Abstract This paper will examine portfolio returns using long-term relative strength and long-term momentum as the only criteria for investment decisions. It will compare various portfolios using quarterly RSI readings as the sole gauge for long-term relative strength and will use the S&P 500 monthly RSI reading for its long-term momentum parameter. The results suggest that long-term relative strength and the S&P 500 long-term momentum are both useful for investment criteria.   Introduction The academic literature is dominated by fundamental analysis. Fundamental analysis itself is built on a handful of foundational concepts. Seemingly most academics and academic institutions have accepted these concepts and their assumptions as fundamentally true. This, in turn, has affected the belief of most market participants. Many professional market participants aspire to beat passive “buy and hold” investing where investors will buy index funds with low management fees. It is quite challenging to beat indexing over the long term and many

Abstract The dearth of empirical studies substantiating the merits of technical analysis limits the legitimacy of the field. This study seeks to address that by offering evidence that Pakistan does not conform to the assumptions of weak form efficiency and therefore offers opportunities to generate abnormal returns using historical data such as that employed by technical analysts. Results of three versions of the runs test, the Augmented Dickey Fuller test, the Phillips Perron test, and an autoregressive model of order one, point to a lack of randomness, and thus a lack of weak form efficiency, over the two periods studied.   Introduction This study employs parametric and non-parametric tests to assess whether Pakistan’s KSE-100 Index is weak form efficient. Eugene Fama (1965, 1970) posited via the Efficient Market Hypothesis (EMH) that stock prices are unpredictable. The weak form of Fama’s EMH implies that stock prices include all past information. When that is the case,

Abstract In 1995 Larry Connors and Linda Bradford Raschke published a book titled: “Street Smarts: High Probability Short-Term Trading Strategies” that soon became a reference milestone for many generations of traders. The book presented multiple strategies inherently discretionary and mostly focused on equities and futures. The strategies were based on three simple swing trading concepts: retracements, pattern breakouts, and climax reversals, which are among the fundamental pillars of Technical Analysis, and by which support and resistance levels are formed. Although several traders have used these strategies for many years, following some structural changes in the markets, and an increasingly adoption of mechanical trading systems, the strategies of Street Smarts lately ended up on a sidetrack, and were no longer considered as mainstream by an increasing number of technical analysts and systematic traders. In this work, some of the most popular strategies described in Street Smarts are reviewed. Moreover, an exhaustive backtesting on

Abstract In the book “The Man Who Solved the Market”[1], the author quotes Renaissance’s co-CEO Rober Mercer saying to a friend – “We’re right 50.75 percent of the time…but we are 100 percent right 50.75 percent of time…You can make billions that way”. This paper intends to provide a similar indicator or system, which is right a bit more than half the time. Drawing inspiration from physics, the paper investigates the applicability of basic principles of physics to arrive at a price prediction system (or indicator) for stocks. The system hence derived, named as “Tyche System”, shows strong performance during back-testing.  The current paper also demonstrates the usage of Tyche system for – a) Market technicians who would like to get a heads-up about an impending technical event in a stock without scanning through the charts of thousands of stocks; and b) Hedge Funds, who would like to use their long-only

Abstract Evidence of outperformance garnered through factor investing is abundant through many sources. Typically, these accepted factors have been shown to add alpha to an index. Although market volume is a crucial piece of investment information, the majority of the public ignores volume as an investment factor. This paper introduces a new factor exhibiting alpha generation: the volume factor.   Introduction Presently baby boomers are piling money into the stock markets preparing for their upcoming retirements. According to Forbes Financial Council, over the course of the next decade, ten thousand baby boomers will retire per day on average. Meanwhile the highest earnings demographic, the millennial population, notorious for doing things ten years late, is just beginning to gear up their investment saving plans.  How are these new fund flows being allocated? According to CNBC, passive investing now accounts for 45% of all United States stock funds, up from 25% just a decade ago.

Introduction What is this topic about? This paper will focus on the study of momentum using a very popular technical analysis indicator, the Moving Average Convergence Divergence (MACD), created by one of the most respected analysts of our time – Gerald Appel.[1] This paper is comprised of 6 parts. In the first section we will focus on the MACD itself. We will do a brief description of its construction, the most elementary ways to use it and then a review of the five limitations it has. This is a section that is familiar territory to all technicians. In the second section, we will show a widely known suggestion to deal with these limitations, that does improve one, but does not solve all of them. In the third and fourth sections we will present our own solution, which remedies the shortcomings, while creating unique advantages (edges) that would not be possible to obtain via the classic MACD. In