Classification Cramer-Rao bounds on stock price prediction

Abstract

In parameter estimation, we take advantage of the Cramer–Rao lower bound (CRLB) to evaluate the performance of estimation algorithms since the CRLB provides a theoretical upper bound on estimation accuracy. In pattern recognition, the same concept can be quite useful in terms of knowing the point of diminishing return. In this paper, we develop an innovative approach to quantifying the classification CRLB by combining the concepts of sufficient statistics and data compression with a metric that measures class separability. This approach allows us to assess the degree of performance optimality attained by each classifier. Instead of ranking performance of each classifier based on a confusion matrix, the proposed approach assigns a quantity called the optimality score that indicates the extent to which a classifier approximates the Bayes classifier. We illustrate the power of this approach with two interesting examples—two‐class prediction problems with known and unknown class‐conditional probability density functions. The latter case deals with prediction of S&P 500 price‐movement direction based on raw price data and technical indicators. Copyright © 1998 John Wiley & Sons, Ltd.

Comparing classification algorithms for prediction on CROBEX data

“…Some of the first research in this area used neural networks to predict the Tokyo stock market (Mizuno, Kosaka, Yajima, Komoda, 1998;Kimoto, Asakawa, Yoda, Takeoka, 1990). Some other similar studies have used the Bayes classifier (Pop, 2006;Shin, Kil, 1998;Tsaih, Hsu, Lai, 1998;) and support vector machines (Ince, Trafalis, 2007;Moreira, Jorge, Soares, Sousa, 2006). Also, many studies compare the performance of different methods such as neural networks, support vector machines, k-nearest neighbours, naïve Bayes classifier, genetic algorithms, decision trees etc.…”

Predicting Stock Market Indices Using Classification Tools

“…Qian and Rasheed (2007) used the Hurst exponent to select a period with enhanced predictability to investigate the predictability of the Dow Jones Industrial Average index using artificial neural network, decision tree, and k-nearest neighbor. For the data mining tools, CART (Feldman and Gross, 2005;Razi and Athappilly, 2005;Moreira et al, 2006) Bayes classifiers (Shin and Kil, 1998;Tsaih et al, 1998;Pop, 2006) and prediction using SVM (Ince and Trafalis, 2004;Moreira et al, 2006) have been used for financial market prediction.…”

Accurate forecasting of the undecided population in a public opinion poll

“…Our choice of the NN technique is also based on its previous successes in finding non-linear relationships between variables in a wide variety of complex systems. It has been utilized successfully in noisy image recognition Saloma 1995, 1998;Fu et al, 1999) and feature identification in different kinds of signals (Zhang et al, 1998;Chen and Ware, 1999;Hoorn et al, 1999;Huse and Gjosaeter, 1999;Li and Bridgewater, 2000;Mittal and Zhang, 2000;Qureshi et al, 2000;Shimada et al, 2000), stock trading and securities (Anders et al, 1998;Gençay and Stengos, 1998;Moody et al, 1998;Shin and Kil, 1998;Gençay, 2000. Hamm andBrorsen, 2000;Lam and Lam, 2000), financial management fraud (Fanning and Cogger 1998), inflation (Moshiri and Cameron, 2000), risk assessment in auditing (Ramamoorti et al, 1999), and financial earnings (Charitou and Charalambous, 1996).…”