On the behaviour of financial markets: Fluctuations and Sentiment

On the behaviour of financial markets: Fluctuations and Sentiment Khurshid Ahmad, Chair of Computer Science Trinity College, Dublin, IRELAND 11-13th November 2013

Price Discovery in Spot Markets A method of determining the price for a specific commodity or security through basic supply and demand factors related to the market. Price discovery is the general process used in determining spot prices. These prices are dependent upon market conditions affecting supply and demand. For example, if the demand for a particular commodity is higher than its supply, the price will typically increase (and vice versa). http://www.investopedia.com/terms/p/pricediscovery.asp#axzz2KmoENsz7

Price Discovery in Futures Markets Garbade and Silber have noted that: Risk transfer and price discovery are two of the major contributions of futures markets to the organization of economic activity […] Risk transfer refers to hedgers using futures contracts to shift price risk to others. Price discovery refers to the use of futures prices for pricing cash market transactions. The significance of both contributions depends upon a close relationship between the prices of futures contracts and cash commodities. Kenneth D. Garbade and William L. Silber (1983). Price Movements and Price Discovery in Futures and Cash Markets. The Review of Economics and Statistics, Vol. 65, No. 2 (May, 1983), pp. 289-297Published

Economics, Finance and BehaviourIndividual and Institutional Investor Sentiment The Investor Behavior Project at Yale University, has been collecting questionnaire survey data on the behavior of US investors since 1984. The questionnaire is sent to individual investors and to institutional investors. One of the longest-running effort to measure investor confidence and related investor attitudes. The differences amongst the individuals and institutions is quite remarkable. This is perhaps one of first systematic field studies to have identified information asymmetry in financial trading. Institutional Investors shown in blue, Individual Investors shown in red.

Economics, Finance and BehaviourIndividual and Institutional Investor Sentiment Confidence that the stock market will go up in the succeeding year rose fairly steadily over the years from 1989 to 2004, both for institutional and for individual investors. At the peak of One-Year Confidence, as of December 2003, 92.52% of institutional investors expected the market to go up over the succeeding year, and as of January 2004 95.62% of individual investors thought the same. After that, there was a brief moment of high confidence among institutional investors in 2006. Individual investor confidence bottomed in April 2008, just before the subprime crisis, and, surprisingly, improved with as the crisis worsened. Institutional Investors shown in blue, Individual Investors shown in red. http://icf.som.yale.edu/stock-market-confidence-indices-united-states-yearindex

Economics, Finance and BehaviourIndividual and Institutional Investor Sentiment Confidence that there will be no stock market crash in the succeeding six months generally declined (though with a lot of ups and downs) over the years since 1989 until the stock market bottomed out in late 2002. Just after the terrorist attacks of September 11, 2001, Crash Confidence actually rose a little. But Crash Confidence reached its lowest point at 20.79% for institutional investors and 28.95% for individual investors as of November 2002. Crash confidence reached its all-time low, both for individual and institutional investors, in early 2009, just months after the Lehman crisis, reflecting the turmoil in the credit markets and the strong depression fears generated by that event, and is plausibly related to the very low stock market valutions then. The recovery of crash confidence starting in 2009 mirrors the strong recovery in the stock market. Institutional Investors shown in blue, Individual Investors shown in red. http://icf.som.yale.edu/stock-market-confidence-indices-united-states-crashindex

Economic Cycles 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.

Economic Cycles 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 Kuznetswings 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

Economic Cycles 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.

Economic Cycles 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

Disruption to the economic cycles The three well documented observations: 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).

Disruption to economic cycles Four states of matter: solid, liquid, gases and plasma; Four kinds of randomness: mild, slow, wild, furious. PS: Mandelbrot has only 3 states of matter and three states of randomness; I have added the fourth! Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Economics and Finance Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Disruption to economic cycles Stable economic systems are like solids, mean reversion of returns and minimal volatility. As the economic systems become more and more unstable prices change much more rapidly, reversion to mean is delayed, or indeed disappears altogether and volatility of returns dramatically. The ‘liquid’ state shows local failure but globally the economic system remains stable. In the gaseous state, large components of the system fail and have to be repaired and/or replaced. The plasma state is the state of total meltdown. Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Stable Economy: full employment Local Shocks but otherwise stable economy Major Shocks and fragile economy Economy in total meltdown Disruption to economic cycles Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Disruption to economic cycles 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 wildmovements in stock market prices that are seemingly unjustified by fundamentals” Ontological commitments in BLUE & terminological conventions in RED 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. (http://www.mccombs.utexas.edu/faculty/Paul.Tetlock/papers/TSM_More_Than_Words_09_06.pdf)

Disruption to economic cycles Kumar, Alok., and Lee, Charles, M.C. (2007). Retail Investor Sentiment and Return Comovements. Journal of Finance. Vol 59 (No.5), pp 2451-2486

Randomness of price variation 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 Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Randomness of price variation 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. Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Randomness of price variation 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) Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Randomness of price variation 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 1050) In August 1997, DJIA fell by 7.7% (1 in 50 billion chances); STUFF happens? Mandelbrot, Benoit, B., & Hudson, Richard L. (2004). The (Mis)Behaviour of Markets. London: Profile Books (Paperback edition printed in 2005)

Randomness of price variation 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.

Randomness of price variation • 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.

Flightiness of price change 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).

Flightiness of price change 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.

Flightiness of price change 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.

Flightiness of price change Not-so random walk of price changes 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

Flightiness of price change Not-so random walk of price changes

Flightiness of price change Movement of daily price changes – actually return of prices  r=log(pt/pt-1) on three stock exchanges between 1996-2005. You can see ‘mild’, slow and wild movements The News Impact Curve

Prices Change and Traded Volumes Fluctuate Not-so random walk of price changes Not-so random walk of price changes

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:

Prices Change and Traded Volumes Fluctuate Why do markets (mis)behave? ‘Empirical 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.

Prices Change and Traded Volumes Fluctuate Why do markets (mis)behave? 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.

Prices Change and Traded Volumes Fluctuate Why do markets (mis)behave? 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 averages 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

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’

Economics and Finance ‘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

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.

Economics, Finance and Behaviour Tighten Belt Market Forces

Economics, Finance and Behaviour: The recurrent ‘moral hazard’ For many thinkers, language is a communications system used to represent reality without interfering with the message. For others, contrarily, language shapes the message and becomes part of the message; language constitutes the message rather merely representing it.

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

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

Economics, Finance and Behaviour • ‘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.

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

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.

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

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

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

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 = -0.358CEFDt+0.402TURNt-1+0.414NIPOt +0.464RIPOt+0.371 St-0.431Pt-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.

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 277-287.

On the behaviour of financial markets: Fluctuations and Sentiment