Intelligent Systems and Financial Forecasting
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Lobato and Savin found no evidence of long memory in daily stock returns. Willinger et al. Baum et al.
Intelligent Systems and Financial Forecasting door Jason Kingdon (Boek) - tensubchutmabes.ml
Characterization 23 Using the spectral regression method, Barkoulas et al. Zhuang et al. They found evidence for long-term dependence in four countries: Korea, Malaysia, Singapore and New Zealand. Cheung and Lai found long memory in JPY-based real exchange rates. Panas found long memory in the Athens Stock Exchange. Cavalcante and Assaf found little evidence of long mem- ory in the returns of the Brazilian Stock Market. Henry investigated long range dependence in nine interna- tional stock index returns.
Tolvi a found long memory in Finnish stock market return data.
Using a monthly data set consisting of stock market indices of 16 OECD countries, Tolvi b found statistically sig- nificant long memory for three countries: Denmark, Finland and Ireland, which are all small markets. In a paper that examines and compares the behaviour of four tests for fractional integration in daily obser- vations of silver prices, de Peretti concluded that one must use at least a bilateral bootstrap test to detect long-range dependence in time series, and deduced that silver prices do not exhibit long memory.
Beine and Laurent investigated the major exchange rates and found no evidence of long memory in the conditional mean. Limam analysed stock index returns in 14 markets and concluded that long memory tends to be associated with thin markets. Sapio used spectral analysis and found long memory in day-ahead electricity prices. Cajueiro and Tabak found that the markets of Hong Kong, Singapore and China exhibit long-range dependence. Naively, Cajueiro and Tabak state that the presence of long-range dependence in asset returns seems to be a stylized fact.
They studied the individual stocks in the Brazilian stock market and found evidence that firm-specific variables can explain, at least partially, the long-range dependence phenomena. There was no evidence of long memory in the returns.
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Oh et al. For all daily and high-frequency market data studied, no significant long-term memory was detected in the return series. Investment Newsletters The third and final experiment on the characterization of financial time series concerns an analysis of investment newsletters, and a literature review is given here. However they did find that the degree of disagreement among letters predicts both realized and expected volatility as well as trading volume.
Graham and Harvey examined the performance of newsletter asset-allocation strategies for the —95 period. They found that, as a group, newsletters do not appear to possess any special information about the future direction of the market.
Nevertheless, they found that investment newsletters that are on a hot streak have correctly anticipated the direction of the market in previous recommendations may pro- vide valuable information about future returns. The Value Line Investment Survey is the best known investment newsletter, it is well-respected and freely available.
Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2016
Jaffe and Mahoney analysed the recommenda- tions of common stocks made by the investment newsletters followed by the Hulbert Financial Digest. Taken as a whole, the securities that newsletters recommend did not outperform appropriate benchmarks and the performance of the newsletters did not exhibit persistence. They found little, if any, evidence of herding.
Newsletters tend to recommend securities that have performed well in the recent past and newsletters with poor past performance are more likely to go out of business. Metrick analysed the equity-portfolio recommendations made by investment newsletters. Kumar and Pons analysed the behaviour and performance of investment newsletters that made asset allocation recommendations during a period covering more than 21 years June - November On aggregate the newsletters failed to outperform a passive investment strategy, but active newsletters and contrarian newsletters exhibited market-timing ability.
When they examined the recommendations of individual newsletters at a higher frequency daily as opposed to monthly , they found considerable evidence of timing-ability.
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Modelling 25 Technical Analysis The primary research in Chapter 4 concerns building an agent-based artificial stock market. Technical analysis is defined in Section 4. Brown and Jennings showed that technical analysis has value in a model in which prices are not fully revealing and traders have rational conjectures about the relation between prices and signals.
Frankel and Froot showed evidence for the rising importance of chartists.
Neftci showed that a few of the rules used in technical analysis generate well-defined techniques of forecasting, but even well-defined rules were shown to be useless in prediction if the economic time series is Gaussian. However, if the processes under consideration are non-linear, then the rules might capture some informa- tion.
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Tests showed that this may indeed be the case for the moving average rule. Taylor and Allen report the results of a survey among chief foreign exchange dealers based in London in November and found that at least 90 per cent of respondents placed some weight on technical analysis, and that there was a skew towards using technical, rather than fundamental, analysis at shorter time horizons. In a comprehensive and influential study Brock et al.
Blume et al. They also show that traders who use information con- tained in market statistics do better than traders who do not. Neely explains and reviews technical analysis in the foreign exchange market. Neely et al.
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The rules generated economically significant out-of-sample excess returns for each of six exchange rates, over the period — Lui and Mole reported the results of a questionnaire survey conducted in February on the use by foreign exchange dealers in Hong Kong of fundamental and technical analyses. Neely reconciled the fact that using technical trading rules to trade against US intervention in foreign exchange markets can be profitable, yet, long-term, the intervention tends to be profitable.
LeBaron showed that, when using technical analysis in the foreign exchange market, after removing periods in which the Federal Reserve is active, exchange rate predictability is dramatically reduced. Lo et al. One criticism I have is that beating the market in the absence of costs seems of little significance unless one is interested in finding a signal which will later be incorporated into a full system. Lee and Swaminathan demonstrated the importance of past trading volume. Modelling 26 Weller used genetic programming to show that technical trading rules can be profitable during US foreign exchange intervention.
Cesari and Cremonini made an extensive simulation compari- son of popular dynamic strategies of asset allocation and find that technical analysis only performs well in Pacific markets. Cheol-Ho Park and Scott H. Kavajecz and Odders- White showed that support and resistance levels coincide with peaks in depth on the limit order book1 and moving average forecasts reveal information about the relative position of depth on the book.
They also show that these relationships stem from technical rules locating depth already in place on the limit order book.
Behavioural Finance The algorithms employed by the artificial stock market are based on the behaviour of real market partic- ipants, rather than the actions of the rational but hypothetical Homo economicus. Behavioural finance is defined in Section 4. Back in , Gustave le Bon wrote The Crowd: A Study of the Popular Mind, one of the greatest and most influential books of social psychology ever written le Bon, Selden wrote Psy- chology of the Stock Market. In the US psychologist Leon Festinger introduced a new concept in social psychology: the theory of cognitive dissonance Festinger et al.
When two simultaneously held cognitions are inconsistent, this will produce a state of cognitive dissonance. Because the experience of dissonance is unpleasant, the person will strive to reduce it by changing their beliefs. Pratt considered utility functions, risk aversion and also risks considered as a proportion of total assets. In , two brilliant psychologists, Amos Tversky and Daniel Kahneman, described three heuristics that are employed when making judgments under uncertainty Tversky and 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.
Modelling 27 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. The expected utility hypothesis also called von-Neumann Morgenstern utility Bernoulli, ; von Neumann and Morgenstern, ; Bernoulli, 2 asserts that when people are faced with mak- ing choices under uncertainty, they do so by maximizing their expected utility.
The second-most cited paper ever to appear in Econometrica, the prestigious academic journal of economics, was written by the two psychologists Kahneman and Tversky They present a critique of expected utility theory as a descriptive model of decision making under risk and develop an alternative model, which they call prospect theory.