Bauer, R. J. Jr., (1994), Genetic Algorithms and Investment Strategies. New York, John Wiley & Sons, Inc.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... and their regulation underscores the need for a systematic investigation [6] While the use of neural networks for analysis of nancial markets data has received wide attention [29] interest in genetic algorithm based approaches is more recent, with a series of studies noting its advantages [1][3][4] 24] 25] 27] This paper reports on our ongoing research eort examining issues in utilizing genetic search in the data mining of nancial time series. Genetic search here is aimed at discovering complex patterns informative for formulating decision rules for trading. Such decision rules propose ....

Bauer R.J. Jr. (1994) Genetic Algorithms and Investment Strategies. John Wiley and Sons, NY

....for a new problem. 2.3 Examples of successful GA applications in finance GAs have been reported to be successful in different financial problem contexts. Many of these examples inhibit the task of learning or optimising trading strategies for foreign exchange, stock or bond markets (see e.g. [Ba94], Le95] FHKNS96] TsLa00] Often they are used in conjunction with other methods like Artificial Neural Networks or Fuzzy Logic (see e.g. HKSZ98] Other examples of successful applications are: multi period asset al..location [BPST00] financial forecasting [Ki97] simulation of stock ....

Bauer, R.: Genetic algorithms and investment strategies, New York: John Wiley & Sons, 1994.

....rules that have the potential to enhance understanding of the problem domain. 1.3 Motivation for study This study was motivated by a number of factors. Much of the existing literature concerning the application of genetic algorithms (GA) or GP to the generation of technical trading rules [1] 6] [2] [15] 8] concentrates on the US and to a lesser extent the Japanese stock markets. Published research on this area is both incomplete and scarce. To date, only a limited number of GA GP methodologies and a limited range of technical indicators have been considered. This study addresses these ....

Bauer R. (1994). Genetic Algorithms and Investment Strategies, New York: John Wiley & Sons Inc.

....enhance understanding of the problem domain. 1.3 Motivation for study This study was motivated by a number of factors. Much of the existing literature concerning the application of genetic algorithms (GA) or GP to the generation of technical trading rules [Allen Karjalainen 1999] Colin 1994] [Bauer 1994] [Neely, Weller Dittmar 1997] Deboeck 1994] concentrates on the US and to a lesser extent the Japanese stock markets. Published research on this area is both incomplete and scarce. To date, only a limited number of GA GP methodologies and a limited range of technical indicators have been ....

Bauer R. (1994). Genetic Algorithms and Investment Strategies, New York: John Wiley & Sons Inc.

.... Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma oe 6 6 6 movements m 1 m 2 m 3 m 4 price L 4 L 1 L 2 L 3 H 1 H 2 H 3 H 4 Figure 1: Point Figure Chart heuristic character, there exist different procedures for creating such charts(e.g. [1, 2, 9, 13, 16, 17]) We used the following one in our work: X signs in the column stand for an increase while O signs stand for decreasing share prices. Such price movements are entered continuously into the chart. Whenever a reversal in the price movement occurs, the sign representing the new price movement is ....

....too much movements are collected within a rule. It is a natural conjecture, that too much movements per rule made the formations too complex. Our tests confirmed this, since then the SCS could not generate a rule set with sufficient quality any more. A further problem, that is also discussed in [1] was, that existing patterns lose their validity after a certain time. In our terms, this means that rules, which initially classified very good, become worse in the course of time, while initially relatively bad rules get better. This corresponds with the fact that the stock market participants ....

R. J. Bauer Jr., "Genetic Algorithms and Investment Strategies", J. Wiley & Sons, Inc., New York, 1994.

.... 6 Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma Gamma oe 6 6 6 movements m 1 m 2 m 3 m 4 price L 4 L 1 L 2 L 3 H 1 H 2 H 3 H 4 Figure 1: Point Figure Chart different procedures for creating such a chart (e.g. [Bau, Eng, Hoc, Mur, Tol, Wel]) We used the following one: X signs in the column stand for an increase while O signs stand for decreasing share prices. Such price movements are entered continuously into the chart. Whenever a reversal in the price movement occurs, the sign representing the new price movement is written ....

....= 1 h1rel2 = 4 l1rel3 = 4 = Signal = SELL # 34 rules with 127 terms Endnotes 1 We note that the term rule is used in two different meanings in Technical Chart Analysis and Machine Learning. 2 The application of Genetic Algorithms was originally applied on capital market data by Bauer [Bau] and Allen Karjalainen [AlKa] In contrast to our work, they do not make use of the P F Chart Technique. 3 Since share prices are always positive all calculated quotients are positive. The number of intervals is also an input parameter to our system. 4 Up to now, the first generation of the ....

R. J. Bauer Jr., "Genetic Algorithms and Investment Strategies", J. Wiley & Sons, Inc., New York, 1994.

....of forms. The choice of an appropriate structure for a particular problem is a major factor in determining a GA s success. Structures utilized in prior research include binary strings (Goldberg, 1989) computer programs (Koza, 1992) neural networks (Whitley et al. 1990) and if then rules (Bauer, 1994). We first examine how a traditional GA performs optimization. In optimization, the goal is ideally to find the best possible solution to a problem. For real world problems, one does not usually know the best possible solution. Therefore, a more realistic objective is to find a good solution or, ....

....After one has decided upon an encoding for each attribute, the GA can process the resulting string, without regard to its underlying meaning. Rule representations from prior GA research studies are too numerous to cover in detail. The type of rule we use is similar to that recommended by Bauer (1994). His GA finds thresholds for one or more financial variables, above or below which a stock is considered attractive. For instance, if the GA s structure consists of two variables representing a particular stock s price and earnings per share (EPS) the final rule the GA returns might look like ....

Bauer, R. J., Jr. 1994. Genetic algorithms and investment strategies. New York: John Wiley.

....Anwedungen des Soft Computing, 241] Chaos theory in the nancial markets. Applying fractals, Fuzzy logic, Genetic algorithms, Swrn Simulation the Monte Carlo Method to Manage Market, 14] Evolutionary Algorithms in Management Application, 42] Genetic Algorithms and Investment Strategies, [13] Seven Methods for Transforming Corporate Data Into Business Intelligence, 154] total 6 books 4.2 Journal articles The following list contains the references to every journal article included in this bibliography. The list is arranged in alphabetical order by the name of the journal. Acta ....

....W. B. 29] Arthur, W. Brian, 188] Avatchanakorn, V. 132] Axmann, Joachim K. 238] Baba, N. 124] Back, Barbro, 40, 80, 114] Bagchi, T. P. 81] Bakirtzis, A. G. 12] Balakrishnan, P. V. 101] Baskaran, Subbiah, 28] Bassanini, A. 205] Bauer, Richard J. 52] Bauer, Jr. R. J. [13, 204] Beaumont, P. M. 41] Beltratti, Andrea, 206] Berry, R. H. 196] Bhattacharyya, S. 179] Biethahn, J org, 76, 207] Birchenhall, C. 62] Bradshaw, P. T. 41] Broekmeulen, Rob A. C. M. 226] Burkhardt, Diana, 256] Burns, Scott A. 131] Campbell, John A. 17] Cao, Y. J. 183] Caskey, ....

[Article contains additional citation context not shown here]

R. J. Bauer, Jr. Genetic Algorithms and Investment Strategies. John Wiley & Sons, New York, 1994. y[262] GAdigest v8 n43 ga94aBauer.

....based system FGP (Financial Genetic Programming) Genetic Programming (GP) Koza 1992, Angeline Kinnear 1996) is a promising variant of genetic algorithms (GAs) that uses tree instead of string representations. Genetic algorithms have been studied in financial markets for quite some time. Bauer (1994) reported his GAs intelligent systems that aimed at finding tactic market timing strategies. Allen Karjalainen (1995) used genetic programming to identify profitable trading rules in the stock market. By conducting experiments using genetic programming, Chen Yeh (1996) attempted to formalise ....

Bauer, R. J. Jr., (1994), Genetic Algorithms and Investment Strategies, New York, John Wiley & Sons, Inc.

.... Books on genetic algorithms that include those that survey the entire field, such as Goldberg (1989) Michalewicz (1992) and Mitchell (1996) as well as others that specialize in particular areas, such as the application of genetic algorithms to robotics (Davidor (1990) financial 2 applications (Bauer 1994), image segmentation (Bhanu and Lee 1994) pattern recognition (Pal and Wang 1996) parallelization (Stender 1993) and simulation an modeling (Stender, Hillebrand, and Kingdon 1994) control and signal processing (Man, Tang, Kwong, and Halang 1997) and engineering design (Gen and Cheng 1997) ....

Bauer, R. J., Jr. 1994. Genetic Algorithms and Investment Strategies. John Wiley.

....for the relevance rating can be freely defined by the user. As procedure for selection we use roulette wheel selection. It assigns slots on a roulette wheel to the chromosomes of the population where the size of the individual slots is a function of the fitness of the individual chromosomes (see [6]) For that purpose we first sum up the fitness values of all chromosomes in the current population, which corresponds to the total area of the roulette wheel. Next, we generate a random number between 0 and 1 and multiply it by the sum of the fitness values. The resulting value represents the ....

R. J. Bauer. Genetic Algorithms and Investment Strategies. John Wiley & Sons, New York, 1994.

....at developing horse racing experts. SEAGUL [de la Maza89] uses a breed of genetic algorithm known as a genetic classifier [Goldberg89] which classifies horses as winners or losers and gives a reasonable ROI. Genetic classifiers have been used in financial markets with a fair amount of success [Bauer94] The problem with SEAGUL is that it only classifies horses as winners or losers. If, however, the horse classified as the winner does not arrive at the race track then all of the other horses, having been classified as losers, will not provide a betting opportunity. HOBBES [McNatton94] is an ....

Bauer, R.J. Genetic Algorithms and Investment Strategies. Wiley.

....increasing parallel computers. An overview of genetic algorithms can be found in Goldberg s Genetic Algorithms in Search, Optimization, and Machine Learning (1989) Additional information on current work in genetic algorithms can be found in Forrest (1993) Davis (1987, 1993) Michalewicz (1992) Bauer (1994), Whitley (1992) Maenner and Manderick (1992) Schaffer and Whitley (1992) Albrecht, Reeves, and Steele (1993) and Langton (1994) The classifier system (Holland 1986) is a cognitive architecture into which the genetic algorithm is embedded so as to allow adaptive modification of a population ....

Bauer, R. J., Jr. 1994. Genetic Algorithms and Investment Strategies. John Wiley.

.... be found in Goldberg (1989) Davis (1987, 1991) Michalewicz (1992) Eshelman (1995) Whitley (1992) Maenner and Manderick (1992) Schaffer and Whitley (1992) Albrecht, Reeves, and Steele (1993) Stender (1993) Buckles and Petry (1992) Stender, Hillebrand, and Kingdon (1994) Forrest (1991) Bauer (1994); and Davidor (1990) Genetic programming is an extension of the genetic algorithm in which the genetic population consists of computer programs. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available primitive programmatic ingredients. The ....

Bauer, R. J., Jr. 1994. Genetic Algorithms and Investment Strategies. John Wiley.

No context found.

Bauer, R. J. Jr., (1994), Genetic Algorithms and Investment Strategies. New York, John Wiley & Sons, Inc.

No context found.

R.J. Bauer Jr., Genetic Algorithms and Investment Strategies, Wiley, New York, 1994.

No context found.

Richard J. Bauer jr. Genetic Algorithms and Investment Strategies. John Wiley & Sons, New York, 1994.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC