stock market anomalies and trading strategies
Abstract
Nonpareil of the leading criticisms of the streamlined market hypothesis is the mien of alleged "anomalies", i.e. empirical evidence of abnormal behaviour of asset prices which is unconformable with market efficiency. However, all but studies do not allow dealings costs. Their existence implies that in point of fact traders might not exist able to prepar abnormal profits. This paper examines whether OR non anomalies such as intraday surgery time of the day effects gift ascension to exploitable gain opportunities by replicating the actions of traders. Specifically, the analysis is based happening a trading automaton which simulates their demeanour, and incorporates adaptable transaction costs (spreads). The results suggest that trading strategies aimed at exploiting day-after-day patterns do not generate extra profits. Further, there are no significant differences between torpedo-periods (2005–2006—"normal"; 2007–2009—"crisis"; 2010–2011—"post-crisis).
Introduction
The efficient grocery store hypothesis (EMH) has been highly criticised during the last twenty years, especially on the basis of empirical tell suggesting the front of so-called "anomalies", i.e. abnormal behaviour of asset prices which is seen as at odds with market efficiency. Since the seminal work of Mandelbrot (1963), several studies have shown that the Gaussian distribution provides a misfortunate tally to the behaviour of asset prices, not being harmonious with the random walk model implied by the EMH. As a ensue of this literature, fat tails, clustered volatility, seven-day memory board etc. have get on long-familiar "stylized facts" characterising the demeanor of plus prices. The draw a bead on of this paper is to show that apparent applied mathematics "anomalies" manage non necessarily mean that the food market is inefficient: if it is non possible to generate extra profits by exploiting them, they should personify seen just as applied math phenomena rather than as evidence of market inefficiency.
In particular, we concenter on unmatchable of the best known anomalies, which is the presence of intraday patterns, i.e. more modifier trading at the root and the end of the trading day combined with higher price volatility (Admati and Pfleiderer 1988). For example, Ellen Price Wood et al. (1985) reported that all positive returns are earned during the commencement thirty minutes and at the market close. Harris (1986) showed that prices and last trades lean to be prepared during the first 45dannbsp;Min dialect of trading sessions (all days except Monday). Such patterns were too mentioned aside Thaler (1987) and Raise (2002). Strawinski and Slepaczuk (2008) institute evidence of intraday patterns in the Warszawa Stock Substitution as well.
The primary limitation of the above mentioned studies is that they neglect dealing costs: incorporating spreads, commissions and other fees and payments connected with the trading process can change the picture dramatically. Specifically, it can suit clear that some of these "anomalies" cannot in fact be exploited, i.e. fat trading is non possible, and this unfitness to obtain extra net profit is fully consistent with the EMH.
The present study examines intraday patterns using a trading robot which simulates the actions of the trader and incorporates both transaction costs (spreads) into the analytic thinking. The aim is to reveal that, Eastern Samoa mentioned higher up, the presence of anomalies by itself does not necessarily represent evidence of market inefficiency, since it power not glucinium realistic to exploit them in practice. Obviously, speculators probing for profit opportunities are not simply subterfuge following of the crew; instead, they promptly react along others' behaviour, and as a result any arbitrage opportunities (founded on deviations from fundamental principle-based asset prices) bequeath quickly disappear; nevertheless, it might atomic number 4 possible to exploit them in the precise short run victimisation an congruent trading strategy. We analyse both a mature and an future securities market, namely 27 US companies included in the Dow Jones index, as intimately arsenic 8 Blue-silicon chip Russian companies. Further, we test different sub-periods (2005–2006—"regular"; 2007–2009—"crisis"; 2010–2011—"post-crisis") to establish whether there is evidence of changing demeanor depending on the phase of the economic hertz.
The remainder of the paper is structured as follows: Sect.dannbsp;2 briefly reviews the lit along the efficient market hypothesis and market anomalies. Sectiondannbsp;3 explains the method acting used for the analysis. Sectiondannbsp;4 presents the trial-and-error results. Sectiondannbsp;5 offers some closing remarks.
Literature Review
The EMH was initially formulated aside Fama (1965), who argued that in an efficient market prices should fully reflect the available information and follow unpredictable (see also Samuelson 1965). Fama (1970) so defined three forms of market efficiency (weak, semi-weapons-grade and strong). This theory has been victimised for the valuation of financial assets in terms of risk and uncertainty, and for devising portfolio strategies (see, inter alia, Sharpe 1965; Lintner 1965; Mossin 1966, and Treynor 1962). In the 1980's, it was extremely criticized as overlooking transaction costs, information dissymmetry (Grossman and Stiglitz 1980), nonrational behaviour etc. American Samoa a result many alternative theories and approaches were developed (behavioural finance, the adaptive market hypothesis, the fractal market hypothesis, etc.).
The main implication of the EMH is that traders should not be competent to "overreach" the market and make abnormal profits. An comprehensive literature analyses whether instead there exist market anomalies that dismiss be exploited through appropriate trading strategies. This term was first used by Kuhn (1970). Schwert (2003) is an example of a study providing evidence of abnormalities which are inconsistent with asset pricing theories. Shiller (2000) and Akerlof and Shiller (2009) take the view that thither are deep reasons for the presence of anomalies in business markets, namely irrational behaviour of investors (animal spirits, the crowd instinct, mass psychosis, mass panic), which is inconsistent with the EMH paradigm.
Jensen (1978) argued that anomalies can only exist considered statistically significant when they generate surfeit returns. Raghubir and Das (1999) classify them as follows:
-
Anomalies related to prices and returns (contrarian trading, value investing, the size effect, momentum effect, the effect of shuttered-end funds);
-
Anomalies associated with trading volume and volatility (affright, bubbles on the markets);
-
Anomalies joint with the time series (the Mdanamp;A effect, the IPO effect);
-
Other anomalies.
Jacobsen et al.. (2005) distinguished between calendar, pricing and size anomalies. Examples of calendar (metre) anomalies (the most frequently determined) are: End-of-Draw Effect, Annual Worldwide Optimism Cycle Effect, Allhallows Eve Effect, 12-Month Cycle for Stock Returns Core, Mid-yr Point Set up, Two-Year Essence, Sector Performance by Calendar Month, Worst and Best Days of the Year Effect, Jan Effect, Time unit Effect, Turn-of-the-Calendar month Effect, Undertaking Daylight Effect, Day of the Dividend Payments Effect, Trading Around Option Expiration Days and others.
Particularly great are intraday anomalies, including Uncomplete-of-the-Solar day Effects (abnormally low returns in the eye of a trading session, attended by a sharp give way trading volumes); Last Hour and First Hour Effects (with the last hour of trading being the best, and the prototypic minute the worst time in terms of returns); and the Time of the Day anomalousness (with securities tending to be up in the first-year 45 and last 15dannbsp;min of the trading day).
Harris (1986) and Thaler (1987) examined 15-Taiwanese intervals in plus prices movement to identify patterns in (the volatility of) returns (see also Levy 2002, and Dimson 1988). Harris (1986) found a time of the Day anomaly in the first 45dannbsp;Min dialect of a trading academic term of wholly days of the week except Monday and at the end of a trading day (approximately the worst 5dannbsp;min of the sitting). In his read of the Spanish neckcloth market, Camino (1996) found positive returns in the basic hour of the trading session in all trading days except Monday and Wednesday, and a strong tendency for prices to rise in the first and parthian 15-Hokkianese periods of trading (see besides Coroneo and Veredas 2006). Wood et al. (1985) reported jumps at the opening and closing of trading. Brooks et al. (2003) found high trading volumes in the NYSE at the beginning and the end of the 24-hour interval. The possibility of using the U-shaped pattern by commercialise participants to build trading strategies was emphasised by Abhyankar et al. (1997). The same pattern was found with respect to trading volume, paying back volatility and liquidity profile by Tissaoui (2012) in the Tunisian Stock Exchange. Tabledannbsp;1 gives details of additional relevant studies.
Data and Methodology
Although nigh studies suggest the presence of anomalies in the first 45dannbsp;min (or number 1 hour) of the trading session, their results differ in terms of the exact time when the end-of-the-day anomaly emerges: the last transaction, the last 5dannbsp;Taiwanese, the last 15dannbsp;Fukien, the last hour. Chan (2005) according that the overall moderate returns per minute in the Hong Kong securities market (over the inalterable 30 Hokkianese, o'er the last 10 min, over the last 5 min, and concluded the last 1 min) are statistically positive. However, the majority of studies consider 15-min intervals. Since the empirical literature does not provide clear evidence on intraday effects on particular weekdays (watch, e.g., Strawinski and Slepaczuk 2008; Harris 1989), and since it is effortful to distinguish between clock time of the day and day of the week effects, we center specifically on the last 15dannbsp;min before the end of the trading academic session (take care Levy 2002).
We deal the intraday anomalousness from the trader's viewpoint: is IT possible to make profits from trading on intraday patterns (which would argue market inefficiency)? In particular, we mental testing the next hypotheses:
-
Guess 1: first 45 min up effect exists (H1):
-
H1a—case of developed countries
-
H1b—case of developing countries
-
Hypothesis 2 last 15 min ahead upshot exists (H2)
-
H2a—case of developed countries
-
H2b—case of developing countries
-
Hypothesis 3 the results for different periods (pre-crisis, crisis, and post-crisis) are statistically different (H3).
We use information at 15-Amoy intervals for 27 US companies included in the Dow-Jones Industrial Average index and 8 Blue-chip Russian companies. For the US the sample period is 2005–2011, and the pursuit sub-periods are also considered:
-
2005–2006—normal;
-
2007–2009—crises;
-
2010–2011—post-crises.
For Russia, owing to want of information, the analysis is carried out only for the period 2011–2013.
Virtually studies connected intraday anomalies do not incorporate transaction costs, even though trading is inevitably connected with spreads, fees and commissions to brokers. These costs can be divided into immobile and variable ones. The latter are present in each transaction. A typical exemplar is the spread, which is incorporated into our depth psychology. Specifically, we programme a trading robot which automatically opens and closes positions according to the clock time of the twenty-four hours effect. Positions (in our case entirely the "long" ones) will live opened along "ask" price and closed on "bid" price, though we will merged the inconsistent part of transactional costs in our analysis. The algorithm is constructed such that long positions are opened at the beginning of the trading session and are closed after 45dannbsp;min (the first 45dannbsp;min up effect mentioned by Harris (1986) and Levy (2002)), and are besides opened at the end of the Clarence Shepard Day Jr.. As we consider 15-min intervals, they are opened in the last 15dannbsp;minute of the trading session and are closed at the end of the session (the last 15dannbsp;Fukkianese of the day up effect mentioned by Levy 2002). We use a course of study in the MetaTrader terminal that has been industrial in MetaQuotes Language 4 (MQL4) and used for the mechanization of analytical and trading processes. Trading robots (called experts in MetaTrader) allow to analyse price information and manage trading activities on the basis of the signals received.
MetaQuotes Language 4 is the nomenclature for programming trade strategies built in the client terminal. The syntax of MQL4 is quite similar to that of the C language. It allows to programme trading robots that automate trade processes and is ideally suited for the implementation of trading strategies. The final allows non only to programme trading robots, but also to test them by checking their efficiency using historical data. These are saved in the MetaTrader terminal as bars and defend records appearing as TOHLCV (HST data format). The trading terminal allows to test experts past various methods. Aside selecting smaller periods it is possible to fancy damage fluctuations within bars, i.e., Leontyne Price changes will be reproduced more precisely. For example, when an expert is tried and true on nonpareil-hour information, Price changes for a bar can be modelled using 1-min data. The price account stored in the client terminal includes only Bid prices. In order to model Ask prices, the strategy tester uses the current spread at the beginning of testing. However, a user can set a tailored spread for testing in the "Spread", thereby approximating better actual price movements. Positive profits \(dangt;50~\%\) imply that H1 and H2 cannot be rejected. Equally for H3, we carry out t tests: H3 is rejected if \(t danlt; tcritical\). The program codes for the trading robots used therein canvas are presented in Appendixdannbsp;4 and 5.
Experimental Results
The testing procedure comprises cardinal steps, i.e. at first examination the first 45dannbsp;min up effect, and then the last-place 15dannbsp;min up core.
The complete results for the previous are given in Vermiform appendixdannbsp;1. A summary for different clock time periods is shown in Tabledannbsp;2.
As crapper be seen, every periods were unprofitable, with the probability of a profitable trade being less than 50dannbsp;%. Hypothesis H1a is rejected, i.e. there is no evidence of a first 45dannbsp;min upfield effect in the US stock exchange. Shelvedannbsp;3 reports the t screen for H3 for different sub-periods: present is rejected in all cases. Mesadannbsp;4 shows that H3 is not rejected for meshwork profit per slew in any of the torpedo-periods.
The complete results for the last 15dannbsp;min up force are presented in Cecal appendagedannbsp;2. A summary for the unlike sentence periods is displayed in Shelvedannbsp;5.
All periods were unprofitable, with the chance of a profitable trade being less than 40dannbsp;%. Possibility H2a is rejected: there is none in conclusion 15dannbsp;min up impression in the US stock market.
The t tests for H3 for antithetical sub periods are displayed in Postponedannbsp;6: this hypothesis cannot beryllium rejected, and this applies to all sub-periods.
Set backdannbsp;7 shows that H3 is rejected for net profit per deal. Thither is no evidence of differences between hoagie-periods.
The complete results for Soviet Russia are bestowed in Appendixdannbsp;3. A summary is provided in Tabledannbsp;8: H1b and H2b are rejected again, indicating the petit mal epilepsy of the intraday anomaly being considered in a less developed market as well.
Conclusions
The medical practice relevance of the EMH has been called into question by many another studies determination evidence of so-called anomalies seemingly giving agents the chance to progress to abnormal profits. This paper argues that the presence of anomalies does not necessarily represent evidence of commercialize inefficiency (risk-free lucre opportunities): using a trading robot simulating the actions of a trader we reveal in the case of intraday patterns that, if transaction costs are taken into account, on that point are nobelium profitable trading strategies (i.e. opportunities to make exceptional profits exploiting this type of anomaly), and therefore no testify against the EMH.
Specifically, we consider a known "time of the day anomaly": prices tend to be sprouted during the first 45dannbsp;min and the last 15dannbsp;min of the trading session.
We test 3 hypotheses:
-
Supposition 1: first 45 min up effect exists (H1):
-
Surmise 2: last 15 min skyward set up exists (H2)
-
Hypothesis 3: results for different periods (pre-crisis, crisis, and post-crisis) are statistically different (H3)
These hypotheses are spurned for some the USA and Russia, a matured and inferior developed stock marketplace respectively. The only exception is H3: the results for the last 15dannbsp;min up effect vary depending on the poor boy-menstruation considered.
On the whole, our analysis implies that it is not possible to exploit intraday patterns to make abnormal profits. This suggests that the results from previous studies purporting to provide evidence of exploitable net income opportunities resulting from market anomalies (which would comprise inconsistent with the EMH) were as a matter of fact deceptive because they did not allow dealing costs. The trading robot approach used in the present study tooshie also Be secondhand to analyse other anomalies, but this is left for future work.
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Acknowledgments
We are glad to a phallus of the editorial board for useful comments and suggestions.
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Appendices
Appendix 1
First 45dannbsp;min up effect
2005–2006
| Fellowship | Total trades | Benefit trades | Benefit trades (% of total) | Total net profit |
|---|---|---|---|---|
| Alcoa | 465 | 195 | 41.94 | \(-\)256.1 |
| Altria Group | 464 | 213 | 45.91 | \(-\)28.9 |
| American Express Company | 465 | 214 | 46.02 | \(-\)46.6 |
| ATT Inc | 458 | 191 | 41.70 | \(-\)84.3 |
| Boeing | 465 | 212 | 45.59 | \(-\)315.7 |
| Imogene Coca-Cola | 465 | 163 | 35.05 | \(-\)247.4 |
| DuPont | 465 | 217 | 46.67 | \(-\)126.3 |
| ExxonMobil Corporation | 465 | 209 | 44.95 | \(-\)185.9 |
| General Galvanic Corporation | 465 | 208 | 44.73 | \(-\)85.2 |
| Hewlett-Packard Ship's company | 485 | 278 | 57.32 | 138.2 |
| Home Depot Corporation | 465 | 208 | 44.73 | \(-\)158.8 |
| Honeywell Foreign Inc | 465 | 219 | 47.10 | \(-\)90.7 |
| IBM Corp | 465 | 168 | 36.13 | \(-\)646.2 |
| Intel Bay window | 465 | 200 | 43.01 | \(-\)101 |
| Planetary Paper Company | 465 | 182 | 39.14 | \(-\)256.9 |
| Johnsondanadenosine monophosphate;Johnson | 464 | 189 | 40.73 | \(-\)159.8 |
| JP Sir Henry Morgan Chase | 465 | 225 | 48.39 | \(-\)26.1 |
| McDonalds Tummy | 465 | 180 | 38.71 | \(-\)270.3 |
| Merck Co Inc | 465 | 229 | 49.25 | \(-\)105.4 |
| Microsoft | 465 | 220 | 47.31 | \(-\)29 |
| MMM Company | 465 | 197 | 42.37 | \(-\)423.8 |
| Pfizer | 465 | 185 | 39.78 | \(-\)195 |
| Procter Gamble Company | 465 | 211 | 45.38 | \(-\)145.4 |
| In agreement Technologies Corporation | 465 | 173 | 37.20 | \(-\)429.1 |
| Verizon Communication theory INC | 485 | 185 | 38.14 | \(-\)249.1 |
| Wal-Marketplace Stores Inc | 464 | 213 | 45.91 | \(-\)129.1 |
| Walt Disney | 465 | 219 | 47.10 | \(-\)54 |
2007–2009
| Troupe | Total trades | Net income trades | Profit trades (% of total) | Total lucre lucre |
|---|---|---|---|---|
| Alcoa | 740 | 322 | 43.51 | \(-\)447.6 |
| Altria Group | 740 | 322 | 43.51 | \(-\)169.3 |
| American Express Company | 728 | 300 | 41.21 | \(-\)629 |
| ATT Inc | 739 | 321 | 43.44 | \(-\)272.7 |
| Boeing | 739 | 330 | 44.65 | \(-\)761.2 |
| Coca-Cola | 740 | 340 | 45.95 | \(-\)326.9 |
| DuPont | 740 | 339 | 45.81 | \(-\)299.6 |
| ExxonMobil Corporation | 740 | 373 | 50.41 | 119.1 |
| General Electric Corporation | 740 | 281 | 37.97 | \(-\)559.6 |
| Hewlett-Packard Company | 740 | 381 | 51.49 | 58.2 |
| Home Terminal Corp | 740 | 311 | 42.03 | \(-\)274.8 |
| Honeywell Supranational Inc | 740 | 328 | 44.32 | \(-\)546.7 |
| IBM Corporation | 740 | 331 | 44.73 | \(-\)1005.4 |
| Intel Corporation | 738 | 328 | 44.44 | \(-\)226.7 |
| International Paper Company | 740 | 338 | 45.68 | \(-\)254.4 |
| Johnsondanamp;Lyndon Johnson | 740 | 332 | 44.86 | \(-\)286.9 |
| JP Morgan Chase | 740 | 322 | 43.51 | \(-\)406.6 |
| McDonalds Corporation | 740 | 317 | 42.84 | \(-\)365.4 |
| Merck Co Inc | 740 | 369 | 49.86 | \(-\)112.2 |
| Microsoft | 740 | 355 | 47.97 | \(-\)102.5 |
| MMM Party | 739 | 335 | 45.33 | \(-\)478 |
| Pfizer | 740 | 301 | 40.68 | \(-\)200.6 |
| Procter Hazard Company | 740 | 358 | 48.38 | \(-\)122.4 |
| Amalgamate Technologies Potbelly | 740 | 301 | 40.68 | \(-\)658.7 |
| Verizon Communication theory Iraqi National Congress | 740 | 319 | 43.11 | \(-\)307.7 |
| Wal-Mart Stores Inc | 740 | 330 | 44.59 | \(-\)224.7 |
| Walt Disney | 740 | 339 | 45.81 | \(-\)208.3 |
2010–2011
| Company | Tote up trades | Profit trades | Profit trades (% of total) | Total net turn a profit |
|---|---|---|---|---|
| Alcoa | 334 | 134 | 40.12 | \(-\)112.1 |
| Altria Group | 339 | 118 | 34.81 | \(-\)129 |
| American Express Company | 339 | 164 | 48.38 | \(-\)110 |
| ATT Inc | 339 | 111 | 32.74 | \(-\)192.7 |
| Boeing | 339 | 159 | 46.90 | \(-\)153.6 |
| Coca-Cola | 339 | 139 | 41.00 | \(-\)213.8 |
| DuPont | 338 | 168 | 49.70 | \(-\)41.5 |
| ExxonMobil Bay window | 339 | 137 | 40.41 | \(-\)215.5 |
| General Electric Corporation | 339 | 142 | 41.89 | \(-\)113.3 |
| Hewlett-Packard Caller | 339 | 177 | 52.21 | \(-\)23.1 |
| Domicile Storehouse Corporation | 339 | 164 | 48.38 | \(-\)44.2 |
| Honeywell International Inc | 339 | 151 | 44.54 | \(-\)125.1 |
| IBM Bay window | 339 | 149 | 43.95 | \(-\)296.5 |
| Intel Corporation | 339 | 135 | 39.82 | \(-\)155.4 |
| Worldwide Paper Company | 339 | 166 | 48.97 | \(-\)80.1 |
| Johnsondanamp;Johnson | 339 | 141 | 41.59 | \(-\)130.8 |
| JP Henry Morgan Chase | 339 | 160 | 47.20 | \(-\)162.8 |
| McDonalds Corporation | 339 | 140 | 41.30 | \(-\)205 |
| Merck Co Inc | 339 | 134 | 39.53 | \(-\)162.2 |
| Microsoft | 339 | 131 | 38.64 | \(-\)186.5 |
| MMM Accompany | 338 | 151 | 44.67 | \(-\)144.5 |
| Pfizer | 339 | 131 | 38.64 | \(-\)109.9 |
| Procter Gamble Company | 339 | 152 | 44.84 | \(-\)141.2 |
| United Technologies Corporation | 339 | 139 | 41.00 | \(-\)252.7 |
| Verizon Communications Inc | 339 | 130 | 38.35 | \(-\)218.4 |
| Wal-Mart Stores Iraqi National Congress | 338 | 157 | 46.45 | \(-\)90.3 |
| Disney | 338 | 158 | 46.75 | \(-\)28.9 |
Appendix 2
Net 15 min up effect
2005–2006
| Company | Total trades | Profit trades | Profit trades (% of total) | Overall profits profit |
|---|---|---|---|---|
| Alcoa | 465 | 195 | 41.94 | \(-\)256.1 |
| Altria Group | 464 | 213 | 45.91 | \(-\)28.9 |
| American English Show Company | 465 | 214 | 46.02 | \(-\)46.6 |
| ATT Iraqi National Congress | 458 | 191 | 41.70 | \(-\)84.3 |
| Boeing | 465 | 212 | 45.59 | \(-\)315.7 |
| Coca-Cola | 465 | 163 | 35.05 | \(-\)247.4 |
| DuPont | 465 | 217 | 46.67 | \(-\)126.3 |
| ExxonMobil Corporation | 465 | 209 | 44.95 | \(-\)185.9 |
| General Electric Corporation | 465 | 208 | 44.73 | \(-\)85.2 |
| Hewlett-Packard Company | 485 | 278 | 57.32 | 138.2 |
| Plate Depot Corp | 465 | 208 | 44.73 | \(-\)158.8 |
| Honeywell International Inc | 465 | 219 | 47.10 | \(-\)90.7 |
| IBM Potbelly | 465 | 168 | 36.13 | \(-\)646.2 |
| Intel Corporation | 465 | 200 | 43.01 | \(-\)101 |
| International Report Company | 465 | 182 | 39.14 | \(-\)256.9 |
| Johnsondanampere;Johnson | 464 | 189 | 40.73 | \(-\)159.8 |
| JP Morgan Chase | 465 | 225 | 48.39 | \(-\)26.1 |
| McDonalds Potbelly | 465 | 180 | 38.71 | \(-\)270.3 |
| Merck Co INC | 465 | 229 | 49.25 | \(-\)105.4 |
| Microsoft | 465 | 220 | 47.31 | \(-\)29 |
| MMM Company | 465 | 197 | 42.37 | \(-\)423.8 |
| Pfizer | 465 | 185 | 39.78 | \(-\)195 |
| Procter Gamble Company | 465 | 211 | 45.38 | \(-\)145.4 |
| United Technologies Corporation | 465 | 173 | 37.20 | \(-\)429.1 |
| Verizon Communication theory Inc | 485 | 185 | 38.14 | \(-\)249.1 |
| Wal-Marketplace Stores Inc | 464 | 213 | 45.91 | \(-\)129.1 |
| Walt Disney | 465 | 219 | 47.10 | \(-\)54 |
2007–2009
| Company | Total trades | Profit trades | Profit trades (% of total) | Full net lucre |
|---|---|---|---|---|
| Alcoa | 740 | 322 | 43.51 | \(-\)447.6 |
| Altria Group | 740 | 322 | 43.51 | \(-\)169.3 |
| American Express Fellowship | 728 | 300 | 41.21 | \(-\)629 |
| ATT Inc | 739 | 321 | 43.44 | \(-\)272.7 |
| Boeing | 739 | 330 | 44.65 | \(-\)761.2 |
| Coca-Cola | 740 | 340 | 45.95 | \(-\)326.9 |
| DuPont | 740 | 339 | 45.81 | \(-\)299.6 |
| ExxonMobil Tummy | 740 | 373 | 50.41 | 119.1 |
| General Galvanizing Corporation | 740 | 281 | 37.97 | \(-\)559.6 |
| Hewlett-Packard Party | 740 | 381 | 51.49 | 58.2 |
| Home Depot Corp | 740 | 311 | 42.03 | \(-\)274.8 |
| Honeywell Global Iraqi National Congress | 740 | 328 | 44.32 | \(-\)546.7 |
| IBM Corporation | 740 | 331 | 44.73 | \(-\)1005.4 |
| Intel Corporation | 738 | 328 | 44.44 | \(-\)226.7 |
| International Paper Company | 740 | 338 | 45.68 | \(-\)254.4 |
| JohnsondanAMP;Johnson | 740 | 332 | 44.86 | \(-\)286.9 |
| JP Morgan Chase | 740 | 322 | 43.51 | \(-\)406.6 |
| McDonalds Tummy | 740 | 317 | 42.84 | \(-\)365.4 |
| Merck Cobalt INC | 740 | 369 | 49.86 | \(-\)112.2 |
| Microsoft | 740 | 355 | 47.97 | \(-\)102.5 |
| MMM Company | 739 | 335 | 45.33 | \(-\)478 |
| Pfizer | 740 | 301 | 40.68 | \(-\)200.6 |
| Procter Gamble Company | 740 | 358 | 48.38 | \(-\)122.4 |
| United Technologies Tummy | 740 | 301 | 40.68 | \(-\)658.7 |
| Verizon Communications Inc | 740 | 319 | 43.11 | \(-\)307.7 |
| Wal-Mart Stores Inc | 740 | 330 | 44.59 | \(-\)224.7 |
| Walt Disney | 740 | 339 | 45.81 | \(-\)208.3 |
2010–2011
| Company | Total trades | Profit trades | Profit trades (% of sum up) | Total net net |
|---|---|---|---|---|
| Alcoa | 308 | 58 | 18.83 | \(-\)95 |
| Altria Group | 308 | 78 | 25.32 | \(-\)101.4 |
| American Express Company | 308 | 127 | 41.23 | \(-\)97.5 |
| ATT Inc | 308 | 112 | 36.36 | \(-\)89.4 |
| Boeing | 308 | 96 | 31.17 | \(-\)210.9 |
| Coca-Dope | 308 | 92 | 29.87 | \(-\)198.1 |
| DuPont | 308 | 124 | 40.26 | \(-\)93.9 |
| ExxonMobil Corporation | 308 | 106 | 34.42 | \(-\)207 |
| General Galvanising Corporation | 308 | 88 | 28.57 | \(-\)94.6 |
| Hewlett-Packard Company | 308 | 107 | 34.74 | \(-\)136.9 |
| Home Depot Corp | 308 | 86 | 27.92 | \(-\)124.9 |
| Honeywell International Inc | 308 | 122 | 39.61 | \(-\)100.2 |
| IBM Tummy | 308 | 34 | 11.04 | \(-\)947.6 |
| Intel Potbelly | 308 | 91 | 29.55 | \(-\)105.5 |
| Global Paper Company | 308 | 115 | 37.34 | \(-\)79.5 |
| President Andrew Johnsondanamp;Johnson | 308 | 118 | 38.31 | \(-\)115.4 |
| JP Morgan Following | 308 | 119 | 38.64 | \(-\)101.1 |
| McDonalds Corporation | 308 | 79 | 25.65 | \(-\)250.4 |
| Merck Co Iraqi National Congress | 308 | 94 | 30.52 | \(-\)110.5 |
| Microsoft | 308 | 99 | 32.14 | \(-\)122.3 |
| MMM Company | 308 | 109 | 35.39 | \(-\)190.7 |
| Pfizer | 308 | 76 | 24.68 | \(-\)106.3 |
| Procter Gamble Ship's company | 308 | 78 | 25.32 | \(-\)236.8 |
| United Technologies Corporation | 308 | 101 | 32.79 | \(-\)224.2 |
| Verizon Communications Inc | 308 | 116 | 37.66 | \(-\)89.2 |
| Wal-Marketplace Stores Inc | 308 | 85 | 27.60 | \(-\)182.6 |
| Walt Disney | 308 | 100 | 32.47 | \(-\)112.8 |
Vermiform process 3
Results for Russian stock markets
Archetypal 45 Hokkianese up effect
| Fellowship | Total trades | Earnings trades | Profit trades (% of total) | Total net profit | Profit per apportion |
|---|---|---|---|---|---|
| GAZPROM | 286 | 148 | 51.75 | 66.5 | 0.23252 |
| GAZPROM NEFT | 264 | 95 | 35.98 | \(-\)173 | \(-\)0.6553 |
| LUKOIL | 287 | 132 | 45.99 | \(-\)557 | \(-\)1.9408 |
| NORILSKY Atomic number 28 | 285 | 106 | 37.19 | \(-\)434 | \(-\)1.5228 |
| ROSNEFT | 287 | 127 | 44.25 | \(-\)123.6 | \(-\)0.4307 |
| SBERBANK | 286 | 136 | 47.55 | \(-\)275 | \(-\)0.9615 |
| SURGUTNEFTEGAZ | 287 | 134 | 46.69 | \(-\)335 | \(-\)1.1672 |
| VTB BANK | 242 | 50 | 20.66 | \(-\)1757 | \(-\)7.2603 |
Penultimate 15 min prepared effect
| Company | Total trades | Profit trades | Profit trades (% of total) | Add profit | Gain per treat |
|---|---|---|---|---|---|
| GAZPROM | 378 | 185 | 48.94 | \(-\)2.4 | \(-\)0.0063 |
| GAZPROM NEFT | 347 | 45 | 12.97 | \(-\)459 | \(-\)1.3228 |
| LUKOIL | 378 | 154 | 40.74 | \(-\)94 | \(-\)0.2487 |
| NORILSKY NICKEL | 378 | 168 | 44.44 | \(-\)236 | \(-\)0.6243 |
| ROSNEFT | 378 | 181 | 47.88 | \(-\)9.9 | \(-\)0.0262 |
| SBERBANK | 378 | 171 | 45.24 | \(-\)547 | \(-\)1.4471 |
| SURGUTNEFTEGAZ | 378 | 152 | 40.21 | \(-\)179 | \(-\)0.4735 |
| VTB BANK | 320 | 38 | 11.88 | \(-\)26.4 | \(-\)0.0825 |
Appendix 4
Curriculum code for the "prototypic 45 min up effect"
Vermiform process 5
Broadcast code for the "last 15 min upwards effect"
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Caporale, G.M., Gil-Alana, L., Plastun, A. et al. Intraday Anomalies and Grocery store Efficiency: A Trading Robot Analysis. Comput Econ 47, 275–295 (2016). https://doi.org/10.1007/s10614-015-9484-9
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DOI : https://Interior Department.org/10.1007/s10614-015-9484-9
Keywords
- Efficient market hypothesis
- Intraday patterns
- Time of the daytime anomaly
- Trading strategy
JEL classification
- G12
- C63
stock market anomalies and trading strategies
Source: https://link.springer.com/article/10.1007/s10614-015-9484-9
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