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32 Seiten, Note: 67
List of Abbreviations
Chapter 1: Introduction
Structure of the paper
Chapter 2: Literature Review
Status quo bias
Utility Hypothesis is questioned
Gender as a factor in decision making
Behavior of Individual Investors
Two types of Investors: Smart Money Vs. Ordinary Investors
The Need for Behavioral Finance
Events leading up to the crash
Flash Crash 2010: a brief summary
Net Result of Flash Crash
Chapter 3: Research Question and framework
Question 1: Which theories explain the Flash Crash?
Question 2: Which Companies?
Question 3: Circuitbreakers
Chapter 4: Research Methodology
Objective of the research
How research questions were answered?
Strength of association
How materials were prepared
Types of data
Justification of study’s design
Chapter 5: Analysis of Reasons for Flash Crash 2010
Large Directional Betting/Trading
Changes in Market structure resulting in the decentralization of trading
Influence of HFT
High frequency traders and their contribution
Order flow Toxicity Metric
Chapter 6: Interpretation of Results
Can proposed reasons adequately explain event
How do these possible explanations differ form traditional finance explanations
Chapter 7: Conclusion
Can financial instruments/regulations prevent future crisis?
Often investors are asked why they have picked a certain stock or investment. While answers range from it was “based on extensive research” to “a lucky guess” or possibly “an inside tip”, it is clear that some market behavior of investors cannot be explained by the logical rules of finance. The “why” of investors’ decisions can often be analyzed using financial theories but for the anomalies, a new topic was developed. The field of behavioral finance was created so as to give foundation to alternative explanations for seemingly unexplainable market trends and swings. As we know behavioral finance is the field that uses “psychology-based theories to explain stock market anomalies” (Investopedia, 2013a). This theory attempts to give some rationale to the decisions that cannot otherwise be explained. This seemingly new field has been used to explain a variety of financial events and has been very useful in helping experts to understand “how” or “why” individuals (mostly investors) make certain decisions. Aimed with this knowledge it becomes easier to draw conclusions about what future steps these individuals or groups of individuals may take. There mare many examples of how behavior finance is used but this research will concentrate on one clear event in the recent financial past of the NYSE, the Flash Crash of 2010.
This stock market investor anomaly was named the “Flash Crash of 2010” because of the severe loss that occurred to the DJIA in such a short financial trading window. This event saw a 1,000-point plunge from the DJIA in minutes only to see a near even recovery just minutes later. In this event and for a few short minutes, $1 trillion in market value disappeared. According to Grocer, “While stock markets do crash, immediate rebounds are unprecedented” (2011, p1). There were some very serious losses and major companies such as Exelon, Accenture and even Center-Point Energy dropped 1 cent per share. This was also odd because other stocks such as Apple and HP increased in to over $100,000 in value (Grocer, 2011). So what was behind this nearly bi-polar financial event? It is in this realm that behavioral finance theories can be used to provide a partial explanation for such an uncommon event.
There have been numerous theories presented and many studies have been performed on the Flash Crash and there is still a lot of discrepancy today over the real cause. Yet despite a report by the SEC, there actually has been no finite conclusion as to the true cause of this potentially disastrous financial event. Many experts, academics and traders still disagree on what the motivating factors were. What this paper sets out to do is use the existing literature to try and understand which of the proposed theories is the most credible. With an extensive literature review as well as analysis of the events, this paper aims to use the presented information and draw its own conclusions about what the underlying cause or causes of the event were, taking into consideration how behavior finance has affected this field. Using behavioral finance as a background to understanding investor behavior, this paper will explore non-financial events such as large directional betting, technological glitches and even possible Greece’s instability to understand to what extent investors were impacted by these mitigating factors. With the help of primary sources, secondary sources, this paper is well grounded in both theoretical as well as practical perspectives.
After this brief introduction, which has introduced the topics to be covered, this paper will cover the research with 6 additional chapters. The chapters will proceed in the following manner: Chapter 2 will be a literature review that will develop a theoretical prism through which to view the paper. The focus of this will be on behavior finance topics such as feedback theories and smart money vs. ordinary investors. Additionally, the need for behavioral finance as well as specific background information on the Flash Crash will be given, including events that led up to the event. The purpose of this section is not to list the event that happened but rather establish the market conditions so when behavioral finance concepts are applied to the situation, rational deductions can be drawn from these seemingly irrational events. Chapter 3 will outline the research questions that will be undertaken. This chapter, while quite short in nature, actually presents the crux of the paper. It points out in detail what specific questions and avenues of investigation will be present in the analysis and the results. Chapter 4 will discuss the research methodology related to the study. Since this is a triangulation-based paper, both quantitative and qualitative research methods will be presented. These include but are not limited to statistics, citations as well as primary data acquired from interviews with individuals who witnessed and/or participated in the events. Chapters 1-4 essentially have the ultimate goal of setting the stage for the research and informing the reader about specific terms as well as market conditions that existed in the pre-crash and cash period 2010.
Beginning with Chapter 5, a new section of the paper begins and this deals with the presentation and analysis of results. Chapter 5 presents the results in full detail but in a very straightforward manner so as to provide data for the reader. In this chapter as well, the results of two different individuals directly connected with the event will be summarized. The information acquired through interviews with investors that experienced the flash crash provides invaluable first-hand accounts of how behavioral finance works in a real-time market setting. Chapter 6 interprets these same results using the most recently applied behavioral financial theories. The goal of chapters 5-6 is to comprise the “results” section of the research project and provide it with credible and valid sources as well as data. Through this data, important deductions about the event are made.
Finally, Chapter 7 is the conclusion to the research. The conclusion is a general summary of the points presented and outlined in the paper. After the major findings of the paper are presented, recommendations for the future of this industry will also be taken into consideration. The recommendations section has the goal of presenting possible solutions to prevent future mini market shocks such as the Flash Crash of 2010. It also outlines what changes have been implemented since the crash. While some of these recommendations will never come to fruition, the point is to present as many options as possible so alternatives to the present system can be understood.
This chapter will focus on past research and studies in order to provide a solid theoretical basis for the paper. This section attempts to set the foundation of the paper and functions as a departure point for the topic. While up until this point generalities have been presented, this chapter and subsequent ones delve into more specific and detailed topics.
In order to develop the scope of this paper it will be necessary to define a number of topics; beginning with behavioral finance, we can see that this definition helps to outline the parameters of the paper. As we aforementioned Phung states that
Behavioral Finance is a relatively new field that seeks to combine behavioral and cognitive psychological theory with conventional economics and finance to provide explanations for why people make irrational financial decisions (2013, p1).
The idea that conventional theories of finance and economics were not enough to explain away certain financial events was quite revolutionary because most experts, academics and even traders constantly looked to the market and its trends as a way of explaining what was happening in this environment. Yet the actual process of decision-making does not merely involve the inputting of financial data. The result is a combination of more than just inputs about finance. When making judgment the three factors that need to be taken into consideration according to Amos Tversky and Daniel Kahneman:
- Representativeness: When people are asked to judge the probability that an object or event A belongs to class or process B, probabilities are evaluated by the degree to which A is representative of B, that is, by the degree to which A resembles B.
- Availability: When people are asked to assess the frequency of a class or the probability of an event, they do so by the ease with which instances or occurrences can be brought to mind.
- Anchoring and adjustment: In numerical prediction, when a relevant value (an anchor) is available, people make estimates by starting from an initial value (the anchor) that is adjusted to yield the final answer. The anchor may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient (1974).
If we consider the aforementioned principles we can understand how psychology makes up a large part of the decision making process. What is troubling about this is that there is a general perception that the market is in fact efficient. Because it is based on principles such as supply and demand, the market and academic theories about it do not factor in the impact of psychological factors, which are largely unpredictable and extremely inefficient at times. We can see that psychological factors are in fact very subjective in nature.
Looking at the three factors according to Tversky and Kahneman, we can see there are some real problems with this type of reasoning’s application to investing. If we take availability for examples, we can see that an individual’s ability to make a decision is based on past events. While this can be helpful in many circumstances, we need to ask the question, what happens in the case there is no past experience or no relevant past experience. If instances cannot be brought to mind easily, the decision making process is impeded. Representativeness fairs no better in the decision making process because the two events mentioned in the definition may not resemble on another at all, leading to an incorrect assumption or decision. Finally, we see that anchoring in fact proves to be one of the more damaging additions to decision making. By using an anchor, investors can be misled as to what the starting point for the decision should be. Beginning with the wrong information most certainly leads to an incorrect solution; additionally, even if adjustments are made to this figure these are often not enough to correct the anchor.
This section covers the major developments and theories in behavioral finance that have been the most influential.
Another set of research that was incredibly influential on the behavioral finance field is that of Samuelson and Zeckhauser who performed a series of decision making experiments in their 1988 work. Along the lines of the anchor aspect to Tversky et all this study found that there is clearly a bias towards the status quo. The study concludes is, “individuals disproportionately stick with the status quo and that the status quo bias is substantial in important real decisions” (1988, p47). When we consider the rapid pace and changing nature of the market, a bias towards a status quo can mean a seriously negative impact on decisions given that the financial environment can change immediately. “Rational explanation can be provided for the status quo bias. However, a variety of psychologically based theories provide more robust explanations; that is, their more specific predictions are validated. The two classes of explanations, we believe, are complementary” (1988, p47). Therefore it has been proven again that individuals when given the chance go with what they are familiar with or their status quo. Adding finance into this equation makes decision-making a bit tricky because then it is the case that investors are basing their decisions on feelings or past experiences rather than actual market data or factual information. Then if we consider that thousands of trades a day are made using software based on algorithms, yet there is always the opportunity for humans to weigh in on these decisions, the environment in which these financial decisions take place becomes more and more cloudy.
Even the methods by which finance is analyzed are questioned when were place this in a behavioral finance context. Poterba and Summers performed a great deal of research on transitory components and “found positive autocorrelation in returns over short horizons and negative autocorrelation over longer horizons, although random-walk price behavior cannot be rejected at conventional statistical levels” (1987, p1). What we understand from this research is that even attempts to adjust decision making based on new information provide a poor guide. Perhaps initially this is useful but eventually it winds up having a negative affect on the situation and the decision.
One other set of research that must be mentioned is the work by Rabin and Thaler. In their 2001, this research pair studied in particular risk aversion in decision making. Their results showed that in this case, the expected utility hypothesis is not longer valid and in a sense is dead. As Rabin and Thaler state, in trying to reason away certain events, “Economists ubiquitously employ a simple and elegant explanation for risk aversion: It derives from the concavity of the utility-of-wealth function within the expected-utility framework” (2001). But as was aforementioned the likelihood that the market is efficient and able to function at this level is not very high. Their research shows that such simplistic explanations are not possible in most situation because “anything more than economically negligible risk aversion over moderate stakes requires a utility-of-wealth function that is so concave that it predicts absurdly severe risk aversion over very large stakes” (2001, p219). The inconsistency and inability of economists to make correct predictions means that this framework had continued to mislead economists and others in the field.
One very interesting aspect of the entire behavioral finance matrix is that relevance of gender. There has been a great deal of psychological research into the idea of overconfidence and it has been suggested as well as proven that men are more likely to be over confident than women. Additionally, Barber and Odean (2001) state, “theoretical models predict that overconfident investors trade excessively”. Since we know a predominant number of traders and investors are male, it is possible that gender plays a large role in the complex finance environment. Their research concludes that excessive Trading reduces men's net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women” (2001). This results in men trading 45% more often than women and also indicates that they are reducing their own returns perhaps in large part due to this overconfidence (2001, p2). Since Behavioral economics does just this, it sets up the possibility that more subjective factors, such as overconfidence do impact the larger decisions that are begin made by individuals. Barber and Odean’s contribution to the behavior finance field has been monumental in helping to assess individual investors’ decision.
In a landmark piece of research Barber and Odean uncover even more interesting research when it comes to investor behavior. In 2011 with their study, The Behavior of Individual Investors the following discoveries were made about individual investors, they:
- (1) underperform standard benchmarks (e.g., a low cost index fund),
- (2) sell winning investments while holding losing investments (the “disposition effect”),
- (3) are heavily influenced by limited attention and past return performance in their purchase decisions,
- (4) engage in naïve reinforcement learning by repeating past behaviors that coincided with pleasure while avoiding past behaviors that generated pain, and
- (5) tend to hold undiversified stock portfolios. These behaviors deleteriously affect the financial well being of individual investors (2011).
These conclusions about the individual investors make for some very interesting perspectives on markets and decision-making. They seem to echo some of the first works by Tversky in which the subjective nature of investors’ decisions were brought to light. While investors and traders would like to believe that they are making informed decisions while working, with all of the research to suggest otherwise, it is hard to believe them.
Shiller, one of the leaders in behavioral finance came to the conclusion that mass psychology has a lot to do with the conditions in the market and that this can create a sort of feedback loop.
When speculative prices go up, creating successes for some investors, this may attract public attention, promote word-of-mouth enthusiasm, and heighten expectations for further price increases. The talk attracts attention to “new era” theories and “popular models” that justify the price increases. This process in turn increases investor demand and thus generates another round of price increases. If the feedback is not interrupted, it may produce after many rounds a speculative “bubble,” in which high expectations for further price increases support very high current prices (Shiller, 2003, p91-92).
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