|Author:||H. Russell Fogler|
|Title:||Analyzing the stock market: Statistical evidence and methodology (Grid series in finance & real estate)|
|Format:||txt docx mbr lrf|
|ePUB size:||1285 kb|
|FB2 size:||1393 kb|
|DJVU size:||1905 kb|
|Publisher:||Grid, inc; 2nd edition (1978)|
Analyzing the stock market book. Unknown Binding, 166 pages. Analyzing the stock market: Statistical evidence and methodology (Grid series in finance & real estate). 0882441388 (ISBN13: 9780882441382).
Analyzing the stock market: Statistical evidence and methodology (Grid series in finance & real estate): ISBN 9780882441382 (978-0-88244-138-2) Softcover, Grid, inc, 1978. Blending Quantitative and Traditional Equity Analysis. by Dean S. Barr, Douglas W. Case, Philip B. Erlanger, H. Russell Fogler, David J. Leinweber, Andrew W. Lo, Gary Koehler Christopher M. Murphy,. ISBN 9781879087415 (978-1-879087-41-5) Softcover, AIMR (CFA Institute), 1994. Find signed collectible books: 'Blending Quantitative and Traditional Equity Analysis'
Books by H. Russell Fogler. Analyzing the stock market: A quantitative approach. by H. Publisher: Grid Publishing Company.
Analyzing the stock market: Statistical evidence and methodology. Author : H.
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We study model-driven statistical arbitrage strategies in . Trading signals are generated in two ways: using Principal Component Analysis and using sector ETFs. In both cases, we consider the residuals, or idiosyncratic components of stock returns, and model them as a mean-reverting process, which leads naturally to "contrarian'' trading signals. The main contribution of the paper is the back-testing and comparison of market-neutral PCA- and ETF- based strategies over the broad universe of . The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. In particular, we study in some detail the performance of the strategies during the liquidity crisis of the summer of 2007. We obtain results which are consistent with Khandani and Lo (2007) and validate their "unwinding'' theory for the quant fund drawndown of August 2007.
Numerous studies have been analyzing the role of stock markets in economic growth; most of them have focused on individual stock market, and some on merged market. This paper we will focus theoretically and empirically on the role of Euronext stock markets on the economic growth of the European countries (Belgium, France, Netherlands, Portugal and the United Kingdom). 3 The data analyzed in this paper consists of economic and financial time series of some European countries. Stock market development is a multi-dimensional concept. Markets that are liquid should be able to handle heavy trading without large price swings (Levine, 1996).
Neural Networks in Finance. Finance is highly nonlinear and sometimes stock price data can even seem completely random. Traditional time series methods such as ARIMA and GARCH models are effective only when the series is stationary, which is a restricting assumption that requires the series to be preprocessed by taking log returns (or other transforms). An important step in using the stock price data is to normalize the data. This would usually mean that you minus the average and divide by standard deviation but in our case, we want to be able to use this system on live trade over a period of time. So taking the statistical moments might not be the most accurate way to normalize the data. So I have merely divided the entire data by 200 (an arbitrary number that makes everything small).
Initially the term ‘efficient market’ applied only to the stock market, but later it was generalised to other asset markets. Market efficiency is known as the speed and accuracy where the current market prices reflect the investor expectations . This paper will discuss the definition and concept of efficient market hypothesis and behavior finance in general. I will be look into market issues for countries of Malaysia, USA, Africa and Jordan.