More Evidence of Blue Monday Effect? Markets: The Monday Effect - The Source. By Alen Mattich Could it be mere coincidence that, since the start of last September, the U.S. equity market has advanced on just about every Monday? Every econometrician knows that, if you dig enough through a long enough series, the data will throw off spurious relationships and patterns. On the other hand, seasonal effects in equity markets have been quite well documented. For instance, buying shares at the start of November and then selling again at the start of May isn’t just some old stockbroker’s superstition: “Sell in May and go away, come back on St Leger’s day”. Over the longer run, it’s been a positive investment strategy. On average, it works, though you get the year or two like last year, and possibly this, when it falls down. Then there used to be the January effect, where equities would invariably outperform strongly during the first few sessions of the new year. This time around, though, the market loves Mondays.
That’s a big turnaround from the historic trend.
Halloween indicator. Sell in May and go away is an investment strategy for stocks. It includes a theory (sometimes known as the Halloween indicator) that the period from November to April inclusive has significantly stronger growth on average than the other months.[1] In such strategies, stocks are sold at the start of May and the proceeds held in cash (e.g. a money market fund); stocks are bought again in the autumn, typically around Halloween.
It is the belief that its better to avoid holding stock during the summer period. Though this seasonality is often mentioned informally, it has largely been ignored in academic circles (perhaps being assumed to be a mere superstition). Nonetheless analysis by Bouman and Jacobsen (2002) shows that the effect has indeed occurred in 36 out of 37 countries examined, and since the 17th century (1694) in the United Kingdom; it is strongest in Europe. According to the efficient-market hypothesis, this is impossible. Cause of the effect[edit] Academic response[edit] January barometer. The January barometer is the hypothesis that stock market performance in January (particularly in the U.S.) predicts its performance for the rest of the year. So if the stock market rises in January, it is likely to continue to rise by the end of December.
The January barometer was first mentioned by Yale Hirsch in 1972.[1] Historically, if the S&P 500 goes up in January, the trend will follow for the rest of the year. Conversely if the S&P falls in January, then it will fall for the rest of the year. From 1950 till 1984 both positive and negative prediction had a certainty of about 70% and 90% respectively with 75% in total. After 1985 however, the negative predictive power had been reduced to 50%, or in other words, no predictive power at all.[2] See also[edit] References[edit]
Mark Twain effect. Fat tail. A fat-tailed distribution is a probability distribution that has the property, along with the other heavy-tailed distributions, that it exhibits large skewness or kurtosis. This comparison is often made relative to the normal distribution, or to the exponential distribution. Fat-tailed distributions have been empirically encountered in a variety of areas: economics, physics, and earth sciences. Some fat-tailed distributions have power law decay in the tail of the distribution, but do not necessarily follow a power law everywhere.[1] Definition[edit] A variety of Cauchy distributions for various location and scale parameters. The distribution of a random variable X is said to have a fat tail if That is, if X has probability density function Here the tilde notation " " refers to the asymptotic equivalence of functions.
Fat tails and risk estimate distortions[edit] Levy flight from a Cauchy Distribution compare to the Brownian Motion (below). Applications in economics[edit] See also[edit] Dividend puzzle. The puzzle evolved from the Modigliani-Miller theorems of 1959 and 1961. The reasons for the dividend puzzle have been attributed to a wide range of factors, including uncertainties, psychological/behavioral economics issues, tax-related matters, and asymmetric information. See also[edit] Dividend investing References[edit] Ruben D. Cohen (2002) “The Relationship Between the Equity Risk Premium, Duration and Dividend Yield [ download,” Wilmott Magazine, pp 84–97, November issue.
Limits to arbitrage. Limits to arbitrage is a theory that, due to restrictions that are placed on funds that would ordinarily be used by rational traders to arbitrage away pricing inefficiencies, prices may remain in a non-equilibrium state for protracted periods of time. Rational traders usually work for professional money management firms, and invest other peoples' money. If they engage in arbitrage in reaction to a stock mispricing, and the mispricing persists for an extended period, clients of the money management firm can (and do) formulate the opinion that the firm is incompetent.
This results in withdrawal of the clients' funds. In order to deliver funds, the manager must unwind the position at a loss. Long-Term Capital Management became a victim of limits to arbitrage in 1998. Inefficient Markets: An Introduction to Behavioral Finance, Andrei Shleifer, 2000, Oxford University Press.Andrei Shleifer and Robert W. Sticky (economics) Nominal rigidity, also known as price-stickiness (and/or wage-stickiness), describes a situation in which the nominal price is resistant to change.
Complete nominal rigidity occurs when a price is fixed in nominal terms for a relevant period of time. For example, the price of a particular good might be fixed at $10 per unit for a year. Partial nominal rigidity occurs when a price may vary in nominal terms, but not as much as it would if perfectly flexible. For example, in a regulated market there might be limits to how much a price can change in a given year. If we look at the whole economy, some prices might be very flexible and others rigid. This will lead to the aggregate price level (which we can think of as an average of the individual prices) becoming "sluggish" or "sticky" in the sense that it does not respond to macroeconomic shocks as much as it would if all prices were flexible.
Examples of stickiness[edit] Many firms, during recessions, lay off workers. Where which implies that. Efficiency wages. In labor economics, the efficiency wage hypothesis argues that wages, at least in some markets, form in a way that is not market-clearing. Specifically, it points to the incentive for managers to pay their employees more than the market-clearing wage in order to increase their productivity or efficiency, or reduce costs associated with turnover, in industries where the costs of replacing labor is high.
This increased labor productivity and/or decreased costs pay for the higher wages. Because workers are paid more than the equilibrium wage, there may be unemployment. Efficiency wages offer therefore a market failure explanation of unemployment – in contrast to theories which emphasize government intervention (such as minimum wages).[1] However, efficiency wages do not necessarily imply unemployment, but only uncleared markets and job rationing in those markets. There may be full employment in the economy, and yet efficiency wages may prevail in some occupations. Overview[edit] Market anomaly. A market anomaly (or market inefficiency) is a price and/or rate of return distortion on a financial market that seems to contradict the efficient market hypothesis.[1][2] The market anomaly usually relates to: There are anomalies in relation to the economic fundamentals of the equity, technical trading rules, and economic calendar events.
References[edit] External links[edit] January effect. Therefore, the main characteristics of the January Effect are an increase in buying securities before the end of the year for a lower price, and selling them in January to generate profit from the price differences. The recurrent nature of this anomaly suggest that the market is not efficient, as market efficiency would suggest that this effect should disappear. The effect was first observed in, or before, 1942 by investment banker Sidney B.
Wachtel.[1] It is the observed phenomenon that since 1925, small stocks have outperformed the broader market in the month of January, with most of the disparity occurring before the middle of the month.[2] When combined with the four-year presidential cycle, historically the largest January effect occurs in year three of a president's term.[3] Alternative definition[edit] The January barometer ("As goes January, so goes the year") is sometimes also called the January effect.[6][7] Criticism[edit] See also[edit] References[edit]
Equity premium puzzle. The equity premium puzzle is a term coined in 1985 by Rajnish Mehra and Edward C. Prescott in their seminal work of the same name,[1][2] and refers to a lack of consensus among economists on why demand for government bonds—which return much less than stocks—is as high as it is, and even why the demand exists at all. The intuitive notion that stocks are much riskier than bonds is not a sufficient explanation as the magnitude of the disparity between the two returns, the equity risk premium (ERP), is so great that it implies an implausibly high level of investor risk aversion that is fundamentally incompatible with other branches of economics, particularly macroeconomics and financial economics. The process of calculating the equity risk premium, and selection of the data used, is highly subjective to the study in question, but is generally accepted to be in the range of 3–7% in the long-run.
The puzzle has led to an extensive research effort in both macroeconomics and finance. 2007 Stock Market Returns by Day of Week. When you think about the timing of economic reports, including Fed announcements, not all days are created equal. That raises the possibility of seeing different patterns of returns as a function of day of week. Above we see 2007 returns in the cash Dow Jones Industrial Average as a function of day of week.
Wednesday produced far and away the largest point gains, much of which was retraced on Thursday. Interestingly, simply buying stocks at the close on Tuesday and selling at the close on Wednesday accounted for all of the market's gains for 2007--and then some. My look at the data suggests that this is not because returns were more volatile on Wednesdays. Stock Market Performance by Hour of the Day.