Dow Theory - Explained
What is Dow Theory?
If you still have questions or prefer to get help directly from an agent, please submit a request.
We’ll get back to you as soon as possible.
- Marketing, Advertising, Sales & PR
- Accounting, Taxation, and Reporting
- Professionalism & Career Development
Law, Transactions, & Risk Management
Government, Legal System, Administrative Law, & Constitutional Law Legal Disputes - Civil & Criminal Law Agency Law HR, Employment, Labor, & Discrimination Business Entities, Corporate Governance & Ownership Business Transactions, Antitrust, & Securities Law Real Estate, Personal, & Intellectual Property Commercial Law: Contract, Payments, Security Interests, & Bankruptcy Consumer Protection Insurance & Risk Management Immigration Law Environmental Protection Law Inheritance, Estates, and Trusts
- Business Management & Operations
- Economics, Finance, & Analytics
What is Dow Theory?
It is a theory based on the editorials written by Charles H. Dow, published in The Wall Street Journal. Charles H. Dow is the founder and first editor of this acclaimed financial news journal. He also founded Dow Jones and Company along with Edward Jones and Charles Bergstresser. The theory was developed by William Peter Hamilton, Robert Rhea and E. George Schaefer. The Dow theory explores the analysis between the Dow Jones Industrial Average (DJIA) and Dow Jones Transportation Average (DJTA). It argues if both of these indexes do not reach new highs or lows in lockstep, the stock market trend is not significant enough. If one of these indexes climbs to an immediate high the other is also expected to follow the same trend within a reasonable time period, if it doesn't the market will revert to its former trading level. The the first index reflects the productive capacity and the second reflects the volume of goods distributed; so, an economic trend will be maintained if they rise or fall together. If they do not and move in opposite directions a reversal in the trend is expected.
How Does Dow Theory Work?
Dow theory has several main components. It argues that: Market Discounts - market discounts everything. It operates on the efficient markets hypothesis that assumes asset prices incorporate all available information including earning potential, competitive advantage, management competence. The future events are also discounted as the risk increases. Market Trends - According to this theory, there are three kinds of market trends. The primary trend lasts a year or more, a secondary trend often works against the primary trend and lasts 3 weeks to 3 months, and the minor trends last less than three weeks, these are generally noise. The primary trend has three phases, accumulation phase or distribution phase, public participation phase and excess phase or the panic phase. DJIA and DJTA must confirm one another. The volume must confirm the trend. If the price is moving in the direction of the primary trend, the volume should increase. A trend continues until a reversal happens clearly. The Dow approach considered to be the core of the modern technical analysis.
Academic Research on Dow Theory
- TheDow theory: William Peter Hamilton's track record reconsidered, Brown, S. J., Goetzmann, W. N., & Kumar, A. (1998). The Journal of finance,53(4), 1311-1333. This research examines the results from the test of Albert Cowles as opposed to that of Hamilton's Dow Theory. The objective of this article is to show how Neural Net Modelling can help to further research the Dow's theory.
- Technical analysis: An asset allocation perspective on the use of moving averages, Zhu, Y., & Zhou, G. (2009). Journal of Financial Economics,92(3), 519-544. This research explores the usefulness of technical analysis in asset allocation. Emphasis is placed on the moving average trading rule, and the objective is to show the behavior of the featured technical analysis with regards to availability of stock price information.
- Window dressing, data mining, or data errors: A re-examination of the Dogs of theDow Theory, Prather, L. J., & Webb, G. L. (2002).Journal of Applied Business Research,18(2), 115-124. This article analyses the Dow dividend yield anomaly to ascertain if data errors create the superior returns of the trading rule. Results from this study show that data errors are not the drivers for superior returns of the trading rule. However, due to the Chow breakpoint point of structural stability, the above result is not deemed valid. Thus, this article aims to create a model which can explain the superior returns of trading rule.
- Naive trading rules in financial markets and wiener-kolmogorov predictiontheory: a study of"technical analysis", Neftci, S. N. (1991).Journal of Business, 549-571. This research examines the technical analysis of different informal rules, also known as Wiener-Kolmogorov predictions in stock trading. The objective of this paper is to explore the different techniques which can be used in situations where the Wiener-Kolmogorov Theory has failed.
- StockMarket Patterns And FinancialAnalysis: Methodological Suggestions, Roberts, H. V. (1959). The Journal of Finance,14(1), 1-10. This article examines the believe of financial analysts, in the existence of various 'patterns' which may help to predict market outcomes in the future if properly studied. It aims to show the use of technical analysis in studying these patterns.
- Technical analysis: An asset allocation perspective on the use of moving averages, Zhu, Y., & Zhou, G. (2009).Journal of Financial Economics,92(3), 519-544. This research explores the usefulness of technical analysis in asset allocation. Emphasis is placed on the moving average trading rule, and the objective is to show the behavior of the featured technical analysis with regards to availability of stock price information.
- Random walks in stock market prices, Fama, E. F. (1995). Financial analysts journal,51(1), 75-80. This research explores the theory of random work, and how it helps the works of market analysts. Emphasis is placed on two approaches; the chartist and intrinsic methods. The main objective of this article is to show these approaches produce more returns than random samples.
- Reexamining the profitability oftechnical analysiswith data snooping checks, Hsu, P. H., & Kuan, C. M. (2005).Journal of Financial Econometrics,3(4), 606-628. This research explores the profitability of technical analysis using two different data snooping checks. The objective is to show that investors' strategies can greatly affect profits when added to a simple trading rule.
- What do we know about the profitability oftechnical analysis?, Park, C. H., & Irwin, S. H. (2007). Journal of Economic Surveys,21(4), 786-826. This paper reviews the evidence on the profitability of technical analysis by use of different testing procedures.
- Stock market trading rule discovery usingtechnicalcharting heuristics, Leigh, W., Modani, N., Purvis, R., & Roberts, T. (2002). Expert Systems with Applications,23(2), 155-159. 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.
- Stock market prediction with multiple classifiers, Qian, B., & Rasheed, K. (2007). Applied Intelligence,26(1), 25-33. This research examines stock predictability using Dow Jones Industrial Average Index. The objective is to show that stock predictions accuracy may go higher than the stipulated rate of 50%.