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However, most studies do not take into account transaction costs.

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Their existence implies that in fact traders might not be able to make abnormal profits. This paper examines whether or not anomalies such as intraday or time of the day effects give rise to exploitable profit opportunities by replicating the actions of traders.

Specifically, the analysis is based on a trading robot which simulates their behaviour, and incorporates variable transaction costs spreads. The results suggest that trading strategies aimed at exploiting robot based on trading volumes patterns do not generate extra profits.

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Since the seminal work of Mandelbrotseveral studies have shown that the Gaussian distribution provides a poor fit to the behaviour of asset prices, not being compatible with the random walk model implied by the EMH. As a result of this literature, fat tails, clustered volatility, long memory etc. In particular, we focus on one of the best known anomalies, which is the presence of intraday patterns, i.

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For example, Wood et al. Such patterns were also mentioned by Thaler and Levy Strawinski and Slepaczuk found evidence of intraday patterns in the Warsaw Stock Exchange as well.

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The main limitation of the above mentioned studies is that they neglect transaction costs: incorporating spreads, commissions and other fees and payments connected with the trading process can change the picture dramatically. The present study examines intraday patterns using a trading robot which simulates the actions of the trader and incorporates some transaction costs spreads into the analysis.

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The aim is robot based on trading volumes show that, as mentioned above, the presence of anomalies by itself does not necessarily represent evidence of market inefficiency, since it might not be possible to exploit them in practice.

We analyse both a mature and an emerging stock market, namely 27 US companies included in the Dow Jones index, as well as 8 Blue-chip Russian companies. The remainder of the paper is structured as follows: Sect. Literature Review The EMH was initially formulated by Famawho argued that in an efficient market prices should fully reflect the available information and be unpredictable see also Samuelson Fama then defined three forms of market efficiency weak, semi-strong and strong.

This theory has been used for the valuation of financial assets in terms of risk and uncertainty, and for devising portfolio strategies see, inter alia, Sharpe ; Lintner ; Mossinand Treynor As a result many alternative theories and approaches were developed behavioural finance, the adaptive market hypothesis, the fractal market hypothesis, etc. An extensive literature analyses whether instead there exist market anomalies that can be exploited through appropriate trading strategies.

This term was first used by Kuhn Schwert is an example of a robot based on trading volumes providing evidence of abnormalities which are inconsistent with asset pricing theories. Shiller and Akerlof and Shiller take the view that strategy binary options pinocchio are deep reasons for the presence of anomalies in financial markets, namely irrational behaviour of investors animal spirits, the herd instinct, mass psychosis, mass panicwhich is inconsistent with the EMH paradigm.

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Jensen argued that anomalies can only be considered statistically significant when they generate excess returns. Jacobsen et al.

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Harris and Thaler examined min intervals in asset prices movement to identify patterns in the volatility of returns see also Levyand Dimson In his study of the Spanish stock market, Camino found positive returns in the first hour of binary options mail trading session in all trading days except Monday and How the bitcoin wallet works, and a strong tendency for prices to rise in the first and last min periods of trading see also Coroneo and Veredas Wood et al.

Brooks et al.

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The possibility of using the U-shaped pattern by market participants to build trading strategies was emphasized by Abhyankar et al. The same pattern was found with respect to trading volume, return volatility and liquidity profile by Tissaoui in the Tunisian Stock Exchange.

Chan reported that the overall average returns per minute in the Hong Kong stock market over the last 30 min, over the last 10 min, over the last 5 min, and over the last 1 min are statistically positive.

However, the majority of studies consider min intervals. Since the empirical literature does not provide clear evidence on intraday effects on specific weekdays see, e.

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In particular, we test the following hypotheses: Hypothesis 1: first 45 min up effect exists H1 : H1a—case of developed countries Hypothesis 2 last 15 min up effect 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 data at min intervals for 27 US companies included in the Dow Jones index and 8 Blue-chip Russian companies. For the US the sample period is —, and the following sub-periods are also considered: ——normal; ——crises; ——post-crises.

The real trading is done automatically by robots. About three-quarters of trades on the New York Stock Exchange and Nasdaq are done by algorithms - computer programs following complex sets of rules. And this "robo-trading" is having a profound effect on the investment world, from global hedge funds right down to personal savers. But what are the advantages and disadvantages of allowing computers to manage the world's trillions of dollars?

For Russia, owing to lack of data, the analysis is carried out only for the period — Most studies on 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 fixed and variable ones. The latter are present in each transaction. A typical example is the spread, which is incorporated into our analysis.

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Specifically, we programme a trading robot which automatically opens and closes positions according to the time of the day effect. We use a programme in the MetaTrader terminal that has been developed in MetaQuotes Language 4 MQL4 and used for the automation of analytical and robot based on trading volumes processes. Trading robots called experts in MetaTrader allow to analyse price data and manage trading activities on the basis of the signals received.

He has over 20 years of experience in the areas of sales, marketing and relationship management. An MBA from Mangalore University, Gopkumar in his current role, leads the business that offers a single window for transacting in a wide range of asset classes such as equities and commodities derivatives. Time is money in the stock market. While investors have warmed up to stock trading, the actual time required to manually keep track of interesting opportunities, enter details and execute trades often eats into profit.

MetaQuotes Language 4 is the language for programming trade strategies built in the client terminal. The syntax of MQL4 is quite similar to that of the C language.

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It allows to programme trading robots that automate trade processes and is ideally suited for the implementation of trading strategies. The terminal allows not only to programme trading robots, but also to test them by checking their efficiency using historical data. The trading terminal allows to test experts by various methods.

By selecting smaller periods it is possible to see price fluctuations within bars, i. For example, when an expert is tested on one-hour data, price changes for a bar can be modelled using 1-min data.

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The price history 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. Empirical Results The testing procedure comprises two steps, i. Hypothesis H1a is rejected, i.