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Alternative Sector Classification Methods

Abstract This paper offers two alternative sector classification methods in order to classify companies more accurately. Introduction During the early 1900s, various departments of the US government initiated research and studies on the various industries and their different functions. Due to the lack of set standards, each department ended up using its own methodology. Consolidating information across multiple sources became a challenge. The Standard Industrial Classification (SIC) was hence proposed as a uniform classification system, aimed to represent major industries, sub-class and specific function/product, and was formally adopted in 1937. However, SIC was facing a challenge because of the change in the economic environment. After that, the Global Industry Classification System (GICS) was launched by Standard & Poor's (S&P) and Morgan Stanley (MSCI) in August 1999. The standard provides a comprehensive, globally consistent definition of economic sectors and industr

1929 Market Crash Cause

Many people believe in the reason why the market value of all share listed in NYSE fell by 30% is that there was an economic bubble and the stock market is irrational. However, is that the truth? We are going to show you three different approaches to the 1929 market crash. Remember that correlation is not causation. These are just references. Approaches: 1. There was no economic bubble before the 1929 market crash. Irving Fisher, a famous US economist said: “prices of stocks are low.” Fisher based his projection on strong earnings reports, fewer industrial disputes, and evidence of high investment in R&D and other intangible capital. 2. There was insider trading. In the months prior to the stock market crash of 1929, the price of a seat on the NYSE was abnormally low. Rising stock prices and volume should have driven up seat prices during the boom of 1929; instead, there were negative cumulative abnormal returns to seats of approximately 20% in the months before the market crash. A

Deflated Sharpe Ratio can reduce false discovery

Introduction With the recent advent of large financial datasets, machine learning, and high-performance computing, analysts can backtest millions of alternative investment strategies. Backtest optimizers search for combinations of parameters to maximize a strategy's simulated historical performance, leading to backtest overfitting. The performance inflation problem goes beyond backtesting. More generally, researchers and investment managers tend to report only positive results, a phenomenon is known as selection bias. Failure to control the number of tests involved in a given finding can lead to overly optimistic performance expectations. The Deflated Sharpe Ratio (DSR) corrects for two major sources of performance inflation: selection bias under multiple tests and non-normally distributed returns . By doing so, DSR helps separate legitimate empirical results from statistical deception. Backtesting is a good example. Backtesting is a historical simulation of how a particular inv

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