Literature Review Of Behavioural Finance In Investment - Free Essay Example

Sample details Pages: 19 Words: 5679 Downloads: 1 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? This chapter is a detailed exploration of the behavioural finance literature relevant to the research objectives. The author will explore the beginnings and the development of the framework and will critically analyse the points of the literature which are essential for the research conducted in this study. 1.0 Introduction Don’t waste time! Our writers will create an original "Literature Review Of Behavioural Finance In Investment" essay for you Create order The financial theory based on Modern Portfolio Theory (Markowitz, 1952) and the Capital Asset Pricing Model (Sharpe, 1964), has long shaped the way in which academics and practitioners analyse investment performance. The theory is based on the notion that all investors act rationally and consider all available information in the decision-making process and that therefore investment markets are efficient, reflecting all available information. According to Baberis and Thaler (2002), rationality is defined thus : Rationality means two things. First, when they receive new information, agents update their beliefs correctly, in the manner described in Bayes Law ¹. Second, given their beliefs, agents make choices that are normatively acceptable, in the sense that they are consistent with Savages notion of subjective expected utility ² Therefore, when an investor learns something new about a future cash flow concerning a particular security, they should respond in an efficient manner to this new information, in turn pushing up the price when the news is good and bringing prices down when news is bad. As a consequence, security prices should fully incorporate all available information almost immediately.  ¹Bayes law is a formula for calculating the probability that something (called A) is true or will be true, given a certain set of circumstances (called B)  ² Expected utility theory predicts that the betting preferences of people, with regard to uncertain outcomes (gambles), can be described by a mathematical relation which takes into account the size of a payout, the probability of occurrence, risk aversion, and the different utility of the same payout to people with different assets or personal preferences. During the 1980s and 1990s, contradictory evidence began to emerge that led many academics to reconsider the foundations of traditional finance. Empirical studies discovered anomalies and excess volatility in the stock markets that could not be explained by traditional finance models and suggested that academics should look to other fields of research to explain these discrepancies. In response to the growing number of problems, a new area of research emerged which offered an alternative explanation to the essential question of why prices deviate from their fundamental values. In the 1990s, a lot of the focus of academic discussions moved away from the rigid models of traditional finance towards developing theories on human behaviour and how it relates to financial markets. Behavioural Finance is the integration of traditional finance and economics with the psychological and decision making sciences. Its main argument is based on the claim that human behaviour and perceptions represent two crucial elements of financial decision making (Hirshleifer, 2001). Behavioural Finance scholars started the search for new models and ideas to help explain and predict investor behaviour. They assumed that investors may be irrational in their reactions to new information and make wrong investment decisions. As a result markets will not always be efficient and asset pricing may deviate from predications of traditional market models. There are a number of behavioural finance models which try to suggest that agents fail to update their beliefs correctly (Kahneman Tversky, 1979). This review will evaluate the literature in the field of behavioural finance and will focus on the two main building blocks the limits of arbitrage and investor psychology. In the first section, the limits of arbitrage will be explored, with the main focus on market efficiency and noise traders. In the following section, the focus will be on the psychological aspect of behavioural finance, focusing on the extensive experimental evidence which illustrates how people form their beliefs and make decisions. 2.0 Traditional Framework In the period between the early 1950s and late 1960s, instrumental research in the area of traditional finance was conducted. This was a very productive time for financial thought with many theorists putting together complex mathematical models to try and explain price behaviour. In these models, human behaviour and reasoning were over-simplified as researchers tried to invent practical empirical models. The most influential model which emerged from this period was the Efficient Markets hypothesis (EMH) (Fama, 1970), a theory which, even in todays changing environment, still represents a cornerstone of academic finance. This theory states that markets are considered to be efficient relative to a given information set, providing there are no abnormal profit opportunities for investors trading on the basis of information (Fama, 1970). The EMH implies that it is virtually impossible for investors to consistently beat the market, unless by luck. The EMH theoretical foundations are based on three underlying principles (Fama, 1970). The first assumption is that investors are fully rational. Therefore, when an investor learns something new about a future cash flow concerning a particular security, they should respond in an efficient manner to this new information, in turn pushing up the price when the news is good and bringing prices down when news is bad. As a consequence, security prices should fully incorporate all available information almost immediately. Secondly, as irrational investors transactions are conducted in a random manner, their irrationalities will offset each other, with the result that stock price will not be affected in any way. Subsequently the market may remain efficient even if not all investors are acting rationally. Their investment decisions are conducted in a random way and every trade made is likely to cancel the others out. Therefore an irrational investor will not be able to gain sufficient momentum to influence fundamental asset prices. The final assumption believes that the rational investor will arbitrage away the irrational investors influences on the underlying stock price. When a large group of irrational investors trade in a similar style and together they manage to move prices away from their equilibrium value, it is presumed that rational arbitrageurs will quickly notice the mispricing and act accordingly to return the prices to normal. If these three underlying principles hold and people are rational, then markets in turn will be efficient. There are a number of other quantitative models which emerged out of this traditional school of thought. The modern Portfolio Theory, the Capital Asset Pricing Model and the Arbitrage Pricing Theory are models which underpin the rational expectations based theory. However, there is a large amount of data and research which contradicts their foundations. For example, Fama and French (1993) have shown that the basic facts about the aggregate stock market, the cross-section average returns and individual trading behaviour are not easily understood in this framework. 3.0 Background of Behavioural Finance As cited by Shiller (2002) : Finance from a broader social science perspective including psychology and sociology is now one of the most vital research programmes and it stands in sharp contradiction to much of the efficient markets theory In the early 1990s, a lot of the focus of academic discussion moved away from the neoclassical frameworks of stock valuation towards developing models of human psychology and its relation to financial markets. As such, the Behavioural Finance paradigm has emerged in response to the difficulties faced by the traditional framework in explaining ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦. and in essence it argues that investment choices are not always made on the basis of full rationality. It also attempts to understand the investment market phenomena by relaxing the two doctrines of the traditional framework, that is, firstly, that agents fail to update their beliefs correctly and, secondly, that there is a systematic deviation from the normative process in making investment choice. Bowman and Buchanan (1995) acknowledge that The knowledge on human behaviour should be used in order to help us understand how investors may misperceive the results of their actions and by extension, the functioning of the share market. The expectations-based models argue that the irrationality exhibited by investors will be undone through the process of arbitrage (Freidman, 1953). Behavioural Finance argues that there are limits to arbitrage, which allow investor irrationality to be substantial and to be able to have a long-term impact on prices. To explain investor irrationality and their decision making processes, Behavioural Finance draws upon the experimental evidence of the cognitive psychology discipline and the biases which arise when people form their beliefs and preferences (Baberis Thaler, 2002). Therefore, limits to arbitrage and psychology are seen as the two major building blocks of behavioural finance. 3.1 Limits of Arbitrage Behavioural Finance does not negate the arbitrage mechanism per se and its price correcting ability. However, it argues that not every deviation from fundamental value created by actions of irrational traders will be an attractive investment opportunity for rational arbitrageurs (Szyszka, 2008). Even when an asset is highly mispriced, many arbitrage strategies, which are designed to correct and eliminate the fundamental mispricing, are ultimately risky and costly for the arbitrageur. Therefore many strategies are perceived to be unattractive and this results in the mispricing remaining unchallenged for a comparatively long period of time. The theory of arbitrage can be traced back to Friedman (1953), who stated that rational traders will quickly undo any mispricing caused by irrational traders. Friedmans argument is based on two underlying assumptions. Firstly, as soon as an asset deviates from its fundamental value, an attractive investment opportunity will arise from this mispricing. Secondly, rational traders will immediately react to the situation by purchasing the asset, thereby correcting the mispricing. Behavioural Finance doesnt dispute the fact that an attractive investment opportunity will be exploited it argues that arbitrage strategies developed to correct the mispricing can be both risky and costly, resulting in the mispricing remaining unchallenged. 3.2 Fundamental Risk When an arbitrageur observes a mispriced asset on the market, he needs to find a similar asset which is priced correctly on another market, to enable him to correct this mispricing, thus taking an opposite arbitrage position. If the trader is unable to take up this position, he faces fundamental risk, that is, the risk that new information comes to the market and changes the fundamental value of the asset in the wrong direction. Even when arbitrageurs are able to hedge the fundamental risk that they face and take a long position in the asset where it is cheaper and a short position in the same asset on another market where it is more expensive, the trader is still exposed to Noise Trader Risk. 3.3 Noise Trader Risk Noise trader risk can be defined as the risk that irrationality on the market may become stronger and may drive mispricing to an even greater extent (Shleifer Vishny, 1997). As the mispricing increases, the gap between long and short positions increases which in turn goes against the belief of rational arbitrageurs. If this trend continues, an arbitrageur whose investment horizon is usually relatively short and who often borrows money to fund his trades, may be forced to close his positions before the mispricing is corrected, ultimately resulting in him suffering significant losses. Shleifer (2000) has argued that noise trader risk, the risk from traders who are attempting to buy into rising markets and sell into declining markets, limits the extent to which one should expect arbitrage to bring prices quickly back to rational values, even in the presence of an apparent bubble. Even the most rational arbitrageurs will regret selling a share short which may collect a greater price in the future, even if that price is unreasonably high. Rational arbitrageurs cannot entirely eliminate the effects of noise traders on the market if the size and the ability of the former group to trade are very limited (Camerer, 1989). Consequently, a single arbitrageur who notices a mispricing in the market faces not only the noise trader risk, but also the risk of synchronization of actions of other rational traders (Abreu Brunnermeier (2002). Typically, a single arbitrageur does not have the momentum to correct the mispricing on his own. The individual needs other arbitrageurs to follow his strategy. However, the individual does not know if and how quickly other rational traders will react to the same arbitrage opportunity and take up a similar positions. Also, the risk aversion of arbitrageurs by itself limits their ability to cancel noise traders, even if arbitrageurs have infinite buy-and-hold horizons (Shiller, 1984). As stated by Black (1986), if noise traders undervalue or overvalue stocks for a long period of time, the short horizon under which arbitrageurs performance is evaluated, limits their ability to force asset prices back to their fundamental values. As a result, due to the limitations of arbitrage, noise traders are able to force asset prices away from their equilibrium value for extended periods of time. Rational arbitrageurs also have to realise that noise trader strategies may become even more extreme and unpredictable, resulting in increased risk for the arbitrageur. This additional risk is referred to as noise investor risk. Noise investor risk is systematic and non-diversifiable which in turn creates additional volatility on the stock markets. Rational arbitrageurs would not bear this risk unless compensated with higher expected returns (De Long et al., 1990). This once again limits the successfulness of rational arbitrageurs. 3.4 Implementation Barriers Arbitrage can become a costly activity for a number of reasons. Firstly, transaction costs, which include bid-ask spreads, commissions and price impact, can limit the arbitrageur in exploiting an obvious mispricing. Secondly, the fees charged for borrowing stocks to take a short position can often be off-putting. As cited by Baberis and Thaler (2002), DAvolie (2002) finds That for most stocks, they range between ten and fifteen basis points but they can be much larger; in some cases, arbitrageurs may not be able to find shares to borrow at any price. A further barrier they may face is legal constraints. For example, in many large pension funds, short-selling is prohibited altogether. Finally, the vast amount of research and learning required to exploit a mispricing in a further deterrent. Shiller (1984) found that even if noise trader demand causes a persistent mispricing, it may not be detectable for arbitrageurs unless they expend large amounts of time and resources. 3.5 Evidence Limits of arbitrage have been confirmed empirically by cases of evident mispricing that remain unchallenged in the market for long periods of time. An example of this is the case of twin shares. In 1907, Royal Dutch and Shell merged their interests on a 60:40 basis while both remained separate entities. The stocks of Royal Dutch traded mostly on the US and Dutch Stock Exchange and were to claim 60 percent of the total cash flow, while shares in Shell, which traded in the UK, were to claim 40 percent of the total cash flow of the two firms. Theoretically, the market value of Royal Dutch equity should always be 1.5 times greater than the market value of Shell. Empirical evidence shows that Royal Dutch was sometimes 35 percent underpriced relative to Shell and at times they were 15 percent overpriced. It took until 2001 for the shares to finally sell at their correct values. This is a key example where two shares which are perfect substitutes for each other, would allow the opportunity of easy arbitrage profits. The main risk in this situation is noise trader risk and there is the fear that the share will become even more undervalu ed in the near future. A further example of the limits of arbitragecomes from the inclusion of a new stock on the SP 500. Schleifer (1986) discovered that when a stock is added to the index, the price jumps on average by 3.5 percent and much of this increase remains. A prime example of this is when Yahoo was added to the index, its share price rocketed 24 percent in a single day. Arbitrage is limited in this case due to the fundamental risk and noise trader risk faced by traders. They may find it is very difficult to find a substitute stock and also there is the risk that the price will continue to rise in the short run. In the case of yahoo, its share price was $115 before its addition in the index and it had risen to $214 a month later. Behavioural theorists show that the strategies required to correct the mispricing can be both costly and risky, thus rendering the mispricing opportunity unattractive and allowing them to continue. The examples of Yahoo and Royal Dutch outlined above confirm this point perfectly. 4.0 Psychology aspect of Behavioural Finance The theory of limited arbitrage shows that if irrational traders cause deviations from fundamental value, rational traders will often be powerless to do anything about it (Baberis Thaler, 2002). In order to explain the various investor behaviours in financial markets, behavioural analysts draw on the knowledge of human cognitive behavioural theories and analyse extensive experimental evidence which shows the systematic biases which arise when people form beliefs or preferences. 4.1 Judgement Under Uncertainty 4.1.1 Overconfidence Behavioural finance assumes that agents may be irrational in their reactions to new information and investment decisions. One of the observations of behavioural theorists has been the overconfidence phenomena. Overconfidence allows many of the anomalies and price deviations in traditional finance to be explained, something which standard economic theory struggles to do. People can make mistakes when they receive information and form their beliefs. Extensive evidence shows that individuals tend to be overconfident in their own beliefs and judgements (Odean and Barber, 2000). These individuals also tend to be over-optimistic and perceive things to be far better than they actually are. In general terms, overconfidence and over-optimism make investors trade at far too high a volume and sometimes at far too high a price. Research also shows that when an individual has formed an opinion, he often adheres to it and inadequately updates his beliefs in the line of new information. As a result of the above behaviours, people tend to take too many risks and fail to change their beliefs even after occurring heavy losses. This is turn could cause the stock market to overreact. 4.1.2 Representativeness and Conservatism Conservatism can be defined as the condition where investors are subconsciously reluctant to alter their beliefs in the face of new evidence (Edwards, 1968). This bias affects an investors decision-making process, as even if an investor changes his beliefs in the light of new information, the extent of that change is relatively small in terms of what it should be under strictly rational conditions. Edwards (1968) conducted an experiment which tested a subjects ability to revise probabilities in the light of new evidence. This experiment was constructed so that there was only one correct answer to the probability revision problem given to the subject. It found that people tended to revise their probabilities in the correct direction, but tended not to revise them enough. It was found that people respond insufficiently to new information and this has been replicated in a number of papers such as Beach and Braun (1994) and is now referred to as conservatism bias. Representativeness Heuristic The representativeness heuristic as documented by Kahneman Tversky (1974) can also play an important role in driving overconfidence. They recognised that in forming subjective judgments, people have a tendency to disregard base rate probabilities, and to make judgements based solely in terms of observed similarities to familiar patterns (Shiller, 2001). For example, when people try to determine the probability that a data set A was generated by a model B, or that an object A belongs to a class B, they often use the representativeness heuristic. This means that they evaluate the probability by the degree to which A reflects the essential characteristics of B (Baberis Thaler, 2002). Under the representativeness heuristic, investors will consider a number of positive company performances as a representative of continuous growth potential and ignore the possibility that this performance is of a random nature. This can encourage the investor to expect instinctively that past price changes will continue, even if his professional training tells him that this should not happen (Shiller, 2001). 4.1.3 Anchoring Kahneman and Tversky (1974) argue that when forming estimates, people often start with some initial, possibly arbitrary value, and then adjust away from it. Experimental evidence shows that the adjustment is often insufficient, resulting in the tendency to anchor too much on the initial value. Anchoring is a term used in psychology to describe the common human tendency to rely too heavily or anchor on one trait or piece of information when making decisions. During the normal decision making process, individuals anchor, or rely too heavily, on specific information or a specific value and then adjust to that value to account for other elements of the circumstances. In Kahneman and Tverskys empirical study, subjects were asked to estimate the percentage of United Nations countries which are African. In each case, a number between zero and one-hundred was assigned as an initial value and the subject was asked if this was too high or too low, and what adjustment was needed to be made. Despite the fact that each of the subjects knew that the initial value had been determined randomly, by spinning a wheel in the subjects presence, there still was a tendency to be biased towards the initial value. This phenomenon can be related to the financial markets by looking at how investors pay too much attention to the past prices of securities. Studies have found that the two most common numbers to which investors appear to anchor, are the fifty-two week high and the fifty-two week low for a stock. There is a market tendency for people to assume that a stock has the potential to get back to its fifty-two week high but not breech its fifty-two week low. The problem with this thought process is that it assumes that those numbers are an indication of value and are not just random outcomes based on the fads that can be witnessed in the market. 4.1.4 Mood and Emotion Judgments based on mood and emotion, rather than rational valuation, can play an important part in investor decision making. A number of recent studies have used environmental factors to investigate whether mood and emotion influence investor choices and stock market prices. Kamstra, Kramner and Levi (2003) investigated Seasonal Affective Disorder (SAD), which tries to find a link between depression and the lack of winter sunlight. Their findings indicate that in markets where SAD is prominent, there is a high amount of seasonal variation in returns. They conclude that the variation is due to the changing risk aversion of SAD investors. Further studies have linked variations in returns to lunar cycles and weather related optimism and pessimism. Stock Markets in the United States of America are slightly more prone to more positive returns on days with less cloud cover (Elton and Gruber, 2007). Additional studies by Saunders (1993) support this notion by suggesting that capital markets , on average, have higher returns on days of good weather than on days with heavy clouds or rain. A recent study by Lo and Repen (2002), as cited in Elton and Gruber (2007), attempts to measure the affect in real time of emotions through documenting physiological changes experienced by investors. Lo and Repen attached wires to a number of investors at a hedge fund company to enable the researchers to monitor their responses to risk and volatility. The results of their findings indicate that news events and volatility extract emotional responses with experienced traders remaining calmer in these circumstances. Lo and Repens study ultimately concluded that emotion plays a role in the decision making of trader, which contradicts the theory of rational investors. 4.2 Behavioural Finance Models There have been a number of models developed in an attempt to capture the behaviour of investors. Perhaps the most influential of these is prospect theory (Kahnemand Tversky, 1979). This theory will be explored below and will provide the foundations for the research study. 4.2.1 Prospect theory The traditional finance theory assumes that investors make decisions under uncertainty by maximising the expected utility of wealth or consumption. The expected utility theory, which was formalised by Von Neuman Margenstein in 1947, shows that if preferences satisfy a number of plausible axioms completeness, transivity, continuity, and independence then they can be represented by the expectation of a utility function (Baberis Thaler, 2002). However, the underlying assumptions of this theory have been shown by many studies to be an inaccurate explanation of how people actually behave when choosing between risky alternatives. Prospect theory seeks to explain decisions which are inconsistent with rational probability assessment and standard utility theory. The proponents of the expected utility theory assume that investors are fully rational when facing a gamble. The theory assumes that every individual has an information set which might differ among investors, but it also assumes that every individual analyses this data in a rational manner. According to expected utility theory, all information is equally weighted. This is, however, one of the main contradictions with both prospect theory and behavioural finance. Under expected utility theory, risk preferences are captured by the shape of the utility function. Decision makers are risk averse if U(x) is concave, and risk seeking if U(x) is convex, with the most classical financial theory based on the tenet that decision makers are risk-averse. One standard interpretation for risk aversion is that the usefulness of an additional dollar decreases as a person gets wealthier, a principle known as diminishing marginal utility. A utility function which exhibits diminishing marginal utility i s concave and hence the decision maker is risk averse. In relation to the investors attitude to risk, the expected utility hypothesis adopts a stance which seems to be contradicted by empirical research. Even though it allows for different attitudes towards risk, it does perceive this attitude to be constant. Therefore, if an investor faces a loss or a gain prospect, they will always have the same attitude to risk. Moreover, this theory relies on the assumption that investors think about final wealth states and not about gains or losses, which once again contradicts the behavioural finance paradigm. Prospect theory seeks to explain decisions which are inconsistent with rational probability assessment and standard utility theory. Prospect theory is an important theory for decision making under uncertainty. It departs from the traditional expected utility framework as it provides a psychological explanation to the behavioural tendencies of portfolio selection. Prospect theory was developed in 1979 by two psychologists, Daniel Khaneman and Amos Tversky, who advised that prospect theory is a descriptive model of decision making under uncertainty which can be used to explain behavioural tendencies. It implies that people value gains and losses differently and as a result will base their decisions on perceived gains rather than losses. Therefore, prospect theory evaluates peoples attitudes to risky gambles and presumes that agents are risk averse in the domain of gains but risk-seeking when all changes in wealth are perceived as losses. According to Kahneman Tversky (1979), people vi olate the expected utility theory in three main ways the certainty effect, the reflection effect and the isolation effect. The first problem Kahneman and Tversky found relates to the fact that people tend to put too much weight on certainties. That is, if the investor is faced with a gamble which has an outcome that is certain, they will favour this if the other option is merely probable. This clearly contradicts the expected utility hypothesis which states that a rational investor will make his choice of action only depending on the outcomes utility. The best know counter-example to expected utility theory which exploits the certainty effect was introduced by the French economist Maurice Allias in 1953. Kahneman Tversky (1979) use a set of experimental questions which are adapted from this original theory to further highlight this effect. They show that people prefer a certain outcome over a probable one, even if the average payoff for the uncertain outcome is far higher. This research agrees with the work previously completed by Allias, and shows a clear contradiction to the expected utility hypothesi s in which the rational individual is assumed to choose the option yielding the highest payoff. The second critique redefines the investors attitude towards risk. In relation to the expected utility hypothesis, the investors attitude towards risk is assumed to be constant and is usually risk averse. However, Kahneman Tversky (1979) state that this is not in line with reality and find that an individuals attitude towards risk is not a constant. They found that when a person is faced with a gamble which yields a positive outcome in relation to their initial level of wealth, then they are risk averse. However, KT find that this attitude changes to risk seeking or loving when the gamble contains negative outcomes in relation to the initial level of wealth. For example, when a gamble was offered where you could either loose 3000 for certain or lose 4000 with a probability of eighty percent, an overwhelming majority selected the uncertain gamble, even though the utility would be lower for a rational individual. If a person was given two identical choices, one expressed in terms of p ossible gains and the other in terms of possible losses, people would choose the latter even if they achieve the same result. According to prospect theory, losses have a more emotional impact than an equivalent amount of gains. For example, based on the traditional way of thinking, the amount of utility gained from receiving 50 should be equal to a situation in which you gained 100 and then lost 50. In both circumstances the end result is a net gain of 50. However, despite the fact that you still end up with the same result, the majority of people would favour a single gain of 50. The following experiment by Kahneman and Tversky (1979) illustrates prospect theory. A number of subjects answered questions which involved making judgements between two monetary decisions involving prospective gains and losses. The following questions were asked in the study You have $1,000 and you must pick one of the following choices : You have a 50% choice of gaining $1,000 and a 50% chance of gaining 0. You have 100% chance of gaining $500 You have $2,000 and you must pick one of the following choice A You have a 50% choice of losing $1,000 and 50% chance of losing 0. B You have 100% chance of losing $500. The results of the study found that the majority of people chose B for the first question and A for the second question. This implies that people are willing to settle for a reasonable level of gains, even if there is a chance of earning more, but are willing to engage in risk-seeking behaviour when they can limit their losses. In other words, people value their losses more heavily than an equivalent amount of gains. This is once again clearly in contrast with the expected utility theory as individuals focus on the gains or losses made instead of on final wealth. The final critique that Kahneman Tversky (1979) explore is the isolation effect. It tries to explain peoples behaviour when facing a complex situation and concludes that in order to be able to evaluate information, investors tend to evaluate it in isolation of other information. The results show that people do not make decisions based on the final outcome, instead investors isolate the later game and the probabilities involved and their decision will wholly depend on the probabilities of the part of the game they are involved in at that time. Prospect theory has an asymmetric attitude towards risk, depending on how the potential gains or losses relate to a certain reference point. This reference point could be current wealth, a neighbours wealth, or the price at which an asset is purchased (Elton Gruber, 2007). Kahneman and Tverskys utility function is concave above the given reference point and convex below the point. This structure creates risk aversion with respect to gains and risk seeking with respect to losses and can lead to different decisions depending on whether the outcomes are posed as gains or losses. Prospect theory can be used to explain the occurrence of the disposition effect. The disposition effect is defined as the tendency for investors to hold on to losing stocks for too long and sell winning stocks too soon. The most logical course of action for an investor would be to hold on to the winning stocks in order to further their gains and to sell losing stocks in order to minimise potential losses. There has been a number of empirical studies which agree with the above theory. Odean (1998) analysed the accounts at a large brokerage firm and found that there was a greater tendency to sell stocks with paper capital gains than losses. A further study found a similar effect among all types of investors in Finland where the investors failed to recognise losses. As cited by Elton and Gruber (2007), a study far afield from investing by Chen, Santos Lakshminarayanan (2005), has identified loss-aversion behaviour in Capuchin monkeys, suggesting that loss aversion is instinctive. Prospect theory can thus be seen to be applied to a diverse range of situations which appear inconsistent with standard economic rationality. 5.0 Conclusion In conclusion, the last five decades have seen the introduction and development of many leading theories in financial literature. One of the main theories to dominate this period was the subject of efficient markets. The Efficient Market hypothesis was initially well-favoured but it relied on many unrealistic assumptions. More recently, scholars and investment professionals have started to investigate an alternative theory to try and explain the mispricing which occurred using the traditional models. Behavioural finance attempts to inform people of the emotional factors and psychological processes which influence the individuals who invest in financial markets. Behavioural finance argues that not all investors are rational and are able to value correctly their decisions or the importance of new information. Behavioural Finance does not attempt to replace the traditional views and models of finance. Instead, it looks to fill some gaps and to offer realistic explanations as to why misp ricing can occur in the stock markets. This chapter has attempted to outline the key factors of behavioural finance and highlighted prospect theory as one of the main models used to demonstrate this theory. Based on this literature review, the subsequent chapter will address the methodologies used to achieve the research objectives.

Essay about The Time Machine A Social Critique of...

H G Wells was cynical of the Victorian class system and thoroughly disapproved of the way people were segregated, according to their wealth. Wells disagreed with England’s capitalist views, as he himself was a socialist. His novel The Time Machine is primarily a social critique of Victorian England projected into the distant future. He has taken segregation to its extremes and shows how far human evolution will go if capitalism continues unhindered. On travelling to the future he finds that this new world is not what he expected, as he feels vulnerable and ‘naked in a strange world.’ (Page 26) This panic then quickly transforms into frenzy as he then meets the Eloi who were all that he despised, creatures who were frail, had lost†¦show more content†¦This is like the Nineteenth Century as the upper class Victorians would walk about carefree as children have the freedom to do. 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The Twilight Saga 3 Eclipse PREFACE Free Essays

To my husband, Pancho, for your patience, love, friendship, humor, and willingness to eat out. And also to my children, Gabe, Seth, and Eli, for letting me experience the kind of love that people freely die for. Fire and Ice Some say the world will end in fire, Some say in ice. We will write a custom essay sample on The Twilight Saga 3: Eclipse PREFACE or any similar topic only for you Order Now From what I’ve tasted of desire I hold with those who favor fire. But if it had to perish twice, I think I know enough of hate To say that for destruction ice Is also great And would suffice. Robert Frost PREFACE ALL OUR ATTEMPTS AT SUBTERFUGE HAD BEEN IN VAIN. With ice in my heart, I watched him prepare to defend me. His intense concentration betrayed no hint of doubt, though he was outnumbered. I knew that we could expect no help – at this moment, his family was fighting for their lives just as surely as he was for ours. Would I ever learn the outcome of that other fight? Find out who the winners and the losers were? Would I live long enough for that? The odds of that didn’t look so great. Black eyes, wild with their fierce craving for my death, watched for the moment when my protector’s attention would be diverted. The moment when I would surely die. Somewhere, far, far away in the cold forest, a wolf howled. How to cite The Twilight Saga 3: Eclipse PREFACE, Essay examples

Crowd Sourcing for an Effective Way for Funding -myassignmenthelp

Question: Discuss about theCrowd Sourcing for an Effective Way for Funding. Answer: Introduction Crowdsourcing is very popular phenomenon through which organizations take help from public to help in achieving activities that are commonly performed by the employees/ contractors (Bayus, 2013). Crowdfunding can be defined as the process where the individuals appeal for contributions from public by making use of online platform, and it is turning out to be a powerful way for raising funds for new entrepreneurial ventures as well as projects. According to Massolution, a crowdsourcing firm specialising in research and advisory indicated that during 2012 there was seen a growth of 81 percent in the crowdfunding platforms and together they raised US$ 2.7 billion (Sally, 2013).Crowdsourcing can act as a tool with multiple benefits if it is leveraged in successful manner. This report will thus introduce the contemporary issues of crowdsourcing by putting forward supporting facts from literature reviews available. Further theoretical entrepreneurship frameworks will be applied to develop t he concept of crowdsourcing. Finally the report will also provide few effective applications as well as judgements so that advanced knowledge about crowdsourcing can be developed. Crowdsourcing A Concept of Funding There are many ways of financing the start-ups ( Bellefalmme, Lambert SChweinbacher 2011) and crowdsourcing is one of them in which crowd is being used to develop a profit-oriented activities of an organization. Crowdfunding is a part of crowdsourcing and both the phenomenon is different from each other. The term crowdsourcing was firstly coined by Jeff Howe in June 2006. The term has been defined as the cat of organisation or firm taking up the function that was earlier performed by employees and outsourcing to the undefined network of people in form of an open call (2). Crowdsourcing is different from other functions like open sourcing; user generated content (UGC), collective intelligence as well as outsourcing (Lehdonvirta Bright, 2015). Crowdsourcing cannot be considered as a silver bullet for economy, but it does provide various approaches that help the enterprises to operate in a successful and efficient manner in on-going changes in policies, technology skills as wells fluidity seen in the world economy. Businesses as well as various organizations believe that crowd can help them in dealing with various challenges ranging from simple rote tasks for example raising money / voting, image labelling to handling complex issues like designing of new products or services , strategic planning or brainstorming ideas. Crowdfunding is a form of crowdsourcing and it is related to outsourcing. Thus it means transferring task of one organization to another, thus crowdsourcing means outsourcing to crowd that is large group of individuals mainly through Internet. Companies use crowdsourcing like, involving customers in the innovation process of the company (Leimeister et al, 2009). Advantages of Crowdsourcing Although many people criticize crowdsourcing because companies treat it as a way for maximising their profits simply by outsourcing their functions which are traditionally conducted by employees to the public. This helps the companies to achieve huge savings in cost. This is the main reason why companys crowdsource, however there are various other reasons due to which organisations adopt crowdsourcing. First advantage of crowdsourcing is the huge size of the crowd itself which acts as a benefit. Whereas most of the companies have less than 10,000 employees and if the contrast is being considered then the potential size of the crowd that also relies on the activity that is crowdsourced. For example 65,000 mew videos were uploaded on YouTube when it was acquired by Google in 2006 (4). Thus it was because of the crowd only that is millions of YouTube users that so many videos were uploaded. It would have been tough even to imagine doing so for 50 odd employees at YouTube. The second adv antage of crowd is being geographically distributed (Luz, Silva, Novais, 2015) . The third advantage of crowdsourcing is diversity present in the crowd. It has been found that diverse group is able to outperform group of experts while solving problem. For example some big corporations cast their scientific issues which even their RD experts cannot resolve to the website known as InnoCentive. Currently InnoCentive users have successfully resolved more than half of the challenges or problems posted (Cheung, 2012). The last and fourth advantage of crowdsourcing is that it reduces the cost of utilising the crowd in order to perform the task. Leveraging Business through Crowdsourcing Crowdsourcing acts as a tool that can be used for boosting efficiency as well as creativity and helps in creating better and positive customer experience. It can help in filling up the labour gaps in efficient manner for basic tasks or short-term projects. Like Amazon Mechanical Turk helps the companies in outsourcing tasks like transcription, verification , photo moderation etc. which needs human intelligence. In the field of creativity it helps in website deigning to logo overhaul. Thus crowdsourcing acts as a cost effective method for the businesses to access custom design work. Design Hill / 99Deisgns are the crowdsourcing platforms which help businesses in crowdsourcing design options related to marketing collateral , branding , websites as well as advertisements (Canarelli, 2017). Crowdsourcing is also a form of crowdsourcing. There are platforms like Fundable , ANgelList and Kickstarter which help the businesses , artists and entrepreneurs in raising money without taking help from traditional funding channels like venture capital or business loans . Thus crowdfunding is a tool which helps in attracting start-ups and it is not just for the new ventures only. Even well established businesses can use crowdfunding for expansion of their services, launch of new products as well as for the fund programs which aim at giving back to the community (Canarelli, 2017). Crowdsourcing to Crowdfunding When a company is started the entrepreneurs in the field of technology face several challenges for example limited funds, lack of resources and wide range of technical issues. Accessing the local funding is very tough as well as restrictive. On top of that searching for resources within a limited budget is a challenging task. In such cases crowdsourcing acts as a compelling idea which tackles all these issues by creating connections with resources as well as talented people all across the globe. These connections act as opportunities for entrepreneurs so that they can grow the community by adding talent form the target crowd, creating solutions for technical issues as well as by finding the desired resources (Smith, Manesh, Alshiakh, 2013). Thus crowd funding acts as a very fresh and recent method for funding the start-ups and it is gaining lot of popularity too. It is just like taking a loan, contribution / investments or pre-order from more than one person and that too at the same time. Crowdfunding works in a way where an entrepreneur puts a detailed description of the business on a crowdfunding platform. The main aims of the business will also be mentioned along with the plans for deriving profits from business, the amount of funding required and the reason why funds are required etc. Then the customers read all the information about the business and then decide to help with money if they find the idea genuine one. The people who give money make online pledge, while promising that they will pre-buy the product or give a donation. Anyone can make monetary contributions to help the business in which they believe(Michel, Gil, Hauder, 2015) . Crowdfunding is a good option for start-ups to raise funds simply because it can generate interest and thus helps the business in marketing the product along with arranging for finances. It also acts as a boon for the business in case one is not sure about the demand of the product being thought of. The process of crowdfunding cuts the professional brokers as well as investors as the funding is fully in the hands of common people. It can also attract venture-capital investment in case the company has a successful campaign. Crowdfunding however, is very competitive place to get funds, is gaining the attention of average customers just with the help of description or images online is very tough. Some of the well knows crowdfunding websites in US include: RocketHub, Onevest, GoFundMe, DreamFunded and Kickstarter (ProfitBooks, 2017). Start-ups and Crowdfunding Crowdsourcings very well-known model is the Open Collaboration (OC) Model where the problems or the opportunities made available are being posted by the organisation to the public with the help of IT systems . The crowds voluntarily engage in these ventures without any expectation of monetary compensations (Mergel, 2015). Some examples of OC crowdsourcing include starting an enterprise like Wiki or making use of online as well as social media communities for gaining contributions (Leimeister et al, 2009). The crowds level of engagement is related to several factors for example efficacy of the open call, the crowd capital of company and reach as well as engagement of the platform that is being used (Prpi? Shukla, 2013). For example as of May, 2015 Twitter quoted it has more than 500 million users and out of all these users more than 310 million are found to be active on monthly basis.. Thus it does not convert into significant engagement from users potential pool on platform. Tourna ment Crowdsourcing (TC) is another form of crowdsourcing where the organisations post their issues on specialised IT platforms like Eyeka or Kaggle or on in-house platforms like Challenge.gov (Zhang, Gu, Song, Pan, Dawy, Han, 2015) . Thus with the help of a platform mediated by IT organisers create a competition with set rules and rewards for competition. These TC platforms mostly attract as well as maintain special crowds who show liking towards special platforms(Brabham, 2013) . Another kind of crowdsourcing is Virtual Labour Markets (VLM) whish is also an IT-mediated markets in which the individuals render online services to be carried out anywhere, offered by the firms mainly with the help of micro-tasks thus indicating the production model of crowd sourcing in exchange of monetary compensation (Luz, Silva, Novais, 2015). Conclusion The above analysis indicates that crowdsourcing is a tool which makes use of IT mediated platform to engage the crowd for the purpose of task completion , production , generation of ideas or for problem solving , where the dispersed skills as well as knowledge of groups or individuals are leveraged with a mixture of innovative crowd derived inputs as well as processes that have top-down set goals as well as organisation initiatives. Crowdsourcing is an emerging field which is evolving. It encompasses various platforms like virtual labour markets (VLMs), Tournament crowdsourcing (TC) as well as Open collaboration (OC). Crowdfunding is a form of crowdsourcing and acts as a tool for financing for start-ups and is current and popular method. The financing gap that emerges in the start-ups can be easily bridged with the help of crowdfunding. Thus crowdfunding acts as a tool for raising funds for start-ups during the first financing rounds. AS far as the start-ups are capable of showing so me value to the crowds, crowd fund will be automatically being considered as financing option by entrepreneurs. Bibliography Bayus, B. (2013). Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community. Management science, 226-244. Brabham, D. (2013). Using Crowdsourcing in Government. IBM Center for The Business of Govt. Canarelli, M. (2017, Feb 22). Harness the Power of Crowdsourcing to Improve Your Business. Retrieved Oct 11, 2017, from Startupnation.com: https://startupnation.com/start-your-business/crowdsourcing-improve-business/ Cheung, S. (2012). How Companies Can Leverage Crowdsourcing. Composite Information Systems Laboratory (CISL). Lehdonvirta, V., Bright, J. (2015). Crowdsourcing for public policy and government. Policy Internet, 263-267. Leimeister, J., et al. (2009). Leveraging crowdsourcing: Activation-Supporting components for IT-based ideas competition. J. Manage. Inform. Syst., 197-224. Luz, N., Silva, N., Novais, P. (2015). A survey of task-oriented crowdsourcing. Artificial Intelligence Review, 187-213. Mergel, I. (2015). Open collaboration in the public sector: The case of social coding on GitHub. Government Information Quarterly, 464-472. Michel, F., Gil, Y., Hauder, M. (2015). A virtual crowdsourcing community for open collaboration in science processes. Americas Conference on Information Systems. ProfitBooks. (2017). 10 Funding Options To Raise Startup Capital For Your Business. Retrieved October 11, 2017, from https://www.profitbooks.net/funding-options-to-raise-startup-capital-for-your-business/ Prpi?, J., Shukla, P. (2013). The Theory of Crowd Capital. 46th Hawaii International Conference on System Sciences, . Computer Society Press. Sally. (2013). Cash from the Crowd: How to crowdfund your ideas and gain fans for your success. Enterprenuer Press. Smith, D., Manesh, M., Alshiakh, A. (2013). How Can Entrepreneurs Motivate Crowdsourcing Participants? Technology Innovation Management Review. Zhang, Y., Gu, Y., Song, L., Pan, M., Dawy, Z., Han, Z. (2015). Tournament Based Incentive Mechanism Designs for Mobile Crowdsourcing. IEEE Global Communications Conference, (pp. 1-6).