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On the behaviour of financial markets: Price and Volume Fluctuations Khurshid Ahmad, Chair of Computer Science Trinity College, Dublin, IRELAND 17-19 Nov.

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Presentation on theme: "On the behaviour of financial markets: Price and Volume Fluctuations Khurshid Ahmad, Chair of Computer Science Trinity College, Dublin, IRELAND 17-19 Nov."— Presentation transcript:

1 On the behaviour of financial markets: Price and Volume Fluctuations Khurshid Ahmad, Chair of Computer Science Trinity College, Dublin, IRELAND Nov 2010

2 Economics and Finance Complex physical systems exhibit repetitive behaviour or cycles: Periodic arrangements of atoms in a crystalline structure leads to robust and elastic materials; a lack of periodicity is regarded as crystal defect. We have weather changes – spring in May, snowfall in December in the Northern Hemisphere- but the ‘early’ onset of spring/summer/winter, or the more/less than average rainfall/snowfall, or the more/less frequent floods, is variously attributed to the disastrous global warming/cooling. Any deviation from the periodic behaviour is described through terms of negative affect – defects, disasters, spikes, and crash of or in the system.

3 Economics and Finance Prices and traded volumes of shares, bonds and commodities, for instance, show a cyclical behaviour over a period of time–Jugular (1862) noted a 10 year cycle, then there are 20 year Kuznet swings and 50 year Kondratieff cycle (Solumu 1998); and for the chaos theorist Benoit Mandelbrot there are 5 year cycles. The unexpected surges and devastating downturns in prices remain largely unexplained

4 Economics and Finance The cyclical behaviour of prices suggests that when an object is underpriced by its seller, a buyer rush towards it and competition encourages the seller to reach the correct price; similarly for an overpriced object, buyers shy away and the seller is forced to sell the object at its true value. Prices move towards an equilibrium value, much like the physical systems where forces of nature (atomic, molecular, gravitational and so on) help the systems to move towards a settled price.

5 Economics and Finance It has been argued that there are market forces that help to realize the optimum prices – and this has lead to the so-called rational market theories, especially the efficient market hypothesis which had dominated the pre-2007/08 credit crunch. Market forces will discount all irrationality and the lender-of-last-resort will be there only to discourage criminal manipulation of prices. However, this (constructivist) Cartesian world of rationally behaved trinity of buyers/sellers/regulators also discounted three well documented observations

6 Economics and Finance The three well documented observations: (a)framing –presentation format of a proposition effects the perception what is being proposed (Kahnemann 2000); (b) human herd behaviour in financial markets (Cipriani and Guarino 2009); and (c) areas of human brain dedicated to seeking risk unnecessarily and avoiding plausible risk (Porcelli and Delgado 2009).

7 Three states of matter: solid, liquid and gases; Three kinds of randomness: mild, slow, and wild. Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

8 Three states of matter: solid, liquid and gases; Three kinds of randomness: mild, slow, and wild. Mandelbrot: Conventional finance theory assumes that the variation of prices can be modeled by random processes that, in effect, follow the simplest ‘mild’ pattern, as if each uptick and downtick were determined by the toss of a coin Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

9 Three states of matter: solid, liquid and gases; Three kinds of randomness: mild, slow, and wild. Mandelbrot: Investigations based on the fractals of mathematics indicate that standard, real prices ‘misbehave’ very badly. Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

10 Three states of matter: solid, liquid and gases; Three kinds of randomness: mild, slow, and wild. August 1998 should not have happened: Random walk theory (mild randomness) suggests that chances of August 31, 1998 collapse was 1 in 20 million (trade for 100,000 years to encountyer such an event; odds of THREE such declines in one month  one in 500 billion. (Mandelbrot and Hudson 2004:4) Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

11 Three states of matter: solid, liquid and gases; Three kinds of randomness: mild, slow, and wild. In October 198, DJIA fell by 29.2% (1 in ) In August 1997, DJIA fell by 7.7% (1 in 50 billion chances); STUFF happens? Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

12 Investor sentiment & stock market bubbles has some causal relationship with: Baker, M., & Wurgler, J. (2003). ‘Investor sentiment and cross-section of stock returns. Proc. Conf on Investor Sentiment tronics mania 1967franchise and computer ‘crazies’ 1983high tech issues 2001dot.com Economics and Finance

13 In his book Irrational Exuberance Robert Shiller (2000) mentions the mass media as an important factor in the generation of overreactions: Due to their capacity to arouse attention the media can create positive feedback and reinforce existent trends – and contribute to the reinforcement of speculative price movements and financial bubbles.

14 Economics and Finance Benoit Mandelbrot (1963) has argued that the rapid rate of change in prices (the flightiness in the change) can and should be studied and not eliminated – ‘large changes [in prices] tend to be followed by large changes –of either sign- and small changes tend to be followed by small changes’. The term volatility clustering is attributed to such clustered changes in prices. Mandelbrot’s paper drew upon the behaviour of commodity prices (cotton, wool and so on), but volatility clustering’ is now used in for almost the whole range of financial instruments (see Taylor 2007 for an excellent and statistically well- grounded, yet readable, account of this subject).

15 Prices Change and Traded Volumes Fluctuate There is a realisation that the various stakeholders in financial markets across the world that we do not understand fully how prices of financial instruments change with time. This realisation is more worrying in that many of the regulators of financial markets have doubts about the ability of the markets to apply endogenous corrections. Somehow it appears that stakeholders – investors, traders, regulators- behave in an irrational manner and their subjective feelings have (indirect) impact on the markets.

16 Prices Change and Traded Volumes Fluctuate The ability to estimate the changes in prices of an asset – asset price dynamics to be more precise- is critical for an estimation of risk associated with that asset. The efficient market hypothesis – that gives credence to the self- correcting markets hypothesis- is based on a random walk model of the prices where the changes in prices are assumed to be distributed according to a normal distribution: 68% of the changes will be within one standard deviation from the mean value, and 99.5% within three standard deviation from the mean. The efficient market hypothesis suggested that price changes are statistically independent.

17 Prices Change and Traded Volumes Fluctuate Benoit Mandelbrot (2005) notes that ‘the bell curve [normal distribution] fits reality very poorly. Form 1916 to 2003, the daily index movements of the Dow Jones Industrial Average do not spread out on a graph paper like a simple bell curve. […] Theory [bell curves] suggests that over that time [97 years] there should be fifty eight days when the Dow moved more than 3.4 percent; in fact there were 1,001 [such days]. Theory predicts six days of index swings beyond 4.5 percent; in fact there were 366. And index swings of more than 7 percent should come once every 300,000 years; in fact twentieth century saw forty eight such days. Truly, a calamitous era that insists on flaunting all predictions. Or, perhaps, our assumptions are wrong’ (pp 13) Mandelbrot, Benoit B., and Hudson, Richard L. (2005), The (Mis)behaviour of Markets – A Fractal View of risk, Ruin and Reward. London: Profile Books Not-so random walk of price changes

18 Prices Change and Traded Volumes Fluctuate Not-so random walk of price changes Normal Distribution Deviation from the meanProbabilityCumulative Value %50.00% %59.87% %69.15% %84.13% %93.32% 25.40%97.72% 30.44%99.87% 40.01%100.00% 50.00%100.00% 60.00%100.00% 70.00%100.00%

19 Prices Change and Traded Volumes Fluctuate Movement of daily price changes – actually return of prices  r=log(p t /p t-1 ) on three stock exchanges between You can see ‘mild’, slow and wild movements

20 Prices Change and Traded Volumes Fluctuate Not-so random walk of price changes Once Every Price Changes Theory (Days) Observation (Days) Year3.4%Once in 1.65 yrs %Once in 16.5 yrs3.8 7%Once in 300K yrsOnce in 2 yrs Once Every Price Changes Theory (Days) Observation (Days) 1,000 Years3.4% %6377 7%Once in 300K yrs49 Once Every Price Changes Theory (Days) Observation (Days) 1,000,000 Years3.4% % % Not-so random walk of price changes

21 Prices Change and Traded Volumes Fluctuate Financial Times, Saturday 21, March 2009 Main Headline: ‘Banker fury over tax ‘witch hunt’ Back Page: The Week in Numbers: 300 bn20%5 Federal Reserve The [Fed] stunned the market by […buying] $300bn of longer-term Treasury bonds. The yield on 10-year Treasury bonds fell 50 basis points US equities The [S&P 500] benchmark set an intraday high of , marking a rise of more than 20% from a 12 year low of struck just nine days earlier Norwegian Kr The Norwegian krone touched a five month high against the dollar as investors sought safer alternatives to the US currency [Oct 2008:7.2 NKr/$; Mar 2009: ~6.4 NKr/$]

22 Prices Change and Traded Volumes Fluctuate ‘Empirial observation of finance markets has often revealed that large movements occur more frequently than would be xpected if returns were normally distributed. For instance, the 1987 equity crash recorded negative returns that were over 20 standard deviations from the mean […] In addition, most return distributions are also skewed, meaning there is a greater likelihood of the portfolio yielding either higher or lower returns than would be expected under normal distributions’ (Lhabitant 2004:47) Lhabitant, François-Serge. (2004). Hedge Funds: Quantitative Insights. Chichester: John Wiley & Sons, Ltd. Why do markets (mis)behave?

23 Prices Change and Traded Volumes Fluctuate The MSCI (Morgan Stanley Capital Investment) World is a stock market index of 'world' stocks. L’habitant (2004) has argued that ‘only when we remove some outliers’ the normality assumption is usually not rejected. But even when as much as 2% outliers are excluded, returns on many hedge funds still do not conform to normal distribution (ibid:48-49) Lhabitant, François-Serge. (2004). Hedge Funds: Quantitative Insights. Chichester: John Wiley & Sons, Ltd. Why do markets (mis)behave?

24 Prices Change and Traded Volumes Fluctuate We can tell that markets misbehave because (a) prices do correlate and exhibit flightiness – or volatility; and (b) the underlying distribution of changes – or returns- does not obey the normal distribution. But why is there the flightiness and non-normality? Because it is Nature’s law – Zipf’s Law; Pareto Distribution; Cauchy’s Distributions, and Mandelbrot’s fractal theory of behaviour. In all these cases, the largest observed value can and does change the avergaes and standard deviations. Mandelbrot, Benoit B., and Hudson, Richard L. (2005), The (Mis)behaviour of Markets – A Fractal View of risk, Ruin and Reward. London: Profile Books Why do markets (mis)behave?

25 Economics and Finance Dan Nelson (1992) ‘recognized that volatility could respond asymmetrically to past forecast errors. In a financial context, negative returns seemed to be more important predictors of volatility than positive returns. Large price declines forecast greater volatility than similarly large price increases. This is an economically interesting effect that has wide ranging implications’

26 Economics and Finance Volatility Clustering Type Clustering CycleInformation Flow SlowSeveral years or longer. Single inventions or unique events that may benefit firms in the longer term High FrequencyFew days or minutes Price Discovery : When agents fail to agree on a price and suspect that other agents have insights/models better than his or her. Prices are revised upwards or downwards quite rapidly. Medium Duration Volatility Weeks or Months Clustered events : Many inventions streaming in; global summits; governmental inquiries; ‘Why it is natural for news to be clustered in time, we must be more specific about the information flow’ (Engle 2003:330) Robert F. Engle III (2003). RISK AND VOLATILITY: ECONOMETRIC MODELS AND FINANCIAL PRACTICE. Nobel Lecture, December 8, 2003

27 Economics and Finance Board of Governors of the Federal Reserve System The January 2008 Senior Loan Officer Opinion Survey on Bank Lending Practices The [..] Survey addressed changes in the supply of, and demand for, bank loans to businesses and households over the past three months. Special questions in the survey queried banks about changes in terms on commercial real estate loans during 2007, expected changes in asset quality in 2008, and loss-mitigation strategies on residential mortgage loans. In addition, the survey included a new set of recurring questions regarding revolving home equity lines of credit. This article is based on responses from fifty-six domestic banks and twenty-three foreign banking institutions.

28 Economics, Finance and Behaviour Tighten Belt Market Forces

29 A multi-sensory world Multisensory Processing is an emergent property of the brain that distorts the neural representation of reality to generate adaptive behaviors.

30 Economics, Finance and Behaviour John R. Nofsinger (2005) Social Mood and Financial Economics. The Journal of Behavioral Finance 2005, Vol. 6, No. 3, 144–160

31 Economics and Finance ‘The ability to forecast financial market volatility is important for portfolio selection and asset management as well for the pricing of primary and derivative assets’. The asymmetric or leverage volatility models: good news and bad news have different predictability for future volatility. Engle, R. F. and Ng, V. K (1993). Measuring and testing the impact of news on volatility, Journal of Finance Vol. 48, pp 1749—1777.

32 Economics and Finance As time goes by, we get more information on these future events and re-value the asset. So at a basic level, financial price volatility is due to the arrival of new information. Volatility clustering is simply clustering of information arrivals. The fact that this is common to so many assets is simply a statement that news is typically clustered in time. Robert F. Engle III (2003). RISK AND VOLATILITY: ECONOMETRIC MODELS AND FINANCIAL PRACTICE. Nobel Lecture, December 8, 2003

33 Economics and Finance Volatility and Information Arrivals ‘The ability to forecast financial market volatility is important for portfolio selection and asset management as well for the pricing of primary and derivative assets’. The asymmetric or leverage volatility models: good news and bad news have different predictability for future volatility. Engle, R. F. and Ng, V. K (1993). Measuring and testing the impact of news on volatility, Journal of Finance Vol. 48, pp 1749—1777.

34 Economics and Finance Griffin concludes that ‘the most likely reason why the stockholder held on to their ENRON positions long after the erosion of firm value became evident is that senior management made several strong endorsements and recommendations as to the holding of ENRON common equity. Management insistence to maintain and even to increase the size of their positions temporarily assuaged investor’s fears and protected their ego.’ (2006:127) Harry F. Griffin. (2006). Did Investor Sentiment Foretell the Fall of ENRON? The Journal of Behavioral Finance 2006, Vol. 7, No. 3, 126–127

35 Economics, Finance and Behaviour John R. Nofsinger (2005) Social Mood and Financial Economics. The Journal of Behavioral Finance 2005, Vol. 6, No. 3, 144–160

36 Economics and Finance A financial economist can analyse quantitative data using a large body of methods and techniques in statistical time series analysis on “fundamental data”, related, for example, to fixed assets of an enterprise, and on “technical data”, for example, share price movement; The economist can study the behaviour of a financial instrument, for example individual shares or currencies, or aggregated indices associated with stock exchanges, by looking at the changes in the value of the instrument at different time scales – ranging from minutes to decades; Financial investors/traders are trying to discover the market sentiment, looking for consensus in expectations, rising prices on falling volumes, and information/assistance from back-office analysts; The efficient market hypothesis suggests that quirks caused by sentiments can be rectified by the supposed inherent rationality of the majority of the players in the market

37 Economics and Finance  Firm-level Information Proxies: Closed-end fund discount (CEFD); Turnover ratio (in NYSE for example) (TURN) Number of Initial Public Offerings (N-IPO); Average First Day Returns on R-IPO Equity share S Dividend Premium Age of the firm, external finance, ‘size’(log(equity))……. Each sentiment proxy is likely to include a sentiment component and as well as idiosyncratic or non-sentiment-related components. Principal components analysis is typically used to isolate the common component.  A novel composite index built using Factor Analysis: Sentiment = CEFD t TURN t NIPO t RIPO t S t P t-1 Baker, M., and Wurgler, J. (2004). "Investor Sentiment and the Cross-Section of Stock Returns," NBER Working Papers 10449, Cambridge, Mass National Bureau of Economic Research, Inc.

38 Economics and Sociology Of all the contested boundaries that define the discipline of sociology, none is more crucial than the divide between sociology and economics […] Talcott Parsons, for all [his] synthesizing ambitions, solidified the divide. “Basically,” […] “Parsons made a pact... you, economists, study value; we, the sociologists, will study values.” If the financial markets are the core of many high-modern economies, so at their core is arbitrage: the exploitation of discrepancies in the prices of identical or similar assets. Arbitrage is pivotal to the economic theory of financial markets. It allows markets to be posited as efficient without all individual investors having to be assumed to be economically rational. MacKenzie, Donald. 2000b. “Long-Term Capital Management: a Sociological Essay.” In (Eds) in Okönomie und Gesellschaft, Herbert Kaltoff, Richard Rottenburg and Hans-Jürgen Wagener. Marberg: Metropolis. pp

39 Defining Rationality MethodTechniques Systematic study of archives detailed observations Mathematical/ Statistical Models

40 Defining Rationality InstancesData Characteristics Econometrics esp. asset dynamics Large data sets of quantitative variables

41 Economics and Psychology Bounded Rationality Herbert Simon( Nobel Prize in Economics 1978 ) Rational Decision Making in Business Organisations: Mechanisms of Bounded Rationality – failures of knowing all of the alternatives, uncertainty about relevant exogenous events, and inability to calculate consequences. Daniel Kahneman ( Nobel Prize in Economics 2002 ) Maps of bounded rationality –intuitive judgement & choice: Two generic modes of cognitive function: an intuitive mode: automatic and rapid decision making; controlled mode deliberate and slower.

42 Economics, Finance and Behaviour The Journal of Behavioral Finance 2004,Vol. 5,No. 2, 70-74

43 Economics, Finance and Behaviour Rumors and the Financial Marketplace In the contemporary financial marketplace, the consequences of speculation and decision making based on unfounded assertions and false rumors can be especially potent and undeniably dangerous. With the emergence of the Internet and other new communication technologies that facilitate the spread of misinformation, it has become essential for managers, investors, and other stakeholders to acquire a better understanding of the forces that give rise to rumors and the most effective strategies for dealing with them. [….] Although relatively little research attention has been paid to the particularities of financial rumors, […] some key characteristics that appear to distinguish financial rumors from rumors about other aspects of business operations, such as greater conciseness, a shorter life cycle, and the potential for significant economic consequences. Editorial (2004). The Journal of Behavioral Finance 2004,Vol. 5,No. 3,

44 Economics, Finance and Behaviour Hardie, Iain & MacKenzie, Donald. (July 2005). An Economy of Calculation: Agencement and Distributed Cognition in a Hedge Fund (available from There is a constant stream of news and s in a dealing room. Some directly from news agencies (*) and some annotated items based on the news

45 Economics, Finance and Behaviour Floyd Norris, of New York Times and Int. Herald Tribune Online Editions, writes acerbically on finance and economics, on a near daily basis. His column attracts bloggers and he replies occasionally and then the bloggers write even more. Norris on March 2, 2007, 2:31 pm Bloggers start on March 2, 2007 at 5.27 My column today warns of the risks involved in tightening subprime credit now, as home prices are falling. In tomorrow’s Times, I will discuss how home prices are falling in many regions ……………………… pm: I agree that tardy regulators can often make a bad situation worse. Posted by Jonsson 6.00 pm: Floyd to Blogger: Mr.Jonsson: No, I do not think we would be better off without them.

46 Economics, Finance and Behaviour DateBlogsLead Sentence Excerpt Apr. 419 A Search for Scapegoats The most amazing diversion now appearing in the credit crisis is the search for scapegoats. [..]. My column today criticizes regulators, who [] did nothing to halt the flurry of highly leveraged products. […] Apr. 214 Does Wall Street Trust Wall Street? Is it all over? The big rally in stocks this week may be a sign that traders believe that governments now stand behind investment banks, as they do commercial banks: Apr. 119 Nail the Rumor- Mongers Have you noticed that financial regulators are all investigating to see who is spreading rumors that financial institutions are less than healthy? Mar Market Plunges, Fed Acts Say this for the Fed. It pays attention to what Wall Street wants. [..] Alan Greenspan fought to keep regulation away from that market,

47 Ever since Maynard Keynes suggestion that there are “animal spirits” in the market, “economists have devoted substantial attention to trying to understand the determinants of wild movements in stock market prices that are seemingly unjustified by fundamentals” Economics, Finance and Behaviour Tetlock, Paul C. (2008). Giving Content to Investor Sentiment: The Role of Media in the StockMarket. Journal of Finance. Paul C. Tetlock, Saar-Tsechansky, Mytal, and Mackskassy, Sofus (2005). More Than Words: Quantifying Language to Measure Firms’ Fundamentals. ( ) Ontological commitments in BLUE & terminological conventions in RED

48 Economics, Finance and Behaviour Market TypeWhy prices change?Role of sentiment? Rational Market (‘Traditional’ View) The current price of a stock closely reflects the present value of its future cash flows. The correlations in the returns of two assets arise from correlations in the changes in the assets’ fundamental values Demand shocks or shifts in investor sentiment plays no role [in price changes] because the actions of arbitrageurs readily offset such shocks. Exuberant Market ('Alternative' view) The dynamic interplay between noise traders and rational arbitrageurs establishes prices. The correlated trading activities of noise traders may induce co-movements and arbitrage forces may not fully absorb these correlated demand shocks. Kumar, Alok., and Lee, Charles, M.C. (2007). Retail Investor Sentiment and Return Comovements. Journal of Finance. Vol 59 (No.5), pp

49 Financial Markets Financial News Financial Traders describe write report analyse affect usecommunicate Financial Language Financial Reporters restrict survey Economics, Finance and Behaviour

50 Financial Markets Financial News Financial Traders describe write report analyse affect usecommunicate Financial Language Financial Reporters restrict survey Economics, Finance and Behaviour Bloggers

51 News Effects I: News Announcements Matter, and Quickly; II: Announcement Timing Matters III: Volatility Adjusts to News Gradually IV: Pure Announcement Effects are Present in Volatility V: Announcement Effects are Asymmetric – Responses Vary with the Sign of the News; VI: The effect on traded volume persists longer than on prices. Andersen, T. G., Bollerslev, T., Diebold, F X., & Vega, C. (2002). Micro effects of macro announcements: Real time price discovery in foreign exchange. National Bureau of Economic Research Working Paper 8959, Economics, Finance and Behaviour

52 Economics, Finance and Neuroscience Richard L. Peterson (2007). Affect and Financial Decision-Making: How Neuroscience Can Inform Market Participants. The Journal of Behavioral Finance 2007, Vol. 8 (no. 2), pp 70–78 Peterson has argued ‘that investors’ undisciplined decisions may be biased in a way that furthers the development of bull and bear markets. When the stock market is rising and most people are experiencing paper gains, many feel hypomanic, they ignore risks, and they overemphasize potential returns. Consequently, the market risk premium tends to decline and stocks rise further, generating more upward movements in the bull market.’

53 Economics, Finance and Neuroscience Evidence indicates the existence of separate brain systems, linked to affect [moods, attitudes, and emotions] processing, that are responsible for risk- taking and risk-avoiding behaviors in financial settings. Excessive activation or suppression of either system can lead to errors in investment choices and trading behaviors. Richard L. Peterson (2007). Affect and Financial Decision-Making: How Neuroscience Can Inform Market Participants. The Journal of Behavioral Finance 2007, Vol. 8 (no. 2), pp 70–78

54 Economics, Finance and Neuroscience Trepela, Fox, and Poldrack (2005) “Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk Cognitive Brain Research Vol 23 pp34–50 Brain areas, neuro- transmitters and prospect theory

55 John R. Nofsinger (2005) Social Mood and Financial Economics. The Journal of Behavioral Finance 2005, Vol. 6, No. 3, 144–160 Proponents of behavioural finance have posited that (a) optimism and/or pessimism within groups in a society, or even a society itself, is ‘reflected by the emotions of financial decision-makers.’; and (b) emotions of one participant or group may effect emotions of the other – the emotions may correlate (Nofsinger 2005:144). This leads authors like Nofsinger to make three major claims Economics, Finance and Behaviour

56 John R. Nofsinger (2005) Social Mood and Financial Economics. The Journal of Behavioral Finance 2005, Vol. 6, No. 3, 144–160 Proponents of behavioural finance, like Nofsinger claim that: 1.Social mood determines the types of decisions made by consumers, investors, and corporate managers alike. Extremes in social mood are characterized by optimistic (pessimistic) aggregate investment and business activity. 2. Due to the efficient and emotional nature of stock transactions, the stock market itself is a direct measure or gauge of social mood. 3. Since the tone and character of business activity follows, rather than leads, social mood, stock market trends help forecast future financial and economic activity. Specific predictions about stock market levels and trading volume, market volatility, firm expansion, leverage use, and IPO and M&A activity are also given. Economics, Finance and Behaviour

57 Iain Hardie and Donald MacKenzie. (2007). Assembling an economic actor: the agencement of a Hedge Fund. Sociological Review. Vol. 77, pp A fundamental question for any discipline that studies financial markets is how we should theorise actors and action in those markets. Dominant approaches in financial economics – and also, for example, in psychology-based ‘behavioural finance’ – explicitly or implicitly theorise actors as equivalent to individual human beings, whether rational, as orthodoxy posits, or subject to systematic biases as behavioural finance suggests. Economics, Finance and Behaviour

58 Economics, Finance and Behaviour Individual and Institutional Investor Sentiment Institutional Investors shown in blue, Individual Investors shown in red. The Investor Behavior Project at Yale University, has been collecting questionnaire survey data on the behavior of US investors since Institutions in BLUE and Individuals in RED One of the longest-running effort to measure investor confidence and related investor attitudes. The individual is ever so hopeful, but the institutions know something else

59 Economics, Finance and Behaviour Individual and Institutional Investor Sentiment Institutional Investors shown in blue, Individual Investors shown in red. The Investor Behavior Project at Yale University, has been collecting questionnaire survey data on the behavior of US investors since Institutions in BLUE and Individuals in RED One of the longest- running effort to measure investor confidence and related investor attitudes. The individual is ever so hopeful, but the institutions know something else

60 ‘Economics and psychology offer contrasting perspectives on the question of how people value things. The economic model of choice is concerned with a rational agent whose preferences obey a tight web of logical rules, formalized in consumer theory and in models of decision making under risk’ (Kahneman, Ritov and Schkade 1999:203) Kahneman, Daniel., Ilana Ritov and David Schkade. (1999). ‘Economic Preferences or Attitude Expressions? An Analysis of Dollar Responses to Public Issues’. Journal of Risk and Uncertainty. Vole 19 (Nos.1-3), pp ; Reprinted in Kahneman and Tversky (Eds.) (2000), pp Economics and Psychology?

61 ‘Economics and psychology offer contrasting perspectives on the question of how people value things. [….] The tradition of psychology, in contrast [to the tradition of economics] is not congenial that a logic of rational choice can serve double duty as a model of actual decision behavior.’ (Kahneman, Ritov and Schkade 1999:203) Kahneman, Daniel., Ilana Ritov and David Schkade. (1999). ‘Economic Preferences or Attitude Expressions? An Analysis of Dollar Responses to Public Issues’. Journal of Risk and Uncertainty. Vole 19 (Nos.1-3), pp ; Reprinted in Kahneman and Tversky (Eds.) (2000), pp Economics and Psychology?

62 What is important is the ‘power and generality of psychological principles’ and not the ‘limitations of rational choice theory’. Phenomena that appears anomalous from the ‘perspective of standard preference models are, in fact, predictable –indeed, inevitable – consequences of well-established rules of judgment and valuation (Kahneman, Ritov and Schkade 1999:233) Kahneman, Daniel., Ilana Ritov and David Schkade. (1999). ‘Economic Preferences or Attitude Expressions? An Analysis of Dollar Responses to Public Issues’. Journal of Risk and Uncertainty. Vole 19 (Nos.1-3), pp ; Reprinted in Kahneman and Tversky (Eds.) (2000), pp Economics and Psychology?

63 According to conventional financial theory, the world and its participants are, for the most part, rational "wealth maximizers". However, there are many instances where emotion and psychology influence our decisions, causing us to behave in unpredictable or irrational ways. Notes on Prospect Theory


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