zwrotów jest grupowana na podstawie wielkości BMR, wyniki estymacji efektu przyczynowego dyspersji zwrotów na zmienność cen zwrotów wykazują nieistotny wpływ dla wszystkich podgrup akcji wartościowych, neutralnych i wzrostowych. W szczególności, gdy firma ma wyższy wskaźnik BMR, zwiększona dyspersja zwrotów na takich akcjach wartościowych nie zmienia ich zmienności zwrotów. W konsekwencji, zarządzający portfelami i uczestnicy rynku mogliby zminimalizować niepewność ruchów cen i wyeliminować opóźnienia w handlu akcjami, wdrażając strategię inwestowania w stylu i wybierając akcje do tworzenia portfela typu wartościowego. Ponadto, firmy powinny zarządzać pozycją swojej wartości księgowej, aby pozostać zaklasyfikowane jako segment akcji wartościowych, co mogłoby utrzymać zainteresowanie uczestników rynku i obniżyć koszt kapitału.
Rocznik
Strony
255--271
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
autor
  • Faculty of Economics and Business, Universitas Jenderal Soedirman, Indonesia
autor
  • Universitas Jenderal Soedirman, Indonesia
autor
  • Universitas Jenderal Soedirman, Indonesia
  • Universitas Borneo Tarakan, Indonesia
Bibliografia
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  • 2. Asgharian, H., Christiansen, C. and Hou, A. J., (2023). The effect of uncertainty on stock market volatility and correlation. Journal of Banking and Finance, 154, 106929.
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  • 4. Bae, K.-H., Chan, K. and Ng, A., (2004). Investibility and return volatility. Journal of Financial Economics, 71, 239-263.
  • 5. Barberis, N., Shleifer, A., (2003). Style investing. Journal of Financial Economics, 68(2), 161-199.
  • 6. Bekaert, G., Harvey, C. R., (1997). Emerging equity market volatility. Journal of Financial Economics, 43(1), 29-77.
  • 7. Ben Mrad Douagi, F. W., Chaouachi, O. and Sow, M., (2021). The Portfolio Management: Investigation of the Fama-French Five- and Six-Factor Asset Pricing Models. Polish Journal of Management Studies, 23(1), 106-118
  • 8. Bohl, M. T., Brzeszczyński, J., (2006). Do institutional investors destabilize stock prices? Evidence from an emerging market. Journal of International Financial Markets, Institutions and Money, 16(4), 370-383.
  • 9. Bollerslev, T., (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
  • 10. Brandt, M. W., Brav, A., Graham, J. R. and Kumar, A., (2010). The Idiosyncratic Volatility Puzzle: Time Trend or Speculative Episodes? Review of Financial Studies, 23(2), 863- 899.
  • 11. Bravo, F., (2016). Forward-looking disclosure and corporate reputation as mechanisms to reduce stock return volatility. Revista de Contabilidad, 19(1), 122-131.
  • 12. Bushee, B. J., Noe, C. F., (2000). Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility. Journal of Accounting Research, 38, 171-202.
  • 13. Byun, S. J., (2016). The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility. Journal of Empirical Finance, 36, 162-180.
  • 14. Campbell, J. Y., Lettau, M., Malkiel, B. G. and Xu, Y., (2001). Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk. The Journal of Finance, 56(1), 1-43.
  • 15. Cao, C., Simin, T. and Zhao, J., (2008). Can Growth Options Explain the Trend in Idiosyncratic Risk?. Review of Financial Studies, 21(6), 2599-2633.
  • 16. Chang, E. C., Cheng, J. W. and Khorana, A., (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking and Finance, 24(10), 1651-1679.
  • 17. Chowdhury, S. S. H., Irfan, M., (2022). A Study on the Time-varying Volatility Connectedness between the Sectors in the Indian Stock Market. Montenegrin Journal of Economics, 18(3), 77-88.
  • 18. Connolly, R., Stivers, C., (2006). Information content and other characteristics of the daily cross-sectional dispersion in stock returns. Journal of Empirical Finance, 13(1), 79-112.
  • 19. Demirer, R., Gupta, R., Lv, Z. and Wong, W.-K., (2019). Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests. Sustainability, 11(2), 351.
  • 20. Fama, E. F., French, K. R., (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427-465.
  • 21. Fei, T., Liu, X. and Wen, C., (2019). Cross-sectional return dispersion and volatility prediction. Pacific-Basin Finance Journal, 58, 101218.
  • 22. Galariotis, E. C., Rong, W. and Spyrou, S. I., (2015). Herding on fundamental information: A comparative study. Journal of Banking and Finance, 50, 589-598.
  • 23. Hwang, S., Satchell, S. E., (2005). GARCH model with cross-sectional volatility: GARCHX models. Applied Financial Economics, 15(3), 203-216.
  • 24. Jose, P. E., (2013). Doing Statistical Mediation and Moderation (1st ed.). Guilford Press.
  • 25. Kakinuma, Y., (2020). Empirical evidence of profitability anomaly in the Thai stock market. Journal of International Studies, 13(4), 89-100.
  • 26. Kumari, J., Mahakud, J. and Hiremath, G. S., (2017). Determinants of idiosyncratic volatility: Evidence from the Indian stock market. Research in International Business and Finance, 41, 172-184.
  • 27. Lakonishok, J., Shleifer, A. and Vishny, R. W., (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32(1), 23-43.
  • 28. Li, D., Nguyen, Q. N., Pham, P. K. and Wei, S. X., (2011). Large Foreign Ownership and Firm-Level Stock Return Volatility in Emerging Markets. Journal of Financial and Quantitative Analysis, 46(04), 1127-1155.
  • 29. Li, D., Zhang, L. and Li, L., (2023). Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model. International Review of Financial Analysis, 88, 102708.
  • 30. Muharam, H., Mawardi, W., Arfinto, E. D. and Najmudin, N., (2019). Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets. Journal of International Studies, 12(1), 134-150.
  • 31. Najmudin, Kurniasih, R., Sulistyandari and Jati, D. P., (2019). The effect of dynamic relationship between domestic market and world market on stock returns volatility. IOP Conference Series: Earth and Environmental Science, 255.
  • 32. Nguyen, D. S., Hoang, T. H. V., Pho, K.-H. and Nhan, D. T. T., (2023). Financial Literacy and Portfolio Diversification: Evidence from Vietnam. Journal of Competitiveness, 15(3), 79-103.
  • 33. Niu, Z., Demirer, R., Suleman, M. T. and Zhang, H., (2023). Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data. Journal of Forecasting, 42(6), 1309-1328.
  • 34. Rajgopal, S., Venkatachalam, M., (2011). Financial reporting quality and idiosyncratic return volatility. Journal of Accounting and Economics, 51(1-2), 1-20.
  • 35. Robiyanto, R., Santoso, M., Rambu Atahau, A. and Harijono, H., (2019). The Indonesia Stock Exchange and Its Dynamics: An Analysis of the Effect of Macroeconomic Variables. Montenegrin Journal of Economics, 15(4), 59-73
  • 36. Schwert, G. W., (2011). Stock Volatility during the Recent Financial Crisis. European Financial Management, 17(5), 789-805.
  • 37. Sedliacikova, M., Moresova, M., Alac, P. and Drabek, J., (2021). How Do Behavioral Aspects Affect the Financial Decisions of Managers and the Competitiveness of Enterprises? Journal of Competitiveness, 13(2), 99-116.
  • 38. Singh, S., Parmar, K. S. and Kaur, J., (2023). Chapter 12 - Forecasting volatility in the stock market data using GARCH, EGARCH, and GJR models. In S. Eslamian and F. Eslamian (Eds.), Handbook of Hydroinformatics (pp. 207-220). Elsevier
  • 39. Stivers, C. T., (2003). Firm-level return dispersion and the future volatility of aggregate stock market returns. Journal of Financial Markets, 6(3), 389-411.
  • 40. Vo, X. V., (2015). Foreign ownership and stock return volatility – Evidence from Vietnam. Journal of Multinational Financial Management, 30, 101-109.
  • 41. Vozlyublennaia, N., (2013). Do firm characteristics matter for the dynamics of idiosyncratic risk? Journal of International Financial Markets, Institutions and Money, 27, 35-46.
  • 42. Wahal, S., and Yavuz, M. D., (2013). Style investing, comovement and return predictability. Journal of Financial Economics, 107(1), 136-154.
  • 43. Wang, J., Ma, F., Wang, T. and Wu, L., (2023). International stock volatility predictability: New evidence from uncertainties. Journal of International Financial Markets, Institutions and Money, 85, 101781.
  • 44. Wei, S. X., Zhang, C., (2006). Why Did Individual Stocks Become More Volatile? The Journal of Business, 79(1), 259-292.
  • 45. Yacob, N., Mustapha, M., Abdullah, S. M. M. and Ya'acob, F. F., (2020). Incorporating Investor Behaviour in Portfolio Management in Developed and Emerging OECD Stock Markets. Polish Journal of Management Studies, 22(1), 611-625.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-441cab45-456b-40a1-8c88-9663470ee821
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