AN EMPIRICAL STUDY OF PRICE CLUSTERING ON THE NAIROBI SECURITIES EXCHANGE
Keywords:Price clustering, stock volatility, securities exchange
Purpose: The purpose of this study was to empirically investigate price clustering phenomenon on the Nairobi Securities Exchange for the period 2009 to 2013.
Materials and methods: The study used secondary sources of data obtained from the Nairobi Securities exchange. The study revealed that there has been a preference by investors for stock whose prices end with the digit 5 and this accounted for 67.88 percent of all the stocks examined and was followed by stocks whose prices ended with the digit 0 which accounted for 4.55 percent. In order to establish the determinants of this observed behavior a multivariate regression model used by Harris (1991) was adopted where price clustering was regressed against stock volatility, number of trades, market capitalization, and own stock price.
Results: The regression results indicated that the number of trades as well as Market Capitalization was positive and significantly related to price clustering. The study also found the stock price to be negative and significantly related to price clustering. On the other hand, Stock volatility was established to be an insignificant predictor of price clustering. The multivariate regression model was found to be significant in explaining the observed relationship and that 15.4 percent of the variance in price clustering was explained by number of trades, stock volatility, own stock price and the market capitalization. The study finds that there is a tendency of prices to cluster around certain numbers as evidenced by the 67.88 percent of numbers clustering around the number 5 and that price clustering is positively related to number of trades
Recommendations: It is thus recommended that if firms are to increase the number of trades of their shares they should consider pricing their shares according to the preferences of investors who prefer shares or stocks whose prices ends with 5 or 0.
Aitken,M., Brown, P., Buckland, C., Izan, H., & Walter, T. (1996).Price clustering on the Australian Stock Exchange.Pacific-Basin Finance Journal (4), 297-314.
Aşçıoğlu, A., Comerton‐Forde, C., & McInish, T. H. (2007). Price clustering on the Tokyo Stock Exchange. The Financial Review(42), 289‐301.
Ball, C. A., Torus, W. A., & Tschoegl, A. E. (1985). The degree of price resolution: The case of the gold market. Journal of Future Markets(5), 29-43.
Bollen, N., Smith, T., & Whaley, R. E. (2003). Optimal contract design: For whom? Journal of Futures Markets(23), 719-750.
Brown, P., Chua, A., & Mitchell, J. (2002). The influence of cultural factors on price clustering: evidence from Asia-Pacific stock markets. Pacific-Basin Finance Journal(10), 307-332.
Christie, W. G., & Schultz, P. H. (1994). Why do NASDAQ market makers avoid odd-eighth quotes. Journal of Finance(49), 1813-1840.
Christie, W. G., Harris, J. H., & Schultz, P. H. (1994). Why did NASDAQ market makers stop avoiding odd-eighth quotes. Journal of Finance(49), 1841-1860.
Chung, K. H., Kim, K. A., & Kitsabunnarat, P. (2005). Liquidity and quote clustering in a market with multiple tick sizes. Journal of Financial Research(28), 177-195.
De Ceuster, M. J., Dhaene, G., & Schatteman, T. (1998). The hypothesis of psychological barriers in stock markets and Benford’s Law. Journal of Empirical Finance, 5(4), 263-279.
De Grauwe, P., & Decupere, D. (1992). Psychological barriers in the foreign exchange market. Journal of International and Comparative Economics, 1, 87-101.
Dickinson, J. P., & Muragu, K. (1994). Market Efficiency in Developing Countries: A Case Study of the Nairobi Stock Exchange. Journal of Business Finance and Accounting, 21(1), 133-149.
Goodhart, C., & Curcio, R. (1991). The clustering of bid-ask prices and the spread in the foreign exchange market. London Schoool of Economics(Discussion Paper 110).
Grossman, S. J., Miller, M. H., Cone, K. R., Fischel, D. R., & Ross, D. J. (1997). Clustering and competition in asset markets. Journal of Law and Economics(40), 23-60.
Gwilym, O. A., Clare, & Thomas, S. (1998). Extreme price clustering in the London equity futures and options markets. Journal of Banking and Finance(22), 1193-1206.
Hameed, A., & Terry, E. (1998). The effect of tick size on price clustering and trading volume. Journal of Business Finance and Accounting(25), 849-867.
Harris, L. (1991). Stock price clustering and discreteness. Review of Financial Studies(4), 389-415.
Hornik, J., Cherian, J., & Zakay, D. (1994). The influence of prototypic values on the validity of studies using time estimates. Journal of the Market Research Society, 36(2), 145-147.
Huang, R. D., & Stoll, H. R. (2001). Tick Size, Bid‐Ask Spreads, and Market Structure. The Journal of Financial and Quantitative Analysis(36 (4)), 503‐522.
Kahn, C., Pennachi, G., & Sopranzetti, B. (1999). Bank deposit rate clustering: Theory and empirical evidence. Journal of Finance(54), 2185-2214.
Ley, E. (1996). On the peculiar distribution of the U.S. stock indexes’ digits. The American Statistician, 50(4), 311-313.
Liu, H.-C., & Witte, M. D. (2013). Price Clustering in the U.S. Dollar/Taiwan Dollar Swap Market. The Financial Review(48), 77–96.
Loomes, G. (1988). Different experimental procedures for obtaining valuations of risky actions: Implications for utility theory. Theory and Decision, 25(1), 1-23.
Mitchell, J. (2001). Clustering and Psychological Barriers: The Importance of Numbers. The Journal of Futures Markets(21), 395–428.
Niederhoffer, V. (1966). A new look at clustering of stock prices. Journal of Business(39), 309-313.
Niederhoffer, V., & Osborne, M. F. (1966). Market making and reversal on the stock exchange. Journal of the American Statistical Association(61), 897-916.
Ohta, W. (2006). An analysis of intraday patterns in price clustering on the Tokyo Stock Exchange. Journal of Banking and Finance(30), 1023-1039.
Osborne, M. F. (1962). Periodic structure in the Brownian motion of stock prices. Operations Research(10), 345-379.
Palmon, O., Smith, B. A., & Sopranzetti, B. J. (2004). Clustering in real estate prices: determinants and consequences. The Journal of Real Estate Research, 26(2), 115-136.
Parkinson, J. M. (1987). The EMH and the CAPM on the Nairobi Stock Exchange. East African Economic Review, 3(2), 105-110.
Preece, D. A. (1981). Distribution of the final digits in data. The Statistician, 30(1), 31-60.
Schelling, T. C. (1960). The Strategy of Conflict. Cambridge Massachusetts: Harvard University Press.
Schwartz, A., Van Ness, B. F., & Ness, R. A. (2004). Clustering in the futures market: Evidence from S&P 500 futures contracts. Journal of Futures Markets(24), 413-428.
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99-118.
Sonnemams, J. (2003). Price clustering and natural resistance points in the Dutch Stock Market: A natural experiment. Discussion Paper, University of Amsterdam.
Sopranzetti, B. J., & Datar, V. (2002). Price clustering in foreign exchange spot markets. Journal of Financial Markets(5), 411-417.
Thaler, R. (1992). The winners’ curse: paradoxes and anomalies of recent life. In: A Russell Sage Foundation Book. New York: Maxwell Macmillan International.
Timarr. (2010, February 24). Irrational Numbers: Price Clustering & Stop Losses. Retrieved from The Psy-Fi Blog: http://www.psyfitec.com/2010/02/irrational-numbers-price-clustering.html
Vogt, Bodo, Uphaus, A., & Albers, W. (2001). Numerical Decision Processing Causing Stock Price Clustering? Homo-economicus(18 (2)), 1‐12.
Yule, G. U. (1927). On reading a scale. Journal of the Royal Statistical Society, 90(3), 570-579.