NYSE Composite Index Research Articles

Temporal Aggregation of the Returns of a Stock Index Series

The effects of temporal aggregation on asymmetry properties and the kurtosis of returns based on the NYSE composite index are studied. There is less asymmetry in responses to shocks for weekly and monthly frequencies than for the daily frequency. Kurtosis is not smaller for the lower frequencies.

Stock market trading rule discovery using technical charting heuristics

Abstract In this case study in knowledge engineering and data mining, we implement a recognizer for two variations of the ‘bull flag’ technical charting heuristic and use this recognizer to discover trading rules on the NYSE Composite Index. Out-of-sample results indicate that these rules are effective.

Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network, and genetic algorithm: a case study in romantic decision support

The 21st century is seeing technological advances that make it possible to build more robust and sophisticated decision support systems than ever before. But the effectiveness of these systems may be limited if we do not consider more eclectic (or romantic) options. This paper exemplifies the potential that lies in the novel application and combination of methods, in this case to evaluating stock market purchasing opportunities using the “technical analysis” school of stock market prediction. Members of the technical analysis school predict market prices and movements based on the dynamics of market price and volume, rather than on economic fundamentals such as earnings and market share. The results of this paper support the effectiveness of the technical analysis approach through use of the “bull flag” price and volume pattern heuristic. The romantic approach to decision support exemplified in this paper is made possible by the recent development of: (1) high-performance desktop computing, (2) the methods and techniques of machine learning and soft computing, including neural networks and genetic algorithms, and (3) approaches recently developed that combine diverse classification and forecasting systems. The contribution of this paper lies in the novel application and combination of the decision-making methods and in the nature and superior quality of the results achieved.

An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index

We introduce a method for combining template matching, from pattern recognition, and the feed-forward neural network, from artificial intelligence, to forecast stock market activity. We evaluate the effectiveness of the method for forecasting increases in the New York Stock Exchange Composite Index at a 5 trading day horizon. Results indicate that the technique is capable of returning results that are superior to those attained by random choice.

OPTIMAL LAG IN DYNAMICAL INVESTMENTS

A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the optimal composition changes and, more important, it turns out that the frequency of transactions must be reduced. This simple observation leads to the definition of an optimal lag between two rearrangement of the portfolio. This idea is tested against an investment in a risky asset and a risk-less one. The price of the first is proportional to NYSE composite index while the price of the second grows according to the American Discount Rate.

Expectation revisions and jumps in asset prices

When fundamentals follow a Markov switching process asset prices must jump when agents revise their inferences about the active regime. We estimate the jump size accompanying regime switches in the fundamental for the NYSE Composite Index and various individual stocks.

Inducing Stock Screening Rules for Portfolio Construction

This study describes a technique originated from the emerging field of machine learning and demonstrates its effectiveness in stock screening. We have derived screening rules by applying a rule induction method, constructed portfolios using the rules, and evaluated the portfolios' performance using the Sharpe, Treynor and Jensen indexes. Results indicate that regularities among stocks can be identified, and portfolios so constructed outperformed the NYSE Composite index and the S&P 500 over the same period.

Inducing Stock Screening Rules for Portfolio Construction

This study describes a technique originated from the emerging field of machine learning and demonstrates its effectiveness in stock screening. We have derived screening rules by applying a rule induction method, constructed portfolios using the rules, and evaluated the portfolios' performance using the Sharpe, Treynor and Jensen indexes. Results indicate that regularities among stocks can be identified, and portfolios so constructed outperformed the NYSE Composite index and the S&P 500 over the same period.

The Use of Interest Rate Futures and Options by Corporate Financial Managers

existence as currency futures began trading on the International Monetary Market of the Chicago Mercantile Exchange. By 1975, the definition of financial futures was greatly expanded when interest rate futures on GNMA certificates were initiated on the Chicago Board of Trade. By early 1976, the Chicago Mercantile Exchange quickly countered with interest rate futures on 90-day U.S. Treasury bills. A number of other products rapidly followed. Futures contracts were introduced for Treasury bonds, Treasury notes, commercial paper, certificates of deposit, and other interest-related instruments. The enthusiasm eventually spread to the equity markets in the early 1980s with the introduction of stock index futures on the Value Line Index by the Kansas City Board of Trade. The Chicago Mercantile Exchange quickly came into the equity picture with the Standard and Poor's 500 Index futures contract as did the New York S ock Exchange with the NYSE Composite Index futures.