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, IRELAND11-13th 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 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 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.




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.

http://icf.som.yale.edu/stock-market-confidence-indices-united-states-yearindex

http://icf.som.yale.edu/financial_data/confidence_index/YearIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/BuyIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/CrashIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/ValueIndex.shtml




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.

http://icf.som.yale.edu/stock-market-confidence-indices-united-states-crashindex

http://icf.som.yale.edu/financial_data/confidence_index/YearIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/BuyIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/CrashIndex.shtml

http://icf.som.yale.edu/financial_data/confidence_index/ValueIndex.shtml

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 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

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: (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).

Four states of matter: solid, liquid, gases and plasma;

Four kinds of randomness: mild, slow, wild, furious.

Disruption to economic cycles

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

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Economics and Finance

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



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.



Stable Economy: full employment

Local Shocks but otherwise stable

economy

Major Shocks and fragile economy

Economy in total meltdown

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”


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)

Ontological commitments in BLUE & terminological conventions in RED


Market Type Why 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 2451-2486

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

Randomness of price variation




Mandelbrot: Investigations based on the fractals of mathematics indicate that standard, real prices ‘misbehave’ very badly.





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)





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?



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.

1961 -tronics mania

1967 franchise and computer ‘crazies’

1983 high tech issues2001 dot.com



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).


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.


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.


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

Flightiness of price changeN

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Normal DistributionDeviation from the mean Probability Cumulative Value

0 39.89% 50.00%0.25 38.67% 59.87%0.5 35.21% 69.15%1 24.20% 84.13%

1.5 12.95% 93.32%2 5.40% 97.72%3 0.44% 99.87%4 0.01% 100.00%5 0.00% 100.00%6 0.00% 100.00%7 0.00% 100.00%


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

Prices Change and Traded Volumes Fluctuate

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Price Changes

Theory (Days)

Observation (Days)

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Observation (Days)

1,000,000 Years 3.4% 597938 10319588 4.5% 61856 3773196

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Not-so random

walk of price changes


Financial Times, Saturday 21, March 2009Main Headline: ‘Banker fury over tax ‘witch hunt’

Back Page: The Week in Numbers:

300 bn 20% 5Federal ReserveThe [Fed] stunned the market by […buying] $300bn of longer-term Treasury bonds. The yield on 10-year Treasury bonds fell 50 basis points

US equitiesThe [S&P 500] benchmark set an intraday high of 802.34, marking a rise of more than 20% from a 12 year low of 669.2 struck just nine days earlier

Norwegian KrThe Norwegian krone touched a five month high against the dollar as investors sought safer alternatives to the US currency [Oct 2008:7.2 NKr/$; Mar 2009: ~6.4 NKr/$]


‘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.

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.



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



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’


Volatility Clustering Type

Clustering Cycle

Information Flow

Slow Several years or longer.

Single inventions or unique events that may benefit firms in the longer term

High Frequency

Few 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


Board of Governors of the Federal Reserve SystemThe January 2008 Senior Loan Officer Opinion Survey on Bank Lending PracticesThe [..] 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.


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 that distorts the neural representation of reality to generate adaptive behaviors.


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


• ‘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 FinanceAs 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.


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


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


• 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.

Defining Rationality

Method TechniquesSystematic study of archives detailed observations

Mathematical/ Statistical Models

Defining Rationality

Instances Data Characteristics

Econometrics esp. asset dynamics

Large data sets of quantitative variables

Economics and Psychology

Bounded RationalityHerbert 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.


The Journal of Behavioral Finance2004,Vol. 5,No. 2, 70-74


Rumors and the Financial MarketplaceIn 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, 134-141


Hardie, Iain & MacKenzie, Donald. (July 2005). An Economy of Calculation: Agencement and Distributed Cognition in a Hedge Fund (available from [email protected])

There is a constant stream of news and e-mails in a dealing room. Some directly from news agencies (*) and some annotated items based on the news


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 ………………………..

5.27 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.


Date Blogs

Lead Sentence

Excerpt

Apr. 4 19 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. 2 14 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. 1 19 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 31 107 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,

Financial Markets

Financial News

Financial Traders

describe

write

report

analyse

affect

use communicateFinancialLanguage

Financial Reporters

restrict

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Financial Markets

Financial News

Financial Traders

describe

write

report

analyse

affect

use communicateFinancialLanguage

Financial Reporters

restrict

survey


Bloggers

Bloggers

Bloggers

Bloggers

• 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, http://www.nber.org/papers/w8959


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.’

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


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



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.


Iain Hardie and Donald MacKenzie. (2007). Assembling an economic actor: the agencement of a Hedge Fund. Sociological Review. Vol. 77, pp 55-80.

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 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 203-235; Reprinted in Kahneman and Tversky (Eds.) (2000), pp 642-671.

Economics and Psychology?

‘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 203-235; Reprinted in Kahneman and Tversky (Eds.) (2000), pp 642-671.


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 203-235; Reprinted in Kahneman and Tversky (Eds.) (2000), pp 642-671.


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

http://www.investopedia.com/university/behavioral_finance/default.asp

Notes on Prospect Theory

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A method for comparing asset dynamics and ‘affect’ changes (Ahmad 2008a, 2008b)

Ahmad K. (2011) The ‘return’ and ‘volatility’ of sentiments: An attempt to quantify the behaviour of the markets? In: Ahmad K. (ed).

on the behaviour of financial markets: fluctuations and sentiment

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