The Impact of Investor Sentiment on the 'Leverage Effect'

Year 2016, Volume: 8 Issue: 1, 4 - 18, 01.04.2016

Semen Son-turan

https://doi.org/10.33818/ier.278043

Cited By: 1

Abstract

With the advent of the Internet and the availability of user search query data on a broader scale, since the early 2000s researchers have started using collective search query information instead of, or, in addition to, traditional investor sentiment proxies. This study examines whether the leverage (bad news) effect, as measured by the EGARCH (1,1) model, changes with the inclusion of a newly emerging sentiment proxy, internet search volume. The sample consists of 14 US companies belonging to the NASDAQ and NYSE Indices and 501 observations of data collected at weekly frequency spanning a nine year period. Empirical findings suggest that, inclusion of the investor sentiment variable has no clear impact on the bad news effect; there is, however, a discernible increase in volatility persistence. The implications of the findings are that the investor sentiment proxy has additional informational content. Behavioral finance theory and the availability and social proof heuristics serve as potential explanations for such findings.

Keywords

EGARCH, Investor Sentiment, Leverage Effect, Behavioral Finance, Internet Search Queries.

References

• Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on
• Automatic Control, 19 (6), 716-723. Antweiler, W. and M.Z. Frank (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59 (3), 1259-1294.
• Araghi, M.K. and M.M. Pak (2012). Assessing the Exchange Rate Fluctuation on Tehran's
• Stock Market Price: A GARCH Application. International Journal of Management and Business Research (IJMBR), 2(2), 95-107. Askitas, N. and K. Zimmermann (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, Duncker & Humblot, Berlin, 55 (2), 107
• Baker, M. and J. Wurgler (2007). Investor sentiment in the stock market. Journal of
• Economic Perspectives, 21, 129-152. Bank, M., M. Larch and G. Peter (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial markets and portfolio management, 25 (3), 264.
• Barber, B.M. and T. Odean (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21 (2), 785-818.
• Barberis, N., A. Shleifer, and R. Vishny (1998). A model of investor sentiment. Journal of
• Financial Economics, 49 (3), 307-343. Black, F. (1976). The pricing of commodity contracts. Journal of financial economics, 3 (1), 179.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307-327.
• Bordino, I., S. Battiston, G. Caldarelli, M. Cristelli, A. Ukkonen and I. Weber (2012). Web search queries can predict stock market volumes. PloS one, 7 (7), e40014.
• Breeden, D.T. (1979). An intertemporal asset pricing model with stochastic consumption and investment opportunities. Journal of financial Economics, 7 (3), 265-296.
• Brooks, R.D., R.W. Faff and T.R. Fry (2001). GARCH modelling of individual stock data: the impact of censoring, firm size and trading volume. Journal of International
• Financial Markets, Institutions and Money, 11 (2), 215-222. Brooks, C. (2002) Introductory Econometrics for Finance. Cambridge, UK: Cambridge University Press.
• Brown, G.W. and M.T. Cliff (2005). Investor Sentiment and Asset Valuation.The Journal of Business, 78 (2), 405-440.
• Cialdini, R.B. (1984). The psychology of persuasion. New York: Quill William Morrow.
• Chen, N.F., R. Roll and S.A. Ross (1986). Economic forces and the stock market. Journal of business, 383-403.
• Christakis, N.A. and H.F. James (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in medicine, 32 (4), 556-577.
• Coval, J.D. and T. Shumway (2001). Is sound just noise? The Journal of Finance, 56 (5), 1910.
• Coviello, L., Y. Sohn, A.D. Kramer, C. Marlow, M. Franceschetti, N.A. Christakis and J.H. Fowler (2014). Detecting emotional contagion in massive social networks. PloS one, (3), e90315.
• Da, Z., J. Engelberg and P. Gao (2011). In search of attention. The Journal of Finance, 66 (5), 1499.
• Daniel, K., D. Hirshleifer and A. Subrahmanyam, (1998). Investor psychology and security market under‐and overreactions. The Journal of Finance, 53 (6), 1839-1885.
• De Bondt, W.F. and R. Thaler (1985). Does the stock market overreact? The Journal of finance, 40 (3), 793-805.
• DeLong, J. B., A. Shleifer, L.H. Summers and R.J. Waldmann, (1990). Positive feedback investment strategies and destabilizing rational speculation. The Journal of Finance, 45 (2), 379-395.
• Depken, C.A. (2001). Good news. bad news and GARCH effects in stock return data. Journal of Applied Economics, 4 (2), 313-327.
• Dimpfl, T. and S. Jank (2011). Can internet search queries help to predict stock market volatility? CFR Working Paper (No. 11-15).
• Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
• Engle, R.F. and A.J. Patton (2001). What good is a volatility model. Quantitative finance, 1 (2), 237-245.
• Fama, E.F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25 (2), 383-417.
• Fama, E.F., L. Fisher, M.C. Jensen and R. Roll (1969). The adjustment of stock prices to new information. International economic review, 10 (1), 1-21.
• Fama, E.F. and K.R. French (1992). The cross‐section of expected stock returns. Journal of Finance, 47 (2), 427-465.
• Fama, E.F. and K.R. French (2001). Disappearing dividends: changing firm characteristics or lower propensity to pay? Journal of Financial economics, 60 (1), 3-43.
• Flannery, M.J. and A.A. Protopapadakis (2002). Macroeconomic factors do influence aggregate stock returns. Review of Financial Studies, 15 (3), 751-782.
• Foucault, T., D. Sraer and D.J. Thesmar (2011). Individual investors and volatility. The Journal of Finance, 66 (4), 1369-1406.
• Frazzini, A. and O.A. Lamont (2005). Dumb money: mutual fund flows and the cross-section of stock returns. National Bureau of Economic Research.
• Geske, R. and R. Roll (1983). The fiscal and monetary linkage between stock returns and inflation. The Journal of Finance, 38 (1), 1-33.
• Hedström, P. and R. Swedberg (1998). Social mechanisms: An analytical approach to social theory. Cambridge University Press.
• Joseph, K., M.B. Wintoki and Z. Zhang (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International
• Journal of Forecasting, 27 (4), 1116-1127.
• Kahneman, D. and A. Tversky (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263-291.
• Kumar, A. and C. Lee (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61 (5), 2451-2486.
• Kyle, A.S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, 1315-1335.
• Lamoureux, C.G. and W.D. Lastrapes (1990). Heteroskedasticity in stock return data: volume versus GARCH effects. The Journal of Finance, 45 (1), 221-229.
• Latoeiro, P., S.B. Ramos and H. Veiga (2013). Predictability of stock market activity using
• Google search queries. Working Paper 13-06. Universidad Carlos III de Madrid. Lee, C., A. Shleifer and R.H. Thaler (1991). Investor sentiment and the closed‐end fund puzzle. The Journal of Finance, 46 (1), 75-109.
• Mangani, R. (2009). Macroeconomic effects on individual JSE Stocks: a GARCH representation. Investment Analysts Journal, 69, 47-57. Nelson, D.B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
• Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19 (3), 425-442.
• Son-Turan, S. (2014). Internet Search Volume and Stock Return Volatility: The Case of
• Turkish Companies. Information Management and Business Review, 6 (6), 317-328. Swaminathan, B. (1996). Time-varying expected small firm returns and closed-end fund discounts. Review of Financial Studies, 9 (3), 845-887.
• Tetlock, P.C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62 (3), 1139-1168.
• Thaler, R. (1980). Toward a positive theory of consumer choice. Journal of Economic
• Behavior & Organization, 1 (1), 39-60. Tversky, A. and D. Kahneman (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5 (2), 207-232.
• Vlastakis, N. and R.N. Markello (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36 (6), 1808-1821.