Effect of overconfidence that happens within the market - Free Essay Example

Sample details Pages: 8 Words: 2346 Downloads: 4 Date added: 2017/06/26 Category Marketing Essay Type Argumentative essay Did you like this example? Odean (1998) theoretically investigated the effects of overconfidence in different market structures which differ in information distribution and in price determination. He presented overconfidence effects on different market measures such as trading volume, market depth, volatility, expected utility and market efficiency taking overconfidence of different traders: price takers in the market where information is broadly disseminated, strategic-trading insiders in markets with concentrated information and market-makers. Odean assumed that investors were rational in all respects except valuing the information. Don’t waste time! Our writers will create an original "Effect of overconfidence that happens within the market" essay for you Create order They overestimate the precision of their information signals. The most robust result presented is that trading volume increases when price-takers, insiders or market-makers are overconfident, however market depth also increases but expected utility of overconfident traders decrease. He also presented that overconfidence of price takers worsens the price quality but overconfidence in insiders improves it. Overconfident traders increase volatility though overconfident market makers may decrease it. Overconfident price takers reduce market efficiency because they are overconfident about their ability to interpret publicly disclosed information while overconfident insiders temporarily increase the market efficiency. In the presence of many overconfident traders market tends to under-react to the information of rational traders. Markets also under-react to highly relevant information and overreact to salient but less relevant information. Daniel, Hirshleifer and Subrahmanyam (1998) developed theoretical models based on investor overconfidence and self attribution biased. They made distinction between public and private information and defined investor overconfidence as overestimation of the precision of private information signals, but not of information signals that are publicly received. In their model, investors are quasi-rational in a way that they are Bayesian optimizers except for over valuing valid private information and biased updating of precision. Overconfidence makes investors to overestimate precision of their own valuation abilities and as a result they overestimate precision of private information signals. The theory predicts stock market under and over reaction on the basis of investor overconfidence and changes in confidence resulting from biased self-attribution of investment outcomes. Primary theme of the study was that stock prices overreact to private information signals and under react to public information signals. Unlike Odean (1998b) who presented trading volume implications of investor overconfidence, Daniel et. al. focus was on return implications of overconfidence. They showed that under certain circumstances security returns are positively auto-correlated in the short run (momentum) but negatively auto-correlated in the long run and overconfidence cause excess volatility. Odean (1998b) examined the disposition effect, the propensity of investors to sell winning investments too early and hold losing investments too long, by analyzing trading records of 10,000 randomly selected accounts from January 1987 to December 1993 at a nationwide discount brokerage house. The data file included 162,948 records of all trades made in 10,000 accounts. Accounts that were closed during the window period were not replaced which made data subject to survivorship bias to some extent in favor of more successful investors. For each day Odean constructed a portfolio of securities for which the purchase date and prices were known and compared the selling price for each stock sold to its average purchase price to determine whether that stock is sold for a gain or a loss. Securities not sold on a particular day were considered to be as unrealized (paper) gain or loss. He constructed two proportions, proportion of gain realized PGR (realized gain divided by realized gain plus p aper gain) and proportion of loss realized PLR (realized loss divided by realized loss plus paper loss). He developed two hypothesis: 1. PGR is greater than PLR (for the entire year), 2. (PLR PGR) in December is greater than (PLR PGR) in January-November. The second hypothesis was for analyzing tax motivated selling in December. He also analyzed PLR and PGR by partitioning the data in time periods and in frequent traders and infrequent traders. He showed the results by comparing proportions using t-test and concluded that investors exhibited a strong preference for selling winners and holding losers throughout the year except December when tax-motivated selling was prominent. Further, desire to rebalance or to avoid the higher trading costs of low priced stocks did not seem to motivate investor behavior. Gervais and Odean (2001) developed a multi-period equilibrium model that describes the process by which traders learn about their abilities and how a bias in this learning makes them overconfident. The model argued that traders initially do not know their ability but they infer it from their success and failure. Traders are biased in assessing their ability when they take too much credit for their success. This leads them to become overconfident. This biasness is more prevalent in early stages of trades careers. With more experience, traders better assess their abilities. Further, overconfident traders are wealthy as a result of success but wealth is not a function of overconfidence. However, process of becoming wealthy can make traders overconfident. Due to wealth of overconfident traders, they are not in immediate danger of being driven out of the marketplace. Overconfidence of a particular trader would not grow indefinitely; it would decrease gradually with time and experience. Bu t, in a market in where new traders are born every minute, overconfidence will flourish. The model showed that higher returns from general market increase make investors overconfident due to their biased attribution of returns to their abilities and therefore they trade more actively. The primary implications of model were: periods of market increase would tend to be followed by periods of increased aggregate trading. Aggregate trading would likely to rise late in a bull market and to fall late in a bear market. Volatility would also increase with the degree of traders learning bias. Due to suboptimal behavior overconfident would lower their profit. Odean (1999) tested the proposition that overconfident investor would trade too much in the market. The data included ten thousand customer accounts randomly selected from accounts that were active in 1987. It was provided by a large discount brokerage house and had 162,948 records of all trades made in sample accounts from 1987 to 1993. Odean (1999) examined that whether the trading profits of discount brokerage customers were enough to cover their trading costs. Return horizons were taken as four months (84 trading days), one year (252 trading days), and two years (504 trading days). The first null hypothesis was established as, the difference in returns (average returns to securities bought minus the average returns to security sold) was greater than or equal to the average total cost of a round-trip trade (i.e about 5.9 percent) over all the return horizons. The second null hypothesis was that average returns to securities bought were greater than or equal to those sold over the same horizons. Due to lack of independence between overlapping periods statistical test that require independence were not employed. Instead, bootstrapping an empirical distribution under the assumption that returns were drawn from the same distribution was used to test the significance of differences in returns. The results showed that investors trade too much in that their returns were reduced through trading even after controlling for trades motivated by tax-loss selling, liquidity demands, portfolio rebalancing and a move towards lower-risk securities. Overconfident investors actually lowered their returns through trading even when trading costs are ignored. De Bondt and Thaler (1995) reviewed the literature on behavioral finance. They argued that the key behavioral factor needed to understand the trading puzzle is overconfidence. They further pointed out that overconfidence can explain why portfolio managers trade too much, why active equity managers are hired by pension funds, and why financial economists often hold actively managed portfolios. Moreover, high trading volume and the pursuit of active investment strategies look to be inconsistent with common knowledge of rationality. Benos (1998) developed a theoretical model of overconfidence. He examined the overconfidence where some risk neutral investors overestimate the precision of their private information. He analyzed a strategic model of trading in a call auction market with many informed traders. Investors overestimate their precision in a way that when they receive imperfect information on asset characteristics, some treat it cautiously, realizing that it may contain irrelevant noise, while, others think signals are perfect. In his model some informed investors were overconfident about their estimates of unknown variables or about their valuation abilities. And also all market participants knew about the beliefs of all their opponents and about the overconfidence of traders and reacted accordingly. And overconfident investors compete in market orders with informed traders who have rational expectations. The model concluded that presence of overconfident traders in the market results in higher trading v olume, more depth, higher volatility and increased market efficiency. Striking result presented was that overconfident traders may make higher expected profits. This is due to first-mover in competition setting. Gallant, Rossi and Tauchen (1992) investigated price and trading volume co-movement. Their empirical study was mainly a data-based effort. It was not organized around the specification and testing of a particular model or class of models. To analyze the relationship between contemporaneous trading volume and volatility and the relationships among prices, volatility, and volume over time were the two main objectives among four. A bi-variate time series of 16,127 daily observations on the SP composite index and total NYSE trading volume from 1928 to 1987 was used. They employed seminonparametric estimation approach, which was a nonparametric estimation strategy, to analyze the data. The nonparametric choice was adopted to avoid risk of specification errors with parametric techniques. Initially data was adjusted for both long term trends and known calendar effects using different dummy variables. Data was further partitioned into three sub-periods to analyze the stability of the finding s. The results explained that there is positive and nonlinear relationship between daily price change and daily trading volume. Conditional volatility and trading volume are also positively related. Positive relationship also revealed between risk and return after conditioning on lagged trading volume. Statman, Thorley and Vorkink (2006) empirically tested the theories of overconfidence developed by Odean (1998) and Gervais and Odean (2001). The focus of the study was on trading volume implications of overconfidence. The study incorporated the overconfidence hypothesis as a separate theory of trading activity related to investors beliefs about trading in general, rather than an attitude about individual stocks they currently hold (disposition effect). The primary hypothesis was that past market returns can explain the current trading volume. In other words, there is long-lag relationship between market returns and trading volume. Database consists of monthly observations on all NYSE/AMEX common stocks, excluding closed-end funds, REITs, and ADRs, from August 1962 to December 2002. Vector autoregressive (VAR) model at market level and individual security level, was used to empirically test the implications of overconfidence. They constructed a market portfolio consisted of all the s ecurities and calculated the variables. Monthly market return and monthly trading volume were endogenous, and monthly volatility based on daily data and value weighted cross sectional mean average deviation from market returns were the control variables for market-wide VAR. Individual security level VAR had only one control variable i.e volatility but had three endogenous variables: security return, market returns and security turnover. Turnover variable was constructed as total shares traded divided by outstanding shares at the beginning of a month. The key findings of study were: there was a significant positive relationship between market-wide turnover and past market returns after controlling for contemporaneous and lagged volatility associations, consistent with the prediction of the overconfidence hypothesis. Individual security turnover was also positively related to both lagged security returns and lagged market returns. The positive security turnover response to own lagged return was interpreted as disposition effect, while positive turnover response to lagged market returns was interpreted as change in investor overconfidence. The phenomena of overconfidence and disposition effect trading were both more prominent in small-cap stocks. Zaiane and Abaoub (2009) examined empirically the theory of overconfidence in a small Tunisian market using Vector autoregressive (VAR) model and related impulse response functions. Database consisted of monthly observations of common stocks from January 2000 to December 2006. Raw shares-traded was treated as proxy for trading volume. Market returns and trading volume were endogenous variables in VAR. Whereas, volatility and value weighted cross sectional dispersion were treated as exogenous or control variables. Variables used were the same as used by Statman et.al. (2006). Hypothesis of the study was that there is positive significant relationship between past returns and current trading volume. Little evidence of relationship between trading volume and lagged returns was found in Tunisian market which was interpreted as absence of overconfidence. Though, past returns affect trading activity over some months but mostly, relationship is insignificant. However, significant positive r elation was found between volume and volatility. Pisedtasalasai and Gunasekarage (2007) examined the causal and dynamic relationships among stock returns, trading volume and return volatility in five emerging markets of South-East Asia: Malaysia, Indonesia, Philippines, Thailand and Singapore using daily data. Specifically, they investigated contemporaneous relationship between return and volume, effect of trading volume on return volatility and causal relationship between return and trading volume. They used simple regression equation for contemporaneous relationship, EGARCH model for trading volume and return volatility relationship and Vector autoregressive model for Causality. The dataset was collected from Datastream. The selection criteria for equity indices were based on representation of majority of securities and availability of corresponding trading volume. Jakarta Stock Exchange Composite Index (JSECI) for Indonesia, the Manila Stock Exchange Composite Index (MSECI) for the Philippines, theKuala Lumpur Stock Exchange Com posite Index (KLSECI) for Malaysia, the Bangkok SET Index (BSETI) for Thailand and the Datastream Market Index (DMI) for Singapore were analyzed. Data series started from January 1990 for MSECI, DMI, and BSETI, from January 1991 for JSECI and from March 1996 for KLSECI. All series ended at December 2004. To check robustness of results dummy variables for Monday effect and for Asian Financial Crisis 1997 were also incorporated. The results showed the evidence of a statistically significant causal relationship from stock returns to trading volume for Indonesia, Malaysia, Singapore and Thailand. Such evidence was not found for Philippines market. Causality from trading volume to stock returns was detected only for Singapore. Evidence of trading volume being useful in predicting return volatility was found only for Singapore and Philippines markets. Striking behavior of Philippines market was suspected as due to lower capitalization of the market.

Questions On Securities Market And Traded On Secondary...

Q. 2. Explain how securities are issued in the primary market and traded on secondary markets. â€Å"There are two different types of securities markets: the primary securities and the secondary securities market† (Kelly Williams, 2016, pg. 165). So, what does the term primary securities mean, it’s a market that describes a portion of the capital market where new securities are issued by companies, government entities; or public institutions. In this market, there is more than one way to issue securities, the first is what is known as the public offering; it’s where anyone that is trying to raise their financial capitals within that market can investing by purchase these financial securities from that of the public. Therefore, these public†¦show more content†¦Though, these investment banks are well compensated for every sell the make on the actual sell of these securities, so they will make every effort to approach the situation any way possible; just to land extra security for themselves. In addition, the bank or financial institution will pledge to buy whatever shares do not sell through these investment banks, and replace them with new ones at an adequate price; that all are on agreement too in order to gain a profit of some sort. Unfortunately, in order for the firm to go out on the limb publicly, it must first file what is called a registration statement through that of the (SEC); containing the necessary requirements the name of the corporation manager, what assets they own, their competitors’ names; and what these funds are intended to be use towards before even being considered any form of offering. Therefore, no sale can move forward, until the (SEC) gives the firm the red light to proceed with the offering of these securities; or confirms the statement to be that of a functional fact. On the other hand, there is yet another method to which one can apply to that of securities, and that is what is called private placement; securities that are sold to one or more investor, individual, or that of an institution; through that of bargaining among the two main investors (the issuing firm and the private investors). However, while knowing the difference between the two methods of issuing isShow MoreRelatedEssay on Financial Management Integrated Case 2 Answers1381 Words   |  6 Pages | |Financial Management Section C | |Homework 2: Integrated Case Questions | Chapter 2 Integrated Case 2-11 A) In a well-functioning economy, capital flows efficiently from those with surplus capital to thoseRead MoreBank Processes1139 Words   |  5 PagesCapital Markets and Investment Banking Process Paper FIN 402 May 6, 2012 Alger Marable Capital Markets and Investment Banking Process Paper Investment banking is a crucial part of our global business environment. Investments, banking, and other capital markets now have a valuable role in business and everyone’s daily lives. 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Wisdom of Realization - 1746 Words

Lesson 15 The wisdom of realization One can say that wisdom can truly be found through a realization in one own life and even through suffering. In Shakespeare’s â€Å"King Lear† and Mitch Albom’s â€Å"Tuesdays with Morrie†, we see that both main characters Morrie Schwartz and King Lear gain wisdom through realizations of certain aspects of life and through their own personal sufferings. Both Morrie and Lear gain wisdom firstly through suffering. Morrie gains wisdom through suffering physically from ALS and having to come to terms that he is no longer independent and never will be. While Lear must suffer through the fact that perhaps those he thought loved him the most, didn’t love him at all and the one person who did love†¦show more content†¦It is through this realization that he realizes what love truly is and gains the wisdom of true real and filial love of a daughter. Even when they end up in jail together he says â€Å"†¦Come, let’s away to prison; we two alone will sing like birds I’th’cage†¦Ã¢â‚¬ ¦..and pray, and sing, and tell old tales, and laugh at guilded butterflies†¦Ã¢â‚¬ (Shakespeare, 5,3,8-13)Therefore King Lear gains wisdom through suffering at the hands of his greedy and selfish daughters, only then does he know what true love and affection means .Therefore one can gain wisdom through realization and suffrage. While one can love material things, material things can never love you. Materialistic people must understand that in life it is not the things that you have, it is the people and the relationships you have even when those things have come and gone. Secondly, Morrie Schwartz comes to accept that he does not need material things now that he is dying and tries to pass on this knowledge to Mitch during one of their Tuesdays together. Money is not a substitute for tenderness, and power is not a substitute for tenderness. 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Implementation of a Robot Controller

Question: Describe about Implementation of a Robot Controller? Answer: Introduction From last two decades, technology has tremendously changed in the field automation technology; robotics system is one of them. Robotics system is a special kind of branch of electrical engineering, mechanical engineering, electronics and communication engineering and computer science which deals with the several kinds of applications of the robot such as implementation of the robot, operational design of the robot, construction, automatic control mechanism with the help of their feedback system, sensing mechanism, mapping of the robot and various kinds of information processing system (Berry, 2012). The robotics system controls the mechanism with the help of automotive machine. The automated machine controller can have several kinds of natures of the human behavior such as ability for sensing, ability for moving and ability for thinking. Several kinds of applications of robotics are crane, defusing bomb in bomb squad and for exploring ship wrecks. This report mainly focuses on the mo bile robotics with different types of behavioral control architecture such as avoidance of the obstacle, wandering and mapping of the road directions. This report also highlights a brief background of the literature review, architectural design of the overall system, behavioral design of the system, various experimental designs of the robotics system and results and findings of the experiments. Coding Coding for Obstacle avoidance: Coding for obstacle on left side # turn right # else if approaching an obstacle on the right side # turn left # else go forward from pyro.brain import Brain class Avoid(Brain): def step(self): safeDistance = 0.1 # in Robot Units #if approaching an obstacle on the left side, turn right if min(self.get('robot/range/front-left/value')) safeDistance: self.robot.move(0,-0.3) #else if approaching an obstacle on the right side, turn left 9 elif min(self.get('robot/range/front-right/value')) safeDistance: 10 self.robot.move(0,0.3) 11 #else go forward 12 else: 13 robot.move(0.5, 0) 14 def INIT(engine): 15 return Avoid('Avoid', engine) Coding for wandering of the robot from pyro.brain import Brain from random import random class Wander(Brain): def step(self): safeDistance = 0.85 # in Robot Units l = min(self.get('robot/range/front-left/value')) r = min(self.get('robot/range/front-right/value')) f = min(self.get('robot/range/front/value')) if (f safeDistance): if (random() 0.5): self.robot.move(0, - random()) else: self.robot.move(0, random()) elif (l safeDistance): self.robot.move(0,-random()) elif (r safeDistance): self.robot.move(0, random()) else: # nothing blocked, go straight self.robot.move(0.2, 0) Coding for mapping of the robot ArTcpConnection con; ArRobot robot; con.setPort (); if (!con.openSimple()) { printf("Open failed."); Aria::shutdown(); return 1; } robot.setDeviceConnection(con); if (!robot.blockingConnect ()) { printf("Could not connect to robot... Exiting."); Aria::shutdown(); return 1; } The coding for obstacle avoidance program depicts the avoidance of the obstacle with the help of pyro framework to control over the different types of robot platform. This kind of controller program is the example of direct control program. Direct control method is most useful method for introducing the robotics subjects. In this kind of controller system simply used in robotics system which provides sensor values and control system for directly output of the motor. The top five line of the coding for obstacle avoidance showcases the basic pseudo code for avoiding the various kinds of obstacles by the robot (Smith, 2001). The implementation of the pseudo code is used with the help of different kinds of abstractions from libraries. The coding which is used for avoiding the obstacle for a robot creates a class called Avoid which can be inherited from the class called Brain. Each pyro brain has some steps methods which are used to execute the control cycle within 10 times in a second. This showcases that the robot continuously avoiding the obstacle. The robot can avoid the obstacle with in safe distance which is 0.1 m for here. The right and left from sensor helps to robot to move in the right or left direction. The line number 6 and 6 depicts the front sensor with left and right direction. The line number 14 and 15 depicts the automatic initialization of the robot. The coding for wandering of the robot depicts the variety of the platforms of the robot. The wander program helps to move the robot without bumping the objects. The pseudo code depicts the sensors of the variety ranges. The pseudo code for wandering also helps to understand the different kinds of the sensors. Here three sonar sensors are used; the khepera cannot be measured by using the single infrared sensor (Frazzoli, 2013). The coding for mapping of the robot depicts the ARIA program for connecting the TCP/IP port. ArtRobot::setDeviceConnection is the connection with TCP/IP port with the number 8101 with the local host. Literature review In this part of the report several kinds of past researchers reviews on the robotics will be evaluated. The literature review should brief, summarized form with the help of a proper format. Different past researchers have researched on this topic their various findings and results can help to carry out the overall report of the robotics. Several kinds of methods are used to detect obstacle with the help of various automated devices. Different types of obstacle detection methods are shadow detection, edge detection, image segmentation and the corner detection. According to Singh, (1996) the good obstacle detection may have several kinds of features which are summarized below; The obstacle detection device should have some specific range for detection of the obstacle. The device should have ability to detect the correct obstacle among various obstacles. The device should the power of indentifying the correct obstacle ignoring all the features of the grounds. The device should be able to detect the obstacle within a specific distance. The detection of the obstacle can be occurred into following two steps such as; the first step is the detection of the edge for processing of the low level images which is used to set the image processing of the higher level. The higher level processing of the image is used to detect the edge of the device which helps to segmentation of the objects. The edge detector is such kind of mathematical operator which can responds to the changes of the spatial and discontinuous grey level pixels which can be helpful for setting the pixels for the continuous variation. The edge can be indicated by the abrupt changes of the spatial of the images. The abrupt changes can be categorized into several kinds. Edge diving areas may have several kinds of rapid variations. Detection of the edge always should be based on the requirements of the output and the information of the edge is shared between the variations. The edge information can be used to determine the different closed areas between the dif ferent objects. Various types of the edge detector can work under different conditions. Singh, (1996) also summarized that different kind of algorithms are used for various edge detection operation. Boolean function based detection of the edge can produce several kinds of thickness of the edges on its output thus it can create a problem for finding fine image. According to Marr-Hildreth, (1998) it can be stated that there is no differences between the strong edges and weak edges detection of the robotics devices as it consist of one operator of the threshold edges. The edge detector of the canny cannot perform well under circumstances of high quality of the images. The quality of the images of the edges is fully dependent on the algorithm of the edges, properties of the surfaces and conditions of then lighting, different densities of the edges and noise (Frazzoli, 2013). The algorithm with the help of several kinds of approximating threshold can be used to overcome the different types of limitation of the edge detection. Intensity variation plays an effective role at the time of image processing of the edge detection. The derivation of the intensity of the edge detection shows the relationship between the existence if the edges and the variation of the intensity of the edges. The above diagram showcases the intensity of the derivatives of an edge. Several kinds of edge detection methods are summarized below; By using gradient operator method Sobel, Prewitt and Roberts methods are used to derivative of the calculation of the map for calculating the gradient changes of the edge (Marques, 2008). The common gradients methods for operating of the edge detection methods are shown below; By using optimum operator Marr-Hildreth Edge detector uses the Gaussian method with the help of invariant Laplace transformation r2f = / which is used to determine the gradient changes of the within the edge of the threshold. This formula shows the accurate results of the edge detection with the help of the algorithm. Canny model edge detection used as the problem detection of the signal processing with the help of signal to noise ratio, various kinds of the signal responses criteria and the accuracy of the location edge criteria. According to Nadernejad and Hassanpour, (1998) it can be found that there are several kinds of differences between the edge detections of the Marr-Hildreth edge detector, detector of the canny edge and several kinds of Boolean function based edge detection. Architecture design In this part of report several kinds of architectural mobile robotics design will be evaluated with the help of various software and hardware functionalities of the design. Several kinds of software functions for implementation of the mobile robotics system are summarized below; The interpretation software implementation helps to detect the several kinds of inputs provided by the sensor of the edge detector (Frazzoli, 2013). To control over the motion of the wheels and other kinds of the movable parts. To plan for the future path of the robotics architecture. Several kinds of complications may arise during the design stage of the mobile robotics system which are described below; Path can be blocked by the various kind of obstacle. Imperfect input of the sensor can block the system. Power supply should be taken into consideration during the stages of the design. Mechanical limitations may damage the system. According to the requirements of the report the behavioral architectural design of the mobile robotics consists of three methods such as the avoidance of the obstacle, wandering and mapping for the edge detection of the object. The evaluation criteria of the designs are; The device should be able to detect the obstacle. The devices should be able to mapping with the help of detection of the edge. The device should be accurate and reactive behaviors. The robot should be able to allow for different kinds of uncertainty. The device should be flexible and real time. The architectural design can be classified into three types of solutions such as control loop design, layered architecture of the robotics system, and invocation of the implicit with the help of TCA and the architecture design of the blackboard (Berry, 2012). First step is the design of the control system which is depicted below; The control system of consists of four parts such as controller, actuator, sensors and the environment. The active component of the robot should coordinate with the several kinds of activities with the help of actuator and the sensor. The environment of the design should be taken into consideration which can detect the several kinds of problems which may cause the fault of the power supply and unsafely performance of the robot. The major components for designing of the control system are motors, supervisors and the sensors. The control system device should be flexible so that it can operate in any environment. Layered architecture consists of several kinds of components such as robot control, control, navigation, supervisor, modeling on real world, interpretation of the sensor and global planning. This stage is used to design the communication between the several kinds of hierarchy. Various interlayer used to support the reconfiguration of the system. TCA is the task control architecture which combined all types of tasks provided by the implicit invocation (Marques, 2008). The several tasks communicated between them. Roles of the task are described below; Handling of the exception. To monitor the different messages. To handle the wire tappers. The last step of the architecture design is the architecture of the blackboard which are depicted below in the figure; Experimental design In this part of the report several kinds of experimental design process will be described which can be helpful for further design of the mobile robotics (Akella, 2008). The lunar obstacle can be classified into two types such as rocks and holes. To detect the two types of obstacle the intensity map are used for detecting the two types of images such as the lowest point of the intensity and the highest point of the intensity. Comparing several kinds of edge detection it can be concluded that the canny edge detector provides the best results among them. Sensitivity and accuracy can be detected by using the canny edge detection. The limitation of the canny edge detection is the shadow. By using the additive removal model the shadow during the detection can be removed. Shadow coefficient also can be used to eliminate the low points of the image (Frazzoli, 2013). The step by step image process flow algorithm is described below; Results Detection of the shadow and removal Blajovici, Kiss, Bonus, Varga showcase the direct and ambient light for detecting the shadows. Shadow is the part of the image, according the model of the shadow it can be defined as; Ii = (tI cos I Ld+ Le) Ri , where Ii is the pixel of the images, Ld is the direct light, Le is the light of the environment, Ri is the surface reflectance, I is the angle of the direct light and tI is the factor of the attenuation. The shadow coefficient can be denoted by ki = (tI cos I) The ratio between the environment light and direct light can be denoted by r= Ld /Le Conclusion Robotics system is a special kind of branch of electrical engineering, mechanical engineering, electronics and communication engineering and computer science which deals with the control, implementation and automatic system (Zhang, 2010). The entire can be classified into several kinds of segmentation such as introduction part, coding part, literature review of the overall robotics system, experimental design part and the results of the design of the robotics. 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