Editor’s note: This article was originally published by Proactive Advisor Magazine and is reprinted here with permission. Subscriptions to Proactive Advisor Magazine are available to financial professionals at no charge. Fundamental analysis is the foundation of stock investing and continues to be the number one source of data for selecting stocks by giant institutions, market professionals, managers of small funds, and retail investors. However, the quality of fundamental data and the timeliness of that data are not the same for each of these market participant groups. The retail investor often receives data from financial reports after the information has already been acquired by institutions and market professionals. Major institutions—responsible for the largest mutual and pension funds—have the resources, talent, and power to investigate companies well ahead of any reports issued by a firm to the general public. Though it may appear that technical patterns lead or predict the market, they merely show information

Editor’s note: this was originally posted at DualMomentum.net by Gary Antonacci, a featured speaker at the MTA’s 2015 Annual Symposium. Like most investors, those using momentum are often guilty of chasing performance. In fact, momentum requires that we do this. But it should be done in a disciplined and systematic way. Performance chasing should not be due to myopia, irrational loss aversion, or other psychological biases. Behavioral Challenges It is not always easy adhering to a disciplined approach. If you are not vigilant, emotions can get the better of you. Even Daniel Kahneman, the father of behavioral economics, admits to being influenced by behavioral heuristics. We may forget our strategy’s long-term expected outperformance when we experience uncomfortable drawdowns. The survival instinct kicks in strongly then. Recency bias can make us feel the drawdown will never end. We may also have to deal with regret aversion when our portfolio underperforms. This will happen sooner or later. No strategy

Editor’s note: This article was originally posted at KeithSelover.net and is reprinted here with permission. It is presented as an example of the new software tools available for technical analysis and includes a sample of the code required to back the widely-used RSI indicator. The article also highlights a number of resources available to analysts and is a primer for those wanting to learn more about programming. To learn more, please visit KeithSelover.net. The above chart was generated in Python. It’s the result of backtesting a basic algorithmic trading strategy that makes use of the Relative Strength Index (RSI). In this tutorial I’ll walk through implementing and graphing a simple strategy. The tutorial should provide a framework that will allow coders to swap out code segments to include strategies and indicators of their preference. It’s meant more to model the beginnings of a customizable backtesting platform than a successful strategy.  Libraries There is a small

Editor’s note: this paper is available at SSRN.com and highlights the history of options pricing models. It demonstrates the long history of quantitative models in the markets and the relevance of history to market analysts. The complete paper, with all citations, can be read at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2831362. Abstract: This paper examines option pricing methods used by investors in the late 19th century. Based on the book called “PUT-AND-CALL” written by Leonard R. Higgins in 1896 and published in 1906 it is shown that investors in that period used routinely the put-call parity for option conversion and static replicating of option positions, and had developed no-arbitrage pricing formulas for determining the prices of at-the-money and slightly out-of-the-money and in-the-money short-term calls and puts. Option traders in the late 19th century understood that the expected return of the underlying does not affect the price of an option and viewed options mainly as instruments