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Moving average trading strategies are examined for trading ten major currencies during the 1997‐2001 time period. Both a traditional trend‐following moving average cross over strategy and a contrarian strategy are tested. Following a simple moving average cross over out performed, on average, the contrarian strategy. However, neither strategy consistently outperformed a simple strategy of holding U.S. dollars during the four‐year test period.

This study examines the relationship between U.S. monetary policy changes and financial market reactions; in particular, the fluctuations in interest rates and exchange rates for the U.S., Britain, Canada, France, Germany, and Japan are related to changes in the U.S. Federal Reserve discount rate for the period 1980–1991. Consistent with previous research, the results indicate a significant relationship between U.S. interest rates and changes in the discount rate throughout the test period. Further, the relationship between non‐U.S. interest rates and the U.S. discount rate is minimal during the first half of the test period but strong in the second half of the period. As expected, statistically significant results were not found with respect to exchange rates. These results suggest an increase both in financial market integration and in the U.S. Federal Reserve's role as an international financial policymaker.

Reviews previous research on contrarian investment strategy (i.e. buying “losers” and selling “winners”) and analyses the results of applying the strategy to US stocks 1928‐1992. Explains the methodology and presents the results, which show no statistically significant holding period returns from the strategy, although selling the “winners” is significant. Considers the implications and limitations of the study and calls for further research.

Discusses research ideas on the distinctions between data, information and knowledge, the categories of knowledge and knowledge‐processing activities in Holsapple and Whinston’s (HW’s) taxonomy (1987, 1988a, 1988b), and their application to markets and the activities of the firm. Describes a manufacturing firm’s inputs, production processes and outputs in terms of HW’s taxonomy, pointing out that management must filter the information surrounding the firm to turn it into knowledge of various types, e.g. descriptive, derived, assimilative etc. Considers the role of information for customers of and investors in the firm; and the relationship between knowledge and efficiency. Identifies three types of knowledge workers (builders, stewards and appliers) and calls for further research on the taxonomy of knowledge and standards of knowledge within the finance discipline.

Lists eight criteria for designing a general trading system for investment, explains how the five steps of genetic (computer) programming work in practice and shows how they can be applied to identify trading rules for a particular stock and stock screening rules for portfolio formation. Warns of some potential problems but believes the system described meets the eight criteria set and is easy to implement.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.