The Total Cost of Transactions on the NYSE

Berkowitz, Stephen A.; Logue, Dennis E.; Noser, Eugene A.

doi:10.2307/2328325

Cited by 132 publications

(87 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Berkowitz, Logue, and Noser (1988) estimate the cost of a transaction on the New York Stock Exchange (NYSE) to be 0.23 percent. Based on this estimate, a speculator who purchases a representative NYSE stock just prior to the turn‐of‐month trading days and sells the stock just after the turn‐of‐month trading days will incur transaction costs totalling

2 \times 0.23 = 0.46

percent.…”

Section: Initial Empirical Resultsmentioning

confidence: 99%

Turn‐of‐Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects

Ogden

1990

The Journal of Finance

168

View full text Add to dashboard Cite

This paper presents and tests a hypothesis that the standardization of payments in the United States at the turn of each calendar month generally induces a surge in stock returns at the turn of each calendar month. The hypothesis also asserts that returns generally will be greater following the month of December and will vary inversely with the stringency of monetary policy. Empirical results using stock index returns for 1969-1986 support the hypothesis. This analysis provides an explanation for the previously documented monthly effect in stock returns and a partial explanation for the January effect.IN A RECENT PAPER, Ariel (1987) documents an important empirical regularity in stock returns and terms it the "monthly effect." Using value-weighted and equally weighted daily stock index returns for the period 1963-1981, Ariel shows that virtually all of the cumulative return on these indexes is realized on ten consecutive trading days of the calendar month, beginning with the last trading day of the month and extending through the first nine trading days of the following month. Ariel considers various explanations for this phenomenon, "but none sufficed to explain the observed empirical regularity" (p. 174). More recently, Lakonishok and Smidt (1988) examine returns on the Dow Jones Industrial Average index for the period 1897-1986 and find that significant mean daily returns are realized consistently on only four consecutive trading days of the calendar month, beginning with the last trading day of the month. These trading days will henceforth be referred to as the turn-of-month trading days.Ariel's and Lakonishok and Smidt's tests indicate that the monthly effect is not merely another manifestation of the previously documented "January effect." Rozeff and Kinney (1976) first documented that average stock index returns in January are higher than in any other month. Keim (1983) and others find that the surge in January returns is largely confined to stocks of small firms and to the first few trading days in January. Branch (1977) and others have suggested that the surge in January returns is due to year-end tax-loss selling and subsequent repurchases in January. Studies by Roll (1983), Reinganum (1983), Ritter (1988), and others provide evidence consistent with the tax-loss selling * School of Management, State University of New York at Buffalo. I am grateful to participants in the finance workshops at Arizona State University and SUNY-Buffalo, especially Richard Smith, an anonymous referee, and editor Rene Stulz for helpful comments on earlier drafts of this paper. Any remaining errors are, of course, my responsibility. 1259 1260 The Journal of Finance hypothesis. In contrast, Constantinides (1984) argues that year-end tax-loss selling cannot explain the January effect, and the empirical results of Pettengill (1986), Chan (1986), and Jones, Pearce, and Wilson (1987) are inconsistent with the tax-loss selling hypothesis.'This paper presents and tests a hypothesis that the monthly and January effects are due, at le...

show abstract

2 \times 0.23 = 0.46

percent.…”

Section: Initial Empirical Resultsmentioning

confidence: 99%

Turn‐of‐Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects

Ogden

1990

The Journal of Finance

168

View full text Add to dashboard Cite

show abstract

“…Berkowitz et al (1988) report that the average round-trip cost for securities transaction is less than 0.5 percent.…”

mentioning

confidence: 99%

Evidence of Predictable Behavior of Security Returns

Jegadeesh¹

1990

The Journal of Finance

497

367

View full text Add to dashboard Cite

“…Trading volume, order flow and order types (limit orders vs. market orders, day orders vs. good'til cancelled orders) potentially differ, with large block orders occurring more frequently on the NYSE 2 , 3 . Individuals face higher per share transaction costs than do institutions (compare Blum, Kracaw, and Lewellen (1986) to Berkowitz, Logue, and Noser (1988)), and this may affect their trading decisions at different stock price levels 4 . Small investors may prefer the lower price created by a split as it facilitates trading round lots; institutions may prefer higher priced shares so as to minimize their transaction costs 5…”

mentioning

confidence: 99%

Volatility increases Subsequent to NYSE and AMEX Stock Splits

Dubofsky¹

1991

The Journal of Finance

View full text Add to dashboard Cite

The post-split increase in daily returns volatility is less for AMEX stocks than for NYSE stocks. The exchange trading location is a significant factor in explaining the volatility shift even after stock price and firm size are considered. Furthermore, when measured on a weekly basis, there is no increase in AMEX stocks' returns volatility. These results suggest that measurement errors created by bid-ask spreads and the 1/8 effect, and also one or more of the elements that make the NYSE different from the AMEX, explain why the estimated volatility of daily stock returns increases after the ex split date.OHLSON AND PENMAN (1985) provide evidence that NYSE stock returns variances increase subsequent to ex stock distribution days for splits of 100% or greater. The increase in volatility is independent of the size of the split alnd the post split price. Seven potential problems that might affect their results are analyzed and rejected.1Ohlson and Penman (1985) suggest that the greater post-split volatility may be due to the activity of relatively ignorant noisy traders who prefer trading low-priced stocks (this "clientele factor" is attributed to Black (1986, pg. 534)) and "institutional factors" (a rather broad category that incorporates measurement problems created by the "1/8 effect" and bid-ask spreads) as possible hypotheses for the post split volatility increase. This paper extends Ohlson and Penman's (1985) analysis to AMEX stocks. Exchange listing can serve as a proxy for several real factors that may cause the increase in post-split variance, many of which are related to the clientele factor and institutional factors cited above. If the increase in volatility differs between NYSE and AMEX stocks, it will suggest that one (or more) of the following contributes to the phenomenon: a) Attributes of the stock and/or company. AMEX stocks are smaller and lower priced than NYSE stocks. Shares of low priced and small sized firms are characterized by wider bid-ask spreads (see Roll (1984) and *Associate Professor, Texas A&M University. The author thanks S. Kannan, S. Lummer, A. Mahajan, D. Mayers, R. Stulz, and an anonymous referee for helpful comments. 1Ohlson and Penman's (1985) paper has also spurred subsequent work on smaller sized distributions and reverse splits (Dravid (1987)), and on split-related beta shifts (Brennan and Copeland (1988)). However, the source of the volatility-stock split relationship still remains a mystery. 421 422 The Journal of Finance Research 9, 291-302. Brennan, Michael J. and Thomas E. Copeland, 1988, Beta changes around stock splits: A note,

show abstract

The Total Cost of Transactions on the NYSE

Cited by 132 publications

References 0 publications

Turn‐of‐Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects

Turn‐of‐Month Evaluations of Liquid Profits and Stock Returns: A Common Explanation for the Monthly and January Effects

Evidence of Predictable Behavior of Security Returns

Volatility increases Subsequent to NYSE and AMEX Stock Splits

Contact Info

Product

Resources

About