Doctor of Philosophy (PhD)
Electrical and Computer Engineering
We consider the problem of trend-following in US stock market and propose a combined economic and technical model to approach this problem. A bank of linear and nonlinear, discrete-time, low-pass filters with different sampling rates is used to generate timing signals for US stock market indexes such as NASDAQ Composite and S&P 500. These timing signals help us find the appropriate times to step in or out of the market. Back-testing and real-time implementation results along with the risk analysis validate our model. According to the trend of the market, we may adopt a long or short position. If we conclude that the market is in an uptrend (rising prices) then, we buy some shares of a stock to sell them for a higher price in future (long position). On the other hand, in a market downtrend (falling prices), we may borrow a number of shares and sell them outright to repurchase them for a lower price in future (short selling). The purpose of the market timing is to recognize the current trend of the market and to find the appropriate times to step in or out of the market. We do not consider market timing for the stocks of individual companies due to the high sensitivity of daily prices to news, the performance of their competitors, the conditions of the economic sector they belong to, and many other sources of randomness. Instead, we consider the timing problem for the large market indexes such as NASDAQ Composite and S&P 500 that are weighted averages of the price of many companies from several economic sectors. Therefore, we use the daily index value and volume (total number of trades) for a large market index in place of an individual company. Such timing signals would be suitable for investing in exchange traded funds (ETFs).
Document Availability at the Time of Submission
Student has submitted appropriate documentation to restrict access to LSU for 365 days after which the document will be released for worldwide access.
Khademi, Iman, "A System Approach to Investing In Uncertain Markets" (2014). LSU Doctoral Dissertations. 1723.